apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-11 15:15:15 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

merged conflict in tutorial's tests

Browse source
This commit is contained in:
Nicolas 2013-06-05 17:31:07 +01:00
commit a56072b159
75 changed files with 1964 additions and 1674 deletions
Download patch file Download diff file Expand all files Collapse all files

5
GPy/core/__init__.py
View file

@ -1,8 +1,9 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from GP import GP
from sparse_GP import sparse_GP
from model import *
from parameterised import *
import priors
from GPy.core.gp import GP
from GPy.core.sparse_gp import SparseGP
from fitc import FITC

128
GPy/models/FITC.py → GPy/core/fitc.py
View file

@ -7,57 +7,62 @@ from ..util.linalg import mdot, jitchol, chol_inv, tdot, symmetrify,pdinv
from ..util.plot import gpplot
from .. import kern
from scipy import stats, linalg
from ..core import sparse_GP
from sparse_gp import SparseGP
def backsub_both_sides(L,X):
""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
class FITC(SparseGP):
"""
sparse FITC approximation
class FITC(sparse_GP):
:param X: inputs
:type X: np.ndarray (num_data x Q)
:param likelihood: a likelihood instance, containing the observed data
:type likelihood: GPy.likelihood.(Gaussian | EP)
:param kernel : the kernel (covariance function). See link kernels
:type kernel: a GPy.kern.kern instance
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (M x Q) | None
:param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
:type normalize_(X|Y): bool
"""
def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
super(FITC, self).__init__(X, likelihood, kernel, normalize_X=normalize_X)
def __init__(self, X, likelihood, kernel, Z, normalize_X=False):
SparseGP.__init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False)
assert self.output_dim == 1, "FITC model is not defined for handling multiple outputs"
def update_likelihood_approximation(self):
"""
Approximates a non-gaussian likelihood using Expectation Propagation
Approximates a non-Gaussian likelihood using Expectation Propagation
For a Gaussian (or direct: TODO) likelihood, no iteration is required:
For a Gaussian likelihood, no iteration is required:
this function does nothing
Diag(Knn - Qnn) is added to the noise term to use the tools already implemented in sparse_GP.
The true precison is now 'true_precision' not 'precision'.
"""
if self.has_uncertain_inputs:
raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
else:
self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
self._set_params(self._get_params()) # update the GP
self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
self._set_params(self._get_params()) # update the GP
def _compute_kernel_matrices(self):
# kernel computations, using BGPLVM notation
self.Kmm = self.kern.K(self.Z)
self.psi0 = self.kern.Kdiag(self.X)
self.psi1 = self.kern.K(self.Z, self.X)
self.psi2 = None
def _computations(self):
#factor Kmm
self.Lm = jitchol(self.Kmm)
self.Lmi,info = linalg.lapack.flapack.dtrtrs(self.Lm,np.eye(self.M),lower=1)
self.Lmi,info = linalg.lapack.flapack.dtrtrs(self.Lm,np.eye(self.num_inducing),lower=1)
Lmipsi1 = np.dot(self.Lmi,self.psi1)
self.Qnn = np.dot(Lmipsi1.T,Lmipsi1).copy()
self.Diag0 = self.psi0 - np.diag(self.Qnn)
self.beta_star = self.likelihood.precision/(1. + self.likelihood.precision*self.Diag0[:,None]) #Includes Diag0 in the precision
self.beta_star = self.likelihood.precision/(1. + self.likelihood.precision*self.Diag0[:,None]) #NOTE: beta_star contains Diag0 and the precision
self.V_star = self.beta_star * self.likelihood.Y
# The rather complex computations of self.A
if self.has_uncertain_inputs:
raise NotImplementedError
else:
if self.likelihood.is_heteroscedastic:
assert self.likelihood.D == 1
tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.N)))
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
self.A = tdot(tmp)
tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.num_data)))
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
self.A = tdot(tmp)
# factor B
self.B = np.eye(self.M) + self.A
self.B = np.eye(self.num_inducing) + self.A
self.LB = jitchol(self.B)
self.LBi = chol_inv(self.LB)
self.psi1V = np.dot(self.psi1, self.V_star)
@ -108,18 +113,12 @@ class FITC(sparse_GP):
self._dpsi1_dX_jkj = 0
self._dpsi1_dtheta_jkj = 0
for i,V_n,alpha_n,gamma_n,gamma_k in zip(range(self.N),self.V_star,alpha,gamma_2,gamma_3):
for i,V_n,alpha_n,gamma_n,gamma_k in zip(range(self.num_data),self.V_star,alpha,gamma_2,gamma_3):
K_pp_K = np.dot(Kmmipsi1[:,i:(i+1)],Kmmipsi1[:,i:(i+1)].T)
#Diag_dpsi1 = Diag_dA_dpsi1: yT*beta_star*y + Diag_dC_dpsi1 +Diag_dD_dpsi1
_dpsi1 = (-V_n**2 - alpha_n + 2.*gamma_k - gamma_n**2) * Kmmipsi1.T[i:(i+1),:]
#Diag_dKmm = Diag_dA_dKmm: yT*beta_star*y +Diag_dC_dKmm +Diag_dD_dKmm
_dKmm = .5*(V_n**2 + alpha_n + gamma_n**2 - 2.*gamma_k) * K_pp_K #Diag_dD_dKmm
self._dpsi1_dtheta += self.kern.dK_dtheta(_dpsi1,self.X[i:i+1,:],self.Z)
self._dKmm_dtheta += self.kern.dK_dtheta(_dKmm,self.Z)
self._dKmm_dX += 2.*self.kern.dK_dX(_dKmm ,self.Z)
self._dpsi1_dX += self.kern.dK_dX(_dpsi1.T,self.Z,self.X[i:i+1,:])
@ -128,7 +127,7 @@ class FITC(sparse_GP):
# save computation here.
self.partial_for_likelihood = None
elif self.likelihood.is_heteroscedastic:
raise NotImplementedError, "heteroscedatic derivates not implemented"
raise NotImplementedError, "heteroscedatic derivates not implemented."
else:
# likelihood is not heterscedatic
dbstar_dnoise = self.likelihood.precision * (self.beta_star**2 * self.Diag0[:,None] - self.beta_star)
@ -138,14 +137,14 @@ class FITC(sparse_GP):
aux_1 = self.likelihood.Y.T * np.dot(self._LBi_Lmi_psi1V.T,LBiLmipsi1)
aux_2 = np.dot(LBiLmipsi1.T,self._LBi_Lmi_psi1V)
dA_dnoise = 0.5 * self.D * (dbstar_dnoise/self.beta_star).sum() - 0.5 * self.D * np.sum(self.likelihood.Y**2 * dbstar_dnoise)
dA_dnoise = 0.5 * self.input_dim * (dbstar_dnoise/self.beta_star).sum() - 0.5 * self.input_dim * np.sum(self.likelihood.Y**2 * dbstar_dnoise)
dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T,self.LBi,Lmi_psi1) * Lmi_psi1 * dbstar_dnoise.T)
dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T,self.LBi,Lmi_psi1) * Lmi_psi1 * dbstar_dnoise.T)
dD_dnoise_1 = mdot(self.V_star*LBiLmipsi1.T,LBiLmipsi1*dbstar_dnoise.T*self.likelihood.Y.T)
alpha = mdot(LBiLmipsi1,self.V_star)
alpha_ = mdot(LBiLmipsi1.T,alpha)
dD_dnoise_2 = -0.5 * self.D * np.sum(alpha_**2 * dbstar_dnoise )
dD_dnoise_2 = -0.5 * self.input_dim * np.sum(alpha_**2 * dbstar_dnoise )
dD_dnoise_1 = mdot(self.V_star.T,self.psi1.T,self.Lmi.T,self.LBi.T,self.LBi,self.Lmi,self.psi1,dbstar_dnoise*self.likelihood.Y)
dD_dnoise_2 = 0.5*mdot(self.V_star.T,self.psi1.T,Hi,self.psi1,dbstar_dnoise*self.psi1.T,Hi,self.psi1,self.V_star)
@ -155,8 +154,8 @@ class FITC(sparse_GP):
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
C = -self.D * (np.sum(np.log(np.diag(self.LB))))
A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
return A + C + D
@ -165,35 +164,30 @@ class FITC(sparse_GP):
return np.hstack((self.dL_dZ().flatten(), self.dL_dtheta(), self.likelihood._gradients(partial=self.partial_for_likelihood)))
def dL_dtheta(self):
if self.has_uncertain_inputs:
raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
else:
dL_dtheta = self.kern.dKdiag_dtheta(self._dL_dpsi0,self.X)
dL_dtheta += self.kern.dK_dtheta(self._dL_dpsi1,self.X,self.Z)
dL_dtheta += self.kern.dK_dtheta(self._dL_dKmm,X=self.Z)
dL_dtheta += self._dKmm_dtheta
dL_dtheta += self._dpsi1_dtheta
dL_dtheta = self.kern.dKdiag_dtheta(self._dL_dpsi0,self.X)
dL_dtheta += self.kern.dK_dtheta(self._dL_dpsi1,self.X,self.Z)
dL_dtheta += self.kern.dK_dtheta(self._dL_dKmm,X=self.Z)
dL_dtheta += self._dKmm_dtheta
dL_dtheta += self._dpsi1_dtheta
return dL_dtheta
def dL_dZ(self):
if self.has_uncertain_inputs:
raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
else:
dL_dZ = self.kern.dK_dX(self._dL_dpsi1.T,self.Z,self.X)
dL_dZ += 2. * self.kern.dK_dX(self._dL_dKmm,X=self.Z)
dL_dZ += self._dpsi1_dX
dL_dZ += self._dKmm_dX
dL_dZ = self.kern.dK_dX(self._dL_dpsi1.T,self.Z,self.X)
dL_dZ += 2. * self.kern.dK_dX(self._dL_dKmm,X=self.Z)
dL_dZ += self._dpsi1_dX
dL_dZ += self._dKmm_dX
return dL_dZ
def _raw_predict(self, Xnew, which_parts, full_cov=False):
def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
assert X_variance_new is None, "FITC model is not defined for handling uncertain inputs."
if self.likelihood.is_heteroscedastic:
Iplus_Dprod_i = 1./(1.+ self.Diag0 * self.likelihood.precision.flatten())
self.Diag = self.Diag0 * Iplus_Dprod_i
self.P = Iplus_Dprod_i[:,None] * self.psi1.T
self.RPT0 = np.dot(self.Lmi,self.psi1)
self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,((1. - Iplus_Dprod_i)/self.Diag0)[:,None]*self.RPT0.T))
self.R,info = linalg.flapack.dtrtrs(self.L,self.Lmi,lower=1)
self.L = np.linalg.cholesky(np.eye(self.num_inducing) + np.dot(self.RPT0,((1. - Iplus_Dprod_i)/self.Diag0)[:,None]*self.RPT0.T))
self.R,info = linalg.lapack.flapack.dtrtrs(self.L,self.Lmi,lower=1)
self.RPT = np.dot(self.R,self.P.T)
self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T,self.RPT)
self.w = self.Diag * self.likelihood.v_tilde
@ -210,13 +204,13 @@ class FITC(sparse_GP):
# q(u|f) = N(u| R0i*mu_u*f, R0i*C*R0i.T)
# Ci = I + (RPT0)Di(RPT0).T
# C = I - [RPT0] * (D+[RPT0].T*[RPT0])^-1*[RPT0].T
# = I - [RPT0] * (D + self.Qnn)^-1 * [RPT0].T
# C = I - [RPT0] * (input_dim+[RPT0].T*[RPT0])^-1*[RPT0].T
# = I - [RPT0] * (input_dim + self.Qnn)^-1 * [RPT0].T
# = I - [RPT0] * (U*U.T)^-1 * [RPT0].T
# = I - V.T * V
U = np.linalg.cholesky(np.diag(self.Diag0) + self.Qnn)
V,info = linalg.flapack.dtrtrs(U,self.RPT0.T,lower=1)
C = np.eye(self.M) - np.dot(V.T,V)
V,info = linalg.lapack.flapack.dtrtrs(U,self.RPT0.T,lower=1)
C = np.eye(self.num_inducing) - np.dot(V.T,V)
mu_u = np.dot(C,self.RPT0)*(1./self.Diag0[None,:])
#self.C = C
#self.RPT0 = np.dot(self.R0,self.Knm.T) P0.T
@ -232,13 +226,13 @@ class FITC(sparse_GP):
mu_star = np.dot(KR0T,mu_H)
if full_cov:
Kxx = self.kern.K(Xnew,which_parts=which_parts)
var = Kxx + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.M),KR0T.T))
var = Kxx + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.num_inducing),KR0T.T))
else:
Kxx = self.kern.Kdiag(Xnew,which_parts=which_parts)
var = (Kxx + np.sum(KR0T.T*np.dot(Sigma_H - np.eye(self.M),KR0T.T),0))[:,None]
var = (Kxx + np.sum(KR0T.T*np.dot(Sigma_H - np.eye(self.num_inducing),KR0T.T),0))[:,None]
return mu_star[:,None],var
else:
raise NotImplementedError, "homoscedastic fitc not implemented"
raise NotImplementedError, "Heteroscedastic case not implemented."
"""
Kx = self.kern.K(self.Z, Xnew)
mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)

18
GPy/core/GP.py → GPy/core/gp.py
View file

@ -33,8 +33,8 @@ class GP(GPBase):
self._set_params(self._get_params())
def _set_params(self, p):
self.kern._set_params_transformed(p[:self.kern.Nparam_transformed()])
self.likelihood._set_params(p[self.kern.Nparam_transformed():])
self.kern._set_params_transformed(p[:self.kern.num_params_transformed()])
self.likelihood._set_params(p[self.kern.num_params_transformed():])
self.K = self.kern.K(self.X)
self.K += self.likelihood.covariance_matrix
@ -46,12 +46,12 @@ class GP(GPBase):
#alpha = np.dot(self.Ki, self.likelihood.Y)
alpha,_ = linalg.lapack.flapack.dpotrs(self.L, self.likelihood.Y,lower=1)
self.dL_dK = 0.5 * (tdot(alpha) - self.D * self.Ki)
self.dL_dK = 0.5 * (tdot(alpha) - self.output_dim * self.Ki)
else:
#tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)
tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
self.dL_dK = 0.5 * (tmp - self.D * self.Ki)
self.dL_dK = 0.5 * (tmp - self.output_dim * self.Ki)
def _get_params(self):
return np.hstack((self.kern._get_params_transformed(), self.likelihood._get_params()))
@ -89,7 +89,7 @@ class GP(GPBase):
model for a new variable Y* = v_tilde/tau_tilde, with a covariance
matrix K* = K + diag(1./tau_tilde) plus a normalization term.
"""
return -0.5 * self.D * self.K_logdet + self._model_fit_term() + self.likelihood.Z
return -0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z
def _log_likelihood_gradients(self):
@ -117,7 +117,7 @@ class GP(GPBase):
var = Kxx - np.sum(np.multiply(KiKx, Kx), 0)
var = var[:, None]
if stop:
debug_this
debug_this # @UndefinedVariable
return mu, var
def predict(self, Xnew, which_parts='all', full_cov=False):
@ -131,12 +131,12 @@ class GP(GPBase):
:type which_parts: ('all', list of bools)
:param full_cov: whether to return the folll covariance matrix, or just the diagonal
:type full_cov: bool
:rtype: posterior mean, a Numpy array, Nnew x self.D
:rtype: posterior mean, a Numpy array, Nnew x self.input_dim
:rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.D
:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
If full_cov and self.D > 1, the return shape of var is Nnew x Nnew x self.D. If self.D == 1, the return shape is Nnew x Nnew.
If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
This is to allow for different normalizations of the output dimensions.
"""

42
GPy/core/gp_base.py
View file

@ -1,34 +1,34 @@
import numpy as np
import model
from .. import kern
from ..util.plot import gpplot, Tango, x_frame1D, x_frame2D
import pylab as pb
from GPy.core.model import Model
class GPBase(model.model):
class GPBase(Model):
"""
Gaussian Process model for holding shared behaviour between
Gaussian Process Model for holding shared behaviour between
sprase_GP and GP models
"""
def __init__(self, X, likelihood, kernel, normalize_X=False):
self.X = X
assert len(self.X.shape) == 2
self.N, self.input_dim = self.X.shape
self.num_data, self.input_dim = self.X.shape
assert isinstance(kernel, kern.kern)
self.kern = kernel
self.likelihood = likelihood
assert self.X.shape[0] == self.likelihood.data.shape[0]
self.N, self.D = self.likelihood.data.shape
self.num_data, self.output_dim = self.likelihood.data.shape
if normalize_X:
self._Xmean = X.mean(0)[None, :]
self._Xstd = X.std(0)[None, :]
self.X = (X.copy() - self._Xmean) / self._Xstd
else:
self._Xmean = np.zeros((1,self.input_dim))
self._Xstd = np.ones((1,self.input_dim))
self._Xmean = np.zeros((1, self.input_dim))
self._Xstd = np.ones((1, self.input_dim))
model.model.__init__(self)
Model.__init__(self)
# All leaf nodes should call self._set_params(self._get_params()) at
# the end
@ -70,7 +70,7 @@ class GPBase(model.model):
else:
m, v = self._raw_predict(Xnew, which_parts=which_parts, full_cov=True)
Ysim = np.random.multivariate_normal(m.flatten(), v, samples)
gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None,], axes=ax)
gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None, ], axes=ax)
for i in range(samples):
ax.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
@ -84,8 +84,8 @@ class GPBase(model.model):
Xnew, xmin, xmax, xx, yy = x_frame2D(self.X, plot_limits, resolution)
m, v = self._raw_predict(Xnew, which_parts=which_parts)
m = m.reshape(resolution, resolution).T
ax.contour(xx, yy, m, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
ax.scatter(self.X[:, 0], self.X[:, 1], 40, self.likelihood.Y, linewidth=0, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max())
ax.contour(xx, yy, m, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet) # @UndefinedVariable
ax.scatter(self.X[:, 0], self.X[:, 1], 40, self.likelihood.Y, linewidth=0, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max()) # @UndefinedVariable
ax.set_xlim(xmin[0], xmax[0])
ax.set_ylim(xmin[1], xmax[1])
else:
@ -94,9 +94,9 @@ class GPBase(model.model):
def plot(self, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None):
"""
TODO: Docstrings!
:param levels: for 2D plotting, the number of contour levels to use
is ax is None, create a new figure
"""
# TODO include samples
if which_data == 'all':
@ -108,27 +108,27 @@ class GPBase(model.model):
if self.X.shape[1] == 1:
Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
m, var, lower, upper = self.predict(Xnew, which_parts=which_parts)
m, _, lower, upper = self.predict(Xnew, which_parts=which_parts)
for d in range(m.shape[1]):
gpplot(Xnew, m[:,d], lower[:,d], upper[:,d],axes=ax)
ax.plot(Xu[which_data], self.likelihood.data[which_data,d], 'kx', mew=1.5)
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax)
ax.plot(Xu[which_data], self.likelihood.data[which_data, d], 'kx', mew=1.5)
ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper))
ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
elif self.X.shape[1] == 2: # FIXME
elif self.X.shape[1] == 2: # FIXME
resolution = resolution or 50
Xnew, xx, yy, xmin, xmax = x_frame2D(self.X, plot_limits, resolution)
Xnew, _, _, xmin, xmax = x_frame2D(self.X, plot_limits, resolution)
x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution)
m, var, lower, upper = self.predict(Xnew, which_parts=which_parts)
m, _, lower, upper = self.predict(Xnew, which_parts=which_parts)
m = m.reshape(resolution, resolution).T
ax.contour(x, y, m, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
ax.contour(x, y, m, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet) # @UndefinedVariable
Yf = self.likelihood.Y.flatten()
ax.scatter(self.X[:, 0], self.X[:, 1], 40, Yf, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
ax.scatter(self.X[:, 0], self.X[:, 1], 40, Yf, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.) # @UndefinedVariable
ax.set_xlim(xmin[0], xmax[0])
ax.set_ylim(xmin[1], xmax[1])

95
GPy/core/model.py
View file

@ -6,37 +6,32 @@ from .. import likelihoods
from ..inference import optimization
from ..util.linalg import jitchol
from GPy.util.misc import opt_wrapper
from parameterised import parameterised
from scipy import optimize
from parameterised import Parameterised
import multiprocessing as mp
import numpy as np
import priors
import re
import sys
import pdb
from GPy.core.domains import POSITIVE, REAL
# import numdifftools as ndt
class model(parameterised):
class Model(Parameterised):
def __init__(self):
parameterised.__init__(self)
Parameterised.__init__(self)
self.priors = None
self.optimization_runs = []
self.sampling_runs = []
self.preferred_optimizer = 'tnc'
#self._set_params(self._get_params()) has been taken out as it should only be called on leaf nodes
self.preferred_optimizer = 'scg'
# self._set_params(self._get_params()) has been taken out as it should only be called on leaf nodes
def _get_params(self):
raise NotImplementedError, "this needs to be implemented to use the model class"
raise NotImplementedError, "this needs to be implemented to use the Model class"
def _set_params(self, x):
raise NotImplementedError, "this needs to be implemented to use the model class"
raise NotImplementedError, "this needs to be implemented to use the Model class"
def log_likelihood(self):
raise NotImplementedError, "this needs to be implemented to use the model class"
raise NotImplementedError, "this needs to be implemented to use the Model class"
def _log_likelihood_gradients(self):
raise NotImplementedError, "this needs to be implemented to use the model class"
raise NotImplementedError, "this needs to be implemented to use the Model class"
def set_prior(self, regexp, what):
"""
Sets priors on the model parameters.
Sets priors on the Model parameters.
Arguments
---------
@ -65,7 +60,7 @@ class model(parameterised):
if len(tie_matches) > 1:
raise ValueError, "cannot place Prior across multiple ties"
elif len(tie_matches) == 1:
which = which[:1] # just place a Prior object on the first parameter
which = which[:1] # just place a Prior object on the first parameter
# check constraints are okay
@ -95,7 +90,7 @@ class model(parameterised):
def get_gradient(self, name, return_names=False):
"""
Get model gradient(s) by name. The name is applied as a regular expression and all parameters that match that regular expression are returned.
Get Model gradient(s) by name. The name is applied as a regular expression and all parameters that match that regular expression are returned.
"""
matches = self.grep_param_names(name)
if len(matches):
@ -135,7 +130,7 @@ class model(parameterised):
def randomize(self):
"""
Randomize the model.
Randomize the Model.
Make this draw from the Prior if one exists, else draw from N(0,1)
"""
# first take care of all parameters (from N(0,1))
@ -147,16 +142,16 @@ class model(parameterised):
if self.priors is not None:
[np.put(x, i, p.rvs(1)) for i, p in enumerate(self.priors) if not p is None]
self._set_params(x)
self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
def optimize_restarts(self, Nrestarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs):
def optimize_restarts(self, num_restarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs):
"""
Perform random restarts of the model, and set the model to the best
Perform random restarts of the Model, and set the Model to the best
seen solution.
If the robust flag is set, exceptions raised during optimizations will
be handled silently. If _all_ runs fail, the model is reset to the
be handled silently. If _all_ runs fail, the Model is reset to the
existing parameter values.
Notes
@ -179,19 +174,19 @@ class model(parameterised):
try:
jobs = []
pool = mp.Pool(processes=num_processes)
for i in range(Nrestarts):
for i in range(num_restarts):
self.randomize()
job = pool.apply_async(opt_wrapper, args=(self,), kwds=kwargs)
jobs.append(job)
pool.close() # signal that no more data coming in
pool.join() # wait for all the tasks to complete
pool.close() # signal that no more data coming in
pool.join() # wait for all the tasks to complete
except KeyboardInterrupt:
print "Ctrl+c received, terminating and joining pool."
pool.terminate()
pool.join()
for i in range(Nrestarts):
for i in range(num_restarts):
try:
if not parallel:
self.randomize()
@ -200,10 +195,10 @@ class model(parameterised):
self.optimization_runs.append(jobs[i].get())
if verbose:
print("Optimization restart {0}/{1}, f = {2}".format(i + 1, Nrestarts, self.optimization_runs[-1].f_opt))
print("Optimization restart {0}/{1}, f = {2}".format(i + 1, num_restarts, self.optimization_runs[-1].f_opt))
except Exception as e:
if robust:
print("Warning - optimization restart {0}/{1} failed".format(i + 1, Nrestarts))
print("Warning - optimization restart {0}/{1} failed".format(i + 1, num_restarts))
else:
raise e
@ -218,20 +213,16 @@ class model(parameterised):
Ensure that any variables which should clearly be positive have been constrained somehow.
"""
positive_strings = ['variance', 'lengthscale', 'precision', 'kappa']
param_names = self._get_param_names()
# param_names = self._get_param_names()
currently_constrained = self.all_constrained_indices()
to_make_positive = []
for s in positive_strings:
for i in self.grep_param_names(".*"+s):
for i in self.grep_param_names(".*" + s):
if not (i in currently_constrained):
#to_make_positive.append(re.escape(param_names[i]))
to_make_positive.append(i)
if len(to_make_positive):
#self.constrain_positive('(' + '|'.join(to_make_positive) + ')')
self.constrain_positive(np.asarray(to_make_positive))
def objective_function(self, x):
"""
The objective function passed to the optimizer. It combines the likelihood and the priors.
@ -244,18 +235,18 @@ class model(parameterised):
Gets the gradients from the likelihood and the priors.
"""
self._set_params_transformed(x)
obj_grads = - self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
return obj_grads
def objective_and_gradients(self, x):
self._set_params_transformed(x)
obj_f = -self.log_likelihood() - self.log_prior()
obj_grads = - self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
return obj_f, obj_grads
def optimize(self, optimizer=None, start=None, **kwargs):
"""
Optimize the model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors.
Optimize the Model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors.
kwargs are passed to the optimizer. They can be:
:max_f_eval: maximum number of function evaluations
@ -278,7 +269,7 @@ class model(parameterised):
def optimize_SGD(self, momentum=0.1, learning_rate=0.01, iterations=20, **kwargs):
# assert self.Y.shape[1] > 1, "SGD only works with D > 1"
sgd = SGD.StochasticGD(self, iterations, learning_rate, momentum, **kwargs)
sgd = SGD.StochasticGD(self, iterations, learning_rate, momentum, **kwargs) # @UndefinedVariable
sgd.run()
self.optimization_runs.append(sgd)
@ -295,7 +286,7 @@ class model(parameterised):
def f(x):
self._set_params(x)
return self.log_likelihood()
h = ndt.Hessian(f)
h = ndt.Hessian(f) # @UndefinedVariable
A = -h(x)
self._set_params(x)
# check for almost zero components on the diagonal which screw up the cholesky
@ -304,7 +295,7 @@ class model(parameterised):
return A
def Laplace_evidence(self):
"""Returns an estiamte of the model evidence based on the Laplace approximation.
"""Returns an estiamte of the Model evidence based on the Laplace approximation.
Uses a numerical estimate of the hessian if none is available analytically"""
A = self.Laplace_covariance()
try:
@ -314,12 +305,12 @@ class model(parameterised):
return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld
def __str__(self):
s = parameterised.__str__(self).split('\n')
s = Parameterised.__str__(self).split('\n')
# add priors to the string
if self.priors is not None:
strs = [str(p) if p is not None else '' for p in self.priors]
else:
strs = ['']*len(self._get_params())
strs = [''] * len(self._get_params())
width = np.array(max([len(p) for p in strs] + [5])) + 4
log_like = self.log_likelihood()
@ -340,7 +331,7 @@ class model(parameterised):
def checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):
"""
Check the gradient of the model by comparing to a numerical estimate.
Check the gradient of the Model by comparing to a numerical estimate.
If the verbose flag is passed, invividual components are tested (and printed)
:param verbose: If True, print a "full" checking of each parameter
@ -392,7 +383,11 @@ class model(parameterised):
if target_param is None:
param_list = range(len(x))
else:
param_list = self.grep_param_names(target_param)
param_list = self.grep_param_names(target_param, transformed=True, search=True)
if not np.any(param_list):
print "No free parameters to check"
return
for i in param_list:
xx = x.copy()
@ -419,15 +414,15 @@ class model(parameterised):
def input_sensitivity(self):
"""
return an array describing the sesitivity of the model to each input
return an array describing the sesitivity of the Model to each input
NB. Right now, we're basing this on the lengthscales (or
variances) of the kernel. TODO: proper sensitivity analysis
where we integrate across the model inputs and evaluate the
effect on the variance of the model output. """
where we integrate across the Model inputs and evaluate the
effect on the variance of the Model output. """
if not hasattr(self, 'kern'):
raise ValueError, "this model has no kernel"
raise ValueError, "this Model has no kernel"
k = [p for p in self.kern.parts if p.name in ['rbf', 'linear']]
if (not len(k) == 1) or (not k[0].ARD):
@ -474,8 +469,8 @@ class model(parameterised):
ll_change = new_ll - last_ll
if ll_change < 0:
self.likelihood = last_approximation # restore previous likelihood approximation
self._set_params(last_params) # restore model parameters
self.likelihood = last_approximation # restore previous likelihood approximation
self._set_params(last_params) # restore Model parameters
print "Log-likelihood decrement: %s \nLast likelihood update discarded." % ll_change
stop = True
else:

65
GPy/core/parameterised.py
View file

@ -6,12 +6,10 @@ import numpy as np
import re
import copy
import cPickle
import os
from ..util.squashers import sigmoid
import warnings
import transformations
class parameterised(object):
class Parameterised(object):
def __init__(self):
"""
This is the base class for model and kernel. Mostly just handles tieing and constraining of parameters
@ -36,7 +34,7 @@ class parameterised(object):
"""
Returns a **copy** of parameters in non transformed space
:see_also: :py:func:`GPy.core.parameterised.params_transformed`
:see_also: :py:func:`GPy.core.Parameterised.params_transformed`
"""
return self._get_params()
@ -49,7 +47,7 @@ class parameterised(object):
"""
Returns a **copy** of parameters in transformed space
:see_also: :py:func:`GPy.core.parameterised.params`
:see_also: :py:func:`GPy.core.Parameterised.params`
"""
return self._get_params_transformed()
@ -85,7 +83,7 @@ class parameterised(object):
else:
return self._get_params()[matches]
else:
raise AttributeError, "no parameter matches %s" % name
raise AttributeError, "no parameter matches %s" % regexp
def __setitem__(self, name, val):
"""
@ -113,13 +111,13 @@ class parameterised(object):
if hasattr(self, 'prior'):
pass
self._set_params_transformed(self._get_params_transformed()) # sets tied parameters to single value
self._set_params_transformed(self._get_params_transformed()) # sets tied parameters to single value
def untie_everything(self):
"""Unties all parameters by setting tied_indices to an empty list."""
self.tied_indices = []
def grep_param_names(self, regexp):
def grep_param_names(self, regexp, transformed=False, search=False):
"""
:param regexp: regular expression to select parameter names
:type regexp: re | str | int
@ -129,15 +127,23 @@ class parameterised(object):
Other objects are passed through - i.e. integers which weren't meant for grepping
"""
if transformed:
names = self._get_param_names_transformed()
else:
names = self._get_param_names()
if type(regexp) in [str, np.string_, np.str]:
regexp = re.compile(regexp)
return np.nonzero([regexp.match(name) for name in self._get_param_names()])[0]
elif type(regexp) is re._pattern_type:
return np.nonzero([regexp.match(name) for name in self._get_param_names()])[0]
pass
else:
return regexp
if search:
return np.nonzero([regexp.search(name) for name in names])[0]
else:
return np.nonzero([regexp.match(name) for name in names])[0]
def Nparam_transformed(self):
def num_params_transformed(self):
removed = 0
for tie in self.tied_indices:
removed += tie.size - 1
@ -151,18 +157,18 @@ class parameterised(object):
"""Unconstrain matching parameters. does not untie parameters"""
matches = self.grep_param_names(regexp)
#tranformed contraints:
# tranformed contraints:
for match in matches:
self.constrained_indices = [i[i<>match] for i in self.constrained_indices]
self.constrained_indices = [i[i <> match] for i in self.constrained_indices]
#remove empty constraints
tmp = zip(*[(i,t) for i,t in zip(self.constrained_indices,self.constraints) if len(i)])
# remove empty constraints
tmp = zip(*[(i, t) for i, t in zip(self.constrained_indices, self.constraints) if len(i)])
if tmp:
self.constrained_indices, self.constraints = zip(*[(i,t) for i,t in zip(self.constrained_indices,self.constraints) if len(i)])
self.constrained_indices, self.constraints = zip(*[(i, t) for i, t in zip(self.constrained_indices, self.constraints) if len(i)])
self.constrained_indices, self.constraints = list(self.constrained_indices), list(self.constraints)
# fixed:
self.fixed_values = [np.delete(values, np.nonzero(np.sum(indices[:, None] == matches[None, :], 1))[0]) for indices,values in zip(self.fixed_indices,self.fixed_values)]
self.fixed_values = [np.delete(values, np.nonzero(np.sum(indices[:, None] == matches[None, :], 1))[0]) for indices, values in zip(self.fixed_indices, self.fixed_values)]
self.fixed_indices = [np.delete(indices, np.nonzero(np.sum(indices[:, None] == matches[None, :], 1))[0]) for indices in self.fixed_indices]
# remove empty elements
@ -181,7 +187,7 @@ class parameterised(object):
""" Set positive constraints. """
self.constrain(regexp, transformations.logexp())
def constrain_bounded(self, regexp,lower, upper):
def constrain_bounded(self, regexp, lower, upper):
""" Set bounded constraints. """
self.constrain(regexp, transformations.logistic(lower, upper))
@ -191,8 +197,8 @@ class parameterised(object):
else:
return np.empty(shape=(0,))
def constrain(self,regexp,transform):
assert isinstance(transform,transformations.transformation)
def constrain(self, regexp, transform):
assert isinstance(transform, transformations.transformation)
matches = self.grep_param_names(regexp)
overlap = set(matches).intersection(set(self.all_constrained_indices()))
@ -223,7 +229,6 @@ class parameterised(object):
To fix multiple parameters to the same value, simply pass a regular expression which matches both parameter names, or pass both of the indexes
"""
matches = self.grep_param_names(regexp)
overlap = set(matches).intersection(set(self.all_constrained_indices()))
if overlap:
self.unconstrain(np.asarray(list(overlap)))
@ -244,7 +249,7 @@ class parameterised(object):
def _get_params_transformed(self):
"""use self._get_params to get the 'true' parameters of the model, which are then tied, constrained and fixed"""
x = self._get_params()
[np.put(x,i,t.finv(x[i])) for i,t in zip(self.constrained_indices,self.constraints)]
[np.put(x, i, t.finv(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]
to_remove = self.fixed_indices + [t[1:] for t in self.tied_indices]
if len(to_remove):
@ -256,7 +261,7 @@ class parameterised(object):
""" takes the vector x, which is then modified (by untying, reparameterising or inserting fixed values), and then call self._set_params"""
self._set_params(self._untransform_params(x))
def _untransform_params(self,x):
def _untransform_params(self, x):
"""
The transformation required for _set_params_transformed.
@ -283,9 +288,9 @@ class parameterised(object):
[np.put(xx, i, v) for i, v in zip(self.fixed_indices, self.fixed_values)]
[np.put(xx, i, v) for i, v in [(t[1:], xx[t[0]]) for t in self.tied_indices] ]
[np.put(xx,i,t.f(xx[i])) for i,t in zip(self.constrained_indices, self.constraints)]
if hasattr(self,'debug'):
stop
[np.put(xx, i, t.f(xx[i])) for i, t in zip(self.constrained_indices, self.constraints)]
if hasattr(self, 'debug'):
stop # @UndefinedVariable
return xx
@ -309,7 +314,7 @@ class parameterised(object):
remove = np.hstack((remove, np.hstack(self.fixed_indices)))
# add markers to show that some variables are constrained
for i,t in zip(self.constrained_indices,self.constraints):
for i, t in zip(self.constrained_indices, self.constraints):
for ii in i:
n[ii] = n[ii] + t.__str__()
@ -326,10 +331,10 @@ class parameterised(object):
if not N:
return "This object has no free parameters."
header = ['Name', 'Value', 'Constraints', 'Ties']
values = self._get_params() # map(str,self._get_params())
values = self._get_params() # map(str,self._get_params())
# sort out the constraints
constraints = [''] * len(names)
for i,t in zip(self.constrained_indices,self.constraints):
for i, t in zip(self.constrained_indices, self.constraints):
for ii in i:
constraints[ii] = t.__str__()
for i in self.fixed_indices:
@ -347,7 +352,7 @@ class parameterised(object):
max_constraint = max([len(constraints[i]) for i in range(len(constraints))] + [len(header[2])])
max_ties = max([len(ties[i]) for i in range(len(ties))] + [len(header[3])])
cols = np.array([max_names, max_values, max_constraint, max_ties]) + 4
columns = cols.sum()
# columns = cols.sum()
header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
header_string = map(lambda x: '|'.join(x), [header_string])

6
GPy/core/priors.py
View file

@ -99,9 +99,9 @@ class MultivariateGaussian:
assert len(self.var.shape) == 2
assert self.var.shape[0] == self.var.shape[1]
assert self.var.shape[0] == self.mu.size
self.D = self.mu.size
self.input_dim = self.mu.size
self.inv, self.hld = pdinv(self.var)
self.constant = -0.5 * self.D * np.log(2 * np.pi) - self.hld
self.constant = -0.5 * self.input_dim * np.log(2 * np.pi) - self.hld
def summary(self):
raise NotImplementedError
@ -121,7 +121,7 @@ class MultivariateGaussian:
return np.random.multivariate_normal(self.mu, self.var, n)
def plot(self):
if self.D == 2:
if self.input_dim == 2:
rvs = self.rvs(200)
pb.plot(rvs[:, 0], rvs[:, 1], 'kx', mew=1.5)
xmin, xmax = pb.xlim()

62
GPy/core/sparse_GP.py → GPy/core/sparse_gp.py
View file

@ -8,22 +8,22 @@ from scipy import linalg
from ..likelihoods import Gaussian
from gp_base import GPBase
class sparse_GP(GPBase):
class SparseGP(GPBase):
"""
Variational sparse GP model
:param X: inputs
:type X: np.ndarray (N x input_dim)
:type X: np.ndarray (num_data x input_dim)
:param likelihood: a likelihood instance, containing the observed data
:type likelihood: GPy.likelihood.(Gaussian | EP | Laplace)
:param kernel : the kernel (covariance function). See link kernels
:type kernel: a GPy.kern.kern instance
:param X_variance: The uncertainty in the measurements of X (Gaussian variance)
:type X_variance: np.ndarray (N x input_dim) | None
:type X_variance: np.ndarray (num_data x input_dim) | None
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (M x input_dim) | None
:param M : Number of inducing points (optional, default 10. Ignored if Z is not None)
:type M: int
:type Z: np.ndarray (num_inducing x input_dim) | None
:param num_inducing : Number of inducing points (optional, default 10. Ignored if Z is not None)
:type num_inducing: int
:param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
:type normalize_(X|Y): bool
"""
@ -32,7 +32,7 @@ class sparse_GP(GPBase):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self.Z = Z
self.M = Z.shape[0]
self.num_inducing = Z.shape[0]
self.likelihood = likelihood
if X_variance is None:
@ -69,7 +69,7 @@ class sparse_GP(GPBase):
# The rather complex computations of self.A
if self.has_uncertain_inputs:
if self.likelihood.is_heteroscedastic:
psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1))).sum(0)
psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.num_data, 1, 1))).sum(0)
else:
psi2_beta = self.psi2.sum(0) * self.likelihood.precision
evals, evecs = linalg.eigh(psi2_beta)
@ -77,7 +77,7 @@ class sparse_GP(GPBase):
tmp = evecs * np.sqrt(clipped_evals)
else:
if self.likelihood.is_heteroscedastic:
tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)))
tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.num_data)))
else:
tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
@ -85,7 +85,7 @@ class sparse_GP(GPBase):
# factor B
self.B = np.eye(self.M) + self.A
self.B = np.eye(self.num_inducing) + self.A
self.LB = jitchol(self.B)
# TODO: make a switch for either first compute psi1V, or VV.T
@ -99,28 +99,28 @@ class sparse_GP(GPBase):
# Compute dL_dKmm
tmp = tdot(self._LBi_Lmi_psi1V)
self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.D * np.eye(self.M) + tmp)
self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.output_dim * np.eye(self.num_inducing) + tmp)
tmp = -0.5 * self.DBi_plus_BiPBi
tmp += -0.5 * self.B * self.D
tmp += self.D * np.eye(self.M)
tmp += -0.5 * self.B * self.output_dim
tmp += self.output_dim * np.eye(self.num_inducing)
self.dL_dKmm = backsub_both_sides(self.Lm, tmp)
# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertain inputs case
self.dL_dpsi0 = -0.5 * self.D * (self.likelihood.precision * np.ones([self.N, 1])).flatten()
self.dL_dpsi0 = -0.5 * self.output_dim * (self.likelihood.precision * np.ones([self.num_data, 1])).flatten()
self.dL_dpsi1 = np.dot(self.Cpsi1V, self.likelihood.V.T)
dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.D * np.eye(self.M) - self.DBi_plus_BiPBi)
dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.output_dim * np.eye(self.num_inducing) - self.DBi_plus_BiPBi)
if self.likelihood.is_heteroscedastic:
if self.has_uncertain_inputs:
self.dL_dpsi2 = self.likelihood.precision.flatten()[:, None, None] * dL_dpsi2_beta[None, :, :]
else:
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.N))
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.num_data))
self.dL_dpsi2 = None
else:
dL_dpsi2 = self.likelihood.precision * dL_dpsi2_beta
if self.has_uncertain_inputs:
# repeat for each of the N psi_2 matrices
self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.N, axis=0)
self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.num_data, axis=0)
else:
# subsume back into psi1 (==Kmn)
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2, self.psi1)
@ -135,26 +135,26 @@ class sparse_GP(GPBase):
raise NotImplementedError, "heteroscedatic derivates not implemented"
else:
# likelihood is not heterscedatic
self.partial_for_likelihood = -0.5 * self.N * self.D * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
self.partial_for_likelihood = -0.5 * self.num_data * self.output_dim * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
self.partial_for_likelihood += 0.5 * self.output_dim * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
self.partial_for_likelihood += self.likelihood.precision * (0.5 * np.sum(self.A * self.DBi_plus_BiPBi) - np.sum(np.square(self._LBi_Lmi_psi1V)))
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
if self.likelihood.is_heteroscedastic:
A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
else:
A = -0.5 * self.N * self.D * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
C = -self.D * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.M * np.log(sf2))
A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
return A + B + C + D + self.likelihood.Z
def _set_params(self, p):
self.Z = p[:self.M * self.input_dim].reshape(self.M, self.input_dim)
self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.Nparam])
self.likelihood._set_params(p[self.Z.size + self.kern.Nparam:])
self.Z = p[:self.num_inducing * self.input_dim].reshape(self.num_inducing, self.input_dim)
self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.num_params])
self.likelihood._set_params(p[self.Z.size + self.kern.num_params:])
self._compute_kernel_matrices()
self._computations()
@ -221,7 +221,7 @@ class sparse_GP(GPBase):
Bi, _ = linalg.lapack.flapack.dpotri(self.LB, lower=0) # WTH? this lower switch should be 1, but that doesn't work!
symmetrify(Bi)
Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.M) - Bi)
Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.num_inducing) - Bi)
if X_variance_new is None:
Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
@ -259,12 +259,12 @@ class sparse_GP(GPBase):
:type which_parts: ('all', list of bools)
:param full_cov: whether to return the folll covariance matrix, or just the diagonal
:type full_cov: bool
:rtype: posterior mean, a Numpy array, Nnew x self.D
:rtype: posterior mean, a Numpy array, Nnew x self.input_dim
:rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.D
:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
If full_cov and self.D > 1, the return shape of var is Nnew x Nnew x self.D. If self.D == 1, the return shape is Nnew x Nnew.
If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
This is to allow for different normalizations of the output dimensions.
"""

2
GPy/examples/__init__.py
View file

@ -4,5 +4,5 @@
import classification
import regression
import dimensionality_reduction
import non_gaussian
import non_Gaussian
import tutorials

43
GPy/examples/classification.py
View file

@ -24,7 +24,7 @@ def crescent_data(seed=default_seed): # FIXME
Y = data['Y']
Y[Y.flatten()==-1] = 0
m = GPy.models.GP_classification(data['X'], Y)
m = GPy.models.GPClassification(data['X'], Y)
m.ensure_default_constraints()
m.update_likelihood_approximation()
m.optimize()
@ -41,7 +41,7 @@ def oil():
Y[Y.flatten()==-1] = 0
# Create GP model
m = GPy.models.GP_classification(data['X'], Y)
m = GPy.models.GPClassification(data['X'], Y)
# Contrain all parameters to be positive
m.constrain_positive('')
@ -66,7 +66,7 @@ def toy_linear_1d_classification(seed=default_seed):
Y[Y.flatten() == -1] = 0
# Model definition
m = GPy.models.GP_classification(data['X'], Y)
m = GPy.models.GPClassification(data['X'], Y)
m.ensure_default_constraints()
# Optimize
@ -95,7 +95,7 @@ def sparse_toy_linear_1d_classification(seed=default_seed):
Y[Y.flatten() == -1] = 0
# Model definition
m = GPy.models.sparse_GP_classification(data['X'], Y)
m = GPy.models.SparseGPClassification(data['X'], Y)
m['.*len']= 2.
m.ensure_default_constraints()
@ -114,7 +114,8 @@ def sparse_toy_linear_1d_classification(seed=default_seed):
return m
def sparse_crescent_data(inducing=10, seed=default_seed):
"""Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
"""
Run a Gaussian process classification with DTC approxiamtion on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
:param seed : seed value for data generation.
@ -127,7 +128,7 @@ def sparse_crescent_data(inducing=10, seed=default_seed):
Y = data['Y']
Y[Y.flatten()==-1]=0
m = GPy.models.sparse_GP_classification(data['X'], Y)
m = GPy.models.SparseGPClassification(data['X'], Y)
m.ensure_default_constraints()
m['.*len'] = 10.
m.update_likelihood_approximation()
@ -135,3 +136,33 @@ def sparse_crescent_data(inducing=10, seed=default_seed):
print(m)
m.plot()
return m
def FITC_crescent_data(inducing=10, seed=default_seed):
"""
Run a Gaussian process classification with FITC approximation on the crescent data. The demonstration uses EP to approximate the likelihood.
:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
:param seed : seed value for data generation.
:type seed: int
:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
:type inducing: int
"""
data = GPy.util.datasets.crescent_data(seed=seed)
Y = data['Y']
Y[Y.flatten()==-1]=0
data = GPy.util.datasets.crescent_data(seed=seed)
Y = data['Y']
Y[Y.flatten()==-1]=0
m = GPy.models.FITCClassification(data['X'], Y)
m.ensure_default_constraints()
m['.*len'] = 3.
m.update_likelihood_approximation()
m.optimize()
print(m)
m.plot()
return m

57
GPy/examples/dimensionality_reduction.py
View file

@ -5,29 +5,28 @@ import numpy as np
from matplotlib import pyplot as plt
import GPy
from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
from GPy.util.datasets import swiss_roll_generated
from GPy.core.transformations import logexp
from GPy.models.bayesian_gplvm import BayesianGPLVM
default_seed = np.random.seed(123344)
def BGPLVM(seed=default_seed):
N = 10
M = 3
num_inducing = 3
Q = 2
D = 4
# generate GPLVM-like data
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, D).T
Y = np.random.multivariate_normal(np.zeros(N), K, Q).T
k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) + GPy.kern.white(Q)
# k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
# k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
m = GPy.models.BayesianGPLVM(Y, Q, kernel=k, num_inducing=num_inducing)
m.constrain_positive('(rbf|bias|noise|white|S)')
# m.constrain_fixed('S', 1)
@ -63,8 +62,8 @@ def GPLVM_oil_100(optimize=True):
m.plot_latent(labels=m.data_labels)
return m
def swiss_roll(optimize=True, N=1000, M=15, Q=4, sigma=.2, plot=False):
from GPy.util.datasets import swiss_roll
def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False):
from GPy.util.datasets import swiss_roll_generated
from GPy.core.transformations import logexp_clipped
data = swiss_roll_generated(N=N, sigma=sigma)
@ -101,24 +100,24 @@ def swiss_roll(optimize=True, N=1000, M=15, Q=4, sigma=.2, plot=False):
S = (var * np.ones_like(X) + np.clip(np.random.randn(N, Q) * var ** 2,
- (1 - var),
(1 - var))) + .001
Z = np.random.permutation(X)[:M]
Z = np.random.permutation(X)[:num_inducing]
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
m = Bayesian_GPLVM(Y, Q, X=X, X_variance=S, M=M, Z=Z, kernel=kernel)
m = BayesianGPLVM(Y, Q, X=X, X_variance=S, num_inducing=num_inducing, Z=Z, kernel=kernel)
m.data_colors = c
m.data_t = t
m.constrain('variance|length', logexp_clipped())
m['lengthscale'] = 1. # X.var(0).max() / X.var(0)
m['noise'] = Y.var() / 100.
m.ensure_default_constraints()
m['rbf_lengthscale'] = 1. # X.var(0).max() / X.var(0)
m['noise_variance'] = Y.var() / 100.
m['bias_variance'] = 0.05
if optimize:
m.optimize('scg', messages=1)
return m
def BGPLVM_oil(optimize=True, N=100, Q=5, M=25, max_f_eval=4e3, plot=False, **k):
def BGPLVM_oil(optimize=True, N=100, Q=5, num_inducing=25, max_f_eval=4e3, plot=False, **k):
np.random.seed(0)
data = GPy.util.datasets.oil()
from GPy.core.transformations import logexp_clipped
@ -129,7 +128,7 @@ def BGPLVM_oil(optimize=True, N=100, Q=5, M=25, max_f_eval=4e3, plot=False, **k)
Yn = Y - Y.mean(0)
Yn /= Yn.std(0)
m = GPy.models.Bayesian_GPLVM(Yn, Q, kernel=kernel, M=M, **k)
m = GPy.models.BayesianGPLVM(Yn, Q, kernel=kernel, num_inducing=num_inducing, **k)
m.data_labels = data['Y'][:N].argmax(axis=1)
# m.constrain('variance|leng', logexp_clipped())
@ -168,7 +167,7 @@ def oil_100():
def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
x = np.linspace(0, 4 * np.pi, N)[:, None]
s1 = np.vectorize(lambda x: np.sin(x))
s2 = np.vectorize(lambda x: np.cos(x))
@ -228,13 +227,13 @@ def bgplvm_simulation_matlab_compare():
Y = sim_data['Y']
S = sim_data['S']
mu = sim_data['mu']
M, [_, Q] = 3, mu.shape
num_inducing, [_, Q] = 3, mu.shape
from GPy.models import mrd
from GPy import kern
reload(mrd); reload(kern)
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k,
# X=mu,
# X_variance=S,
_debug=False)
@ -248,8 +247,8 @@ def bgplvm_simulation(optimize='scg',
plot=True,
max_f_eval=2e4):
# from GPy.core.transformations import logexp_clipped
D1, D2, D3, N, M, Q = 15, 8, 8, 100, 3, 5
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot)
D1, D2, D3, N, num_inducing, Q = 15, 8, 8, 100, 3, 5
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot)
from GPy.models import mrd
from GPy import kern
@ -259,7 +258,7 @@ def bgplvm_simulation(optimize='scg',
Y = Ylist[0]
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k, _debug=True)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k, _debug=True)
# m.constrain('variance|noise', logexp_clipped())
m.ensure_default_constraints()
m['noise'] = Y.var() / 100.
@ -276,8 +275,8 @@ def bgplvm_simulation(optimize='scg',
return m
def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
D1, D2, D3, N, M, Q = 150, 200, 400, 500, 3, 7
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
D1, D2, D3, N, num_inducing, Q = 150, 200, 400, 500, 3, 7
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
from GPy.models import mrd
from GPy import kern
@ -285,7 +284,7 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
reload(mrd); reload(kern)
k = kern.linear(Q, [.05] * Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = mrd.MRD(Ylist, Q=Q, M=M, kernels=k, initx="", initz='permute', **kw)
m = mrd.MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
for i, Y in enumerate(Ylist):
m['{}_noise'.format(i + 1)] = Y.var() / 100.
@ -297,7 +296,7 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
if optimize:
print "Optimizing Model:"
m.optimize('scg', messages=1, max_iters=5e4, max_f_eval=5e4)
m.optimize('scg', messages=1, max_iters=5e4, max_f_eval=5e4, gtol=.05)
if plot:
m.plot_X_1d("MRD Latent Space 1D")
m.plot_scales("MRD Scales")
@ -313,7 +312,7 @@ def brendan_faces():
Yn /= Yn.std()
m = GPy.models.GPLVM(Yn, Q)
# m = GPy.models.Bayesian_GPLVM(Yn, Q, M=100)
# m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
# optimize
m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
@ -377,16 +376,16 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True):
# X /= X.std(axis=0)
#
# Q = 10
# M = 30
# num_inducing = 30
#
# kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
# m = GPy.models.Bayesian_GPLVM(X, Q, kernel=kernel, M=M)
# m = GPy.models.BayesianGPLVM(X, Q, kernel=kernel, num_inducing=num_inducing)
# # m.scale_factor = 100.0
# m.constrain_positive('(white|noise|bias|X_variance|rbf_variance|rbf_length)')
# from sklearn import cluster
# km = cluster.KMeans(M, verbose=10)
# km = cluster.KMeans(num_inducing, verbose=10)
# Z = km.fit(m.X).cluster_centers_
# # Z = GPy.util.misc.kmm_init(m.X, M)
# # Z = GPy.util.misc.kmm_init(m.X, num_inducing)
# m.set('iip', Z)
# m.set('bias', 1e-4)
# # optimize

0
GPy/examples/non_gaussian.py → GPy/examples/non_Gaussian.py
View file

148
GPy/examples/regression.py
View file

@ -10,71 +10,71 @@ import numpy as np
import GPy
def toy_rbf_1d(max_nb_eval_optim=100):
def toy_rbf_1d(optimizer='tnc', max_nb_eval_optim=100):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
data = GPy.util.datasets.toy_rbf_1d()
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
# create simple GP Model
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
m.optimize(max_f_eval=max_nb_eval_optim)
m.optimize(optimizer, max_f_eval=max_nb_eval_optim)
# plot
m.plot()
print(m)
return m
def rogers_girolami_olympics(max_nb_eval_optim=100):
def rogers_girolami_olympics(optim_iters=100):
"""Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
data = GPy.util.datasets.rogers_girolami_olympics()
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
# create simple GP Model
m = GPy.models.GPRegression(data['X'],data['Y'])
#set the lengthscale to be something sensible (defaults to 1)
m['rbf_lengthscale'] = 10
# optimize
m.ensure_default_constraints()
m.optimize(max_f_eval=max_nb_eval_optim)
m.optimize(max_f_eval=optim_iters)
# plot
m.plot(plot_limits = (1850, 2050))
print(m)
return m
def toy_rbf_1d_50(max_nb_eval_optim=100):
def toy_rbf_1d_50(optim_iters=100):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
data = GPy.util.datasets.toy_rbf_1d_50()
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
# create simple GP Model
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
m.optimize(max_f_eval=max_nb_eval_optim)
m.optimize(max_f_eval=optim_iters)
# plot
m.plot()
print(m)
return m
def silhouette(max_nb_eval_optim=100):
def silhouette(optim_iters=100):
"""Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
data = GPy.util.datasets.silhouette()
# create simple GP model
m = GPy.models.GP_regression(data['X'],data['Y'])
# create simple GP Model
m = GPy.models.GPRegression(data['X'],data['Y'])
# optimize
m.ensure_default_constraints()
m.optimize(messages=True,max_f_eval=max_nb_eval_optim)
m.optimize(messages=True,max_f_eval=optim_iters)
print(m)
return m
def coregionalisation_toy2(max_nb_eval_optim=100):
def coregionalisation_toy2(optim_iters=100):
"""
A simple demonstration of coregionalisation on two sinusoidal functions.
"""
@ -87,13 +87,13 @@ def coregionalisation_toy2(max_nb_eval_optim=100):
Y = np.vstack((Y1,Y2))
k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
k2 = GPy.kern.coregionalise(2,1)
k2 = GPy.kern.Coregionalise(2,1)
k = k1.prod(k2,tensor=True)
m = GPy.models.GP_regression(X,Y,kernel=k)
m = GPy.models.GPRegression(X,Y,kernel=k)
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('.*kappa')
m.ensure_default_constraints()
m.optimize('sim',messages=1,max_f_eval=max_nb_eval_optim)
m.optimize('sim',messages=1,max_f_eval=optim_iters)
pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
@ -106,7 +106,7 @@ def coregionalisation_toy2(max_nb_eval_optim=100):
pb.plot(X2[:,0],Y2[:,0],'gx',mew=2)
return m
def coregionalisation_toy(max_nb_eval_optim=100):
def coregionalisation_toy(optim_iters=100):
"""
A simple demonstration of coregionalisation on two sinusoidal functions.
"""
@ -119,13 +119,13 @@ def coregionalisation_toy(max_nb_eval_optim=100):
Y = np.vstack((Y1,Y2))
k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2,2)
k2 = GPy.kern.Coregionalise(2,2)
k = k1.prod(k2,tensor=True)
m = GPy.models.GP_regression(X,Y,kernel=k)
m = GPy.models.GPRegression(X,Y,kernel=k)
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('kappa')
m.ensure_default_constraints()
m.optimize(max_f_eval=max_nb_eval_optim)
m.optimize(max_f_eval=optim_iters)
pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
@ -139,7 +139,7 @@ def coregionalisation_toy(max_nb_eval_optim=100):
return m
def coregionalisation_sparse(max_nb_eval_optim=100):
def coregionalisation_sparse(optim_iters=100):
"""
A simple demonstration of coregionalisation on two sinusoidal functions using sparse approximations.
"""
@ -151,21 +151,21 @@ def coregionalisation_sparse(max_nb_eval_optim=100):
Y2 = -np.sin(X2) + np.random.randn(*X2.shape)*0.05
Y = np.vstack((Y1,Y2))
M = 40
Z = np.hstack((np.random.rand(M,1)*8,np.random.randint(0,2,M)[:,None]))
num_inducing = 40
Z = np.hstack((np.random.rand(num_inducing,1)*8,np.random.randint(0,2,num_inducing)[:,None]))
k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2,2)
k2 = GPy.kern.Coregionalise(2,2)
k = k1.prod(k2,tensor=True) + GPy.kern.white(2,0.001)
m = GPy.models.sparse_GP_regression(X,Y,kernel=k,Z=Z)
m.scale_factor = 10000.
m = GPy.models.SparseGPRegression(X,Y,kernel=k,Z=Z)
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('kappa')
m.constrain_fixed('iip')
m.constrain_bounded('noise_variance',1e-3,1e-1)
m.ensure_default_constraints()
m.optimize_restarts(5, robust=True, messages=1, max_f_eval=max_nb_eval_optim)
m.optimize_restarts(5, robust=True, messages=1, max_f_eval=optim_iters)
#plotting:
pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
Xtest2 = np.hstack((np.linspace(0,9,100)[:,None],np.ones((100,1))))
@ -181,7 +181,7 @@ def coregionalisation_sparse(max_nb_eval_optim=100):
return m
def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000, max_nb_eval_optim=100):
def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000, optim_iters=300):
"""Show an example of a multimodal error surface for Gaussian process regression. Gene 939 has bimodal behaviour where the noisey mode is higher."""
# Contour over a range of length scales and signal/noise ratios.
@ -197,7 +197,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
data['Y'] = data['Y'] - np.mean(data['Y'])
lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
pb.contour(length_scales, log_SNRs, np.exp(lls), 20)
pb.contour(length_scales, log_SNRs, np.exp(lls), 20, cmap=pb.cm.jet)
ax = pb.gca()
pb.xlabel('length scale')
pb.ylabel('log_10 SNR')
@ -211,18 +211,20 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
optim_point_y = np.empty(2)
np.random.seed(seed=seed)
for i in range(0, model_restarts):
kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.)) + GPy.kern.white(1,variance=np.random.exponential(1.))
#kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.))
kern = GPy.kern.rbf(1, variance=np.random.uniform(1e-3,1), lengthscale=np.random.uniform(5,50))
m = GPy.models.GP_regression(data['X'],data['Y'], kernel=kern)
optim_point_x[0] = m.get('rbf_lengthscale')
optim_point_y[0] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance'));
m = GPy.models.GPRegression(data['X'],data['Y'], kernel=kern)
m['noise_variance'] = np.random.uniform(1e-3,1)
optim_point_x[0] = m['rbf_lengthscale']
optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);
# optimize
m.ensure_default_constraints()
m.optimize(xtol=1e-6, ftol=1e-6, max_f_eval=max_nb_eval_optim)
m.optimize('scg', xtol=1e-6, ftol=1e-6, max_f_eval=optim_iters)
optim_point_x[1] = m.get('rbf_lengthscale')
optim_point_y[1] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance'));
optim_point_x[1] = m['rbf_lengthscale']
optim_point_y[1] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);
pb.arrow(optim_point_x[0], optim_point_y[0], optim_point_x[1]-optim_point_x[0], optim_point_y[1]-optim_point_y[0], label=str(i), head_length=1, head_width=0.5, fc='k', ec='k')
models.append(m)
@ -231,42 +233,35 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
ax.set_ylim(ylim)
return (models, lls)
def _contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf):
def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.rbf):
"""Evaluate the GP objective function for a given data set for a range of signal to noise ratios and a range of lengthscales.
:data_set: A data set from the utils.datasets director.
:length_scales: a list of length scales to explore for the contour plot.
:log_SNRs: a list of base 10 logarithm signal to noise ratios to explore for the contour plot.
:signal_kernel: a kernel to use for the 'signal' portion of the data."""
:kernel: a kernel to use for the 'signal' portion of the data."""
lls = []
total_var = np.var(data['Y'])
kernel = kernel_call(1, variance=1., lengthscale=1.)
Model = GPy.models.GPRegression(data['X'], data['Y'], kernel=kernel)
for log_SNR in log_SNRs:
SNR = 10**log_SNR
SNR = 10.**log_SNR
noise_var = total_var/(1.+SNR)
signal_var = total_var - noise_var
Model.kern['.*variance'] = signal_var
Model['noise_variance'] = noise_var
length_scale_lls = []
for length_scale in length_scales:
noise_var = 1.
signal_var = SNR
noise_var = noise_var/(noise_var + signal_var)*total_var
signal_var = signal_var/(noise_var + signal_var)*total_var
Model['.*lengthscale'] = length_scale
length_scale_lls.append(Model.log_likelihood())
signal_kernel = signal_kernel_call(1, variance=signal_var, lengthscale=length_scale)
noise_kernel = GPy.kern.white(1, variance=noise_var)
kernel = signal_kernel + noise_kernel
K = kernel.K(data['X'])
total_var = (np.dot(np.dot(data['Y'].T,GPy.util.linalg.pdinv(K)[0]), data['Y'])/data['Y'].shape[0])[0,0]
noise_var *= total_var
signal_var *= total_var
kernel = signal_kernel_call(1, variance=signal_var, lengthscale=length_scale) + GPy.kern.white(1, variance=noise_var)
model = GPy.models.GP_regression(data['X'], data['Y'], kernel=kernel)
model.constrain_positive('')
length_scale_lls.append(model.log_likelihood())
lls.append(length_scale_lls)
return np.array(lls)
def sparse_GP_regression_1D(N = 400, M = 5, max_nb_eval_optim=100):
def sparse_GP_regression_1D(N = 400, num_inducing = 5, optim_iters=100):
"""Run a 1D example of a sparse GP regression."""
# sample inputs and outputs
X = np.random.uniform(-3.,3.,(N,1))
@ -275,17 +270,17 @@ def sparse_GP_regression_1D(N = 400, M = 5, max_nb_eval_optim=100):
rbf = GPy.kern.rbf(1)
noise = GPy.kern.white(1)
kernel = rbf + noise
# create simple GP model
m = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
# create simple GP Model
m = GPy.models.SparseGPRegression(X, Y, kernel, num_inducing=num_inducing)
m.ensure_default_constraints()
m.checkgrad(verbose=1)
m.optimize('tnc', messages = 1, max_f_eval=max_nb_eval_optim)
m.optimize('tnc', messages = 1, max_f_eval=optim_iters)
m.plot()
return m
def sparse_GP_regression_2D(N = 400, M = 50, max_nb_eval_optim=100):
def sparse_GP_regression_2D(N = 400, num_inducing = 50, optim_iters=100):
"""Run a 2D example of a sparse GP regression."""
X = np.random.uniform(-3.,3.,(N,2))
Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(N,1)*0.05
@ -295,8 +290,8 @@ def sparse_GP_regression_2D(N = 400, M = 50, max_nb_eval_optim=100):
noise = GPy.kern.white(2)
kernel = rbf + noise
# create simple GP model
m = GPy.models.sparse_GP_regression(X,Y,kernel, M = M)
# create simple GP Model
m = GPy.models.SparseGPRegression(X,Y,kernel, num_inducing = num_inducing)
# contrain all parameters to be positive (but not inducing inputs)
m.ensure_default_constraints()
@ -305,13 +300,12 @@ def sparse_GP_regression_2D(N = 400, M = 50, max_nb_eval_optim=100):
m.checkgrad()
# optimize and plot
pb.figure()
m.optimize('tnc', messages = 1, max_f_eval=max_nb_eval_optim)
m.optimize('tnc', messages = 1, max_f_eval=optim_iters)
m.plot()
print(m)
return m
def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
def uncertain_inputs_sparse_regression(optim_iters=100):
"""Run a 1D example of a sparse GP regression with uncertain inputs."""
fig, axes = pb.subplots(1,2,figsize=(12,5))
@ -324,18 +318,18 @@ def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
k = GPy.kern.rbf(1) + GPy.kern.white(1)
# create simple GP model - no input uncertainty on this one
m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z)
# create simple GP Model - no input uncertainty on this one
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=max_nb_eval_optim)
m.optimize('scg', messages=1, max_f_eval=optim_iters)
m.plot(ax=axes[0])
axes[0].set_title('no input uncertainty')
#the same model with uncertainty
m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z, X_variance=S)
#the same Model with uncertainty
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z, X_variance=S)
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=max_nb_eval_optim)
m.optimize('scg', messages=1, max_f_eval=optim_iters)
m.plot(ax=axes[1])
axes[1].set_title('with input uncertainty')
print(m)

11
GPy/examples/tutorials.py
View file

@ -19,7 +19,7 @@ def tuto_GP_regression():
kernel = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)
m = GPy.models.GP_regression(X,Y,kernel)
m = GPy.models.GPRegression(X, Y, kernel)
print m
m.plot()
@ -46,7 +46,7 @@ def tuto_GP_regression():
ker = GPy.kern.Matern52(2,ARD=True) + GPy.kern.white(2)
# create simple GP model
m = GPy.models.GP_regression(X,Y,ker)
m = GPy.models.GPRegression(X, Y, ker)
# contrain all parameters to be positive
m.constrain_positive('')
@ -114,7 +114,12 @@ def tuto_kernel_overview():
Y = 0.5*X[:,:1] + 0.5*X[:,1:] + 2*np.sin(X[:,:1]) * np.sin(X[:,1:])
# Create GP regression model
<<<<<<< HEAD
m = GPy.models.GP_regression(X,Y,Kanova)
=======
m = GPy.models.GPRegression(X, Y, Kanova)
pb.figure(figsize=(5,5))
>>>>>>> efbf169a6a17d824234d538553ffcbe0c4bddc40
m.plot()
pb.figure(figsize=(20,3))
@ -140,5 +145,5 @@ def model_interaction():
X = np.random.randn(20,1)
Y = np.sin(X) + np.random.randn(*X.shape)*0.01 + 5.
k = GPy.kern.rbf(1) + GPy.kern.bias(1)
return GPy.models.GP_regression(X,Y,kernel=k)
return GPy.models.GPRegression(X, Y, kernel=k)

38
GPy/inference/optimization.py
View file

@ -1,18 +1,16 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import pdb
import pylab as pb
import datetime as dt
from scipy import optimize
import numpy as np
try:
import rasmussens_minimize as rasm
rasm_available = True
except ImportError:
rasm_available = False
from SCG import SCG
from scg import SCG
class Optimizer():
"""
@ -51,9 +49,9 @@ class Optimizer():
start = dt.datetime.now()
self.opt(**kwargs)
end = dt.datetime.now()
self.time = str(end-start)
self.time = str(end - start)
def opt(self, f_fp = None, f = None, fp = None):
def opt(self, f_fp=None, f=None, fp=None):
raise NotImplementedError, "this needs to be implemented to use the optimizer class"
def plot(self):
@ -78,7 +76,7 @@ class opt_tnc(Optimizer):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "TNC (Scipy implementation)"
def opt(self, f_fp = None, f = None, fp = None):
def opt(self, f_fp=None, f=None, fp=None):
"""
Run the TNC optimizer
@ -96,8 +94,8 @@ class opt_tnc(Optimizer):
if self.gtol is not None:
opt_dict['pgtol'] = self.gtol
opt_result = optimize.fmin_tnc(f_fp, self.x_init, messages = self.messages,
maxfun = self.max_f_eval, **opt_dict)
opt_result = optimize.fmin_tnc(f_fp, self.x_init, messages=self.messages,
maxfun=self.max_f_eval, **opt_dict)
self.x_opt = opt_result[0]
self.f_opt = f_fp(self.x_opt)[0]
self.funct_eval = opt_result[1]
@ -108,7 +106,7 @@ class opt_lbfgsb(Optimizer):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "L-BFGS-B (Scipy implementation)"
def opt(self, f_fp = None, f = None, fp = None):
def opt(self, f_fp=None, f=None, fp=None):
"""
Run the optimizer
@ -130,8 +128,8 @@ class opt_lbfgsb(Optimizer):
if self.gtol is not None:
opt_dict['pgtol'] = self.gtol
opt_result = optimize.fmin_l_bfgs_b(f_fp, self.x_init, iprint = iprint,
maxfun = self.max_f_eval, **opt_dict)
opt_result = optimize.fmin_l_bfgs_b(f_fp, self.x_init, iprint=iprint,
maxfun=self.max_f_eval, **opt_dict)
self.x_opt = opt_result[0]
self.f_opt = f_fp(self.x_opt)[0]
self.funct_eval = opt_result[2]['funcalls']
@ -142,12 +140,12 @@ class opt_simplex(Optimizer):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "Nelder-Mead simplex routine (via Scipy)"
def opt(self, f_fp = None, f = None, fp = None):
def opt(self, f_fp=None, f=None, fp=None):
"""
The simplex optimizer does not require gradients.
"""
statuses = ['Converged', 'Maximum number of function evaluations made','Maximum number of iterations reached']
statuses = ['Converged', 'Maximum number of function evaluations made', 'Maximum number of iterations reached']
opt_dict = {}
if self.xtol is not None:
@ -157,8 +155,8 @@ class opt_simplex(Optimizer):
if self.gtol is not None:
print "WARNING: simplex doesn't have an gtol arg, so I'm going to ignore it"
opt_result = optimize.fmin(f, self.x_init, (), disp = self.messages,
maxfun = self.max_f_eval, full_output=True, **opt_dict)
opt_result = optimize.fmin(f, self.x_init, (), disp=self.messages,
maxfun=self.max_f_eval, full_output=True, **opt_dict)
self.x_opt = opt_result[0]
self.f_opt = opt_result[1]
@ -172,7 +170,7 @@ class opt_rasm(Optimizer):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "Rasmussen's Conjugate Gradient"
def opt(self, f_fp = None, f = None, fp = None):
def opt(self, f_fp=None, f=None, fp=None):
"""
Run Rasmussen's Conjugate Gradient optimizer
"""
@ -189,8 +187,8 @@ class opt_rasm(Optimizer):
if self.gtol is not None:
print "WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it"
opt_result = rasm.minimize(self.x_init, f_fp, (), messages = self.messages,
maxnumfuneval = self.max_f_eval)
opt_result = rasm.minimize(self.x_init, f_fp, (), messages=self.messages,
maxnumfuneval=self.max_f_eval)
self.x_opt = opt_result[0]
self.f_opt = opt_result[1][-1]
self.funct_eval = opt_result[2]
@ -203,7 +201,7 @@ class opt_SCG(Optimizer):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "Scaled Conjugate Gradients"
def opt(self, f_fp = None, f = None, fp = None):
def opt(self, f_fp=None, f=None, fp=None):
assert not f is None
assert not fp is None
opt_result = SCG(f, fp, self.x_init, display=self.messages,
@ -218,7 +216,7 @@ class opt_SCG(Optimizer):
self.status = opt_result[3]
def get_optimizer(f_min):
from SGD import opt_SGD
from sgd import opt_SGD
optimizers = {'fmin_tnc': opt_tnc,
'simplex': opt_simplex,

0
GPy/inference/SCG.py → GPy/inference/scg.py
View file

178
GPy/inference/SGD.py → GPy/inference/sgd.py
View file

@ -11,17 +11,17 @@ class opt_SGD(Optimizer):
Optimize using stochastic gradient descent.
*** Parameters ***
model: reference to the model object
Model: reference to the Model object
iterations: number of iterations
learning_rate: learning rate
momentum: momentum
"""
def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, model = None, messages = False, batch_size = 1, self_paced = False, center = True, iteration_file = None, learning_rate_adaptation=None, actual_iter=None, schedule=None, **kwargs):
def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, Model = None, messages = False, batch_size = 1, self_paced = False, center = True, iteration_file = None, learning_rate_adaptation=None, actual_iter=None, schedule=None, **kwargs):
self.opt_name = "Stochastic Gradient Descent"
self.model = model
self.Model = Model
self.iterations = iterations
self.momentum = momentum
self.learning_rate = learning_rate
@ -42,17 +42,17 @@ class opt_SGD(Optimizer):
self.learning_rate_0 = self.learning_rate.mean()
self.schedule = schedule
# if len([p for p in self.model.kern.parts if p.name == 'bias']) == 1:
# if len([p for p in self.Model.kern.parts if p.name == 'bias']) == 1:
# self.param_traces.append(('bias',[]))
# if len([p for p in self.model.kern.parts if p.name == 'linear']) == 1:
# if len([p for p in self.Model.kern.parts if p.name == 'linear']) == 1:
# self.param_traces.append(('linear',[]))
# if len([p for p in self.model.kern.parts if p.name == 'rbf']) == 1:
# if len([p for p in self.Model.kern.parts if p.name == 'rbf']) == 1:
# self.param_traces.append(('rbf_var',[]))
self.param_traces = dict(self.param_traces)
self.fopt_trace = []
num_params = len(self.model._get_params())
num_params = len(self.Model._get_params())
if isinstance(self.learning_rate, float):
self.learning_rate = np.ones((num_params,)) * self.learning_rate
@ -84,7 +84,7 @@ class opt_SGD(Optimizer):
return (np.isnan(data).sum(axis=1) == 0)
def check_for_missing(self, data):
if sp.sparse.issparse(self.model.likelihood.Y):
if sp.sparse.issparse(self.Model.likelihood.Y):
return True
else:
return np.isnan(data).sum() > 0
@ -107,32 +107,32 @@ class opt_SGD(Optimizer):
def shift_constraints(self, j):
constrained_indices = copy.deepcopy(self.model.constrained_indices)
constrained_indices = copy.deepcopy(self.Model.constrained_indices)
for c, constraint in enumerate(constrained_indices):
mask = (np.ones_like(constrained_indices[c]) == 1)
for i in range(len(constrained_indices[c])):
pos = np.where(j == constrained_indices[c][i])[0]
if len(pos) == 1:
self.model.constrained_indices[c][i] = pos
self.Model.constrained_indices[c][i] = pos
else:
mask[i] = False
self.model.constrained_indices[c] = self.model.constrained_indices[c][mask]
self.Model.constrained_indices[c] = self.Model.constrained_indices[c][mask]
return constrained_indices
# back them up
# bounded_i = copy.deepcopy(self.model.constrained_bounded_indices)
# bounded_l = copy.deepcopy(self.model.constrained_bounded_lowers)
# bounded_u = copy.deepcopy(self.model.constrained_bounded_uppers)
# bounded_i = copy.deepcopy(self.Model.constrained_bounded_indices)
# bounded_l = copy.deepcopy(self.Model.constrained_bounded_lowers)
# bounded_u = copy.deepcopy(self.Model.constrained_bounded_uppers)
# for b in range(len(bounded_i)): # for each group of constraints
# for bc in range(len(bounded_i[b])):
# pos = np.where(j == bounded_i[b][bc])[0]
# if len(pos) == 1:
# pos2 = np.where(self.model.constrained_bounded_indices[b] == bounded_i[b][bc])[0][0]
# self.model.constrained_bounded_indices[b][pos2] = pos[0]
# pos2 = np.where(self.Model.constrained_bounded_indices[b] == bounded_i[b][bc])[0][0]
# self.Model.constrained_bounded_indices[b][pos2] = pos[0]
# else:
# if len(self.model.constrained_bounded_indices[b]) == 1:
# if len(self.Model.constrained_bounded_indices[b]) == 1:
# # if it's the last index to be removed
# # the logic here is just a mess. If we remove the last one, then all the
# # b-indices change and we have to iterate through everything to find our
@ -140,35 +140,35 @@ class opt_SGD(Optimizer):
# raise NotImplementedError
# else: # just remove it from the indices
# mask = self.model.constrained_bounded_indices[b] != bc
# self.model.constrained_bounded_indices[b] = self.model.constrained_bounded_indices[b][mask]
# mask = self.Model.constrained_bounded_indices[b] != bc
# self.Model.constrained_bounded_indices[b] = self.Model.constrained_bounded_indices[b][mask]
# # here we shif the positive constraints. We cycle through each positive
# # constraint
# positive = self.model.constrained_positive_indices.copy()
# positive = self.Model.constrained_positive_indices.copy()
# mask = (np.ones_like(positive) == 1)
# for p in range(len(positive)):
# # we now check whether the constrained index appears in the j vector
# # (the vector of the "active" indices)
# pos = np.where(j == self.model.constrained_positive_indices[p])[0]
# pos = np.where(j == self.Model.constrained_positive_indices[p])[0]
# if len(pos) == 1:
# self.model.constrained_positive_indices[p] = pos
# self.Model.constrained_positive_indices[p] = pos
# else:
# mask[p] = False
# self.model.constrained_positive_indices = self.model.constrained_positive_indices[mask]
# self.Model.constrained_positive_indices = self.Model.constrained_positive_indices[mask]
# return (bounded_i, bounded_l, bounded_u), positive
def restore_constraints(self, c):#b, p):
# self.model.constrained_bounded_indices = b[0]
# self.model.constrained_bounded_lowers = b[1]
# self.model.constrained_bounded_uppers = b[2]
# self.model.constrained_positive_indices = p
self.model.constrained_indices = c
# self.Model.constrained_bounded_indices = b[0]
# self.Model.constrained_bounded_lowers = b[1]
# self.Model.constrained_bounded_uppers = b[2]
# self.Model.constrained_positive_indices = p
self.Model.constrained_indices = c
def get_param_shapes(self, N = None, input_dim = None):
model_name = self.model.__class__.__name__
model_name = self.Model.__class__.__name__
if model_name == 'GPLVM':
return [(N, input_dim)]
if model_name == 'Bayesian_GPLVM':
@ -179,37 +179,37 @@ class opt_SGD(Optimizer):
def step_with_missing_data(self, f_fp, X, step, shapes):
N, input_dim = X.shape
if not sp.sparse.issparse(self.model.likelihood.Y):
Y = self.model.likelihood.Y
samples = self.non_null_samples(self.model.likelihood.Y)
self.model.N = samples.sum()
if not sp.sparse.issparse(self.Model.likelihood.Y):
Y = self.Model.likelihood.Y
samples = self.non_null_samples(self.Model.likelihood.Y)
self.Model.N = samples.sum()
Y = Y[samples]
else:
samples = self.model.likelihood.Y.nonzero()[0]
self.model.N = len(samples)
Y = np.asarray(self.model.likelihood.Y[samples].todense(), dtype = np.float64)
samples = self.Model.likelihood.Y.nonzero()[0]
self.Model.N = len(samples)
Y = np.asarray(self.Model.likelihood.Y[samples].todense(), dtype = np.float64)
if self.model.N == 0 or Y.std() == 0.0:
return 0, step, self.model.N
if self.Model.N == 0 or Y.std() == 0.0:
return 0, step, self.Model.N
self.model.likelihood._offset = Y.mean()
self.model.likelihood._scale = Y.std()
self.model.likelihood.set_data(Y)
# self.model.likelihood.V = self.model.likelihood.Y*self.model.likelihood.precision
self.Model.likelihood._offset = Y.mean()
self.Model.likelihood._scale = Y.std()
self.Model.likelihood.set_data(Y)
# self.Model.likelihood.V = self.Model.likelihood.Y*self.Model.likelihood.precision
sigma = self.model.likelihood._variance
self.model.likelihood._variance = None # invalidate cache
self.model.likelihood._set_params(sigma)
sigma = self.Model.likelihood._variance
self.Model.likelihood._variance = None # invalidate cache
self.Model.likelihood._set_params(sigma)
j = self.subset_parameter_vector(self.x_opt, samples, shapes)
self.model.X = X[samples]
self.Model.X = X[samples]
model_name = self.model.__class__.__name__
model_name = self.Model.__class__.__name__
if model_name == 'Bayesian_GPLVM':
self.model.likelihood.YYT = np.dot(self.model.likelihood.Y, self.model.likelihood.Y.T)
self.model.likelihood.trYYT = np.trace(self.model.likelihood.YYT)
self.Model.likelihood.YYT = np.dot(self.Model.likelihood.Y, self.Model.likelihood.Y.T)
self.Model.likelihood.trYYT = np.trace(self.Model.likelihood.YYT)
ci = self.shift_constraints(j)
f, fp = f_fp(self.x_opt[j])
@ -218,18 +218,18 @@ class opt_SGD(Optimizer):
self.x_opt[j] -= step[j]
self.restore_constraints(ci)
self.model.grads[j] = fp
self.Model.grads[j] = fp
# restore likelihood _offset and _scale, otherwise when we call set_data(y) on
# the next feature, it will get normalized with the mean and std of this one.
self.model.likelihood._offset = 0
self.model.likelihood._scale = 1
self.Model.likelihood._offset = 0
self.Model.likelihood._scale = 1
return f, step, self.model.N
return f, step, self.Model.N
def adapt_learning_rate(self, t, D):
if self.learning_rate_adaptation == 'adagrad':
if t > 0:
g_k = self.model.grads
g_k = self.Model.grads
self.s_k += np.square(g_k)
t0 = 100.0
self.learning_rate = 0.1/(t0 + np.sqrt(self.s_k))
@ -245,8 +245,8 @@ class opt_SGD(Optimizer):
elif self.learning_rate_adaptation == 'semi_pesky':
if self.model.__class__.__name__ == 'Bayesian_GPLVM':
g_t = self.model.grads
if self.Model.__class__.__name__ == 'Bayesian_GPLVM':
g_t = self.Model.grads
if t == 0:
self.hbar_t = 0.0
self.tau_t = 100.0
@ -259,28 +259,28 @@ class opt_SGD(Optimizer):
def opt(self, f_fp=None, f=None, fp=None):
self.x_opt = self.model._get_params_transformed()
self.x_opt = self.Model._get_params_transformed()
self.grads = []
X, Y = self.model.X.copy(), self.model.likelihood.Y.copy()
X, Y = self.Model.X.copy(), self.Model.likelihood.Y.copy()
self.model.likelihood.YYT = 0
self.model.likelihood.trYYT = 0
self.model.likelihood._offset = 0.0
self.model.likelihood._scale = 1.0
self.Model.likelihood.YYT = 0
self.Model.likelihood.trYYT = 0
self.Model.likelihood._offset = 0.0
self.Model.likelihood._scale = 1.0
N, input_dim = self.model.X.shape
D = self.model.likelihood.Y.shape[1]
num_params = self.model._get_params()
N, input_dim = self.Model.X.shape
D = self.Model.likelihood.Y.shape[1]
num_params = self.Model._get_params()
self.trace = []
missing_data = self.check_for_missing(self.model.likelihood.Y)
missing_data = self.check_for_missing(self.Model.likelihood.Y)
step = np.zeros_like(num_params)
for it in range(self.iterations):
if self.actual_iter != None:
it = self.actual_iter
self.model.grads = np.zeros_like(self.x_opt) # TODO this is ugly
self.Model.grads = np.zeros_like(self.x_opt) # TODO this is ugly
if it == 0 or self.self_paced is False:
features = np.random.permutation(Y.shape[1])
@ -292,29 +292,29 @@ class opt_SGD(Optimizer):
NLL = []
import pylab as plt
for count, j in enumerate(features):
self.model.D = len(j)
self.model.likelihood.D = len(j)
self.model.likelihood.set_data(Y[:, j])
# self.model.likelihood.V = self.model.likelihood.Y*self.model.likelihood.precision
self.Model.input_dim = len(j)
self.Model.likelihood.input_dim = len(j)
self.Model.likelihood.set_data(Y[:, j])
# self.Model.likelihood.V = self.Model.likelihood.Y*self.Model.likelihood.precision
sigma = self.model.likelihood._variance
self.model.likelihood._variance = None # invalidate cache
self.model.likelihood._set_params(sigma)
sigma = self.Model.likelihood._variance
self.Model.likelihood._variance = None # invalidate cache
self.Model.likelihood._set_params(sigma)
if missing_data:
shapes = self.get_param_shapes(N, input_dim)
f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes)
else:
self.model.likelihood.YYT = np.dot(self.model.likelihood.Y, self.model.likelihood.Y.T)
self.model.likelihood.trYYT = np.trace(self.model.likelihood.YYT)
self.Model.likelihood.YYT = np.dot(self.Model.likelihood.Y, self.Model.likelihood.Y.T)
self.Model.likelihood.trYYT = np.trace(self.Model.likelihood.YYT)
Nj = N
f, fp = f_fp(self.x_opt)
self.model.grads = fp.copy()
self.Model.grads = fp.copy()
step = self.momentum * step + self.learning_rate * fp
self.x_opt -= step
if self.messages == 2:
noise = self.model.likelihood._variance
noise = self.Model.likelihood._variance
status = "evaluating {feature: 5d}/{tot: 5d} \t f: {f: 2.3f} \t non-missing: {nm: 4d}\t noise: {noise: 2.4f}\r".format(feature = count, tot = len(features), f = f, nm = Nj, noise = noise)
sys.stdout.write(status)
sys.stdout.flush()
@ -328,19 +328,19 @@ class opt_SGD(Optimizer):
# plt.plot(self.param_traces['noise'])
# for k in self.param_traces.keys():
# self.param_traces[k].append(self.model.get(k)[0])
self.grads.append(self.model.grads.tolist())
# self.param_traces[k].append(self.Model.get(k)[0])
self.grads.append(self.Model.grads.tolist())
# should really be a sum(), but earlier samples in the iteration will have a very crappy ll
self.f_opt = np.mean(NLL)
self.model.N = N
self.model.X = X
self.model.D = D
self.model.likelihood.N = N
self.model.likelihood.D = D
self.model.likelihood.Y = Y
sigma = self.model.likelihood._variance
self.model.likelihood._variance = None # invalidate cache
self.model.likelihood._set_params(sigma)
self.Model.N = N
self.Model.X = X
self.Model.input_dim = D
self.Model.likelihood.N = N
self.Model.likelihood.input_dim = D
self.Model.likelihood.Y = Y
sigma = self.Model.likelihood._variance
self.Model.likelihood._variance = None # invalidate cache
self.Model.likelihood._set_params(sigma)
self.trace.append(self.f_opt)
if self.iteration_file is not None:

16
GPy/kern/Brownian.py
View file

@ -2,26 +2,26 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
def theta(x):
"""Heavisdie step function"""
return np.where(x>=0.,1.,0.)
class Brownian(kernpart):
class Brownian(Kernpart):
"""
Brownian Motion kernel.
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance:
:type variance: float
"""
def __init__(self,D,variance=1.):
self.D = D
assert self.D==1, "Brownian motion in 1D only"
self.Nparam = 1.
def __init__(self,input_dim,variance=1.):
self.input_dim = input_dim
assert self.input_dim==1, "Brownian motion in 1D only"
self.num_params = 1.
self.name = 'Brownian'
self._set_params(np.array([variance]).flatten())

114
GPy/kern/Matern32.py
View file

@ -2,22 +2,20 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
import hashlib
from ..util.linalg import pdinv,mdot
from scipy import integrate
class Matern32(kernpart):
class Matern32(Kernpart):
"""
Matern 3/2 kernel:
.. math::
k(r) = \\sigma^2 (1 + \\sqrt{3} r) \exp(- \sqrt{3} r) \\ \\ \\ \\ \\text{ where } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
k(r) = \\sigma^2 (1 + \\sqrt{3} r) \exp(- \sqrt{3} r) \\ \\ \\ \\ \\text{ where } r = \sqrt{\sum_{i=1}^input_dim \\frac{(x_i-y_i)^2}{\ell_i^2} }
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the vector of lengthscale :math:`\ell_i`
@ -28,11 +26,11 @@ class Matern32(kernpart):
"""
def __init__(self,D,variance=1.,lengthscale=None,ARD=False):
self.D = D
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False):
self.input_dim = input_dim
self.ARD = ARD
if ARD == False:
self.Nparam = 2
self.num_params = 2
self.name = 'Mat32'
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
@ -40,78 +38,78 @@ class Matern32(kernpart):
else:
lengthscale = np.ones(1)
else:
self.Nparam = self.D + 1
self.num_params = self.input_dim + 1
self.name = 'Mat32'
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == self.D, "bad number of lengthscales"
assert lengthscale.size == self.input_dim, "bad number of lengthscales"
else:
lengthscale = np.ones(self.D)
self._set_params(np.hstack((variance,lengthscale.flatten())))
lengthscale = np.ones(self.input_dim)
self._set_params(np.hstack((variance, lengthscale.flatten())))
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale))
return np.hstack((self.variance, self.lengthscale))
def _set_params(self,x):
def _set_params(self, x):
"""set the value of the parameters."""
assert x.size == self.Nparam
assert x.size == self.num_params
self.variance = x[0]
self.lengthscale = x[1:]
def _get_param_names(self):
"""return parameter names."""
if self.Nparam == 2:
return ['variance','lengthscale']
if self.num_params == 2:
return ['variance', 'lengthscale']
else:
return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscale.size)]
return ['variance'] + ['lengthscale_%i' % i for i in range(self.lengthscale.size)]
def K(self,X,X2,target):
def K(self, X, X2, target):
"""Compute the covariance matrix between X and X2."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
np.add(self.variance*(1+np.sqrt(3.)*dist)*np.exp(-np.sqrt(3.)*dist), target,target)
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))
np.add(self.variance * (1 + np.sqrt(3.) * dist) * np.exp(-np.sqrt(3.) * dist), target, target)
def Kdiag(self,X,target):
def Kdiag(self, X, target):
"""Compute the diagonal of the covariance matrix associated to X."""
np.add(target,self.variance,target)
np.add(target, self.variance, target)
def dK_dtheta(self,dL_dK,X,X2,target):
def dK_dtheta(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
dvar = (1+np.sqrt(3.)*dist)*np.exp(-np.sqrt(3.)*dist)
invdist = 1./np.where(dist!=0.,dist,np.inf)
dist2M = np.square(X[:,None,:]-X2[None,:,:])/self.lengthscale**3
#dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
target[0] += np.sum(dvar*dL_dK)
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))
dvar = (1 + np.sqrt(3.) * dist) * np.exp(-np.sqrt(3.) * dist)
invdist = 1. / np.where(dist != 0., dist, np.inf)
dist2M = np.square(X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 3
# dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
target[0] += np.sum(dvar * dL_dK)
if self.ARD == True:
dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
#dl = self.variance*dvar[:,:,None]*dist2M*invdist[:,:,None]
target[1:] += (dl*dL_dK[:,:,None]).sum(0).sum(0)
dl = (self.variance * 3 * dist * np.exp(-np.sqrt(3.) * dist))[:, :, np.newaxis] * dist2M * invdist[:, :, np.newaxis]
# dl = self.variance*dvar[:,:,None]*dist2M*invdist[:,:,None]
target[1:] += (dl * dL_dK[:, :, None]).sum(0).sum(0)
else:
dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist)) * dist2M.sum(-1)*invdist
#dl = self.variance*dvar*dist2M.sum(-1)*invdist
target[1] += np.sum(dl*dL_dK)
dl = (self.variance * 3 * dist * np.exp(-np.sqrt(3.) * dist)) * dist2M.sum(-1) * invdist
# dl = self.variance*dvar*dist2M.sum(-1)*invdist
target[1] += np.sum(dl * dL_dK)
def dKdiag_dtheta(self,dL_dKdiag,X,target):
def dKdiag_dtheta(self, dL_dKdiag, X, target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def dK_dX(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscale**2/np.where(dist!=0.,dist,np.inf)
dK_dX = - np.transpose(3*self.variance*dist*np.exp(-np.sqrt(3)*dist)*ddist_dX,(1,0,2))
target += np.sum(dK_dX*dL_dK.T[:,:,None],0)
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))[:, :, None]
ddist_dX = (X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 2 / np.where(dist != 0., dist, np.inf)
dK_dX = -np.transpose(3 * self.variance * dist * np.exp(-np.sqrt(3) * dist) * ddist_dX, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self,dL_dKdiag,X,target):
def dKdiag_dX(self, dL_dKdiag, X, target):
pass
def Gram_matrix(self,F,F1,F2,lower,upper):
def Gram_matrix(self, F, F1, F2, lower, upper):
"""
Return the Gram matrix of the vector of functions F with respect to the RKHS norm. The use of this function is limited to D=1.
Return the Gram matrix of the vector of functions F with respect to the RKHS norm. The use of this function is limited to input_dim=1.
:param F: vector of functions
:type F: np.array
@ -122,16 +120,16 @@ class Matern32(kernpart):
:param lower,upper: boundaries of the input domain
:type lower,upper: floats
"""
assert self.D == 1
def L(x,i):
return(3./self.lengthscale**2*F[i](x) + 2*np.sqrt(3)/self.lengthscale*F1[i](x) + F2[i](x))
assert self.input_dim == 1
def L(x, i):
return(3. / self.lengthscale ** 2 * F[i](x) + 2 * np.sqrt(3) / self.lengthscale * F1[i](x) + F2[i](x))
n = F.shape[0]
G = np.zeros((n,n))
G = np.zeros((n, n))
for i in range(n):
for j in range(i,n):
G[i,j] = G[j,i] = integrate.quad(lambda x : L(x,i)*L(x,j),lower,upper)[0]
Flower = np.array([f(lower) for f in F])[:,None]
F1lower = np.array([f(lower) for f in F1])[:,None]
#print "OLD \n", np.dot(F1lower,F1lower.T), "\n \n"
#return(G)
return(self.lengthscale**3/(12.*np.sqrt(3)*self.variance) * G + 1./self.variance*np.dot(Flower,Flower.T) + self.lengthscale**2/(3.*self.variance)*np.dot(F1lower,F1lower.T))
for j in range(i, n):
G[i, j] = G[j, i] = integrate.quad(lambda x : L(x, i) * L(x, j), lower, upper)[0]
Flower = np.array([f(lower) for f in F])[:, None]
F1lower = np.array([f(lower) for f in F1])[:, None]
# print "OLD \n", np.dot(F1lower,F1lower.T), "\n \n"
# return(G)
return(self.lengthscale ** 3 / (12.*np.sqrt(3) * self.variance) * G + 1. / self.variance * np.dot(Flower, Flower.T) + self.lengthscale ** 2 / (3.*self.variance) * np.dot(F1lower, F1lower.T))

30
GPy/kern/Matern52.py
View file

@ -2,21 +2,21 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
import hashlib
from scipy import integrate
class Matern52(kernpart):
class Matern52(Kernpart):
"""
Matern 5/2 kernel:
.. math::
k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) \ \ \ \ \ \\text{ where } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) \ \ \ \ \ \\text{ where } r = \sqrt{\sum_{i=1}^input_dim \\frac{(x_i-y_i)^2}{\ell_i^2} }
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the vector of lengthscale :math:`\ell_i`
@ -26,11 +26,11 @@ class Matern52(kernpart):
:rtype: kernel object
"""
def __init__(self,D,variance=1.,lengthscale=None,ARD=False):
self.D = D
def __init__(self,input_dim,variance=1.,lengthscale=None,ARD=False):
self.input_dim = input_dim
self.ARD = ARD
if ARD == False:
self.Nparam = 2
self.num_params = 2
self.name = 'Mat52'
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
@ -38,13 +38,13 @@ class Matern52(kernpart):
else:
lengthscale = np.ones(1)
else:
self.Nparam = self.D + 1
self.num_params = self.input_dim + 1
self.name = 'Mat52'
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == self.D, "bad number of lengthscales"
assert lengthscale.size == self.input_dim, "bad number of lengthscales"
else:
lengthscale = np.ones(self.D)
lengthscale = np.ones(self.input_dim)
self._set_params(np.hstack((variance,lengthscale.flatten())))
def _get_params(self):
@ -53,13 +53,13 @@ class Matern52(kernpart):
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size == self.Nparam
assert x.size == self.num_params
self.variance = x[0]
self.lengthscale = x[1:]
def _get_param_names(self):
"""return parameter names."""
if self.Nparam == 2:
if self.num_params == 2:
return ['variance','lengthscale']
else:
return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscale.size)]
@ -109,7 +109,7 @@ class Matern52(kernpart):
def Gram_matrix(self,F,F1,F2,F3,lower,upper):
"""
Return the Gram matrix of the vector of functions F with respect to the RKHS norm. The use of this function is limited to D=1.
Return the Gram matrix of the vector of functions F with respect to the RKHS norm. The use of this function is limited to input_dim=1.
:param F: vector of functions
:type F: np.array
@ -122,7 +122,7 @@ class Matern52(kernpart):
:param lower,upper: boundaries of the input domain
:type lower,upper: floats
"""
assert self.D == 1
assert self.input_dim == 1
def L(x,i):
return(5*np.sqrt(5)/self.lengthscale**3*F[i](x) + 15./self.lengthscale**2*F1[i](x)+ 3*np.sqrt(5)/self.lengthscale*F2[i](x) + F3[i](x))
n = F.shape[0]

2
GPy/kern/__init__.py
View file

@ -2,7 +2,7 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, periodic_exponential, periodic_Matern32, periodic_Matern52, prod, symmetric, coregionalise, rational_quadratic, fixed, rbfcos, independent_outputs
from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, periodic_exponential, periodic_Matern32, periodic_Matern52, prod, symmetric, Coregionalise, rational_quadratic, Fixed, rbfcos, IndependentOutputs
try:
from constructors import rbf_sympy, sympykern # these depend on sympy
except:

14
GPy/kern/bias.py
View file

@ -2,20 +2,20 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
import hashlib
class bias(kernpart):
def __init__(self,D,variance=1.):
class bias(Kernpart):
def __init__(self,input_dim,variance=1.):
"""
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
"""
self.D = D
self.Nparam = 1
self.input_dim = input_dim
self.num_params = 1
self.name = 'bias'
self._set_params(np.array([variance]).flatten())

70
GPy/kern/constructors.py
View file

@ -12,7 +12,7 @@ from exponential import exponential as exponentialpart
from Matern32 import Matern32 as Matern32part
from Matern52 import Matern52 as Matern52part
from bias import bias as biaspart
from fixed import fixed as fixedpart
from fixed import Fixed as fixedpart
from finite_dimensional import finite_dimensional as finite_dimensionalpart
from spline import spline as splinepart
from Brownian import Brownian as Brownianpart
@ -21,10 +21,10 @@ from periodic_Matern32 import periodic_Matern32 as periodic_Matern32part
from periodic_Matern52 import periodic_Matern52 as periodic_Matern52part
from prod import prod as prodpart
from symmetric import symmetric as symmetric_part
from coregionalise import coregionalise as coregionalise_part
from coregionalise import Coregionalise as coregionalise_part
from rational_quadratic import rational_quadratic as rational_quadraticpart
from rbfcos import rbfcos as rbfcospart
from independent_outputs import independent_outputs as independent_output_part
from independent_outputs import IndependentOutputs as independent_output_part
#TODO these s=constructors are not as clean as we'd like. Tidy the code up
#using meta-classes to make the objects construct properly wthout them.
@ -33,8 +33,8 @@ def rbf(D,variance=1., lengthscale=None,ARD=False):
"""
Construct an RBF kernel
:param D: dimensionality of the kernel, obligatory
:type D: int
:param input_dim: dimensionality of the kernel, obligatory
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
@ -51,7 +51,7 @@ def linear(D,variances=None,ARD=False):
Arguments
---------
D (int), obligatory
input_dimD (int), obligatory
variances (np.ndarray)
ARD (boolean)
"""
@ -64,7 +64,7 @@ def white(D,variance=1.):
Arguments
---------
D (int), obligatory
input_dimD (int), obligatory
variance (float)
"""
part = whitepart(D,variance)
@ -74,8 +74,8 @@ def exponential(D,variance=1., lengthscale=None, ARD=False):
"""
Construct an exponential kernel
:param D: dimensionality of the kernel, obligatory
:type D: int
:param input_dim: dimensionality of the kernel, obligatory
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
@ -90,8 +90,8 @@ def Matern32(D,variance=1., lengthscale=None, ARD=False):
"""
Construct a Matern 3/2 kernel.
:param D: dimensionality of the kernel, obligatory
:type D: int
:param input_dim: dimensionality of the kernel, obligatory
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
@ -106,8 +106,8 @@ def Matern52(D,variance=1., lengthscale=None, ARD=False):
"""
Construct a Matern 5/2 kernel.
:param D: dimensionality of the kernel, obligatory
:type D: int
:param input_dim: dimensionality of the kernel, obligatory
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
@ -124,7 +124,7 @@ def bias(D,variance=1.):
Arguments
---------
D (int), obligatory
input_dim (int), obligatory
variance (float)
"""
part = biaspart(D,variance)
@ -133,7 +133,7 @@ def bias(D,variance=1.):
def finite_dimensional(D,F,G,variances=1.,weights=None):
"""
Construct a finite dimensional kernel.
D: int - the number of input dimensions
input_dim: int - the number of input dimensions
F: np.array of functions with shape (n,) - the n basis functions
G: np.array with shape (n,n) - the Gram matrix associated to F
variances : np.ndarray with shape (n,)
@ -145,8 +145,8 @@ def spline(D,variance=1.):
"""
Construct a spline kernel.
:param D: Dimensionality of the kernel
:type D: int
:param input_dim: Dimensionality of the kernel
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
"""
@ -157,8 +157,8 @@ def Brownian(D,variance=1.):
"""
Construct a Brownian motion kernel.
:param D: Dimensionality of the kernel
:type D: int
:param input_dim: Dimensionality of the kernel
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
"""
@ -204,8 +204,8 @@ def periodic_exponential(D=1,variance=1., lengthscale=None, period=2*np.pi,n_fre
"""
Construct an periodic exponential kernel
:param D: dimensionality, only defined for D=1
:type D: int
:param input_dim: dimensionality, only defined for input_dim=1
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
@ -222,8 +222,8 @@ def periodic_Matern32(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
"""
Construct a periodic Matern 3/2 kernel.
:param D: dimensionality, only defined for D=1
:type D: int
:param input_dim: dimensionality, only defined for input_dim=1
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
@ -240,8 +240,8 @@ def periodic_Matern52(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
"""
Construct a periodic Matern 5/2 kernel.
:param D: dimensionality, only defined for D=1
:type D: int
:param input_dim: dimensionality, only defined for input_dim=1
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the lengthscale of the kernel
@ -256,14 +256,14 @@ def periodic_Matern52(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
def prod(k1,k2,tensor=False):
"""
Construct a product kernel over D from two kernels over D
Construct a product kernel over input_dim from two kernels over input_dim
:param k1, k2: the kernels to multiply
:type k1, k2: kernpart
:rtype: kernel object
"""
part = prodpart(k1,k2,tensor)
return kern(part.D, [part])
return kern(part.input_dim, [part])
def symmetric(k):
"""
@ -273,7 +273,7 @@ def symmetric(k):
k_.parts = [symmetric_part(p) for p in k.parts]
return k_
def coregionalise(Nout,R=1, W=None, kappa=None):
def Coregionalise(Nout,R=1, W=None, kappa=None):
p = coregionalise_part(Nout,R,W,kappa)
return kern(1,[p])
@ -282,8 +282,8 @@ def rational_quadratic(D,variance=1., lengthscale=1., power=1.):
"""
Construct rational quadratic kernel.
:param D: the number of input dimensions
:type D: int (D=1 is the only value currently supported)
:param input_dim: the number of input dimensions
:type input_dim: int (input_dim=1 is the only value currently supported)
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the lengthscale :math:`\ell`
@ -294,13 +294,13 @@ def rational_quadratic(D,variance=1., lengthscale=1., power=1.):
part = rational_quadraticpart(D,variance, lengthscale, power)
return kern(D, [part])
def fixed(D, K, variance=1.):
def Fixed(D, K, variance=1.):
"""
Construct a fixed effect kernel.
Construct a Fixed effect kernel.
Arguments
---------
D (int), obligatory
input_dim (int), obligatory
K (np.array), obligatory
variance (float)
"""
@ -314,13 +314,13 @@ def rbfcos(D,variance=1.,frequencies=None,bandwidths=None,ARD=False):
part = rbfcospart(D,variance,frequencies,bandwidths,ARD)
return kern(D,[part])
def independent_outputs(k):
def IndependentOutputs(k):
"""
Construct a kernel with independent outputs from an existing kernel
"""
for sl in k.input_slices:
assert (sl.start is None) and (sl.stop is None), "cannot adjust input slices! (TODO)"
parts = [independent_output_part(p) for p in k.parts]
return kern(k.D+1,parts)
return kern(k.input_dim+1,parts)

26
GPy/kern/coregionalise.py
View file

@ -1,18 +1,18 @@
# Copyright (c) 2012, James Hensman and Ricardo Andrade
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
from GPy.util.linalg import mdot, pdinv
import pdb
from scipy import weave
class coregionalise(kernpart):
class Coregionalise(Kernpart):
"""
Kernel for Intrinsic Corregionalization Models
"""
def __init__(self,Nout,R=1, W=None, kappa=None):
self.D = 1
self.input_dim = 1
self.name = 'coregion'
self.Nout = Nout
self.R = R
@ -26,14 +26,14 @@ class coregionalise(kernpart):
else:
assert kappa.shape==(self.Nout,)
self.kappa = kappa
self.Nparam = self.Nout*(self.R + 1)
self.num_params = self.Nout*(self.R + 1)
self._set_params(np.hstack([self.W.flatten(),self.kappa]))
def _get_params(self):
return np.hstack([self.W.flatten(),self.kappa])
def _set_params(self,x):
assert x.size == self.Nparam
assert x.size == self.num_params
self.kappa = x[-self.Nout:]
self.W = x[:-self.Nout].reshape(self.Nout,self.R)
self.B = np.dot(self.W,self.W.T) + np.diag(self.kappa)
@ -69,14 +69,14 @@ class coregionalise(kernpart):
else:
index2 = np.asarray(index2,dtype=np.int)
code="""
for(int i=0;i<M; i++){
for(int i=0;i<num_inducing; i++){
for(int j=0; j<N; j++){
target[i+j*M] += B[Nout*index[j]+index2[i]];
target[i+j*num_inducing] += B[Nout*index[j]+index2[i]];
}
}
"""
N,M,B,Nout = index.size,index2.size, self.B, self.Nout
weave.inline(code,['target','index','index2','N','M','B','Nout'])
N,num_inducing,B,Nout = index.size,index2.size, self.B, self.Nout
weave.inline(code,['target','index','index2','N','num_inducing','B','Nout'])
def Kdiag(self,index,target):
@ -91,14 +91,14 @@ class coregionalise(kernpart):
index2 = np.asarray(index2,dtype=np.int)
code="""
for(int i=0; i<M; i++){
for(int i=0; i<num_inducing; i++){
for(int j=0; j<N; j++){
dL_dK_small[index[j] + Nout*index2[i]] += dL_dK[i+j*M];
dL_dK_small[index[j] + Nout*index2[i]] += dL_dK[i+j*num_inducing];
}
}
"""
N, M, Nout = index.size, index2.size, self.Nout
weave.inline(code, ['N','M','Nout','dL_dK','dL_dK_small','index','index2'])
N, num_inducing, Nout = index.size, index2.size, self.Nout
weave.inline(code, ['N','num_inducing','Nout','dL_dK','dL_dK_small','index','index2'])
dkappa = np.diag(dL_dK_small)
dL_dK_small += dL_dK_small.T

101
GPy/kern/exponential.py
View file

@ -2,21 +2,20 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
import hashlib
from scipy import integrate
class exponential(kernpart):
class exponential(Kernpart):
"""
Exponential kernel (aka Ornstein-Uhlenbeck or Matern 1/2)
.. math::
k(r) = \sigma^2 \exp(- r) \ \ \ \ \ \\text{ where } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
k(r) = \sigma^2 \exp(- r) \ \ \ \ \ \\text{ where } r = \sqrt{\sum_{i=1}^input_dim \\frac{(x_i-y_i)^2}{\ell_i^2} }
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the vector of lengthscale :math:`\ell_i`
@ -26,11 +25,11 @@ class exponential(kernpart):
:rtype: kernel object
"""
def __init__(self,D,variance=1.,lengthscale=None,ARD=False):
self.D = D
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False):
self.input_dim = input_dim
self.ARD = ARD
if ARD == False:
self.Nparam = 2
self.num_params = 2
self.name = 'exp'
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
@ -38,76 +37,76 @@ class exponential(kernpart):
else:
lengthscale = np.ones(1)
else:
self.Nparam = self.D + 1
self.num_params = self.input_dim + 1
self.name = 'exp'
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == self.D, "bad number of lengthscales"
assert lengthscale.size == self.input_dim, "bad number of lengthscales"
else:
lengthscale = np.ones(self.D)
self._set_params(np.hstack((variance,lengthscale.flatten())))
lengthscale = np.ones(self.input_dim)
self._set_params(np.hstack((variance, lengthscale.flatten())))
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale))
return np.hstack((self.variance, self.lengthscale))
def _set_params(self,x):
def _set_params(self, x):
"""set the value of the parameters."""
assert x.size == self.Nparam
assert x.size == self.num_params
self.variance = x[0]
self.lengthscale = x[1:]
def _get_param_names(self):
"""return parameter names."""
if self.Nparam == 2:
return ['variance','lengthscale']
if self.num_params == 2:
return ['variance', 'lengthscale']
else:
return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscale.size)]
return ['variance'] + ['lengthscale_%i' % i for i in range(self.lengthscale.size)]
def K(self,X,X2,target):
def K(self, X, X2, target):
"""Compute the covariance matrix between X and X2."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
np.add(self.variance*np.exp(-dist), target,target)
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))
np.add(self.variance * np.exp(-dist), target, target)
def Kdiag(self,X,target):
def Kdiag(self, X, target):
"""Compute the diagonal of the covariance matrix associated to X."""
np.add(target,self.variance,target)
np.add(target, self.variance, target)
def dK_dtheta(self,dL_dK,X,X2,target):
def dK_dtheta(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
invdist = 1./np.where(dist!=0.,dist,np.inf)
dist2M = np.square(X[:,None,:]-X2[None,:,:])/self.lengthscale**3
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))
invdist = 1. / np.where(dist != 0., dist, np.inf)
dist2M = np.square(X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 3
dvar = np.exp(-dist)
target[0] += np.sum(dvar*dL_dK)
target[0] += np.sum(dvar * dL_dK)
if self.ARD == True:
dl = self.variance*dvar[:,:,None]*dist2M*invdist[:,:,None]
target[1:] += (dl*dL_dK[:,:,None]).sum(0).sum(0)
dl = self.variance * dvar[:, :, None] * dist2M * invdist[:, :, None]
target[1:] += (dl * dL_dK[:, :, None]).sum(0).sum(0)
else:
dl = self.variance*dvar*dist2M.sum(-1)*invdist
target[1] += np.sum(dl*dL_dK)
dl = self.variance * dvar * dist2M.sum(-1) * invdist
target[1] += np.sum(dl * dL_dK)
def dKdiag_dtheta(self,dL_dKdiag,X,target):
def dKdiag_dtheta(self, dL_dKdiag, X, target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
#NB: derivative of diagonal elements wrt lengthscale is 0
# NB: derivative of diagonal elements wrt lengthscale is 0
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def dK_dX(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscale**2/np.where(dist!=0.,dist,np.inf)
dK_dX = - np.transpose(self.variance*np.exp(-dist)*ddist_dX,(1,0,2))
target += np.sum(dK_dX*dL_dK.T[:,:,None],0)
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))[:, :, None]
ddist_dX = (X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 2 / np.where(dist != 0., dist, np.inf)
dK_dX = -np.transpose(self.variance * np.exp(-dist) * ddist_dX, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self,dL_dKdiag,X,target):
def dKdiag_dX(self, dL_dKdiag, X, target):
pass
def Gram_matrix(self,F,F1,lower,upper):
def Gram_matrix(self, F, F1, lower, upper):
"""
Return the Gram matrix of the vector of functions F with respect to the RKHS norm. The use of this function is limited to D=1.
Return the Gram matrix of the vector of functions F with respect to the RKHS norm. The use of this function is limited to input_dim=1.
:param F: vector of functions
:type F: np.array
@ -116,13 +115,13 @@ class exponential(kernpart):
:param lower,upper: boundaries of the input domain
:type lower,upper: floats
"""
assert self.D == 1
def L(x,i):
return(1./self.lengthscale*F[i](x) + F1[i](x))
assert self.input_dim == 1
def L(x, i):
return(1. / self.lengthscale * F[i](x) + F1[i](x))
n = F.shape[0]
G = np.zeros((n,n))
G = np.zeros((n, n))
for i in range(n):
for j in range(i,n):
G[i,j] = G[j,i] = integrate.quad(lambda x : L(x,i)*L(x,j),lower,upper)[0]
Flower = np.array([f(lower) for f in F])[:,None]
return(self.lengthscale/2./self.variance * G + 1./self.variance * np.dot(Flower,Flower.T))
for j in range(i, n):
G[i, j] = G[j, i] = integrate.quad(lambda x : L(x, i) * L(x, j), lower, upper)[0]
Flower = np.array([f(lower) for f in F])[:, None]
return(self.lengthscale / 2. / self.variance * G + 1. / self.variance * np.dot(Flower, Flower.T))

16
GPy/kern/finite_dimensional.py
View file

@ -2,21 +2,21 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
from ..util.linalg import pdinv,mdot
class finite_dimensional(kernpart):
def __init__(self, D, F, G, variance=1., weights=None):
class finite_dimensional(Kernpart):
def __init__(self, input_dim, F, G, variance=1., weights=None):
"""
Argumnents
----------
D: int - the number of input dimensions
input_dim: int - the number of input dimensions
F: np.array of functions with shape (n,) - the n basis functions
G: np.array with shape (n,n) - the Gram matrix associated to F
weights : np.ndarray with shape (n,)
"""
self.D = D
self.input_dim = input_dim
self.F = F
self.G = G
self.G_1 ,L,Li,logdet = pdinv(G)
@ -25,14 +25,14 @@ class finite_dimensional(kernpart):
assert weights.shape==(self.n,)
else:
weights = np.ones(self.n)
self.Nparam = self.n + 1
self.num_params = self.n + 1
self.name = 'finite_dim'
self._set_params(np.hstack((variance,weights)))
def _get_params(self):
return np.hstack((self.variance,self.weights))
def _set_params(self,x):
assert x.size == (self.Nparam)
assert x.size == (self.num_params)
self.variance = x[0]
self.weights = x[1:]
def _get_param_names(self):
@ -48,7 +48,7 @@ class finite_dimensional(kernpart):
product = self.variance * mdot(FX,np.diag(np.sqrt(self.weights)),self.G_1,np.diag(np.sqrt(self.weights)),FX2.T)
np.add(product,target,target)
def Kdiag(self,X,target):
product = np.diag(self.K(X,X2))
product = np.diag(self.K(X, X))
np.add(target,product,target)
def dK_dtheta(self,X,X2,target):
"""Return shape is NxMx(Ntheta)"""

29
GPy/kern/fixed.py
View file

@ -1,42 +1,41 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
import hashlib
class fixed(kernpart):
def __init__(self,D,K,variance=1.):
class Fixed(Kernpart):
def __init__(self, input_dim, K, variance=1.):
"""
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
"""
self.D = D
self.input_dim = input_dim
self.fixed_K = K
self.Nparam = 1
self.name = 'fixed'
self.num_params = 1
self.name = 'Fixed'
self._set_params(np.array([variance]).flatten())
def _get_params(self):
return self.variance
def _set_params(self,x):
assert x.shape==(1,)
def _set_params(self, x):
assert x.shape == (1,)
self.variance = x
def _get_param_names(self):
return ['variance']
def K(self,X,X2,target):
def K(self, X, X2, target):
target += self.variance * self.fixed_K
def dK_dtheta(self,partial,X,X2,target):
def dK_dtheta(self, partial, X, X2, target):
target += (partial * self.fixed_K).sum()
def dK_dX(self, partial,X, X2, target):
def dK_dX(self, partial, X, X2, target):
pass
def dKdiag_dX(self,partial,X,target):
def dKdiag_dX(self, partial, X, target):
pass

8
GPy/kern/independent_outputs.py
View file

@ -2,7 +2,7 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
def index_to_slices(index):
@ -31,7 +31,7 @@ def index_to_slices(index):
[ret[ind_i].append(slice(*indexes_i)) for ind_i,indexes_i in zip(ind[switchpoints[:-1]],zip(switchpoints,switchpoints[1:]))]
return ret
class independent_outputs(kernpart):
class IndependentOutputs(Kernpart):
"""
A kernel part shich can reopresent several independent functions.
this kernel 'switches off' parts of the matrix where the output indexes are different.
@ -41,8 +41,8 @@ class independent_outputs(kernpart):
"""
def __init__(self,k):
self.D = k.D + 1
self.Nparam = k.Nparam
self.input_dim = k.input_dim + 1
self.num_params = k.num_params
self.name = 'iops('+ k.name + ')'
self.k = k

123
GPy/kern/kern.py
View file

@ -4,32 +4,31 @@
import numpy as np
import pylab as pb
from ..core.parameterised import parameterised
from kernpart import kernpart
from ..core.parameterised import Parameterised
from kernpart import Kernpart
import itertools
from prod import prod
from ..util.linalg import symmetrify
class kern(parameterised):
def __init__(self, D, parts=[], input_slices=None):
class kern(Parameterised):
def __init__(self, input_dim, parts=[], input_slices=None):
"""
This is the main kernel class for GPy. It handles multiple (additive) kernel functions, and keeps track of variaous things like which parameters live where.
The technical code for kernels is divided into _parts_ (see e.g. rbf.py). This obnject contains a list of parts, which are computed additively. For multiplication, special _prod_ parts are used.
:param D: The dimensioality of the kernel's input space
:type D: int
:param input_dim: The dimensionality of the kernel's input space
:type input_dim: int
:param parts: the 'parts' (PD functions) of the kernel
:type parts: list of kernpart objects
:type parts: list of Kernpart objects
:param input_slices: the slices on the inputs which apply to each kernel
:type input_slices: list of slice objects, or list of bools
"""
self.parts = parts
self.Nparts = len(parts)
self.Nparam = sum([p.Nparam for p in self.parts])
self.num_params = sum([p.num_params for p in self.parts])
self.D = D
self.input_dim = input_dim
# deal with input_slices
if input_slices is None:
@ -39,11 +38,11 @@ class kern(parameterised):
self.input_slices = [sl if type(sl) is slice else slice(None) for sl in input_slices]
for p in self.parts:
assert isinstance(p, kernpart), "bad kernel part"
assert isinstance(p, Kernpart), "bad kernel part"
self.compute_param_slices()
parameterised.__init__(self)
Parameterised.__init__(self)
def plot_ARD(self, fignum=None, ax=None):
@ -80,8 +79,8 @@ class kern(parameterised):
self.param_slices = []
count = 0
for p in self.parts:
self.param_slices.append(slice(count, count + p.Nparam))
count += p.Nparam
self.param_slices.append(slice(count, count + p.num_params))
count += p.num_params
def __add__(self, other):
"""
@ -96,29 +95,29 @@ class kern(parameterised):
:type other: GPy.kern
"""
if tensor:
D = self.D + other.D
self_input_slices = [slice(*sl.indices(self.D)) for sl in self.input_slices]
other_input_indices = [sl.indices(other.D) for sl in other.input_slices]
other_input_slices = [slice(i[0] + self.D, i[1] + self.D, i[2]) for i in other_input_indices]
D = self.input_dim + other.input_dim
self_input_slices = [slice(*sl.indices(self.input_dim)) for sl in self.input_slices]
other_input_indices = [sl.indices(other.input_dim) for sl in other.input_slices]
other_input_slices = [slice(i[0] + self.input_dim, i[1] + self.input_dim, i[2]) for i in other_input_indices]
newkern = kern(D, self.parts + other.parts, self_input_slices + other_input_slices)
# transfer constraints:
newkern.constrained_indices = self.constrained_indices + [x + self.Nparam for x in other.constrained_indices]
newkern.constrained_indices = self.constrained_indices + [x + self.num_params for x in other.constrained_indices]
newkern.constraints = self.constraints + other.constraints
newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
newkern.fixed_indices = self.fixed_indices + [self.num_params + x for x in other.fixed_indices]
newkern.fixed_values = self.fixed_values + other.fixed_values
newkern.constraints = self.constraints + other.constraints
newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
newkern.tied_indices = self.tied_indices + [self.num_params + x for x in other.tied_indices]
else:
assert self.D == other.D
newkern = kern(self.D, self.parts + other.parts, self.input_slices + other.input_slices)
assert self.input_dim == other.input_dim
newkern = kern(self.input_dim, self.parts + other.parts, self.input_slices + other.input_slices)
# transfer constraints:
newkern.constrained_indices = self.constrained_indices + [i + self.Nparam for i in other.constrained_indices]
newkern.constrained_indices = self.constrained_indices + [i + self.num_params for i in other.constrained_indices]
newkern.constraints = self.constraints + other.constraints
newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
newkern.fixed_indices = self.fixed_indices + [self.num_params + x for x in other.fixed_indices]
newkern.fixed_values = self.fixed_values + other.fixed_values
newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
newkern.tied_indices = self.tied_indices + [self.num_params + x for x in other.tied_indices]
return newkern
def __mul__(self, other):
@ -138,16 +137,16 @@ class kern(parameterised):
slices = []
for sl1, sl2 in itertools.product(K1.input_slices, K2.input_slices):
s1, s2 = [False] * K1.D, [False] * K2.D
s1, s2 = [False] * K1.input_dim, [False] * K2.input_dim
s1[sl1], s2[sl2] = [True], [True]
slices += [s1 + s2]
newkernparts = [prod(k1, k2, tensor) for k1, k2 in itertools.product(K1.parts, K2.parts)]
if tensor:
newkern = kern(K1.D + K2.D, newkernparts, slices)
newkern = kern(K1.input_dim + K2.input_dim, newkernparts, slices)
else:
newkern = kern(K1.D, newkernparts, slices)
newkern = kern(K1.input_dim, newkernparts, slices)
newkern._follow_constrains(K1, K2)
return newkern
@ -158,13 +157,13 @@ class kern(parameterised):
K1_param = []
n = 0
for k1 in K1.parts:
K1_param += [range(n, n + k1.Nparam)]
n += k1.Nparam
K1_param += [range(n, n + k1.num_params)]
n += k1.num_params
n = 0
K2_param = []
for k2 in K2.parts:
K2_param += [range(K1.Nparam + n, K1.Nparam + n + k2.Nparam)]
n += k2.Nparam
K2_param += [range(K1.num_params + n, K1.num_params + n + k2.num_params)]
n += k2.num_params
index_param = []
for p1 in K1_param:
for p2 in K2_param:
@ -172,12 +171,12 @@ class kern(parameterised):
index_param = np.array(index_param)
# Get the ties and constrains of the kernels before the multiplication
prev_ties = K1.tied_indices + [arr + K1.Nparam for arr in K2.tied_indices]
prev_ties = K1.tied_indices + [arr + K1.num_params for arr in K2.tied_indices]
prev_constr_ind = [K1.constrained_indices] + [K1.Nparam + i for i in K2.constrained_indices]
prev_constr_ind = [K1.constrained_indices] + [K1.num_params + i for i in K2.constrained_indices]
prev_constr = K1.constraints + K2.constraints
# prev_constr_fix = K1.fixed_indices + [arr + K1.Nparam for arr in K2.fixed_indices]
# prev_constr_fix = K1.fixed_indices + [arr + K1.num_params for arr in K2.fixed_indices]
# prev_constr_fix_values = K1.fixed_values + K2.fixed_values
# follow the previous ties
@ -186,7 +185,7 @@ class kern(parameterised):
index_param[np.where(index_param == j)[0]] = arr[0]
# ties and constrains
for i in range(K1.Nparam + K2.Nparam):
for i in range(K1.num_params + K2.num_params):
index = np.where(index_param == i)[0]
if index.size > 1:
self.tie_params(index)
@ -211,7 +210,7 @@ class kern(parameterised):
def K(self, X, X2=None, which_parts='all'):
if which_parts == 'all':
which_parts = [True] * self.Nparts
assert X.shape[1] == self.D
assert X.shape[1] == self.input_dim
if X2 is None:
target = np.zeros((X.shape[0], X.shape[0]))
[p.K(X[:, i_s], None, target=target) for p, i_s, part_i_used in zip(self.parts, self.input_slices, which_parts) if part_i_used]
@ -223,14 +222,14 @@ class kern(parameterised):
def dK_dtheta(self, dL_dK, X, X2=None):
"""
:param dL_dK: An array of dL_dK derivaties, dL_dK
:type dL_dK: Np.ndarray (N x M)
:type dL_dK: Np.ndarray (N x num_inducing)
:param X: Observed data inputs
:type X: np.ndarray (N x D)
:type X: np.ndarray (N x input_dim)
:param X2: Observed dara inputs (optional, defaults to X)
:type X2: np.ndarray (M x D)
:type X2: np.ndarray (num_inducing x input_dim)
"""
assert X.shape[1] == self.D
target = np.zeros(self.Nparam)
assert X.shape[1] == self.input_dim
target = np.zeros(self.num_params)
if X2 is None:
[p.dK_dtheta(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self.parts, self.input_slices, self.param_slices)]
else:
@ -251,20 +250,20 @@ class kern(parameterised):
def Kdiag(self, X, which_parts='all'):
if which_parts == 'all':
which_parts = [True] * self.Nparts
assert X.shape[1] == self.D
assert X.shape[1] == self.input_dim
target = np.zeros(X.shape[0])
[p.Kdiag(X[:, i_s], target=target) for p, i_s, part_on in zip(self.parts, self.input_slices, which_parts) if part_on]
return target
def dKdiag_dtheta(self, dL_dKdiag, X):
assert X.shape[1] == self.D
assert X.shape[1] == self.input_dim
assert dL_dKdiag.size == X.shape[0]
target = np.zeros(self.Nparam)
target = np.zeros(self.num_params)
[p.dKdiag_dtheta(dL_dKdiag, X[:, i_s], target[ps]) for p, i_s, ps in zip(self.parts, self.input_slices, self.param_slices)]
return self._transform_gradients(target)
def dKdiag_dX(self, dL_dKdiag, X):
assert X.shape[1] == self.D
assert X.shape[1] == self.input_dim
target = np.zeros_like(X)
[p.dKdiag_dX(dL_dKdiag, X[:, i_s], target[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
return target
@ -275,7 +274,7 @@ class kern(parameterised):
return target
def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S):
target = np.zeros(self.Nparam)
target = np.zeros(self.num_params)
[p.dpsi0_dtheta(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self.parts, self.param_slices, self.input_slices)]
return self._transform_gradients(target)
@ -290,7 +289,7 @@ class kern(parameterised):
return target
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S):
target = np.zeros((self.Nparam))
target = np.zeros((self.num_params))
[p.dpsi1_dtheta(dL_dpsi1, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self.parts, self.param_slices, self.input_slices)]
return self._transform_gradients(target)
@ -300,16 +299,16 @@ class kern(parameterised):
return target
def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S):
"""return shapes are N,M,input_dim"""
"""return shapes are N,num_inducing,input_dim"""
target_mu, target_S = np.zeros((2, mu.shape[0], mu.shape[1]))
[p.dpsi1_dmuS(dL_dpsi1, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
return target_mu, target_S
def psi2(self, Z, mu, S):
"""
:param Z: np.ndarray of inducing inputs (M x input_dim)
:param Z: np.ndarray of inducing inputs (num_inducing x input_dim)
:param mu, S: np.ndarrays of means and variances (each N x input_dim)
:returns psi2: np.ndarray (N,M,M)
:returns psi2: np.ndarray (N,num_inducing,num_inducing)
"""
target = np.zeros((mu.shape[0], Z.shape[0], Z.shape[0]))
[p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
@ -327,13 +326,13 @@ class kern(parameterised):
p2.psi1(Z, mu, S, tmp2)
prod = np.multiply(tmp1, tmp2)
crossterms += prod[:,:,None] + prod[:, None, :]
crossterms += prod[:, :, None] + prod[:, None, :]
target += crossterms
return target
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S):
target = np.zeros(self.Nparam)
target = np.zeros(self.num_params)
[p.dpsi2_dtheta(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, i_s, ps in zip(self.parts, self.input_slices, self.param_slices)]
# compute the "cross" terms
@ -345,14 +344,14 @@ class kern(parameterised):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
p1.psi1(Z, mu, S, tmp)
p2.dpsi1_dtheta((tmp[:,None,:]*dL_dpsi2).sum(1)*2., Z, mu, S, target[ps2])
p2.dpsi1_dtheta((tmp[:, None, :] * dL_dpsi2).sum(1) * 2., Z, mu, S, target[ps2])
return self._transform_gradients(target)
def dpsi2_dZ(self, dL_dpsi2, Z, mu, S):
target = np.zeros_like(Z)
[p.dpsi2_dZ(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
#target *= 2
# target *= 2
# compute the "cross" terms
# TODO: we need input_slices here.
@ -362,7 +361,7 @@ class kern(parameterised):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
p1.psi1(Z, mu, S, tmp)
tmp2 = np.zeros_like(target)
p2.dpsi1_dZ((tmp[:,None,:]*dL_dpsi2).sum(1).T, Z, mu, S, tmp2)
p2.dpsi1_dZ((tmp[:, None, :] * dL_dpsi2).sum(1).T, Z, mu, S, tmp2)
target += tmp2
return target * 2
@ -379,14 +378,14 @@ class kern(parameterised):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
p1.psi1(Z, mu, S, tmp)
p2.dpsi1_dmuS((tmp[:,None,:]*dL_dpsi2).sum(1).T*2., Z, mu, S, target_mu, target_S)
p2.dpsi1_dmuS((tmp[:, None, :] * dL_dpsi2).sum(1).T * 2., Z, mu, S, target_mu, target_S)
return target_mu, target_S
def plot(self, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs):
if which_parts == 'all':
which_parts = [True] * self.Nparts
if self.D == 1:
if self.input_dim == 1:
if x is None:
x = np.zeros((1, 1))
else:
@ -408,7 +407,7 @@ class kern(parameterised):
pb.xlabel("x")
pb.ylabel("k(x,%0.1f)" % x)
elif self.D == 2:
elif self.input_dim == 2:
if x is None:
x = np.zeros((1, 2))
else:
@ -430,7 +429,7 @@ class kern(parameterised):
Xnew = np.vstack((xx.flatten(), yy.flatten())).T
Kx = self.K(Xnew, x, which_parts)
Kx = Kx.reshape(resolution, resolution).T
pb.contour(xg, yg, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, *args, **kwargs)
pb.contour(xg, yg, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, *args, **kwargs) # @UndefinedVariable
pb.xlim(xmin[0], xmax[0])
pb.ylim(xmin[1], xmax[1])
pb.xlabel("x1")

12
GPy/kern/kernpart.py
View file

@ -2,18 +2,18 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
class kernpart(object):
def __init__(self,D):
class Kernpart(object):
def __init__(self,input_dim):
"""
The base class for a kernpart: a positive definite function which forms part of a kernel
:param D: the number of input dimensions to the function
:type D: int
:param input_dim: the number of input dimensions to the function
:type input_dim: int
Do not instantiate.
"""
self.D = D
self.Nparam = 1
self.input_dim = input_dim
self.num_params = 1
self.name = 'unnamed'
def _get_params(self):

52
GPy/kern/linear.py
View file

@ -2,21 +2,21 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
from ..util.linalg import tdot
from scipy import weave
class linear(kernpart):
class linear(Kernpart):
"""
Linear kernel
.. math::
k(x,y) = \sum_{i=1}^D \sigma^2_i x_iy_i
k(x,y) = \sum_{i=1}^input_dim \sigma^2_i x_iy_i
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variances: the vector of variances :math:`\sigma^2_i`
:type variances: array or list of the appropriate size (or float if there is only one variance parameter)
:param ARD: Auto Relevance Determination. If equal to "False", the kernel has only one variance parameter \sigma^2, otherwise there is one variance parameter per dimension.
@ -24,11 +24,11 @@ class linear(kernpart):
:rtype: kernel object
"""
def __init__(self, D, variances=None, ARD=False):
self.D = D
def __init__(self, input_dim, variances=None, ARD=False):
self.input_dim = input_dim
self.ARD = ARD
if ARD == False:
self.Nparam = 1
self.num_params = 1
self.name = 'linear'
if variances is not None:
variances = np.asarray(variances)
@ -37,13 +37,13 @@ class linear(kernpart):
variances = np.ones(1)
self._Xcache, self._X2cache = np.empty(shape=(2,))
else:
self.Nparam = self.D
self.num_params = self.input_dim
self.name = 'linear'
if variances is not None:
variances = np.asarray(variances)
assert variances.size == self.D, "bad number of lengthscales"
assert variances.size == self.input_dim, "bad number of lengthscales"
else:
variances = np.ones(self.D)
variances = np.ones(self.input_dim)
self._set_params(variances.flatten())
# initialize cache
@ -54,12 +54,12 @@ class linear(kernpart):
return self.variances
def _set_params(self, x):
assert x.size == (self.Nparam)
assert x.size == (self.num_params)
self.variances = x
self.variances2 = np.square(self.variances)
def _get_param_names(self):
if self.Nparam == 1:
if self.num_params == 1:
return ['variance']
else:
return ['variance_%i' % i for i in range(self.variances.size)]
@ -82,7 +82,7 @@ class linear(kernpart):
def dK_dtheta(self, dL_dK, X, X2, target):
if self.ARD:
if X2 is None:
[np.add(target[i:i + 1], np.sum(dL_dK * tdot(X[:, i:i + 1])), target[i:i + 1]) for i in range(self.D)]
[np.add(target[i:i + 1], np.sum(dL_dK * tdot(X[:, i:i + 1])), target[i:i + 1]) for i in range(self.input_dim)]
else:
product = X[:, None, :] * X2[None, :, :]
target += (dL_dK[:, :, None] * product).sum(0).sum(0)
@ -138,7 +138,7 @@ class linear(kernpart):
def psi2(self, Z, mu, S, target):
"""
returns N,M,M matrix
returns N,num_inducing,num_inducing matrix
"""
self._psi_computations(Z, mu, S)
# psi2_old = self.ZZ * np.square(self.variances) * self.mu2_S[:, None, None, :]
@ -153,7 +153,7 @@ class linear(kernpart):
# psi2_real[n, m, m_prime] = np.dot(tmp, (
# self._Z[m_prime:m_prime + 1] * self.variances).T)
# mu2_S = (self._mu[:, None, :] * self._mu[:, :, None])
# mu2_S[:, np.arange(self.D), np.arange(self.D)] += self._S
# mu2_S[:, np.arange(self.input_dim), np.arange(self.input_dim)] += self._S
# psi2 = (self.ZA[None, :, None, :] * mu2_S[:, None]).sum(-1)
# psi2 = (psi2[:, :, None] * self.ZA[None, None]).sum(-1)
# psi2_tensor = np.tensordot(self.ZZ[None, :, :, :] * np.square(self.variances), self.mu2_S[:, None, None, :], ((3), (3))).squeeze().T
@ -168,7 +168,7 @@ class linear(kernpart):
target += tmp.sum()
def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
"""Think N,M,M,input_dim """
"""Think N,num_inducing,num_inducing,input_dim """
self._psi_computations(Z, mu, S)
AZZA = self.ZA.T[:, None, :, None] * self.ZA[None, :, None, :]
AZZA = AZZA + AZZA.swapaxes(1, 2)
@ -184,7 +184,7 @@ class linear(kernpart):
double factor,tmp;
#pragma omp parallel for private(m,mm,q,qq,factor,tmp)
for(n=0;n<N;n++){
for(m=0;m<M;m++){
for(m=0;m<num_inducing;m++){
for(mm=0;mm<=m;mm++){
//add in a factor of 2 for the off-diagonal terms (and then count them only once)
if(m==mm)
@ -215,9 +215,9 @@ class linear(kernpart):
'extra_compile_args': ['-fopenmp -O3'], #-march=native'],
'extra_link_args' : ['-lgomp']}
N,M,input_dim = mu.shape[0],Z.shape[0],mu.shape[1]
N,num_inducing,input_dim = mu.shape[0],Z.shape[0],mu.shape[1]
weave.inline(code, support_code=support_code, libraries=['gomp'],
arg_names=['N','M','input_dim','mu','AZZA','AZZA_2','target_mu','target_S','dL_dpsi2'],
arg_names=['N','num_inducing','input_dim','mu','AZZA','AZZA_2','target_mu','target_S','dL_dpsi2'],
type_converters=weave.converters.blitz,**weave_options)
@ -231,9 +231,9 @@ class linear(kernpart):
code="""
int n,m,mm,q;
#pragma omp parallel for private(n,mm,q)
for(m=0;m<M;m++){
for(m=0;m<num_inducing;m++){
for(q=0;q<input_dim;q++){
for(mm=0;mm<M;mm++){
for(mm=0;mm<num_inducing;mm++){
for(n=0;n<N;n++){
target(m,q) += dL_dpsi2(n,m,mm)*AZA(n,mm,q);
}
@ -249,9 +249,9 @@ class linear(kernpart):
'extra_compile_args': ['-fopenmp -O3'], #-march=native'],
'extra_link_args' : ['-lgomp']}
N,M,input_dim = mu.shape[0],Z.shape[0],mu.shape[1]
N,num_inducing,input_dim = mu.shape[0],Z.shape[0],mu.shape[1]
weave.inline(code, support_code=support_code, libraries=['gomp'],
arg_names=['N','M','input_dim','AZA','target','dL_dpsi2'],
arg_names=['N','num_inducing','input_dim','AZA','target','dL_dpsi2'],
type_converters=weave.converters.blitz,**weave_options)
@ -278,7 +278,7 @@ class linear(kernpart):
muS_changed = not (np.array_equal(mu, self._mu) and np.array_equal(S, self._S))
if Zv_changed:
# Z has changed, compute Z specific stuff
# self.ZZ = Z[:,None,:]*Z[None,:,:] # M,M,input_dim
# self.ZZ = Z[:,None,:]*Z[None,:,:] # num_inducing,num_inducing,input_dim
# self.ZZ = np.empty((Z.shape[0], Z.shape[0], Z.shape[1]), order='F')
# [tdot(Z[:, i:i + 1], self.ZZ[:, :, i].T) for i in xrange(Z.shape[1])]
self.ZA = Z * self.variances
@ -291,5 +291,5 @@ class linear(kernpart):
self.inner[:, diag_indices[0], diag_indices[1]] += S
self._mu, self._S = mu.copy(), S.copy()
if Zv_changed or muS_changed:
self.ZAinner = np.dot(self.ZA, self.inner).swapaxes(0, 1) # NOTE: self.ZAinner \in [M x N x input_dim]!
self.ZAinner = np.dot(self.ZA, self.inner).swapaxes(0, 1) # NOTE: self.ZAinner \in [num_inducing x N x input_dim]!
self._psi2 = np.dot(self.ZAinner, self.ZA.T)

26
GPy/kern/periodic_Matern32.py
View file

@ -2,21 +2,21 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
from GPy.util.linalg import mdot, pdinv
from GPy.util.linalg import mdot
from GPy.util.decorators import silence_errors
class periodic_Matern32(kernpart):
class periodic_Matern32(Kernpart):
"""
Kernel of the periodic subspace (up to a given frequency) of a Matern 3/2 RKHS. Only defined for D=1.
Kernel of the periodic subspace (up to a given frequency) of a Matern 3/2 RKHS. Only defined for input_dim=1.
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance of the Matern kernel
:type variance: float
:param lengthscale: the lengthscale of the Matern kernel
:type lengthscale: np.ndarray of size (D,)
:type lengthscale: np.ndarray of size (input_dim,)
:param period: the period
:type period: float
:param n_freq: the number of frequencies considered for the periodic subspace
@ -25,17 +25,17 @@ class periodic_Matern32(kernpart):
"""
def __init__(self,D=1,variance=1.,lengthscale=None,period=2*np.pi,n_freq=10,lower=0.,upper=4*np.pi):
assert D==1, "Periodic kernels are only defined for D=1"
def __init__(self, input_dim=1, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi):
assert input_dim==1, "Periodic kernels are only defined for input_dim=1"
self.name = 'periodic_Mat32'
self.D = D
self.input_dim = input_dim
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == 1, "Wrong size: only one lengthscale needed"
else:
lengthscale = np.ones(1)
self.lower,self.upper = lower, upper
self.Nparam = 3
self.num_params = 3
self.n_freq = n_freq
self.n_basis = 2*n_freq
self._set_params(np.hstack((variance,lengthscale,period)))
@ -64,7 +64,7 @@ class periodic_Matern32(kernpart):
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale,self.period))
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size==3
@ -113,7 +113,7 @@ class periodic_Matern32(kernpart):
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)"""
"""derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

28
GPy/kern/periodic_Matern52.py
View file

@ -2,21 +2,21 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
from GPy.util.linalg import mdot, pdinv
from GPy.util.linalg import mdot
from GPy.util.decorators import silence_errors
class periodic_Matern52(kernpart):
class periodic_Matern52(Kernpart):
"""
Kernel of the periodic subspace (up to a given frequency) of a Matern 5/2 RKHS. Only defined for D=1.
Kernel of the periodic subspace (up to a given frequency) of a Matern 5/2 RKHS. Only defined for input_dim=1.
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance of the Matern kernel
:type variance: float
:param lengthscale: the lengthscale of the Matern kernel
:type lengthscale: np.ndarray of size (D,)
:type lengthscale: np.ndarray of size (input_dim,)
:param period: the period
:type period: float
:param n_freq: the number of frequencies considered for the periodic subspace
@ -25,17 +25,17 @@ class periodic_Matern52(kernpart):
"""
def __init__(self,D=1,variance=1.,lengthscale=None,period=2*np.pi,n_freq=10,lower=0.,upper=4*np.pi):
assert D==1, "Periodic kernels are only defined for D=1"
def __init__(self,input_dim=1,variance=1.,lengthscale=None,period=2*np.pi,n_freq=10,lower=0.,upper=4*np.pi):
assert input_dim==1, "Periodic kernels are only defined for input_dim=1"
self.name = 'periodic_Mat52'
self.D = D
self.input_dim = input_dim
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == 1, "Wrong size: only one lengthscale needed"
else:
lengthscale = np.ones(1)
self.lower,self.upper = lower, upper
self.Nparam = 3
self.num_params = 3
self.n_freq = n_freq
self.n_basis = 2*n_freq
self._set_params(np.hstack((variance,lengthscale,period)))
@ -64,7 +64,7 @@ class periodic_Matern52(kernpart):
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale,self.period))
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size==3
@ -115,7 +115,7 @@ class periodic_Matern52(kernpart):
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)"""
"""derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)
@ -209,7 +209,7 @@ class periodic_Matern52(kernpart):
F2lower = np.array(self._cos(self.basis_alpha*self.basis_omega**2,self.basis_omega,self.basis_phi+np.pi)(self.lower))[:,None]
#dK_dvar
dK_dvar = 1./self.variance*mdot(FX,self.Gi,FX2.T)
dK_dvar = 1. / self.variance * mdot(FX, self.Gi, FX.T)
#dK_dlen
da_dlen = [-3*self.a[0]/self.lengthscale, -2*self.a[1]/self.lengthscale, -self.a[2]/self.lengthscale, 0.]

26
GPy/kern/periodic_exponential.py
View file

@ -2,21 +2,21 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
from GPy.util.linalg import mdot, pdinv
from GPy.util.linalg import mdot
from GPy.util.decorators import silence_errors
class periodic_exponential(kernpart):
class periodic_exponential(Kernpart):
"""
Kernel of the periodic subspace (up to a given frequency) of a exponential (Matern 1/2) RKHS. Only defined for D=1.
Kernel of the periodic subspace (up to a given frequency) of a exponential (Matern 1/2) RKHS. Only defined for input_dim=1.
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance of the Matern kernel
:type variance: float
:param lengthscale: the lengthscale of the Matern kernel
:type lengthscale: np.ndarray of size (D,)
:type lengthscale: np.ndarray of size (input_dim,)
:param period: the period
:type period: float
:param n_freq: the number of frequencies considered for the periodic subspace
@ -25,17 +25,17 @@ class periodic_exponential(kernpart):
"""
def __init__(self,D=1,variance=1.,lengthscale=None,period=2*np.pi,n_freq=10,lower=0.,upper=4*np.pi):
assert D==1, "Periodic kernels are only defined for D=1"
def __init__(self, input_dim=1, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi):
assert input_dim==1, "Periodic kernels are only defined for input_dim=1"
self.name = 'periodic_exp'
self.D = D
self.input_dim = input_dim
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == 1, "Wrong size: only one lengthscale needed"
else:
lengthscale = np.ones(1)
self.lower,self.upper = lower, upper
self.Nparam = 3
self.num_params = 3
self.n_freq = n_freq
self.n_basis = 2*n_freq
self._set_params(np.hstack((variance,lengthscale,period)))
@ -64,7 +64,7 @@ class periodic_exponential(kernpart):
def _get_params(self):
"""return the value of the parameters."""
return np.hstack((self.variance,self.lengthscale,self.period))
def _set_params(self,x):
"""set the value of the parameters."""
assert x.size==3
@ -111,7 +111,7 @@ class periodic_exponential(kernpart):
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)"""
"""derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

38
GPy/kern/prod.py
View file

@ -1,35 +1,35 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
import hashlib
class prod(kernpart):
class prod(Kernpart):
"""
Computes the product of 2 kernels
:param k1, k2: the kernels to multiply
:type k1, k2: kernpart
:type k1, k2: Kernpart
:param tensor: The kernels are either multiply as functions defined on the same input space (default) or on the product of the input spaces
:type tensor: Boolean
:rtype: kernel object
"""
def __init__(self,k1,k2,tensor=False):
self.Nparam = k1.Nparam + k2.Nparam
self.num_params = k1.num_params + k2.num_params
self.name = k1.name + '<times>' + k2.name
self.k1 = k1
self.k2 = k2
if tensor:
self.D = k1.D + k2.D
self.slice1 = slice(0,self.k1.D)
self.slice2 = slice(self.k1.D,self.k1.D+self.k2.D)
self.input_dim = k1.input_dim + k2.input_dim
self.slice1 = slice(0,self.k1.input_dim)
self.slice2 = slice(self.k1.input_dim,self.k1.input_dim+self.k2.input_dim)
else:
assert k1.D == k2.D, "Error: The input spaces of the kernels to sum don't have the same dimension."
self.D = k1.D
self.slice1 = slice(0,self.D)
self.slice2 = slice(0,self.D)
assert k1.input_dim == k2.input_dim, "Error: The input spaces of the kernels to sum don't have the same dimension."
self.input_dim = k1.input_dim
self.slice1 = slice(0,self.input_dim)
self.slice2 = slice(0,self.input_dim)
self._X, self._X2, self._params = np.empty(shape=(3,1))
self._set_params(np.hstack((k1._get_params(),k2._get_params())))
@ -40,8 +40,8 @@ class prod(kernpart):
def _set_params(self,x):
"""set the value of the parameters."""
self.k1._set_params(x[:self.k1.Nparam])
self.k2._set_params(x[self.k1.Nparam:])
self.k1._set_params(x[:self.k1.num_params])
self.k2._set_params(x[self.k1.num_params:])
def _get_param_names(self):
"""return parameter names."""
@ -55,11 +55,11 @@ class prod(kernpart):
"""derivative of the covariance matrix with respect to the parameters."""
self._K_computations(X,X2)
if X2 is None:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], None, target[:self.k1.Nparam])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], None, target[self.k1.Nparam:])
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], None, target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], None, target[self.k1.num_params:])
else:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:self.k1.Nparam])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[self.k1.Nparam:])
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[self.k1.num_params:])
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""
@ -74,8 +74,8 @@ class prod(kernpart):
K2 = np.zeros(X.shape[0])
self.k1.Kdiag(X[:,self.slice1],K1)
self.k2.Kdiag(X[:,self.slice2],K2)
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,self.slice1],target[:self.k1.Nparam])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.slice2],target[self.k1.Nparam:])
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,self.slice1],target[:self.k1.num_params])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.slice2],target[self.k1.num_params:])
def dK_dX(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""

54
GPy/kern/prod_orthogonal.py
View file

@ -1,23 +1,23 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
import hashlib
#from scipy import integrate # This may not be necessary (Nicolas, 20th Feb)
class prod_orthogonal(kernpart):
class prod_orthogonal(Kernpart):
"""
Computes the product of 2 kernels
:param k1, k2: the kernels to multiply
:type k1, k2: kernpart
:type k1, k2: Kernpart
:rtype: kernel object
"""
def __init__(self,k1,k2):
self.D = k1.D + k2.D
self.Nparam = k1.Nparam + k2.Nparam
self.input_dim = k1.input_dim + k2.input_dim
self.num_params = k1.num_params + k2.num_params
self.name = k1.name + '<times>' + k2.name
self.k1 = k1
self.k2 = k2
@ -30,8 +30,8 @@ class prod_orthogonal(kernpart):
def _set_params(self,x):
"""set the value of the parameters."""
self.k1._set_params(x[:self.k1.Nparam])
self.k2._set_params(x[self.k1.Nparam:])
self.k1._set_params(x[:self.k1.num_params])
self.k2._set_params(x[self.k1.num_params:])
def _get_param_names(self):
"""return parameter names."""
@ -45,42 +45,42 @@ class prod_orthogonal(kernpart):
"""derivative of the covariance matrix with respect to the parameters."""
self._K_computations(X,X2)
if X2 is None:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.D], None, target[:self.k1.Nparam])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.D:], None, target[self.k1.Nparam:])
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.input_dim], None, target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.input_dim:], None, target[self.k1.num_params:])
else:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.D], X2[:,:self.k1.D], target[:self.k1.Nparam])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.D:], X2[:,self.k1.D:], target[self.k1.Nparam:])
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target[self.k1.num_params:])
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""
target1 = np.zeros(X.shape[0])
target2 = np.zeros(X.shape[0])
self.k1.Kdiag(X[:,:self.k1.D],target1)
self.k2.Kdiag(X[:,self.k1.D:],target2)
self.k1.Kdiag(X[:,:self.k1.input_dim],target1)
self.k2.Kdiag(X[:,self.k1.input_dim:],target2)
target += target1 * target2
def dKdiag_dtheta(self,dL_dKdiag,X,target):
K1 = np.zeros(X.shape[0])
K2 = np.zeros(X.shape[0])
self.k1.Kdiag(X[:,:self.k1.D],K1)
self.k2.Kdiag(X[:,self.k1.D:],K2)
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,:self.k1.D],target[:self.k1.Nparam])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.k1.D:],target[self.k1.Nparam:])
self.k1.Kdiag(X[:,:self.k1.input_dim],K1)
self.k2.Kdiag(X[:,self.k1.input_dim:],K2)
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,:self.k1.input_dim],target[:self.k1.num_params])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.k1.input_dim:],target[self.k1.num_params:])
def dK_dX(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
self._K_computations(X,X2)
self.k1.dK_dX(dL_dK*self._K2, X[:,:self.k1.D], X2[:,:self.k1.D], target)
self.k2.dK_dX(dL_dK*self._K1, X[:,self.k1.D:], X2[:,self.k1.D:], target)
self.k1.dK_dX(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target)
self.k2.dK_dX(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target)
def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0])
K2 = np.zeros(X.shape[0])
self.k1.Kdiag(X[:,0:self.k1.D],K1)
self.k2.Kdiag(X[:,self.k1.D:],K2)
self.k1.Kdiag(X[:,0:self.k1.input_dim],K1)
self.k2.Kdiag(X[:,self.k1.input_dim:],K2)
self.k1.dK_dX(dL_dKdiag*K2, X[:,:self.k1.D], target)
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.k1.D:], target)
self.k1.dK_dX(dL_dKdiag*K2, X[:,:self.k1.input_dim], target)
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.k1.input_dim:], target)
def _K_computations(self,X,X2):
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):
@ -90,12 +90,12 @@ class prod_orthogonal(kernpart):
self._X2 = None
self._K1 = np.zeros((X.shape[0],X.shape[0]))
self._K2 = np.zeros((X.shape[0],X.shape[0]))
self.k1.K(X[:,:self.k1.D],None,self._K1)
self.k2.K(X[:,self.k1.D:],None,self._K2)
self.k1.K(X[:,:self.k1.input_dim],None,self._K1)
self.k2.K(X[:,self.k1.input_dim:],None,self._K2)
else:
self._X2 = X2.copy()
self._K1 = np.zeros((X.shape[0],X2.shape[0]))
self._K2 = np.zeros((X.shape[0],X2.shape[0]))
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],self._K1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],self._K2)
self.k1.K(X[:,:self.k1.input_dim],X2[:,:self.k1.input_dim],self._K1)
self.k2.K(X[:,self.k1.input_dim:],X2[:,self.k1.input_dim:],self._K2)

18
GPy/kern/rational_quadratic.py
View file

@ -2,10 +2,10 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
class rational_quadratic(kernpart):
class rational_quadratic(Kernpart):
"""
rational quadratic kernel
@ -13,21 +13,21 @@ class rational_quadratic(kernpart):
k(r) = \sigma^2 \\bigg( 1 + \\frac{r^2}{2 \ell^2} \\bigg)^{- \\alpha} \ \ \ \ \ \\text{ where } r^2 = (x-y)^2
:param D: the number of input dimensions
:type D: int (D=1 is the only value currently supported)
:param input_dim: the number of input dimensions
:type input_dim: int (input_dim=1 is the only value currently supported)
:param variance: the variance :math:`\sigma^2`
:type variance: float
:param lengthscale: the lengthscale :math:`\ell`
:type lengthscale: float
:param power: the power :math:`\\alpha`
:type power: float
:rtype: kernpart object
:rtype: Kernpart object
"""
def __init__(self,D,variance=1.,lengthscale=1.,power=1.):
assert D == 1, "For this kernel we assume D=1"
self.D = D
self.Nparam = 3
def __init__(self,input_dim,variance=1.,lengthscale=1.,power=1.):
assert input_dim == 1, "For this kernel we assume input_dim=1"
self.input_dim = input_dim
self.num_params = 3
self.name = 'rat_quad'
self.variance = variance
self.lengthscale = lengthscale

251
GPy/kern/rbf.py
View file

@ -2,13 +2,13 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
import hashlib
from scipy import weave
from ..util.linalg import tdot
class rbf(kernpart):
class rbf(Kernpart):
"""
Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:
@ -18,8 +18,8 @@ class rbf(kernpart):
where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input.
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
:param lengthscale: the vector of lengthscale of the kernel
@ -31,76 +31,76 @@ class rbf(kernpart):
.. Note: this object implements both the ARD and 'spherical' version of the function
"""
def __init__(self,D,variance=1.,lengthscale=None,ARD=False):
self.D = D
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False):
self.input_dim = input_dim
self.name = 'rbf'
self.ARD = ARD
if not ARD:
self.Nparam = 2
self.num_params = 2
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == 1, "Only one lengthscale needed for non-ARD kernel"
else:
lengthscale = np.ones(1)
else:
self.Nparam = self.D + 1
self.num_params = self.input_dim + 1
if lengthscale is not None:
lengthscale = np.asarray(lengthscale)
assert lengthscale.size == self.D, "bad number of lengthscales"
assert lengthscale.size == self.input_dim, "bad number of lengthscales"
else:
lengthscale = np.ones(self.D)
lengthscale = np.ones(self.input_dim)
self._set_params(np.hstack((variance,lengthscale.flatten())))
self._set_params(np.hstack((variance, lengthscale.flatten())))
#initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3,1))
self._X, self._X2, self._params = np.empty(shape=(3,1))
# initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3, 1))
self._X, self._X2, self._params = np.empty(shape=(3, 1))
#a set of optional args to pass to weave
# a set of optional args to pass to weave
self.weave_options = {'headers' : ['<omp.h>'],
'extra_compile_args': ['-fopenmp -O3'], #-march=native'],
'extra_compile_args': ['-fopenmp -O3'], # -march=native'],
'extra_link_args' : ['-lgomp']}
def _get_params(self):
return np.hstack((self.variance,self.lengthscale))
return np.hstack((self.variance, self.lengthscale))
def _set_params(self,x):
assert x.size==(self.Nparam)
def _set_params(self, x):
assert x.size == (self.num_params)
self.variance = x[0]
self.lengthscale = x[1:]
self.lengthscale2 = np.square(self.lengthscale)
#reset cached results
self._X, self._X2, self._params = np.empty(shape=(3,1))
self._Z, self._mu, self._S = np.empty(shape=(3,1)) # cached versions of Z,mu,S
# reset cached results
self._X, self._X2, self._params = np.empty(shape=(3, 1))
self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
def _get_param_names(self):
if self.Nparam == 2:
return ['variance','lengthscale']
if self.num_params == 2:
return ['variance', 'lengthscale']
else:
return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscale.size)]
return ['variance'] + ['lengthscale_%i' % i for i in range(self.lengthscale.size)]
def K(self,X,X2,target):
self._K_computations(X,X2)
target += self.variance*self._K_dvar
def K(self, X, X2, target):
self._K_computations(X, X2)
target += self.variance * self._K_dvar
def Kdiag(self,X,target):
np.add(target,self.variance,target)
def Kdiag(self, X, target):
np.add(target, self.variance, target)
def dK_dtheta(self,dL_dK,X,X2,target):
self._K_computations(X,X2)
target[0] += np.sum(self._K_dvar*dL_dK)
def dK_dtheta(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
target[0] += np.sum(self._K_dvar * dL_dK)
if self.ARD:
dvardLdK = self._K_dvar*dL_dK
var_len3 = self.variance/np.power(self.lengthscale,3)
dvardLdK = self._K_dvar * dL_dK
var_len3 = self.variance / np.power(self.lengthscale, 3)
if X2 is None:
#save computation for the symmetrical case
# save computation for the symmetrical case
dvardLdK += dvardLdK.T
code = """
int q,i,j;
double tmp;
for(q=0; q<D; q++){
for(q=0; q<input_dim; q++){
tmp = 0;
for(i=0; i<N; i++){
for(j=0; j<i; j++){
@ -110,39 +110,39 @@ class rbf(kernpart):
target(q+1) += var_len3(q)*tmp;
}
"""
N,M,D = X.shape[0], X.shape[0], self.D
N, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
else:
code = """
int q,i,j;
double tmp;
for(q=0; q<D; q++){
for(q=0; q<input_dim; q++){
tmp = 0;
for(i=0; i<N; i++){
for(j=0; j<M; j++){
for(j=0; j<num_inducing; j++){
tmp += (X(i,q)-X2(j,q))*(X(i,q)-X2(j,q))*dvardLdK(i,j);
}
}
target(q+1) += var_len3(q)*tmp;
}
"""
N,M,D = X.shape[0], X2.shape[0], self.D
#[np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.D)]
weave.inline(code, arg_names=['N','M','D','X','X2','target','dvardLdK','var_len3'],
type_converters=weave.converters.blitz,**self.weave_options)
N, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
# [np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.input_dim)]
weave.inline(code, arg_names=['N','num_inducing','input_dim','X','X2','target','dvardLdK','var_len3'],
type_converters=weave.converters.blitz, **self.weave_options)
else:
target[1] += (self.variance/self.lengthscale)*np.sum(self._K_dvar*self._K_dist2*dL_dK)
target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK)
def dKdiag_dtheta(self,dL_dKdiag,X,target):
#NB: derivative of diagonal elements wrt lengthscale is 0
def dKdiag_dtheta(self, dL_dKdiag, X, target):
# NB: derivative of diagonal elements wrt lengthscale is 0
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
self._K_computations(X,X2)
_K_dist = X[:,None,:]-X2[None,:,:] #don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance/self.lengthscale2)*np.transpose(self._K_dvar[:,:,np.newaxis]*_K_dist,(1,0,2))
target += np.sum(dK_dX*dL_dK.T[:,:,None],0)
def dK_dX(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self,dL_dKdiag,X,target):
def dKdiag_dX(self, dL_dKdiag, X, target):
pass
@ -150,101 +150,100 @@ class rbf(kernpart):
# PSI statistics #
#---------------------------------------#
def psi0(self,Z,mu,S,target):
def psi0(self, Z, mu, S, target):
target += self.variance
def dpsi0_dtheta(self,dL_dpsi0,Z,mu,S,target):
def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S, target):
target[0] += np.sum(dL_dpsi0)
def dpsi0_dmuS(self,dL_dpsi0,Z,mu,S,target_mu,target_S):
def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S, target_mu, target_S):
pass
def psi1(self,Z,mu,S,target):
self._psi_computations(Z,mu,S)
def psi1(self, Z, mu, S, target):
self._psi_computations(Z, mu, S)
target += self._psi1
def dpsi1_dtheta(self,dL_dpsi1,Z,mu,S,target):
self._psi_computations(Z,mu,S)
denom_deriv = S[:,None,:]/(self.lengthscale**3+self.lengthscale*S[:,None,:])
d_length = self._psi1[:,:,None]*(self.lengthscale*np.square(self._psi1_dist/(self.lengthscale2+S[:,None,:])) + denom_deriv)
target[0] += np.sum(dL_dpsi1*self._psi1/self.variance)
dpsi1_dlength = d_length*dL_dpsi1[:,:,None]
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target):
self._psi_computations(Z, mu, S)
denom_deriv = S[:, None, :] / (self.lengthscale ** 3 + self.lengthscale * S[:, None, :])
d_length = self._psi1[:, :, None] * (self.lengthscale * np.square(self._psi1_dist / (self.lengthscale2 + S[:, None, :])) + denom_deriv)
target[0] += np.sum(dL_dpsi1 * self._psi1 / self.variance)
dpsi1_dlength = d_length * dL_dpsi1[:, :, None]
if not self.ARD:
target[1] += dpsi1_dlength.sum()
else:
target[1:] += dpsi1_dlength.sum(0).sum(0)
def dpsi1_dZ(self,dL_dpsi1,Z,mu,S,target):
self._psi_computations(Z,mu,S)
denominator = (self.lengthscale2*(self._psi1_denom))
dpsi1_dZ = - self._psi1[:,:,None] * ((self._psi1_dist/denominator))
target += np.sum(dL_dpsi1.T[:,:,None] * dpsi1_dZ, 0)
def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
self._psi_computations(Z, mu, S)
denominator = (self.lengthscale2 * (self._psi1_denom))
dpsi1_dZ = -self._psi1[:, :, None] * ((self._psi1_dist / denominator))
target += np.sum(dL_dpsi1.T[:, :, None] * dpsi1_dZ, 0)
def dpsi1_dmuS(self,dL_dpsi1,Z,mu,S,target_mu,target_S):
self._psi_computations(Z,mu,S)
tmp = self._psi1[:,:,None]/self.lengthscale2/self._psi1_denom
target_mu += np.sum(dL_dpsi1.T[:, :, None]*tmp*self._psi1_dist,1)
target_S += np.sum(dL_dpsi1.T[:, :, None]*0.5*tmp*(self._psi1_dist_sq-1),1)
def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S, target_mu, target_S):
self._psi_computations(Z, mu, S)
tmp = self._psi1[:, :, None] / self.lengthscale2 / self._psi1_denom
target_mu += np.sum(dL_dpsi1.T[:, :, None] * tmp * self._psi1_dist, 1)
target_S += np.sum(dL_dpsi1.T[:, :, None] * 0.5 * tmp * (self._psi1_dist_sq - 1), 1)
def psi2(self,Z,mu,S,target):
self._psi_computations(Z,mu,S)
def psi2(self, Z, mu, S, target):
self._psi_computations(Z, mu, S)
target += self._psi2
def dpsi2_dtheta(self,dL_dpsi2,Z,mu,S,target):
"""Shape N,M,M,Ntheta"""
self._psi_computations(Z,mu,S)
d_var = 2.*self._psi2/self.variance
d_length = 2.*self._psi2[:,:,:,None]*(self._psi2_Zdist_sq*self._psi2_denom + self._psi2_mudist_sq + S[:,None,None,:]/self.lengthscale2)/(self.lengthscale*self._psi2_denom)
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
"""Shape N,num_inducing,num_inducing,Ntheta"""
self._psi_computations(Z, mu, S)
d_var = 2.*self._psi2 / self.variance
d_length = 2.*self._psi2[:, :, :, None] * (self._psi2_Zdist_sq * self._psi2_denom + self._psi2_mudist_sq + S[:, None, None, :] / self.lengthscale2) / (self.lengthscale * self._psi2_denom)
target[0] += np.sum(dL_dpsi2*d_var)
dpsi2_dlength = d_length*dL_dpsi2[:,:,:,None]
target[0] += np.sum(dL_dpsi2 * d_var)
dpsi2_dlength = d_length * dL_dpsi2[:, :, :, None]
if not self.ARD:
target[1] += dpsi2_dlength.sum()
else:
target[1:] += dpsi2_dlength.sum(0).sum(0).sum(0)
def dpsi2_dZ(self,dL_dpsi2,Z,mu,S,target):
self._psi_computations(Z,mu,S)
term1 = self._psi2_Zdist/self.lengthscale2 # M, M, input_dim
term2 = self._psi2_mudist/self._psi2_denom/self.lengthscale2 # N, M, M, input_dim
dZ = self._psi2[:,:,:,None] * (term1[None] + term2)
target += (dL_dpsi2[:,:,:,None]*dZ).sum(0).sum(0)
def dpsi2_dmuS(self,dL_dpsi2,Z,mu,S,target_mu,target_S):
"""Think N,M,M,input_dim """
self._psi_computations(Z,mu,S)
tmp = self._psi2[:,:,:,None]/self.lengthscale2/self._psi2_denom
target_mu += -2.*(dL_dpsi2[:,:,:,None]*tmp*self._psi2_mudist).sum(1).sum(1)
target_S += (dL_dpsi2[:,:,:,None]*tmp*(2.*self._psi2_mudist_sq-1)).sum(1).sum(1)
def dpsi2_dZ(self, dL_dpsi2, Z, mu, S, target):
self._psi_computations(Z, mu, S)
term1 = self._psi2_Zdist / self.lengthscale2 # num_inducing, num_inducing, input_dim
term2 = self._psi2_mudist / self._psi2_denom / self.lengthscale2 # N, num_inducing, num_inducing, input_dim
dZ = self._psi2[:, :, :, None] * (term1[None] + term2)
target += (dL_dpsi2[:, :, :, None] * dZ).sum(0).sum(0)
def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
"""Think N,num_inducing,num_inducing,input_dim """
self._psi_computations(Z, mu, S)
tmp = self._psi2[:, :, :, None] / self.lengthscale2 / self._psi2_denom
target_mu += -2.*(dL_dpsi2[:, :, :, None] * tmp * self._psi2_mudist).sum(1).sum(1)
target_S += (dL_dpsi2[:, :, :, None] * tmp * (2.*self._psi2_mudist_sq - 1)).sum(1).sum(1)
#---------------------------------------#
# Precomputations #
#---------------------------------------#
def _K_computations(self,X,X2):
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):
def _K_computations(self, X, X2):
if not (np.array_equal(X, self._X) and np.array_equal(X2, self._X2) and np.array_equal(self._params , self._get_params())):
self._X = X.copy()
self._params == self._get_params().copy()
if X2 is None:
self._X2 = None
X = X/self.lengthscale
Xsquare = np.sum(np.square(X),1)
self._K_dist2 = -2.*tdot(X) + (Xsquare[:,None] + Xsquare[None,:])
X = X / self.lengthscale
Xsquare = np.sum(np.square(X), 1)
self._K_dist2 = -2.*tdot(X) + (Xsquare[:, None] + Xsquare[None, :])
else:
self._X2 = X2.copy()
X = X/self.lengthscale
X2 = X2/self.lengthscale
self._K_dist2 = -2.*np.dot(X, X2.T) + (np.sum(np.square(X),1)[:,None] + np.sum(np.square(X2),1)[None,:])
self._K_dvar = np.exp(-0.5*self._K_dist2)
X = X / self.lengthscale
X2 = X2 / self.lengthscale
self._K_dist2 = -2.*np.dot(X, X2.T) + (np.sum(np.square(X), 1)[:, None] + np.sum(np.square(X2), 1)[None, :])
self._K_dvar = np.exp(-0.5 * self._K_dist2)
def _psi_computations(self,Z,mu,S):
#here are the "statistics" for psi1 and psi2
def _psi_computations(self, Z, mu, S):
# here are the "statistics" for psi1 and psi2
if not np.array_equal(Z, self._Z):
#Z has changed, compute Z specific stuff
self._psi2_Zhat = 0.5*(Z[:,None,:] +Z[None,:,:]) # M,M,input_dim
self._psi2_Zdist = 0.5*(Z[:,None,:]-Z[None,:,:]) # M,M,input_dim
self._psi2_Zdist_sq = np.square(self._psi2_Zdist/self.lengthscale) # M,M,input_dim
self._psi2_Zhat = 0.5*(Z[:,None,:] +Z[None,:,:]) # num_inducing,num_inducing,input_dim
self._psi2_Zdist = 0.5*(Z[:,None,:]-Z[None,:,:]) # num_inducing,num_inducing,input_dim
self._psi2_Zdist_sq = np.square(self._psi2_Zdist/self.lengthscale) # num_inducing,num_inducing,input_dim
self._Z = Z
if not (np.array_equal(Z, self._Z) and np.array_equal(mu, self._mu) and np.array_equal(S, self._S)):
@ -258,39 +257,39 @@ class rbf(kernpart):
self._psi1 = self.variance*np.exp(self._psi1_exponent)
#psi2
self._psi2_denom = 2.*S[:,None,None,:]/self.lengthscale2+1. # N,M,M,input_dim
self._psi2_denom = 2.*S[:,None,None,:]/self.lengthscale2+1. # N,num_inducing,num_inducing,input_dim
self._psi2_mudist, self._psi2_mudist_sq, self._psi2_exponent, _ = self.weave_psi2(mu,self._psi2_Zhat)
#self._psi2_mudist = mu[:,None,None,:]-self._psi2_Zhat #N,M,M,input_dim
#self._psi2_mudist = mu[:,None,None,:]-self._psi2_Zhat #N,num_inducing,num_inducing,input_dim
#self._psi2_mudist_sq = np.square(self._psi2_mudist)/(self.lengthscale2*self._psi2_denom)
#self._psi2_exponent = np.sum(-self._psi2_Zdist_sq -self._psi2_mudist_sq -0.5*np.log(self._psi2_denom),-1) #N,M,M
self._psi2 = np.square(self.variance)*np.exp(self._psi2_exponent) # N,M,M
#self._psi2_exponent = np.sum(-self._psi2_Zdist_sq -self._psi2_mudist_sq -0.5*np.log(self._psi2_denom),-1) #N,num_inducing,num_inducing
self._psi2 = np.square(self.variance)*np.exp(self._psi2_exponent) # N,num_inducing,num_inducing
#store matrices for caching
self._Z, self._mu, self._S = Z, mu,S
def weave_psi2(self,mu,Zhat):
N,input_dim = mu.shape
M = Zhat.shape[0]
num_inducing = Zhat.shape[0]
mudist = np.empty((N,M,M,input_dim))
mudist_sq = np.empty((N,M,M,input_dim))
psi2_exponent = np.zeros((N,M,M))
psi2 = np.empty((N,M,M))
mudist = np.empty((N,num_inducing,num_inducing,input_dim))
mudist_sq = np.empty((N,num_inducing,num_inducing,input_dim))
psi2_exponent = np.zeros((N,num_inducing,num_inducing))
psi2 = np.empty((N,num_inducing,num_inducing))
psi2_Zdist_sq = self._psi2_Zdist_sq
_psi2_denom = self._psi2_denom.squeeze().reshape(N,self.D)
half_log_psi2_denom = 0.5*np.log(self._psi2_denom).squeeze().reshape(N,self.D)
_psi2_denom = self._psi2_denom.squeeze().reshape(N, self.input_dim)
half_log_psi2_denom = 0.5 * np.log(self._psi2_denom).squeeze().reshape(N, self.input_dim)
variance_sq = float(np.square(self.variance))
if self.ARD:
lengthscale2 = self.lengthscale2
else:
lengthscale2 = np.ones(input_dim)*self.lengthscale2
lengthscale2 = np.ones(input_dim) * self.lengthscale2
code = """
double tmp;
#pragma omp parallel for private(tmp)
for (int n=0; n<N; n++){
for (int m=0; m<M; m++){
for (int m=0; m<num_inducing; m++){
for (int mm=0; mm<(m+1); mm++){
for (int q=0; q<input_dim; q++){
//compute mudist
@ -325,7 +324,7 @@ class rbf(kernpart):
#include <math.h>
"""
weave.inline(code, support_code=support_code, libraries=['gomp'],
arg_names=['N','M','input_dim','mu','Zhat','mudist_sq','mudist','lengthscale2','_psi2_denom','psi2_Zdist_sq','psi2_exponent','half_log_psi2_denom','psi2','variance_sq'],
type_converters=weave.converters.blitz,**self.weave_options)
arg_names=['N','num_inducing','input_dim','mu','Zhat','mudist_sq','mudist','lengthscale2','_psi2_denom','psi2_Zdist_sq','psi2_exponent','half_log_psi2_denom','psi2','variance_sq'],
type_converters=weave.converters.blitz, **self.weave_options)
return mudist,mudist_sq, psi2_exponent, psi2
return mudist, mudist_sq, psi2_exponent, psi2

42
GPy/kern/rbfcos.py
View file

@ -3,32 +3,32 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
class rbfcos(kernpart):
def __init__(self,D,variance=1.,frequencies=None,bandwidths=None,ARD=False):
self.D = D
class rbfcos(Kernpart):
def __init__(self,input_dim,variance=1.,frequencies=None,bandwidths=None,ARD=False):
self.input_dim = input_dim
self.name = 'rbfcos'
if self.D>10:
if self.input_dim>10:
print "Warning: the rbfcos kernel requires a lot of memory for high dimensional inputs"
self.ARD = ARD
#set the default frequencies and bandwidths, appropriate Nparam
#set the default frequencies and bandwidths, appropriate num_params
if ARD:
self.Nparam = 2*self.D + 1
self.num_params = 2*self.input_dim + 1
if frequencies is not None:
frequencies = np.asarray(frequencies)
assert frequencies.size == self.D, "bad number of frequencies"
assert frequencies.size == self.input_dim, "bad number of frequencies"
else:
frequencies = np.ones(self.D)
frequencies = np.ones(self.input_dim)
if bandwidths is not None:
bandwidths = np.asarray(bandwidths)
assert bandwidths.size == self.D, "bad number of bandwidths"
assert bandwidths.size == self.input_dim, "bad number of bandwidths"
else:
bandwidths = np.ones(self.D)
bandwidths = np.ones(self.input_dim)
else:
self.Nparam = 3
self.num_params = 3
if frequencies is not None:
frequencies = np.asarray(frequencies)
assert frequencies.size == 1, "Exactly one frequency needed for non-ARD kernel"
@ -51,19 +51,19 @@ class rbfcos(kernpart):
return np.hstack((self.variance,self.frequencies, self.bandwidths))
def _set_params(self,x):
assert x.size==(self.Nparam)
assert x.size==(self.num_params)
if self.ARD:
self.variance = x[0]
self.frequencies = x[1:1+self.D]
self.bandwidths = x[1+self.D:]
self.frequencies = x[1:1+self.input_dim]
self.bandwidths = x[1+self.input_dim:]
else:
self.variance, self.frequencies, self.bandwidths = x
def _get_param_names(self):
if self.Nparam == 3:
if self.num_params == 3:
return ['variance','frequency','bandwidth']
else:
return ['variance']+['frequency_%i'%i for i in range(self.D)]+['bandwidth_%i'%i for i in range(self.D)]
return ['variance']+['frequency_%i'%i for i in range(self.input_dim)]+['bandwidth_%i'%i for i in range(self.input_dim)]
def K(self,X,X2,target):
self._K_computations(X,X2)
@ -76,9 +76,9 @@ class rbfcos(kernpart):
self._K_computations(X,X2)
target[0] += np.sum(dL_dK*self._dvar)
if self.ARD:
for q in xrange(self.D):
for q in xrange(self.input_dim):
target[q+1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.tan(2.*np.pi*self._dist[:,:,q]*self.frequencies[q])*self._dist[:,:,q])
target[q+1+self.D] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self._dist2[:,:,q])
target[q+1+self.input_dim] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self._dist2[:,:,q])
else:
target[1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.sum(np.tan(2.*np.pi*self._dist*self.frequencies)*self._dist,-1))
target[2] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self._dist2.sum(-1))
@ -100,13 +100,13 @@ class rbfcos(kernpart):
self._X = X.copy()
self._X2 = X2.copy()
#do the distances: this will be high memory for large D
#do the distances: this will be high memory for large input_dim
#NB: we don't take the abs of the dist because cos is symmetric
self._dist = X[:,None,:] - X2[None,:,:]
self._dist2 = np.square(self._dist)
#ensure the next section is computed:
self._params = np.empty(self.Nparam)
self._params = np.empty(self.num_params)
if not np.all(self._params == self._get_params()):
self._params == self._get_params().copy()

16
GPy/kern/spline.py
View file

@ -2,28 +2,28 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
import hashlib
def theta(x):
"""Heaviside step function"""
return np.where(x>=0.,1.,0.)
class spline(kernpart):
class spline(Kernpart):
"""
Spline kernel
:param D: the number of input dimensions (fixed to 1 right now TODO)
:type D: int
:param input_dim: the number of input dimensions (fixed to 1 right now TODO)
:type input_dim: int
:param variance: the variance of the kernel
:type variance: float
"""
def __init__(self,D,variance=1.,lengthscale=1.):
self.D = D
assert self.D==1
self.Nparam = 1
def __init__(self,input_dim,variance=1.,lengthscale=1.):
self.input_dim = input_dim
assert self.input_dim==1
self.num_params = 1
self.name = 'spline'
self._set_params(np.squeeze(variance))

18
GPy/kern/symmetric.py
View file

@ -1,27 +1,27 @@
# Copyright (c) 2012 James Hensman
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
class symmetric(kernpart):
class symmetric(Kernpart):
"""
Symmetrical kernels
:param k: the kernel to symmetrify
:type k: kernpart
:type k: Kernpart
:param transform: the transform to use in symmetrification (allows symmetry on specified axes)
:type transform: A numpy array (D x D) specifiying the transform
:rtype: kernpart
:type transform: A numpy array (input_dim x input_dim) specifiying the transform
:rtype: Kernpart
"""
def __init__(self,k,transform=None):
if transform is None:
transform = np.eye(k.D)*-1.
assert transform.shape == (k.D, k.D)
transform = np.eye(k.input_dim)*-1.
assert transform.shape == (k.input_dim, k.input_dim)
self.transform = transform
self.D = k.D
self.Nparam = k.Nparam
self.input_dim = k.input_dim
self.num_params = k.num_params
self.name = k.name + '_symm'
self.k = k
self._set_params(k._get_params())

74
GPy/kern/sympykern.py
View file

@ -9,9 +9,9 @@ import sys
current_dir = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
import tempfile
import pdb
from kernpart import kernpart
from kernpart import Kernpart
class spkern(kernpart):
class spkern(Kernpart):
"""
A kernel object, where all the hard work in done by sympy.
@ -26,7 +26,7 @@ class spkern(kernpart):
- to handle multiple inputs, call them x1, z1, etc
- to handle multpile correlated outputs, you'll need to define each covariance function and 'cross' variance function. TODO
"""
def __init__(self,D,k,param=None):
def __init__(self,input_dim,k,param=None):
self.name='sympykern'
self._sp_k = k
sp_vars = [e for e in k.atoms() if e.is_Symbol]
@ -35,15 +35,15 @@ class spkern(kernpart):
assert all([x.name=='x%i'%i for i,x in enumerate(self._sp_x)])
assert all([z.name=='z%i'%i for i,z in enumerate(self._sp_z)])
assert len(self._sp_x)==len(self._sp_z)
self.D = len(self._sp_x)
assert self.D == D
self.input_dim = len(self._sp_x)
assert self.input_dim == input_dim
self._sp_theta = sorted([e for e in sp_vars if not (e.name[0]=='x' or e.name[0]=='z')],key=lambda e:e.name)
self.Nparam = len(self._sp_theta)
self.num_params = len(self._sp_theta)
#deal with param
if param is None:
param = np.ones(self.Nparam)
assert param.size==self.Nparam
param = np.ones(self.num_params)
assert param.size==self.num_params
self._set_params(param)
#Differentiate!
@ -69,15 +69,15 @@ class spkern(kernpart):
def compute_psi_stats(self):
#define some normal distributions
mus = [sp.var('mu%i'%i,real=True) for i in range(self.D)]
Ss = [sp.var('S%i'%i,positive=True) for i in range(self.D)]
mus = [sp.var('mu%i'%i,real=True) for i in range(self.input_dim)]
Ss = [sp.var('S%i'%i,positive=True) for i in range(self.input_dim)]
normals = [(2*sp.pi*Si)**(-0.5)*sp.exp(-0.5*(xi-mui)**2/Si) for xi, mui, Si in zip(self._sp_x, mus, Ss)]
#do some integration!
#self._sp_psi0 = ??
self._sp_psi1 = self._sp_k
for i in range(self.D):
print 'perfoming integrals %i of %i'%(i+1,2*self.D)
for i in range(self.input_dim):
print 'perfoming integrals %i of %i'%(i+1,2*self.input_dim)
sys.stdout.flush()
self._sp_psi1 *= normals[i]
self._sp_psi1 = sp.integrate(self._sp_psi1,(self._sp_x[i],-sp.oo,sp.oo))
@ -85,10 +85,10 @@ class spkern(kernpart):
self._sp_psi1 = self._sp_psi1.simplify()
#and here's psi2 (eek!)
zprime = [sp.Symbol('zp%i'%i) for i in range(self.D)]
zprime = [sp.Symbol('zp%i'%i) for i in range(self.input_dim)]
self._sp_psi2 = self._sp_k.copy()*self._sp_k.copy().subs(zip(self._sp_z,zprime))
for i in range(self.D):
print 'perfoming integrals %i of %i'%(self.D+i+1,2*self.D)
for i in range(self.input_dim):
print 'perfoming integrals %i of %i'%(self.input_dim+i+1,2*self.input_dim)
sys.stdout.flush()
self._sp_psi2 *= normals[i]
self._sp_psi2 = sp.integrate(self._sp_psi2,(self._sp_x[i],-sp.oo,sp.oo))
@ -113,21 +113,21 @@ class spkern(kernpart):
self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
#Here's some code to do the looping for K
arglist = ", ".join(["X[i*D+%s]"%x.name[1:] for x in self._sp_x]\
+ ["Z[j*D+%s]"%z.name[1:] for z in self._sp_z]\
+ ["param[%i]"%i for i in range(self.Nparam)])
arglist = ", ".join(["X[i*input_dim+%s]"%x.name[1:] for x in self._sp_x]\
+ ["Z[j*input_dim+%s]"%z.name[1:] for z in self._sp_z]\
+ ["param[%i]"%i for i in range(self.num_params)])
self._K_code =\
"""
int i;
int j;
int N = target_array->dimensions[0];
int M = target_array->dimensions[1];
int D = X_array->dimensions[1];
int num_inducing = target_array->dimensions[1];
int input_dim = X_array->dimensions[1];
//#pragma omp parallel for private(j)
for (i=0;i<N;i++){
for (j=0;j<M;j++){
target[i*M+j] = k(%s);
for (j=0;j<num_inducing;j++){
target[i*num_inducing+j] = k(%s);
}
}
%s
@ -140,7 +140,7 @@ class spkern(kernpart):
"""
int i;
int N = target_array->dimensions[0];
int D = X_array->dimensions[1];
int input_dim = X_array->dimensions[1];
//#pragma omp parallel for
for (i=0;i<N;i++){
target[i] = k(%s);
@ -149,17 +149,17 @@ class spkern(kernpart):
"""%(diag_arglist,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
#here's some code to compute gradients
funclist = '\n'.join([' '*16 + 'target[%i] += partial[i*M+j]*dk_d%s(%s);'%(i,theta.name,arglist) for i,theta in enumerate(self._sp_theta)])
funclist = '\n'.join([' '*16 + 'target[%i] += partial[i*num_inducing+j]*dk_d%s(%s);'%(i,theta.name,arglist) for i,theta in enumerate(self._sp_theta)])
self._dK_dtheta_code =\
"""
int i;
int j;
int N = partial_array->dimensions[0];
int M = partial_array->dimensions[1];
int D = X_array->dimensions[1];
int num_inducing = partial_array->dimensions[1];
int input_dim = X_array->dimensions[1];
//#pragma omp parallel for private(j)
for (i=0;i<N;i++){
for (j=0;j<M;j++){
for (j=0;j<num_inducing;j++){
%s
}
}
@ -169,12 +169,12 @@ class spkern(kernpart):
#here's some code to compute gradients for Kdiag TODO: thius is yucky.
diag_funclist = re.sub('Z','X',funclist,count=0)
diag_funclist = re.sub('j','i',diag_funclist)
diag_funclist = re.sub('partial\[i\*M\+i\]','partial[i]',diag_funclist)
diag_funclist = re.sub('partial\[i\*num_inducing\+i\]','partial[i]',diag_funclist)
self._dKdiag_dtheta_code =\
"""
int i;
int N = partial_array->dimensions[0];
int D = X_array->dimensions[1];
int input_dim = X_array->dimensions[1];
for (i=0;i<N;i++){
%s
}
@ -182,20 +182,20 @@ class spkern(kernpart):
"""%(diag_funclist,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
#Here's some code to do gradients wrt x
gradient_funcs = "\n".join(["target[i*D+%i] += partial[i*M+j]*dk_dx%i(%s);"%(q,q,arglist) for q in range(self.D)])
gradient_funcs = "\n".join(["target[i*input_dim+%i] += partial[i*num_inducing+j]*dk_dx%i(%s);"%(q,q,arglist) for q in range(self.input_dim)])
self._dK_dX_code = \
"""
int i;
int j;
int N = partial_array->dimensions[0];
int M = partial_array->dimensions[1];
int D = X_array->dimensions[1];
int num_inducing = partial_array->dimensions[1];
int input_dim = X_array->dimensions[1];
//#pragma omp parallel for private(j)
for (i=0;i<N; i++){
for (j=0; j<M; j++){
for (j=0; j<num_inducing; j++){
%s
//if(isnan(target[i*D+2])){printf("%%f\\n",dk_dx2(X[i*D+0], X[i*D+1], X[i*D+2], Z[j*D+0], Z[j*D+1], Z[j*D+2], param[0], param[1], param[2], param[3], param[4], param[5]));}
//if(isnan(target[i*D+2])){printf("%%f,%%f,%%i,%%i\\n", X[i*D+2], Z[j*D+2],i,j);}
//if(isnan(target[i*input_dim+2])){printf("%%f\\n",dk_dx2(X[i*input_dim+0], X[i*input_dim+1], X[i*input_dim+2], Z[j*input_dim+0], Z[j*input_dim+1], Z[j*input_dim+2], param[0], param[1], param[2], param[3], param[4], param[5]));}
//if(isnan(target[i*input_dim+2])){printf("%%f,%%f,%%i,%%i\\n", X[i*input_dim+2], Z[j*input_dim+2],i,j);}
}
}
@ -208,8 +208,8 @@ class spkern(kernpart):
int i;
int j;
int N = partial_array->dimensions[0];
int M = 0;
int D = X_array->dimensions[1];
int num_inducing = 0;
int input_dim = X_array->dimensions[1];
for (i=0;i<N; i++){
j = i;
%s

14
GPy/kern/white.py
View file

@ -2,20 +2,20 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import kernpart
from kernpart import Kernpart
import numpy as np
class white(kernpart):
class white(Kernpart):
"""
White noise kernel.
:param D: the number of input dimensions
:type D: int
:param input_dim: the number of input dimensions
:type input_dim: int
:param variance:
:type variance: float
"""
def __init__(self,D,variance=1.):
self.D = D
self.Nparam = 1
def __init__(self,input_dim,variance=1.):
self.input_dim = input_dim
self.num_params = 1
self.name = 'white'
self._set_params(np.array([variance]).flatten())
self._psi1 = 0 # TODO: more elegance here

4
GPy/likelihoods/__init__.py
View file

@ -1,4 +1,4 @@
from EP import EP
from Gaussian import Gaussian
from ep import EP
from gaussian import Gaussian
# TODO: from Laplace import Laplace
import likelihood_functions as functions

30
GPy/likelihoods/EP.py → GPy/likelihoods/ep.py
View file

@ -4,23 +4,23 @@ from ..util.linalg import pdinv,mdot,jitchol,chol_inv,DSYR,tdot
from likelihood import likelihood
class EP(likelihood):
def __init__(self,data,likelihood_function,epsilon=1e-3,power_ep=[1.,1.]):
def __init__(self,data,LikelihoodFunction,epsilon=1e-3,power_ep=[1.,1.]):
"""
Expectation Propagation
Arguments
---------
epsilon : Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
likelihood_function : a likelihood function (see likelihood_functions.py)
LikelihoodFunction : a likelihood function (see likelihood_functions.py)
"""
self.likelihood_function = likelihood_function
self.LikelihoodFunction = LikelihoodFunction
self.epsilon = epsilon
self.eta, self.delta = power_ep
self.data = data
self.N, self.D = self.data.shape
self.N, self.output_dim = self.data.shape
self.is_heteroscedastic = True
self.Nparams = 0
self._transf_data = self.likelihood_function._preprocess_values(data)
self._transf_data = self.LikelihoodFunction._preprocess_values(data)
#Initial values - Likelihood approximation parameters:
#p(y|f) = t(f|tau_tilde,v_tilde)
@ -48,7 +48,7 @@ class EP(likelihood):
def predictive_values(self,mu,var,full_cov):
if full_cov:
raise NotImplementedError, "Cannot make correlated predictions with an EP likelihood"
return self.likelihood_function.predictive_values(mu,var)
return self.LikelihoodFunction.predictive_values(mu,var)
def _get_params(self):
return np.zeros(0)
@ -110,7 +110,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma[i,i] - self.eta*self.v_tilde[i]
#Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.LikelihoodFunction.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
#Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i])
@ -139,7 +139,7 @@ class EP(likelihood):
The expectation-propagation algorithm with sparse pseudo-input.
For nomenclature see ... 2013.
"""
M = Kmm.shape[0]
num_inducing = Kmm.shape[0]
#TODO: this doesn't work with uncertain inputs!
@ -200,7 +200,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
#Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.LikelihoodFunction.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
#Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])
@ -235,7 +235,7 @@ class EP(likelihood):
The expectation-propagation algorithm with sparse pseudo-input.
For nomenclature see Naish-Guzman and Holden, 2008.
"""
M = Kmm.shape[0]
num_inducing = Kmm.shape[0]
"""
Prior approximation parameters:
@ -258,7 +258,7 @@ class EP(likelihood):
mu = w + P*Gamma
"""
self.w = np.zeros(self.N)
self.Gamma = np.zeros(M)
self.Gamma = np.zeros(num_inducing)
mu = np.zeros(self.N)
P = P0.copy()
R = R0.copy()
@ -295,7 +295,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
#Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.LikelihoodFunction.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
#Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])
@ -305,10 +305,10 @@ class EP(likelihood):
dtd1 = Delta_tau*Diag[i] + 1.
dii = Diag[i]
Diag[i] = dii - (Delta_tau * dii**2.)/dtd1
pi_ = P[i,:].reshape(1,M)
pi_ = P[i,:].reshape(1,num_inducing)
P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_
Rp_i = np.dot(R,pi_.T)
RTR = np.dot(R.T,np.dot(np.eye(M) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))
RTR = np.dot(R.T,np.dot(np.eye(num_inducing) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))
R = jitchol(RTR).T
self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1
self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)
@ -321,7 +321,7 @@ class EP(likelihood):
Diag = Diag0 * Iplus_Dprod_i
P = Iplus_Dprod_i[:,None] * P0
safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)
L = jitchol(np.eye(M) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))
L = jitchol(np.eye(num_inducing) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))
R,info = linalg.lapack.flapack.dtrtrs(L,R0,lower=1)
RPT = np.dot(R,P.T)
Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)

16
GPy/likelihoods/Gaussian.py → GPy/likelihoods/gaussian.py
View file

@ -15,7 +15,7 @@ class Gaussian(likelihood):
self.is_heteroscedastic = False
self.Nparams = 1
self.Z = 0. # a correction factor which accounts for the approximation made
N, self.D = data.shape
N, self.output_dim = data.shape
# normalization
if normalize:
@ -24,8 +24,8 @@ class Gaussian(likelihood):
# Don't scale outputs which have zero variance to zero.
self._scale[np.nonzero(self._scale == 0.)] = 1.0e-3
else:
self._offset = np.zeros((1, self.D))
self._scale = np.ones((1, self.D))
self._offset = np.zeros((1, self.output_dim))
self._scale = np.ones((1, self.output_dim))
self.set_data(data)
@ -35,7 +35,7 @@ class Gaussian(likelihood):
def set_data(self, data):
self.data = data
self.N, D = data.shape
assert D == self.D
assert D == self.output_dim
self.Y = (self.data - self._offset) / self._scale
if D > self.N:
self.YYT = np.dot(self.Y, self.Y.T)
@ -52,9 +52,9 @@ class Gaussian(likelihood):
def _set_params(self, x):
x = np.float64(x)
if self._variance != x:
if np.all(self._variance != x):
if x == 0.:
self.precision = None
self.precision = np.inf
self.V = None
else:
self.precision = 1. / x
@ -68,9 +68,9 @@ class Gaussian(likelihood):
"""
mean = mu * self._scale + self._offset
if full_cov:
if self.D > 1:
if self.output_dim > 1:
raise NotImplementedError, "TODO"
# Note. for D>1, we need to re-normalise all the outputs independently.
# Note. for output_dim>1, we need to re-normalise all the outputs independently.
# This will mess up computations of diag(true_var), below.
# note that the upper, lower quantiles should be the same shape as mean
# Augment the output variance with the likelihood variance and rescale.

10
GPy/likelihoods/likelihood_functions.py
View file

@ -10,12 +10,12 @@ from ..util.plot import gpplot
from ..util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
import link_functions
class likelihood_function(object):
class LikelihoodFunction(object):
"""
Likelihood class for doing Expectation propagation
:param Y: observed output (Nx1 numpy.darray)
..Note:: Y values allowed depend on the likelihood_function used
..Note:: Y values allowed depend on the LikelihoodFunction used
"""
def __init__(self,link):
if link == self._analytical:
@ -69,7 +69,7 @@ class likelihood_function(object):
sigma2_hat = m2 - mu_hat**2 # Second central moment
return float(Z_hat), float(mu_hat), float(sigma2_hat)
class binomial(likelihood_function):
class Binomial(LikelihoodFunction):
"""
Probit likelihood
Y is expected to take values in {-1,1}
@ -82,7 +82,7 @@ class binomial(likelihood_function):
self._analytical = link_functions.probit
if not link:
link = self._analytical
super(binomial, self).__init__(link)
super(Binomial, self).__init__(link)
def _distribution(self,gp,obs):
pass
@ -134,7 +134,7 @@ class binomial(likelihood_function):
p_975 = stats.norm.cdf(norm_975/np.sqrt(1+var))
return mean[:,None], np.nan*var, p_025[:,None], p_975[:,None] # TODO: var
class Poisson(likelihood_function):
class Poisson(LikelihoodFunction):
"""
Poisson likelihood
Y is expected to take values in {0,1,2,...}

19
GPy/models/__init__.py
View file

@ -1,15 +1,12 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from GP_regression import GP_regression
from GP_classification import GP_classification
from sparse_GP_regression import sparse_GP_regression
from sparse_GP_classification import sparse_GP_classification
from GPLVM import GPLVM
from warped_GP import warpedGP
from sparse_GPLVM import sparse_GPLVM
from Bayesian_GPLVM import Bayesian_GPLVM
from gp_regression import GPRegression
from gp_classification import GPClassification
from sparse_gp_regression import SparseGPRegression
from sparse_gp_classification import SparseGPClassification
from fitc_classification import FITCClassification
from gplvm import GPLVM
from warped_gp import WarpedGP
from bayesian_gplvm import BayesianGPLVM
from mrd import MRD
from generalized_FITC import generalized_FITC
from FITC import FITC

67
GPy/models/Bayesian_GPLVM.py → GPy/models/bayesian_gplvm.py
View file

@ -2,21 +2,16 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import pylab as pb
import sys, pdb
from GPLVM import GPLVM
from ..core import sparse_GP
from GPy.util.linalg import pdinv
from ..core import SparseGP
from ..likelihoods import Gaussian
from .. import kern
from numpy.linalg.linalg import LinAlgError
import itertools
from matplotlib.colors import colorConverter
from matplotlib.figure import SubplotParams
from GPy.inference.optimization import SCG
from GPy.util import plot_latent
from GPy.models.gplvm import GPLVM
class Bayesian_GPLVM(sparse_GP, GPLVM):
class BayesianGPLVM(SparseGP, GPLVM):
"""
Bayesian Gaussian Process Latent Variable Model
@ -28,7 +23,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
:type init: 'PCA'|'random'
"""
def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', M=10,
def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, oldpsave=10, _debug=False,
**kwargs):
if type(likelihood_or_Y) is np.ndarray:
@ -44,7 +39,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
X_variance = np.clip((np.ones_like(X) * 0.5) + .01 * np.random.randn(*X.shape), 0.001, 1)
if Z is None:
Z = np.random.permutation(X.copy())[:M]
Z = np.random.permutation(X.copy())[:num_inducing]
assert Z.shape[1] == X.shape[1]
if kernel is None:
@ -64,7 +59,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
self._savedpsiKmm = []
self._savedABCD = []
sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
self._set_params(self._get_params())
@property
@ -78,21 +73,21 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
self._oldps.insert(0, p.copy())
def _get_param_names(self):
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
return (X_names + S_names + sparse_GP._get_param_names(self))
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
return (X_names + S_names + SparseGP._get_param_names(self))
def _get_params(self):
"""
Horizontally stacks the parameters in order to present them to the optimizer.
The resulting 1-D array has this structure:
The resulting 1-input_dim array has this structure:
===============================================================
| mu | S | Z | theta | beta |
===============================================================
"""
x = np.hstack((self.X.flatten(), self.X_variance.flatten(), sparse_GP._get_params(self)))
x = np.hstack((self.X.flatten(), self.X_variance.flatten(), SparseGP._get_params(self)))
return x
def _clipped(self, x):
@ -101,10 +96,10 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
def _set_params(self, x, save_old=True, save_count=0):
# try:
x = self._clipped(x)
N, input_dim = self.N, self.input_dim
N, input_dim = self.num_data, self.input_dim
self.X = x[:self.X.size].reshape(N, input_dim).copy()
self.X_variance = x[(N * input_dim):(2 * N * input_dim)].reshape(N, input_dim).copy()
sparse_GP._set_params(self, x[(2 * N * input_dim):])
SparseGP._set_params(self, x[(2 * N * input_dim):])
# self.oldps = x
# except (LinAlgError, FloatingPointError, ZeroDivisionError):
# print "\rWARNING: Caught LinAlgError, continueing without setting "
@ -131,10 +126,10 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
def KL_divergence(self):
var_mean = np.square(self.X).sum()
var_S = np.sum(self.X_variance - np.log(self.X_variance))
return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.N
return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.num_data
def log_likelihood(self):
ll = sparse_GP.log_likelihood(self)
ll = SparseGP.log_likelihood(self)
kl = self.KL_divergence()
# if ll < -2E4:
@ -151,14 +146,14 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
self._savedpsiKmm.append([self.f_call, [self.Kmm, self.dL_dKmm]])
# sf2 = self.scale_factor ** 2
if self.likelihood.is_heteroscedastic:
A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
# B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
A = -0.5 * self.num_data * self.input_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
# B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
else:
A = -0.5 * self.N * self.D * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
# B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
C = -self.D * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.M * np.log(sf2))
A = -0.5 * self.num_data * self.input_dim * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
# B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
C = -self.input_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
self._savedABCD.append([self.f_call, A, B, C, D])
@ -181,7 +176,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
# d_dS = (dL_dS).flatten()
# ========================
self.dbound_dmuS = np.hstack((d_dmu, d_dS))
self.dbound_dZtheta = sparse_GP._log_likelihood_gradients(self)
self.dbound_dZtheta = SparseGP._log_likelihood_gradients(self)
return self._clipped(np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta)))
def plot_latent(self, *args, **kwargs):
@ -200,7 +195,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
means = np.zeros((N_test, input_dim))
covars = np.zeros((N_test, input_dim))
dpsi0 = -0.5 * self.D * self.likelihood.precision
dpsi0 = -0.5 * self.input_dim * self.likelihood.precision
dpsi2 = self.dL_dpsi2[0][None, :, :] # TODO: this may change if we ignore het. likelihoods
V = self.likelihood.precision * Y
dpsi1 = np.dot(self.Cpsi1V, V.T)
@ -263,7 +258,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
def __getstate__(self):
return (self.likelihood, self.input_dim, self.X, self.X_variance,
self.init, self.M, self.Z, self.kern,
self.init, self.num_inducing, self.Z, self.kern,
self.oldpsave, self._debug)
def __setstate__(self, state):
@ -271,11 +266,11 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
def _debug_filter_params(self, x):
start, end = 0, self.X.size,
X = x[start:end].reshape(self.N, self.input_dim)
X = x[start:end].reshape(self.num_data, self.input_dim)
start, end = end, end + self.X_variance.size
X_v = x[start:end].reshape(self.N, self.input_dim)
start, end = end, end + (self.M * self.input_dim)
Z = x[start:end].reshape(self.M, self.input_dim)
X_v = x[start:end].reshape(self.num_data, self.input_dim)
start, end = end, end + (self.num_inducing * self.input_dim)
Z = x[start:end].reshape(self.num_inducing, self.input_dim)
start, end = end, end + self.input_dim
theta = x[start:]
return X, X_v, Z, theta
@ -353,12 +348,12 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
figs.append(pylab.figure("BGPLVM DEBUG Kmm", figsize=(12, 6)))
fig = figs[-1]
ax8 = fig.add_subplot(121)
ax8.text(.5, .5, r"${\mathbf{A,B,C,D}}$", color='k', alpha=.5, transform=ax8.transAxes,
ax8.text(.5, .5, r"${\mathbf{A,B,C,input_dim}}$", color='k', alpha=.5, transform=ax8.transAxes,
ha='center', va='center')
ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 1], label='A')
ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 2], label='B')
ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 3], label='C')
ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 4], label='D')
ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 4], label='input_dim')
ax8.legend()
figs[-1].canvas.draw()
figs[-1].tight_layout(rect=(.15, 0, 1, .86))

252
GPy/models/fitc.py Normal file
View file

@ -0,0 +1,252 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import pylab as pb
from ..util.linalg import mdot, jitchol, chol_inv, tdot, symmetrify, pdinv
from ..util.plot import gpplot
from .. import kern
from scipy import stats, linalg
from GPy.core.sparse_gp import SparseGP
def backsub_both_sides(L, X):
""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
tmp, _ = linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
return linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
class FITC(SparseGP):
def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
super(FITC, self).__init__(X, likelihood, kernel, normalize_X=normalize_X)
def update_likelihood_approximation(self):
"""
Approximates a non-gaussian likelihood using Expectation Propagation
For a Gaussian (or direct: TODO) likelihood, no iteration is required:
this function does nothing
Diag(Knn - Qnn) is added to the noise term to use the tools already implemented in SparseGP.
The true precison is now 'true_precision' not 'precision'.
"""
if self.has_uncertain_inputs:
raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
else:
self.likelihood.fit_FITC(self.Kmm, self.psi1, self.psi0)
self._set_params(self._get_params()) # update the GP
def _computations(self):
# factor Kmm
self.Lm = jitchol(self.Kmm)
self.Lmi, info = linalg.lapack.flapack.dtrtrs(self.Lm, np.eye(self.num_inducing), lower=1)
Lmipsi1 = np.dot(self.Lmi, self.psi1)
self.Qnn = np.dot(Lmipsi1.T, Lmipsi1).copy()
self.Diag0 = self.psi0 - np.diag(self.Qnn)
self.beta_star = self.likelihood.precision / (1. + self.likelihood.precision * self.Diag0[:, None]) # Includes Diag0 in the precision
self.V_star = self.beta_star * self.likelihood.Y
# The rather complex computations of self.A
if self.has_uncertain_inputs:
raise NotImplementedError
else:
if self.likelihood.is_heteroscedastic:
assert self.likelihood.input_dim == 1
tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.num_data)))
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
self.A = tdot(tmp)
# factor B
self.B = np.eye(self.num_inducing) + self.A
self.LB = jitchol(self.B)
self.LBi = chol_inv(self.LB)
self.psi1V = np.dot(self.psi1, self.V_star)
Lmi_psi1V, info = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
self._LBi_Lmi_psi1V, _ = linalg.lapack.flapack.dtrtrs(self.LB, np.asfortranarray(Lmi_psi1V), lower=1, trans=0)
Kmmipsi1 = np.dot(self.Lmi.T, Lmipsi1)
b_psi1_Ki = self.beta_star * Kmmipsi1.T
Ki_pbp_Ki = np.dot(Kmmipsi1, b_psi1_Ki)
Kmmi = np.dot(self.Lmi.T, self.Lmi)
LBiLmi = np.dot(self.LBi, self.Lmi)
LBL_inv = np.dot(LBiLmi.T, LBiLmi)
VVT = np.outer(self.V_star, self.V_star)
VV_p_Ki = np.dot(VVT, Kmmipsi1.T)
Ki_pVVp_Ki = np.dot(Kmmipsi1, VV_p_Ki)
psi1beta = self.psi1 * self.beta_star.T
H = self.Kmm + mdot(self.psi1, psi1beta.T)
LH = jitchol(H)
LHi = chol_inv(LH)
Hi = np.dot(LHi.T, LHi)
betapsi1TLmiLBi = np.dot(psi1beta.T, LBiLmi.T)
alpha = np.array([np.dot(a.T, a) for a in betapsi1TLmiLBi])[:, None]
gamma_1 = mdot(VVT, self.psi1.T, Hi)
pHip = mdot(self.psi1.T, Hi, self.psi1)
gamma_2 = mdot(self.beta_star * pHip, self.V_star)
gamma_3 = self.V_star * gamma_2
self._dL_dpsi0 = -0.5 * self.beta_star # dA_dpsi0: logdet(self.beta_star)
self._dL_dpsi0 += .5 * self.V_star ** 2 # dA_psi0: yT*beta_star*y
self._dL_dpsi0 += .5 * alpha # dC_dpsi0
self._dL_dpsi0 += 0.5 * mdot(self.beta_star * pHip, self.V_star) ** 2 - self.V_star * mdot(self.V_star.T, pHip * self.beta_star).T # dD_dpsi0
self._dL_dpsi1 = b_psi1_Ki.copy() # dA_dpsi1: logdet(self.beta_star)
self._dL_dpsi1 += -np.dot(psi1beta.T, LBL_inv) # dC_dpsi1
self._dL_dpsi1 += gamma_1 - mdot(psi1beta.T, Hi, self.psi1, gamma_1) # dD_dpsi1
self._dL_dKmm = -0.5 * np.dot(Kmmipsi1, b_psi1_Ki) # dA_dKmm: logdet(self.beta_star)
self._dL_dKmm += .5 * (LBL_inv - Kmmi) + mdot(LBL_inv, psi1beta, Kmmipsi1.T) # dC_dKmm
self._dL_dKmm += -.5 * mdot(Hi, self.psi1, gamma_1) # dD_dKmm
self._dpsi1_dtheta = 0
self._dpsi1_dX = 0
self._dKmm_dtheta = 0
self._dKmm_dX = 0
self._dpsi1_dX_jkj = 0
self._dpsi1_dtheta_jkj = 0
for i, V_n, alpha_n, gamma_n, gamma_k in zip(range(self.num_data), self.V_star, alpha, gamma_2, gamma_3):
K_pp_K = np.dot(Kmmipsi1[:, i:(i + 1)], Kmmipsi1[:, i:(i + 1)].T)
# Diag_dpsi1 = Diag_dA_dpsi1: yT*beta_star*y + Diag_dC_dpsi1 +Diag_dD_dpsi1
_dpsi1 = (-V_n ** 2 - alpha_n + 2.*gamma_k - gamma_n ** 2) * Kmmipsi1.T[i:(i + 1), :]
# Diag_dKmm = Diag_dA_dKmm: yT*beta_star*y +Diag_dC_dKmm +Diag_dD_dKmm
_dKmm = .5 * (V_n ** 2 + alpha_n + gamma_n ** 2 - 2.*gamma_k) * K_pp_K # Diag_dD_dKmm
self._dpsi1_dtheta += self.kern.dK_dtheta(_dpsi1, self.X[i:i + 1, :], self.Z)
self._dKmm_dtheta += self.kern.dK_dtheta(_dKmm, self.Z)
self._dKmm_dX += 2.*self.kern.dK_dX(_dKmm , self.Z)
self._dpsi1_dX += self.kern.dK_dX(_dpsi1.T, self.Z, self.X[i:i + 1, :])
# the partial derivative vector for the likelihood
if self.likelihood.Nparams == 0:
# save computation here.
self.partial_for_likelihood = None
elif self.likelihood.is_heteroscedastic:
raise NotImplementedError, "heteroscedatic derivates not implemented"
else:
# likelihood is not heterscedatic
dbstar_dnoise = self.likelihood.precision * (self.beta_star ** 2 * self.Diag0[:, None] - self.beta_star)
Lmi_psi1 = mdot(self.Lmi, self.psi1)
LBiLmipsi1 = np.dot(self.LBi, Lmi_psi1)
aux_0 = np.dot(self._LBi_Lmi_psi1V.T, LBiLmipsi1)
aux_1 = self.likelihood.Y.T * np.dot(self._LBi_Lmi_psi1V.T, LBiLmipsi1)
aux_2 = np.dot(LBiLmipsi1.T, self._LBi_Lmi_psi1V)
dA_dnoise = 0.5 * self.input_dim * (dbstar_dnoise / self.beta_star).sum() - 0.5 * self.input_dim * np.sum(self.likelihood.Y ** 2 * dbstar_dnoise)
dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T, self.LBi, Lmi_psi1) * Lmi_psi1 * dbstar_dnoise.T)
dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T, self.LBi, Lmi_psi1) * Lmi_psi1 * dbstar_dnoise.T)
dD_dnoise_1 = mdot(self.V_star * LBiLmipsi1.T, LBiLmipsi1 * dbstar_dnoise.T * self.likelihood.Y.T)
alpha = mdot(LBiLmipsi1, self.V_star)
alpha_ = mdot(LBiLmipsi1.T, alpha)
dD_dnoise_2 = -0.5 * self.input_dim * np.sum(alpha_ ** 2 * dbstar_dnoise)
dD_dnoise_1 = mdot(self.V_star.T, self.psi1.T, self.Lmi.T, self.LBi.T, self.LBi, self.Lmi, self.psi1, dbstar_dnoise * self.likelihood.Y)
dD_dnoise_2 = 0.5 * mdot(self.V_star.T, self.psi1.T, Hi, self.psi1, dbstar_dnoise * self.psi1.T, Hi, self.psi1, self.V_star)
dD_dnoise = dD_dnoise_1 + dD_dnoise_2
self.partial_for_likelihood = dA_dnoise + dC_dnoise + dD_dnoise
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
A = -0.5 * self.num_data * self.input_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
C = -self.input_dim * (np.sum(np.log(np.diag(self.LB))))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
return A + C + D
def _log_likelihood_gradients(self):
pass
return np.hstack((self.dL_dZ().flatten(), self.dL_dtheta(), self.likelihood._gradients(partial=self.partial_for_likelihood)))
def dL_dtheta(self):
if self.has_uncertain_inputs:
raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
else:
dL_dtheta = self.kern.dKdiag_dtheta(self._dL_dpsi0, self.X)
dL_dtheta += self.kern.dK_dtheta(self._dL_dpsi1, self.X, self.Z)
dL_dtheta += self.kern.dK_dtheta(self._dL_dKmm, X=self.Z)
dL_dtheta += self._dKmm_dtheta
dL_dtheta += self._dpsi1_dtheta
return dL_dtheta
def dL_dZ(self):
if self.has_uncertain_inputs:
raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
else:
dL_dZ = self.kern.dK_dX(self._dL_dpsi1.T, self.Z, self.X)
dL_dZ += 2. * self.kern.dK_dX(self._dL_dKmm, X=self.Z)
dL_dZ += self._dpsi1_dX
dL_dZ += self._dKmm_dX
return dL_dZ
def _raw_predict(self, Xnew, which_parts, full_cov=False):
if self.likelihood.is_heteroscedastic:
Iplus_Dprod_i = 1. / (1. + self.Diag0 * self.likelihood.precision.flatten())
self.Diag = self.Diag0 * Iplus_Dprod_i
self.P = Iplus_Dprod_i[:, None] * self.psi1.T
self.RPT0 = np.dot(self.Lmi, self.psi1)
self.L = np.linalg.cholesky(np.eye(self.num_inducing) + np.dot(self.RPT0, ((1. - Iplus_Dprod_i) / self.Diag0)[:, None] * self.RPT0.T))
self.R, info = linalg.flapack.dtrtrs(self.L, self.Lmi, lower=1)
self.RPT = np.dot(self.R, self.P.T)
self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T, self.RPT)
self.w = self.Diag * self.likelihood.v_tilde
self.Gamma = np.dot(self.R.T, np.dot(self.RPT, self.likelihood.v_tilde))
self.mu = self.w + np.dot(self.P, self.Gamma)
"""
Make a prediction for the generalized FITC model
Arguments
---------
X : Input prediction data - Nx1 numpy array (floats)
"""
# q(u|f) = N(u| R0i*mu_u*f, R0i*C*R0i.T)
# Ci = I + (RPT0)Di(RPT0).T
# C = I - [RPT0] * (input_dim+[RPT0].T*[RPT0])^-1*[RPT0].T
# = I - [RPT0] * (input_dim + self.Qnn)^-1 * [RPT0].T
# = I - [RPT0] * (U*U.T)^-1 * [RPT0].T
# = I - V.T * V
U = np.linalg.cholesky(np.diag(self.Diag0) + self.Qnn)
V, info = linalg.flapack.dtrtrs(U, self.RPT0.T, lower=1)
C = np.eye(self.num_inducing) - np.dot(V.T, V)
mu_u = np.dot(C, self.RPT0) * (1. / self.Diag0[None, :])
# self.C = C
# self.RPT0 = np.dot(self.R0,self.Knm.T) P0.T
# self.mu_u = mu_u
# self.U = U
# q(u|y) = N(u| R0i*mu_H,R0i*Sigma_H*R0i.T)
mu_H = np.dot(mu_u, self.mu)
self.mu_H = mu_H
Sigma_H = C + np.dot(mu_u, np.dot(self.Sigma, mu_u.T))
# q(f_star|y) = N(f_star|mu_star,sigma2_star)
Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
KR0T = np.dot(Kx.T, self.Lmi.T)
mu_star = np.dot(KR0T, mu_H)
if full_cov:
Kxx = self.kern.K(Xnew, which_parts=which_parts)
var = Kxx + np.dot(KR0T, np.dot(Sigma_H - np.eye(self.num_inducing), KR0T.T))
else:
Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
var = (Kxx + np.sum(KR0T.T * np.dot(Sigma_H - np.eye(self.num_inducing), KR0T.T), 0))[:, None]
return mu_star[:, None], var
else:
raise NotImplementedError, "homoscedastic fitc not implemented"
"""
Kx = self.kern.K(self.Z, Xnew)
mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)
if full_cov:
Kxx = self.kern.K(Xnew)
var = Kxx - mdot(Kx.T, (self.Kmmi - self.C/self.scale_factor**2), Kx) #NOTE this won't work for plotting
else:
Kxx = self.kern.Kdiag(Xnew)
var = Kxx - np.sum(Kx*np.dot(self.Kmmi - self.C/self.scale_factor**2, Kx),0)
return mu,var[:,None]
"""

47
GPy/models/fitc_classification.py Normal file
View file

@ -0,0 +1,47 @@
# Copyright (c) 2013, Ricardo Andrade
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core import FITC
from .. import likelihoods
from .. import kern
from ..likelihoods import likelihood
class FITCClassification(FITC):
"""
FITC approximation for classification
This is a thin wrapper around the FITC class, with a set of sensible defaults
:param X: input observations
:param Y: observed values
:param likelihood: a GPy likelihood, defaults to Binomial with probit link_function
:param kernel: a GPy kernel, defaults to rbf+white
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True
:param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_Y: False|True
:rtype: model object
"""
def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10):
if kernel is None:
kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
if likelihood is None:
distribution = likelihoods.likelihood_functions.Binomial()
likelihood = likelihoods.EP(Y, distribution)
elif Y is not None:
if not all(Y.flatten() == likelihood.data.flatten()):
raise Warning, 'likelihood.data and Y are different.'
if Z is None:
i = np.random.permutation(X.shape[0])[:M]
Z = X[i].copy()
else:
assert Z.shape[1]==X.shape[1]
FITC.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
self._set_params(self._get_params())

150
GPy/models/generalized_FITC.py → GPy/models/generalized_fitc.py
View file

@ -2,20 +2,17 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import pylab as pb
from ..util.linalg import mdot, jitchol, chol_inv, pdinv, trace_dot
from ..util.plot import gpplot
from .. import kern
from scipy import stats, linalg
from ..core import sparse_GP
from scipy import linalg
from GPy.core.sparse_gp import SparseGP
from GPy.util.linalg import mdot
def backsub_both_sides(L,X):
def backsub_both_sides(L, X):
""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
tmp, _ = linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
return linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
class generalized_FITC(sparse_GP):
class GeneralizedFITC(SparseGP):
"""
Naish-Guzman, A. and Holden, S. (2008) implemantation of EP with FITC.
@ -28,25 +25,26 @@ class generalized_FITC(sparse_GP):
:param X_variance: The variance in the measurements of X (Gaussian variance)
:type X_variance: np.ndarray (N x input_dim) | None
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (M x input_dim) | None
:param M : Number of inducing points (optional, default 10. Ignored if Z is not None)
:type M: int
:type Z: np.ndarray (num_inducing x input_dim) | None
:param num_inducing : Number of inducing points (optional, default 10. Ignored if Z is not None)
:type num_inducing: int
:param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
:type normalize_(X|Y): bool
"""
def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
self.Z = Z
self.M = self.Z.shape[0]
self.num_inducing = self.Z.shape[0]
self.true_precision = likelihood.precision
super(generalized_FITC, self).__init__(X, likelihood, kernel=kernel, Z=self.Z, X_variance=X_variance, normalize_X=normalize_X)
super(GeneralizedFITC, self).__init__(X, likelihood, kernel=kernel, Z=self.Z, X_variance=X_variance, normalize_X=normalize_X)
self._set_params(self._get_params())
def _set_params(self, p):
self.Z = p[:self.M*self.input_dim].reshape(self.M, self.input_dim)
self.kern._set_params(p[self.Z.size:self.Z.size+self.kern.Nparam])
self.likelihood._set_params(p[self.Z.size+self.kern.Nparam:])
self.Z = p[:self.num_inducing * self.input_dim].reshape(self.num_inducing, self.input_dim)
self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.num_params])
self.likelihood._set_params(p[self.Z.size + self.kern.num_params:])
self._compute_kernel_matrices()
self._computations()
self._FITC_computations()
@ -58,15 +56,15 @@ class generalized_FITC(sparse_GP):
For a Gaussian (or direct: TODO) likelihood, no iteration is required:
this function does nothing
Diag(Knn - Qnn) is added to the noise term to use the tools already implemented in sparse_GP.
Diag(Knn - Qnn) is added to the noise term to use the tools already implemented in SparseGP.
The true precison is now 'true_precision' not 'precision'.
"""
if self.has_uncertain_inputs:
raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
else:
self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
self.likelihood.fit_FITC(self.Kmm, self.psi1, self.psi0)
self.true_precision = self.likelihood.precision # Save the true precision
self.likelihood.precision = self.true_precision/(1. + self.true_precision*self.Diag0[:,None]) # Add the diagonal element of the FITC approximation
self.likelihood.precision = self.true_precision / (1. + self.true_precision * self.Diag0[:, None]) # Add the diagonal element of the FITC approximation
self._set_params(self._get_params()) # update the GP
def _FITC_computations(self):
@ -75,40 +73,40 @@ class generalized_FITC(sparse_GP):
but adds a diagonal term to the covariance matrix: diag(Knn - Qnn).
This function:
- computes the FITC diagonal term
- removes the extra terms computed in the sparse_GP approximation
- removes the extra terms computed in the SparseGP approximation
- computes the likelihood gradients wrt the true precision.
"""
#NOTE the true precison is now 'true_precision' not 'precision'
# NOTE the true precison is now 'true_precision' not 'precision'
if self.likelihood.is_heteroscedastic:
# Compute generalized FITC's diagonal term of the covariance
self.Lmi,info = linalg.lapack.flapack.dtrtrs(self.Lm,np.eye(self.M),lower=1)
Lmipsi1 = np.dot(self.Lmi,self.psi1)
self.Qnn = np.dot(Lmipsi1.T,Lmipsi1)
#self.Kmmi, Lm, Lmi, Kmm_logdet = pdinv(self.Kmm)
#self.Qnn = mdot(self.psi1.T,self.Kmmi,self.psi1)
#a = kj
self.Lmi, info = linalg.lapack.flapack.dtrtrs(self.Lm, np.eye(self.num_inducing), lower=1)
Lmipsi1 = np.dot(self.Lmi, self.psi1)
self.Qnn = np.dot(Lmipsi1.T, Lmipsi1)
# self.Kmmi, Lm, Lmi, Kmm_logdet = pdinv(self.Kmm)
# self.Qnn = mdot(self.psi1.T,self.Kmmi,self.psi1)
# a = kj
self.Diag0 = self.psi0 - np.diag(self.Qnn)
Iplus_Dprod_i = 1./(1.+ self.Diag0 * self.true_precision.flatten())
Iplus_Dprod_i = 1. / (1. + self.Diag0 * self.true_precision.flatten())
self.Diag = self.Diag0 * Iplus_Dprod_i
self.P = Iplus_Dprod_i[:,None] * self.psi1.T
self.RPT0 = np.dot(self.Lmi,self.psi1)
self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,((1. - Iplus_Dprod_i)/self.Diag0)[:,None]*self.RPT0.T))
self.R,info = linalg.flapack.dtrtrs(self.L,self.Lmi,lower=1)
self.RPT = np.dot(self.R,self.P.T)
self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T,self.RPT)
self.P = Iplus_Dprod_i[:, None] * self.psi1.T
self.RPT0 = np.dot(self.Lmi, self.psi1)
self.L = np.linalg.cholesky(np.eye(self.num_inducing) + np.dot(self.RPT0, ((1. - Iplus_Dprod_i) / self.Diag0)[:, None] * self.RPT0.T))
self.R, info = linalg.lapack.dtrtrs(self.L, self.Lmi, lower=1)
self.RPT = np.dot(self.R, self.P.T)
self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T, self.RPT)
self.w = self.Diag * self.likelihood.v_tilde
self.Gamma = np.dot(self.R.T, np.dot(self.RPT,self.likelihood.v_tilde))
self.mu = self.w + np.dot(self.P,self.Gamma)
self.Gamma = np.dot(self.R.T, np.dot(self.RPT, self.likelihood.v_tilde))
self.mu = self.w + np.dot(self.P, self.Gamma)
# Remove extra term from dL_dpsi1
self.dL_dpsi1 -= mdot(self.Lmi.T,Lmipsi1*self.likelihood.precision.flatten().reshape(1,self.N))
self.dL_dpsi1 -= mdot(self.Lmi.T,Lmipsi1 * self.likelihood.precision.flatten().reshape(1,self.num_data))
#self.Kmmi, Lm, Lmi, Kmm_logdet = pdinv(self.Kmm)
#self.dL_dpsi1 -= mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dB
#self.dL_dpsi1 -= mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.num_data)) #dB
#########333333
#self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
# self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
#########333333
@ -116,16 +114,16 @@ class generalized_FITC(sparse_GP):
else:
raise NotImplementedError, "homoscedastic fitc not implemented"
# Remove extra term from dL_dpsi1
#self.dL_dpsi1 += -mdot(self.Kmmi,self.psi1*self.likelihood.precision) #dB
# self.dL_dpsi1 += -mdot(self.Kmmi,self.psi1*self.likelihood.precision) #dB
sf = self.scale_factor
sf2 = sf**2
sf2 = sf ** 2
# Remove extra term from dL_dKmm
self.dL_dKmm += 0.5 * self.D * mdot(self.Lmi.T, self.A, self.Lmi)*sf2 # dB
self.dL_dKmm += 0.5 * self.input_dim * mdot(self.Lmi.T, self.A, self.Lmi) * sf2 # dB
self.dL_dpsi0 = None
#the partial derivative vector for the likelihood
# the partial derivative vector for the likelihood
if self.likelihood.Nparams == 0:
self.partial_for_likelihood = None
elif self.likelihood.is_heteroscedastic:
@ -133,8 +131,8 @@ class generalized_FITC(sparse_GP):
else:
raise NotImplementedError, "homoscedastic derivatives not implemented"
#likelihood is not heterscedatic
#self.partial_for_likelihood = - 0.5 * self.N*self.D*self.likelihood.precision + 0.5 * np.sum(np.square(self.likelihood.Y))*self.likelihood.precision**2
#self.partial_for_likelihood += 0.5 * self.D * trace_dot(self.Bi,self.A)*self.likelihood.precision
#self.partial_for_likelihood = - 0.5 * self.num_data*self.input_dim*self.likelihood.precision + 0.5 * np.sum(np.square(self.likelihood.Y))*self.likelihood.precision**2
#self.partial_for_likelihood += 0.5 * self.input_dim * trace_dot(self.Bi,self.A)*self.likelihood.precision
#self.partial_for_likelihood += self.likelihood.precision*(0.5*trace_dot(self.psi2_beta_scaled,self.E*sf2) - np.trace(self.Cpsi1VVpsi1))
#TODO partial derivative vector for the likelihood not implemented
@ -142,28 +140,28 @@ class generalized_FITC(sparse_GP):
"""
Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
"""
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
if self.has_uncertain_inputs:
raise NotImplementedError, "heteroscedatic derivates not implemented"
else:
#NOTE in sparse_GP this would include the gradient wrt psi0
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1,self.Z,self.X)
# NOTE in SparseGP this would include the gradient wrt psi0
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.Z, self.X)
return dL_dtheta
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
sf2 = self.scale_factor**2
sf2 = self.scale_factor ** 2
if self.likelihood.is_heteroscedastic:
A = -0.5*self.N*self.D*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
A = -0.5*self.num_data*self.input_dim*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
else:
A = -0.5*self.N*self.D*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
C = -self.D * (np.sum(np.log(np.diag(self.LB))) + 0.5*self.M*np.log(sf2))
#C = -0.5*self.D * (self.B_logdet + self.M*np.log(sf2))
A = -0.5*self.num_data*self.input_dim*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
C = -self.input_dim * (np.sum(np.log(np.diag(self.LB))) + 0.5*self.num_inducing*np.log(sf2))
#C = -0.5*self.input_dim * (self.B_logdet + self.num_inducing*np.log(sf2))
D = 0.5*np.sum(np.square(self._LBi_Lmi_psi1V))
#self.Cpsi1VVpsi1 = np.dot(self.Cpsi1V,self.psi1V.T)
#D_ = 0.5*np.trace(self.Cpsi1VVpsi1)
return A+C+D
return A + C + D
def _raw_predict(self, Xnew, which_parts, full_cov=False):
if self.likelihood.is_heteroscedastic:
@ -177,35 +175,35 @@ class generalized_FITC(sparse_GP):
# q(u|f) = N(u| R0i*mu_u*f, R0i*C*R0i.T)
# Ci = I + (RPT0)Di(RPT0).T
# C = I - [RPT0] * (D+[RPT0].T*[RPT0])^-1*[RPT0].T
# = I - [RPT0] * (D + self.Qnn)^-1 * [RPT0].T
# C = I - [RPT0] * (input_dim+[RPT0].T*[RPT0])^-1*[RPT0].T
# = I - [RPT0] * (input_dim + self.Qnn)^-1 * [RPT0].T
# = I - [RPT0] * (U*U.T)^-1 * [RPT0].T
# = I - V.T * V
U = np.linalg.cholesky(np.diag(self.Diag0) + self.Qnn)
V,info = linalg.flapack.dtrtrs(U,self.RPT0.T,lower=1)
C = np.eye(self.M) - np.dot(V.T,V)
mu_u = np.dot(C,self.RPT0)*(1./self.Diag0[None,:])
#self.C = C
#self.RPT0 = np.dot(self.R0,self.Knm.T) P0.T
#self.mu_u = mu_u
#self.U = U
V, info = linalg.flapack.dtrtrs(U, self.RPT0.T, lower=1)
C = np.eye(self.num_inducing) - np.dot(V.T, V)
mu_u = np.dot(C, self.RPT0) * (1. / self.Diag0[None, :])
# self.C = C
# self.RPT0 = np.dot(self.R0,self.Knm.T) P0.T
# self.mu_u = mu_u
# self.U = U
# q(u|y) = N(u| R0i*mu_H,R0i*Sigma_H*R0i.T)
mu_H = np.dot(mu_u,self.mu)
mu_H = np.dot(mu_u, self.mu)
self.mu_H = mu_H
Sigma_H = C + np.dot(mu_u,np.dot(self.Sigma,mu_u.T))
Sigma_H = C + np.dot(mu_u, np.dot(self.Sigma, mu_u.T))
# q(f_star|y) = N(f_star|mu_star,sigma2_star)
Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
KR0T = np.dot(Kx.T,self.Lmi.T)
mu_star = np.dot(KR0T,mu_H)
KR0T = np.dot(Kx.T, self.Lmi.T)
mu_star = np.dot(KR0T, mu_H)
if full_cov:
Kxx = self.kern.K(Xnew,which_parts=which_parts)
var = Kxx + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.M),KR0T.T))
Kxx = self.kern.K(Xnew, which_parts=which_parts)
var = Kxx + np.dot(KR0T, np.dot(Sigma_H - np.eye(self.num_inducing), KR0T.T))
else:
Kxx = self.kern.Kdiag(Xnew,which_parts=which_parts)
Kxx_ = self.kern.K(Xnew,which_parts=which_parts) # TODO: RA, is this line needed?
var_ = Kxx_ + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.M),KR0T.T)) # TODO: RA, is this line needed?
var = (Kxx + np.sum(KR0T.T*np.dot(Sigma_H - np.eye(self.M),KR0T.T),0))[:,None]
return mu_star[:,None],var
Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
Kxx_ = self.kern.K(Xnew, which_parts=which_parts) # TODO: RA, is this line needed?
var_ = Kxx_ + np.dot(KR0T, np.dot(Sigma_H - np.eye(self.num_inducing), KR0T.T)) # TODO: RA, is this line needed?
var = (Kxx + np.sum(KR0T.T * np.dot(Sigma_H - np.eye(self.num_inducing), KR0T.T), 0))[:, None]
return mu_star[:, None], var
else:
raise NotImplementedError, "homoscedastic fitc not implemented"
"""

8
GPy/models/GP_classification.py → GPy/models/gp_classification.py
View file

@ -7,15 +7,15 @@ from ..core import GP
from .. import likelihoods
from .. import kern
class GP_classification(GP):
class GPClassification(GP):
"""
Gaussian Process classification
This is a thin wrapper around the models.GP class, with a set of sensible defalts
This is a thin wrapper around the models.GP class, with a set of sensible defaults
:param X: input observations
:param Y: observed values
:param likelihood: a GPy likelihood, defaults to binomial with probit link_function
:param likelihood: a GPy likelihood, defaults to Binomial with probit link_function
:param kernel: a GPy kernel, defaults to rbf
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True
@ -31,7 +31,7 @@ class GP_classification(GP):
kernel = kern.rbf(X.shape[1])
if likelihood is None:
distribution = likelihoods.likelihood_functions.binomial()
distribution = likelihoods.likelihood_functions.Binomial()
likelihood = likelihoods.EP(Y, distribution)
elif Y is not None:
if not all(Y.flatten() == likelihood.data.flatten()):

4
GPy/models/GP_regression.py → GPy/models/gp_regression.py
View file

@ -7,11 +7,11 @@ from ..core import GP
from .. import likelihoods
from .. import kern
class GP_regression(GP):
class GPRegression(GP):
"""
Gaussian Process model for regression
This is a thin wrapper around the models.GP class, with a set of sensible defalts
This is a thin wrapper around the models.GP class, with a set of sensible defaults
:param X: input observations
:param Y: observed values

6
GPy/models/GPLVM.py → GPy/models/gplvm.py
View file

@ -6,7 +6,7 @@ import numpy as np
import pylab as pb
import sys, pdb
from .. import kern
from ..core import model
from ..core import Model
from ..util.linalg import pdinv, PCA
from ..core import GP
from ..likelihoods import Gaussian
@ -42,13 +42,13 @@ class GPLVM(GP):
return np.random.randn(Y.shape[0], input_dim)
def _get_param_names(self):
return sum([['X_%i_%i'%(n,q) for q in range(self.input_dim)] for n in range(self.N)],[]) + GP._get_param_names(self)
return sum([['X_%i_%i'%(n,q) for q in range(self.input_dim)] for n in range(self.num_data)],[]) + GP._get_param_names(self)
def _get_params(self):
return np.hstack((self.X.flatten(), GP._get_params(self)))
def _set_params(self,x):
self.X = x[:self.N*self.input_dim].reshape(self.N,self.input_dim).copy()
self.X = x[:self.num_data*self.input_dim].reshape(self.num_data,self.input_dim).copy()
GP._set_params(self, x[self.X.size:])
def _log_likelihood_gradients(self):

55
GPy/models/mrd.py
View file

@ -3,17 +3,16 @@ Created on 10 Apr 2013
@author: Max Zwiessele
'''
from GPy.core import model
from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
from GPy.core import sparse_GP
from GPy.core import Model
from GPy.core import SparseGP
from GPy.util.linalg import PCA
from scipy import linalg
import numpy
import itertools
import pylab
from GPy.kern.kern import kern
from GPy.models.bayesian_gplvm import BayesianGPLVM
class MRD(model):
class MRD(Model):
"""
Do MRD on given Datasets in Ylist.
All Ys in likelihood_list are in [N x Dn], where Dn can be different per Yn,
@ -34,18 +33,18 @@ class MRD(model):
:param X_variance:
Initial latent space variance
:param init: [cooncat|single|random]
initialization method to use:
initialization method to use:
*concat: PCA on concatenated outputs
*single: PCA on each output
*random: random
:param M:
:param num_inducing:
number of inducing inputs to use
:param Z:
initial inducing inputs
:param kernels: list of kernels or kernel shared for all BGPLVMS
:type kernels: [GPy.kern.kern] | GPy.kern.kern | None (default)
"""
def __init__(self, likelihood_or_Y_list, input_dim, M=10, names=None,
def __init__(self, likelihood_or_Y_list, input_dim, num_inducing=10, names=None,
kernels=None, initx='PCA',
initz='permute', _debug=False, **kw):
if names is None:
@ -62,24 +61,24 @@ class MRD(model):
assert not ('kernel' in kw), "pass kernels through `kernels` argument"
self.input_dim = input_dim
self.M = M
self.num_inducing = num_inducing
self._debug = _debug
self._init = True
X = self._init_X(initx, likelihood_or_Y_list)
Z = self._init_Z(initz, X)
self.bgplvms = [Bayesian_GPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, M=self.M, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
self.bgplvms = [BayesianGPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, num_inducing=self.num_inducing, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
del self._init
self.gref = self.bgplvms[0]
nparams = numpy.array([0] + [sparse_GP._get_params(g).size - g.Z.size for g in self.bgplvms])
nparams = numpy.array([0] + [SparseGP._get_params(g).size - g.Z.size for g in self.bgplvms])
self.nparams = nparams.cumsum()
self.N = self.gref.N
self.NQ = self.N * self.input_dim
self.MQ = self.M * self.input_dim
self.num_data = self.gref.num_data
self.NQ = self.num_data * self.input_dim
self.MQ = self.num_inducing * self.input_dim
model.__init__(self) # @UndefinedVariable
Model.__init__(self) # @UndefinedVariable
self._set_params(self._get_params())
@property
@ -143,15 +142,15 @@ class MRD(model):
self._init_Z(initz, self.X)
def _get_param_names(self):
# X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
# S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
# X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
# S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
n1 = self.gref._get_param_names()
n1var = n1[:self.NQ * 2 + self.MQ]
map_names = lambda ns, name: map(lambda x: "{1}_{0}".format(*x),
itertools.izip(ns,
itertools.repeat(name)))
return list(itertools.chain(n1var, *(map_names(\
sparse_GP._get_param_names(g)[self.MQ:], n) \
SparseGP._get_param_names(g)[self.MQ:], n) \
for g, n in zip(self.bgplvms, self.names))))
def _get_params(self):
@ -165,14 +164,14 @@ class MRD(model):
X = self.gref.X.ravel()
X_var = self.gref.X_variance.ravel()
Z = self.gref.Z.ravel()
thetas = [sparse_GP._get_params(g)[g.Z.size:] for g in self.bgplvms]
thetas = [SparseGP._get_params(g)[g.Z.size:] for g in self.bgplvms]
params = numpy.hstack([X, X_var, Z, numpy.hstack(thetas)])
return params
# def _set_var_params(self, g, X, X_var, Z):
# g.X = X.reshape(self.N, self.input_dim)
# g.X_variance = X_var.reshape(self.N, self.input_dim)
# g.Z = Z.reshape(self.M, self.input_dim)
# g.X = X.reshape(self.num_data, self.input_dim)
# g.X_variance = X_var.reshape(self.num_data, self.input_dim)
# g.Z = Z.reshape(self.num_inducing, self.input_dim)
#
# def _set_kern_params(self, g, p):
# g.kern._set_params(p[:g.kern.Nparam])
@ -206,7 +205,7 @@ class MRD(model):
def log_likelihood(self):
ll = -self.gref.KL_divergence()
for g in self.bgplvms:
ll += sparse_GP.log_likelihood(g)
ll += SparseGP.log_likelihood(g)
return ll
def _log_likelihood_gradients(self):
@ -215,7 +214,7 @@ class MRD(model):
dLdmu -= dKLmu
dLdS -= dKLdS
dLdmuS = numpy.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
dldzt1 = reduce(lambda a, b: a + b, (sparse_GP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms))
dldzt1 = reduce(lambda a, b: a + b, (SparseGP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms))
return numpy.hstack((dLdmuS,
dldzt1,
@ -250,9 +249,9 @@ class MRD(model):
if X is None:
X = self.X
if init in "permute":
Z = numpy.random.permutation(X.copy())[:self.M]
Z = numpy.random.permutation(X.copy())[:self.num_inducing]
elif init in "random":
Z = numpy.random.randn(self.M, self.input_dim) * X.var()
Z = numpy.random.randn(self.num_inducing, self.input_dim) * X.var()
self.Z = Z
return Z
@ -274,8 +273,8 @@ class MRD(model):
else:
return pylab.gcf()
def plot_X_1d(self):
return self.gref.plot_X_1d()
def plot_X_1d(self, *a, **kw):
return self.gref.plot_X_1d(*a, **kw)
def plot_X(self, fignum=None, ax=None):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.X))

61
GPy/models/sparse_GPLVM.py
View file

@ -1,61 +0,0 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import pylab as pb
import sys, pdb
# from .. import kern
# from ..core import model
# from ..util.linalg import pdinv, PCA
from GPLVM import GPLVM
from sparse_GP_regression import sparse_GP_regression
class sparse_GPLVM(sparse_GP_regression, GPLVM):
"""
Sparse Gaussian Process Latent Variable Model
:param Y: observed data
:type Y: np.ndarray
:param input_dim: latent dimensionality
:type input_dim: int
:param init: initialisation method for the latent space
:type init: 'PCA'|'random'
"""
def __init__(self, Y, input_dim, kernel=None, init='PCA', M=10):
X = self.initialise_latent(init, input_dim, Y)
sparse_GP_regression.__init__(self, X, Y, kernel=kernel,M=M)
def _get_param_names(self):
return (sum([['X_%i_%i'%(n,q) for q in range(self.input_dim)] for n in range(self.N)],[])
+ sparse_GP_regression._get_param_names(self))
def _get_params(self):
return np.hstack((self.X.flatten(), sparse_GP_regression._get_params(self)))
def _set_params(self,x):
self.X = x[:self.X.size].reshape(self.N,self.input_dim).copy()
sparse_GP_regression._set_params(self, x[self.X.size:])
def log_likelihood(self):
return sparse_GP_regression.log_likelihood(self)
def dL_dX(self):
dL_dX = self.kern.dKdiag_dX(self.dL_dpsi0,self.X)
dL_dX += self.kern.dK_dX(self.dL_dpsi1.T,self.X,self.Z)
return dL_dX
def _log_likelihood_gradients(self):
return np.hstack((self.dL_dX().flatten(), sparse_GP_regression._log_likelihood_gradients(self)))
def plot(self):
GPLVM.plot(self)
#passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
mu, var, upper, lower = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001)
pb.plot(mu[:, 0] , mu[:, 1], 'ko')
def plot_latent(self, *args, **kwargs):
input_1, input_2 = GPLVM.plot_latent(*args, **kwargs)
pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w')

19
GPy/models/sparse_GP_classification.py → GPy/models/sparse_gp_classification.py
View file

@ -3,21 +3,20 @@
import numpy as np
from ..core import sparse_GP
from ..core import SparseGP
from .. import likelihoods
from .. import kern
from ..likelihoods import likelihood
from GP_regression import GP_regression
class sparse_GP_classification(sparse_GP):
class SparseGPClassification(SparseGP):
"""
sparse Gaussian Process model for classification
This is a thin wrapper around the sparse_GP class, with a set of sensible defalts
This is a thin wrapper around the sparse_GP class, with a set of sensible defaults
:param X: input observations
:param Y: observed values
:param likelihood: a GPy likelihood, defaults to binomial with probit link_function
:param likelihood: a GPy likelihood, defaults to Binomial with probit link_function
:param kernel: a GPy kernel, defaults to rbf+white
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True
@ -25,26 +24,24 @@ class sparse_GP_classification(sparse_GP):
:type normalize_Y: False|True
:rtype: model object
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10):
def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
if kernel is None:
kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
if likelihood is None:
distribution = likelihoods.likelihood_functions.binomial()
distribution = likelihoods.likelihood_functions.Binomial()
likelihood = likelihoods.EP(Y, distribution)
elif Y is not None:
if not all(Y.flatten() == likelihood.data.flatten()):
raise Warning, 'likelihood.data and Y are different.'
if Z is None:
i = np.random.permutation(X.shape[0])[:M]
i = np.random.permutation(X.shape[0])[:num_inducing]
Z = X[i].copy()
else:
assert Z.shape[1]==X.shape[1]
sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
self._set_params(self._get_params())

26
GPy/models/sparse_GP_regression.py → GPy/models/sparse_gp_regression.py
View file

@ -3,17 +3,15 @@
import numpy as np
from ..core import sparse_GP
from ..core import SparseGP
from .. import likelihoods
from .. import kern
from ..likelihoods import likelihood
from GP_regression import GP_regression
class sparse_GP_regression(sparse_GP):
class SparseGPRegression(SparseGP):
"""
Gaussian Process model for regression
This is a thin wrapper around the sparse_GP class, with a set of sensible defalts
This is a thin wrapper around the SparseGP class, with a set of sensible defalts
:param X: input observations
:param Y: observed values
@ -28,20 +26,20 @@ class sparse_GP_regression(sparse_GP):
"""
def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10, X_variance=None):
#kern defaults to rbf (plus white for stability)
def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10, X_variance=None):
# kern defaults to rbf (plus white for stability)
if kernel is None:
kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1], 1e-3)
#Z defaults to a subset of the data
# Z defaults to a subset of the data
if Z is None:
i = np.random.permutation(X.shape[0])[:M]
i = np.random.permutation(X.shape[0])[:num_inducing]
Z = X[i].copy()
else:
assert Z.shape[1]==X.shape[1]
assert Z.shape[1] == X.shape[1]
#likelihood defaults to Gaussian
likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
# likelihood defaults to Gaussian
likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
self._set_params(self._get_params())

61
GPy/models/sparse_gplvm.py Normal file
View file

@ -0,0 +1,61 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import pylab as pb
import sys, pdb
from GPy.models.sparse_gp_regression import SparseGPRegression
from GPy.models.gplvm import GPLVM
# from .. import kern
# from ..core import model
# from ..util.linalg import pdinv, PCA
class SparseGPLVM(SparseGPRegression, GPLVM):
"""
Sparse Gaussian Process Latent Variable Model
:param Y: observed data
:type Y: np.ndarray
:param input_dim: latent dimensionality
:type input_dim: int
:param init: initialisation method for the latent space
:type init: 'PCA'|'random'
"""
def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10):
X = self.initialise_latent(init, input_dim, Y)
SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
def _get_param_names(self):
return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
+ SparseGPRegression._get_param_names(self))
def _get_params(self):
return np.hstack((self.X.flatten(), SparseGPRegression._get_params(self)))
def _set_params(self, x):
self.X = x[:self.X.size].reshape(self.num_data, self.input_dim).copy()
SparseGPRegression._set_params(self, x[self.X.size:])
def log_likelihood(self):
return SparseGPRegression.log_likelihood(self)
def dL_dX(self):
dL_dX = self.kern.dKdiag_dX(self.dL_dpsi0, self.X)
dL_dX += self.kern.dK_dX(self.dL_dpsi1.T, self.X, self.Z)
return dL_dX
def _log_likelihood_gradients(self):
return np.hstack((self.dL_dX().flatten(), SparseGPRegression._log_likelihood_gradients(self)))
def plot(self):
GPLVM.plot(self)
# passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
mu, var, upper, lower = SparseGPRegression.predict(self, self.Z + np.random.randn(*self.Z.shape) * 0.0001)
pb.plot(mu[:, 0] , mu[:, 1], 'ko')
def plot_latent(self, *args, **kwargs):
input_1, input_2 = GPLVM.plot_latent(*args, **kwargs)
pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w')

26
GPy/models/warped_GP.py → GPy/models/warped_gp.py
View file

@ -3,25 +3,21 @@
import numpy as np
from .. import kern
from ..core import model
from ..util.linalg import pdinv
from ..util.plot import gpplot
from ..util.warping_functions import *
from GP_regression import GP_regression
from ..core import GP
from .. import likelihoods
from .. import kern
from GPy.util.warping_functions import TanhWarpingFunction_d
from GPy import kern
class warpedGP(GP):
def __init__(self, X, Y, kernel=None, warping_function = None, warping_terms = 3, normalize_X=False, normalize_Y=False):
class WarpedGP(GP):
def __init__(self, X, Y, kernel=None, warping_function=None, warping_terms=3, normalize_X=False, normalize_Y=False):
if kernel is None:
kernel = kern.rbf(X.shape[1])
if warping_function == None:
self.warping_function = TanhWarpingFunction_d(warping_terms)
self.warping_params = (np.random.randn(self.warping_function.n_terms*3+1,) * 1)
self.warping_params = (np.random.randn(self.warping_function.n_terms * 3 + 1,) * 1)
Y = self._scale_data(Y)
self.has_uncertain_inputs = False
@ -35,10 +31,10 @@ class warpedGP(GP):
def _scale_data(self, Y):
self._Ymax = Y.max()
self._Ymin = Y.min()
return (Y-self._Ymin)/(self._Ymax-self._Ymin) - 0.5
return (Y - self._Ymin) / (self._Ymax - self._Ymin) - 0.5
def _unscale_data(self, Y):
return (Y + 0.5)*(self._Ymax - self._Ymin) + self._Ymin
return (Y + 0.5) * (self._Ymax - self._Ymin) + self._Ymin
def _set_params(self, x):
self.warping_params = x[:self.warping_function.num_parameters]
@ -68,15 +64,15 @@ class warpedGP(GP):
alpha = np.dot(self.Ki, self.likelihood.Y.flatten())
warping_grads = self.warping_function_gradients(alpha)
warping_grads = np.append(warping_grads[:,:-1].flatten(), warping_grads[0,-1])
warping_grads = np.append(warping_grads[:, :-1].flatten(), warping_grads[0, -1])
return np.hstack((warping_grads.flatten(), ll_grads.flatten()))
def warping_function_gradients(self, Kiy):
grad_y = self.warping_function.fgrad_y(self.Y_untransformed, self.warping_params)
grad_y_psi, grad_psi = self.warping_function.fgrad_y_psi(self.Y_untransformed, self.warping_params,
return_covar_chain = True)
djac_dpsi = ((1.0/grad_y[:,:, None, None])*grad_y_psi).sum(axis=0).sum(axis=0)
dquad_dpsi = (Kiy[:,None,None,None] * grad_psi).sum(axis=0).sum(axis=0)
return_covar_chain=True)
djac_dpsi = ((1.0 / grad_y[:, :, None, None]) * grad_y_psi).sum(axis=0).sum(axis=0)
dquad_dpsi = (Kiy[:, None, None, None] * grad_psi).sum(axis=0).sum(axis=0)
return -dquad_dpsi + djac_dpsi

31
GPy/testing/bgplvm_tests.py
View file

@ -4,70 +4,71 @@
import unittest
import numpy as np
import GPy
from GPy.models.bayesian_gplvm import BayesianGPLVM
class BGPLVMTests(unittest.TestCase):
def test_bias_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
Y -= Y.mean(axis=0)
k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel = k, M=M)
m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
def test_linear_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
Y -= Y.mean(axis=0)
k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel = k, M=M)
m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
def test_rbf_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
Y -= Y.mean(axis=0)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel = k, M=M)
m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
def test_rbf_bias_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
Y -= Y.mean(axis=0)
k = GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel = k, M=M)
m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
#@unittest.skip('psi2 cross terms are NotImplemented for this combination')
def test_linear_bias_kern(self):
N, M, input_dim, D = 30, 5, 4, 30
N, num_inducing, input_dim, D = 30, 5, 4, 30
X = np.random.rand(N, input_dim)
k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
Y -= Y.mean(axis=0)
k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel = k, M=M)
m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())

50
GPy/testing/examples_tests.py
View file

@ -9,40 +9,41 @@ import pkgutil
import os
import random
from nose.tools import nottest
import sys
class ExamplesTests(unittest.TestCase):
def _checkgrad(self, model):
self.assertTrue(model.checkgrad())
def _checkgrad(self, Model):
self.assertTrue(Model.checkgrad())
def _model_instance(self, model):
self.assertTrue(isinstance(model, GPy.models))
def _model_instance(self, Model):
self.assertTrue(isinstance(Model, GPy.models))
"""
def model_instance_generator(model):
def model_instance_generator(Model):
def check_model_returned(self):
self._model_instance(model)
self._model_instance(Model)
return check_model_returned
def checkgrads_generator(model):
def checkgrads_generator(Model):
def model_checkgrads(self):
self._checkgrad(model)
self._checkgrad(Model)
return model_checkgrads
"""
def model_checkgrads(model):
model.randomize()
assert model.checkgrad()
def model_checkgrads(Model):
Model.randomize()
assert Model.checkgrad()
def model_instance(model):
assert isinstance(model, GPy.core.model)
def model_instance(Model):
assert isinstance(Model, GPy.core.Model)
@nottest
def test_models():
examples_path = os.path.dirname(GPy.examples.__file__)
#Load modules
# Load modules
for loader, module_name, is_pkg in pkgutil.iter_modules([examples_path]):
#Load examples
# Load examples
module_examples = loader.find_module(module_name).load_module(module_name)
print "MODULE", module_examples
print "Before"
@ -56,26 +57,27 @@ def test_models():
continue
print "Testing example: ", example[0]
#Generate model
model = example[1]()
print model
# Generate Model
Model = example[1]()
print Model
#Create tests for instance check
# Create tests for instance check
"""
test = model_instance_generator(model)
test = model_instance_generator(Model)
test.__name__ = 'test_instance_%s' % example[0]
setattr(ExamplesTests, test.__name__, test)
#Create tests for checkgrads check
test = checkgrads_generator(model)
test = checkgrads_generator(Model)
test.__name__ = 'test_checkgrads_%s' % example[0]
setattr(ExamplesTests, test.__name__, test)
"""
model_checkgrads.description = 'test_checkgrads_%s' % example[0]
yield model_checkgrads, model
yield model_checkgrads, Model
model_instance.description = 'test_instance_%s' % example[0]
yield model_instance, model
yield model_instance, Model
if __name__ == "__main__":
print "Running unit tests, please be (very) patient..."
unittest.main()
# unittest.main()
test_models()

12
GPy/testing/gplvm_tests.py
View file

@ -7,11 +7,11 @@ import GPy
class GPLVMTests(unittest.TestCase):
def test_bias_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.GPLVM(Y, input_dim, kernel = k)
m.ensure_default_constraints()
@ -19,11 +19,11 @@ class GPLVMTests(unittest.TestCase):
self.assertTrue(m.checkgrad())
def test_linear_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.GPLVM(Y, input_dim, kernel = k)
m.ensure_default_constraints()
@ -31,11 +31,11 @@ class GPLVMTests(unittest.TestCase):
self.assertTrue(m.checkgrad())
def test_rbf_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.GPLVM(Y, input_dim, kernel = k)
m.ensure_default_constraints()

10
GPy/testing/kernel_tests.py
View file

@ -12,7 +12,7 @@ class KernelTests(unittest.TestCase):
K.constrain_fixed('2')
X = np.random.rand(5,5)
Y = np.ones((5,1))
m = GPy.models.GP_regression(X,Y,K)
m = GPy.models.GPRegression(X,Y,K)
self.assertTrue(m.checkgrad())
def test_fixedkernel(self):
@ -21,9 +21,9 @@ class KernelTests(unittest.TestCase):
"""
X = np.random.rand(30, 4)
K = np.dot(X, X.T)
kernel = GPy.kern.fixed(4, K)
kernel = GPy.kern.Fixed(4, K)
Y = np.ones((30,1))
m = GPy.models.GP_regression(X,Y,kernel=kernel)
m = GPy.models.GPRegression(X,Y,kernel=kernel)
self.assertTrue(m.checkgrad())
def test_coregionalisation(self):
@ -36,9 +36,9 @@ class KernelTests(unittest.TestCase):
Y = np.vstack((Y1,Y2))
k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
k2 = GPy.kern.coregionalise(2,1)
k2 = GPy.kern.Coregionalise(2,1)
k = k1.prod(k2,tensor=True)
m = GPy.models.GP_regression(X,Y,kernel=k)
m = GPy.models.GPRegression(X,Y,kernel=k)
self.assertTrue(m.checkgrad())

6
GPy/testing/mrd_tests.py
View file

@ -14,16 +14,16 @@ class MRDTests(unittest.TestCase):
def test_gradients(self):
num_m = 3
N, M, input_dim, D = 20, 8, 6, 20
N, num_inducing, input_dim, D = 20, 8, 6, 20
X = np.random.rand(N, input_dim)
k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
K = k.K(X)
Ylist = [np.random.multivariate_normal(np.zeros(N), K, D).T for _ in range(num_m)]
Ylist = [np.random.multivariate_normal(np.zeros(N), K, input_dim).T for _ in range(num_m)]
likelihood_list = [GPy.likelihoods.Gaussian(Y) for Y in Ylist]
m = GPy.models.MRD(likelihood_list, input_dim=input_dim, kernels=k, M=M)
m = GPy.models.MRD(likelihood_list, input_dim=input_dim, kernels=k, num_inducing=num_inducing)
m.ensure_default_constraints()
self.assertTrue(m.checkgrad())

6
GPy/testing/prior_tests.py
View file

@ -13,7 +13,7 @@ class PriorTests(unittest.TestCase):
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GP_regression(X, y)
m = GPy.models.GPRegression(X, y)
m.ensure_default_constraints()
lognormal = GPy.priors.LogGaussian(1, 2)
m.set_prior('rbf', lognormal)
@ -27,7 +27,7 @@ class PriorTests(unittest.TestCase):
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GP_regression(X, y)
m = GPy.models.GPRegression(X, y)
m.ensure_default_constraints()
Gamma = GPy.priors.Gamma(1, 1)
m.set_prior('rbf', Gamma)
@ -41,7 +41,7 @@ class PriorTests(unittest.TestCase):
y = b*X + C + 1*np.sin(X)
y += 0.05*np.random.randn(len(X))
X, y = X[:, None], y[:, None]
m = GPy.models.GP_regression(X, y)
m = GPy.models.GPRegression(X, y)
m.ensure_default_constraints()
gaussian = GPy.priors.Gaussian(1, 1)
success = False

52
GPy/testing/psi_stat_expactation_tests.py
View file

@ -21,36 +21,36 @@ def ard(p):
@testing.deepTest(__test__)
class Test(unittest.TestCase):
D = 9
M = 4
input_dim = 9
num_inducing = 4
N = 3
Nsamples = 6e6
def setUp(self):
self.kerns = (
# (GPy.kern.rbf(self.D, ARD=True) +
# GPy.kern.linear(self.D, ARD=True) +
# GPy.kern.bias(self.D) +
# GPy.kern.white(self.D)),
(GPy.kern.rbf(self.D, np.random.rand(), np.random.rand(self.D), ARD=True) +
GPy.kern.rbf(self.D, np.random.rand(), np.random.rand(self.D), ARD=True) +
GPy.kern.linear(self.D, np.random.rand(self.D), ARD=True) +
GPy.kern.bias(self.D) +
GPy.kern.white(self.D)),
# GPy.kern.rbf(self.D), GPy.kern.rbf(self.D, ARD=True),
# GPy.kern.linear(self.D, ARD=False), GPy.kern.linear(self.D, ARD=True),
# GPy.kern.linear(self.D) + GPy.kern.bias(self.D),
# GPy.kern.rbf(self.D) + GPy.kern.bias(self.D),
# GPy.kern.linear(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
# GPy.kern.rbf(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
# GPy.kern.bias(self.D), GPy.kern.white(self.D),
# (GPy.kern.rbf(self.input_dim, ARD=True) +
# GPy.kern.linear(self.input_dim, ARD=True) +
# GPy.kern.bias(self.input_dim) +
# GPy.kern.white(self.input_dim)),
(GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True) +
GPy.kern.bias(self.input_dim) +
GPy.kern.white(self.input_dim)),
# GPy.kern.rbf(self.input_dim), GPy.kern.rbf(self.input_dim, ARD=True),
# GPy.kern.linear(self.input_dim, ARD=False), GPy.kern.linear(self.input_dim, ARD=True),
# GPy.kern.linear(self.input_dim) + GPy.kern.bias(self.input_dim),
# GPy.kern.rbf(self.input_dim) + GPy.kern.bias(self.input_dim),
# GPy.kern.linear(self.input_dim) + GPy.kern.bias(self.input_dim) + GPy.kern.white(self.input_dim),
# GPy.kern.rbf(self.input_dim) + GPy.kern.bias(self.input_dim) + GPy.kern.white(self.input_dim),
# GPy.kern.bias(self.input_dim), GPy.kern.white(self.input_dim),
)
self.q_x_mean = np.random.randn(self.D)
self.q_x_variance = np.exp(np.random.randn(self.D))
self.q_x_samples = np.random.randn(self.Nsamples, self.D) * np.sqrt(self.q_x_variance) + self.q_x_mean
self.Z = np.random.randn(self.M, self.D)
self.q_x_mean.shape = (1, self.D)
self.q_x_variance.shape = (1, self.D)
self.q_x_mean = np.random.randn(self.input_dim)
self.q_x_variance = np.exp(np.random.randn(self.input_dim))
self.q_x_samples = np.random.randn(self.Nsamples, self.input_dim) * np.sqrt(self.q_x_variance) + self.q_x_mean
self.Z = np.random.randn(self.num_inducing, self.input_dim)
self.q_x_mean.shape = (1, self.input_dim)
self.q_x_variance.shape = (1, self.input_dim)
def test_psi0(self):
for kern in self.kerns:
@ -63,7 +63,7 @@ class Test(unittest.TestCase):
for kern in self.kerns:
Nsamples = 100
psi1 = kern.psi1(self.Z, self.q_x_mean, self.q_x_variance)
K_ = np.zeros((Nsamples, self.M))
K_ = np.zeros((Nsamples, self.num_inducing))
diffs = []
for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
K = kern.K(q_x_sample_stripe, self.Z)
@ -89,7 +89,7 @@ class Test(unittest.TestCase):
for kern in self.kerns:
Nsamples = 100
psi2 = kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
K_ = np.zeros((self.M, self.M))
K_ = np.zeros((self.num_inducing, self.num_inducing))
diffs = []
for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
K = kern.K(q_x_sample_stripe, self.Z)

65
GPy/testing/psi_stat_gradient_tests.py
View file

@ -8,23 +8,23 @@ import numpy
import GPy
import itertools
from GPy.core import model
from GPy.core import Model
class PsiStatModel(model):
def __init__(self, which, X, X_variance, Z, M, kernel):
class PsiStatModel(Model):
def __init__(self, which, X, X_variance, Z, num_inducing, kernel):
self.which = which
self.X = X
self.X_variance = X_variance
self.Z = Z
self.N, self.input_dim = X.shape
self.M, input_dim = Z.shape
self.num_inducing, input_dim = Z.shape
assert self.input_dim == input_dim, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
self.kern = kernel
super(PsiStatModel, self).__init__()
self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance)
def _get_param_names(self):
Xnames = ["{}_{}_{}".format(what, i, j) for what, i, j in itertools.product(['X', 'X_variance'], range(self.N), range(self.input_dim))]
Znames = ["Z_{}_{}".format(i, j) for i, j in itertools.product(range(self.M), range(self.input_dim))]
Znames = ["Z_{}_{}".format(i, j) for i, j in itertools.product(range(self.num_inducing), range(self.input_dim))]
return Xnames + Znames + self.kern._get_param_names()
def _get_params(self):
return numpy.hstack([self.X.flatten(), self.X_variance.flatten(), self.Z.flatten(), self.kern._get_params()])
@ -34,7 +34,7 @@ class PsiStatModel(model):
start, end = end, end + self.X_variance.size
self.X_variance = x[start: end].reshape(self.N, self.input_dim)
start, end = end, end + self.Z.size
self.Z = x[start: end].reshape(self.M, self.input_dim)
self.Z = x[start: end].reshape(self.num_inducing, self.input_dim)
self.kern._set_params(x[end:])
def log_likelihood(self):
return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
@ -43,19 +43,19 @@ class PsiStatModel(model):
try:
psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
except AttributeError:
psiZ = numpy.zeros(self.M * self.input_dim)
psiZ = numpy.zeros(self.num_inducing * self.input_dim)
thetagrad = self.kern.__getattribute__("d" + self.which + "_dtheta")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance).flatten()
return numpy.hstack((psimu.flatten(), psiS.flatten(), psiZ.flatten(), thetagrad))
class DPsiStatTest(unittest.TestCase):
input_dim = 5
N = 50
M = 10
D = 20
num_inducing = 10
input_dim = 20
X = numpy.random.randn(N, input_dim)
X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
Z = numpy.random.permutation(X)[:M]
Y = X.dot(numpy.random.randn(input_dim, D))
Z = numpy.random.permutation(X)[:num_inducing]
Y = X.dot(numpy.random.randn(input_dim, input_dim))
# kernels = [GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)), GPy.kern.rbf(input_dim, ARD=True), GPy.kern.bias(input_dim)]
kernels = [GPy.kern.linear(input_dim), GPy.kern.rbf(input_dim), GPy.kern.bias(input_dim),
@ -65,42 +65,39 @@ class DPsiStatTest(unittest.TestCase):
def testPsi0(self):
for k in self.kernels:
m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.M, kernel=k)
try:
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
except:
import ipdb;ipdb.set_trace()
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
# def testPsi1(self):
# for k in self.kernels:
# m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
# M=self.M, kernel=k)
# num_inducing=self.num_inducing, kernel=k)
# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_lin(self):
k = self.kernels[0]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.M, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_lin_bia(self):
k = self.kernels[3]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.M, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_rbf(self):
k = self.kernels[1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.M, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_rbf_bia(self):
k = self.kernels[-1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.M, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_bia(self):
k = self.kernels[2]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.M, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
@ -108,25 +105,25 @@ if __name__ == "__main__":
import sys
interactive = 'i' in sys.argv
if interactive:
# N, M, input_dim, D = 30, 5, 4, 30
# N, num_inducing, input_dim, input_dim = 30, 5, 4, 30
# X = numpy.random.rand(N, input_dim)
# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# K = k.K(X)
# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, D).T
# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, input_dim).T
# Y -= Y.mean(axis=0)
# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, M=M)
# m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
# m.ensure_default_constraints()
# m.randomize()
# # self.assertTrue(m.checkgrad())
numpy.random.seed(0)
input_dim = 5
N = 50
M = 10
num_inducing = 10
D = 15
X = numpy.random.randn(N, input_dim)
X_var = .5 * numpy.ones_like(X) + .1 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
Z = numpy.random.permutation(X)[:M]
Z = numpy.random.permutation(X)[:num_inducing]
Y = X.dot(numpy.random.randn(input_dim, D))
# kernel = GPy.kern.bias(input_dim)
#
@ -136,22 +133,22 @@ if __name__ == "__main__":
# for k in kernels:
# m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=k)
# num_inducing=num_inducing, kernel=k)
# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
#
# m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.linear(input_dim))
# num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim))
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=kernel)
# num_inducing=num_inducing, kernel=kernel)
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=kernel)
# num_inducing=num_inducing, kernel=kernel)
# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.rbf(input_dim))
# num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim))
m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
M=M, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
m3.ensure_default_constraints()
# + GPy.kern.bias(input_dim))
# m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))
# num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))
else:
unittest.main()

19
GPy/testing/sparse_gplvm_tests.py
View file

@ -4,41 +4,42 @@
import unittest
import numpy as np
import GPy
from GPy.models.sparse_gplvm import SparseGPLVM
class sparse_GPLVMTests(unittest.TestCase):
def test_bias_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.sparse_GPLVM(Y, input_dim, kernel = k, M=M)
m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
@unittest.skip('linear kernels do not have dKdiag_dX')
def test_linear_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.sparse_GPLVM(Y, input_dim, kernel = k, M=M)
m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
def test_rbf_kern(self):
N, M, input_dim, D = 10, 3, 2, 4
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = GPy.models.sparse_GPLVM(Y, input_dim, kernel = k, M=M)
m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())

147
GPy/testing/unit_tests.py
View file

@ -5,33 +5,33 @@
import unittest
import numpy as np
import GPy
from GPy.likelihoods.likelihood_functions import Binomial
class GradientTests(unittest.TestCase):
def setUp(self):
######################################
## 1 dimensional example
# # 1 dimensional example
# sample inputs and outputs
self.X1D = np.random.uniform(-3.,3.,(20,1))
self.Y1D = np.sin(self.X1D)+np.random.randn(20,1)*0.05
self.X1D = np.random.uniform(-3., 3., (20, 1))
self.Y1D = np.sin(self.X1D) + np.random.randn(20, 1) * 0.05
######################################
## 2 dimensional example
# # 2 dimensional example
# sample inputs and outputs
self.X2D = np.random.uniform(-3.,3.,(40,2))
self.Y2D = np.sin(self.X2D[:,0:1]) * np.sin(self.X2D[:,1:2])+np.random.randn(40,1)*0.05
self.X2D = np.random.uniform(-3., 3., (40, 2))
self.Y2D = np.sin(self.X2D[:, 0:1]) * np.sin(self.X2D[:, 1:2]) + np.random.randn(40, 1) * 0.05
def check_model_with_white(self, kern, model_type='GP_regression', dimension=1):
#Get the correct gradients
def check_model_with_white(self, kern, model_type='GPRegression', dimension=1):
# Get the correct gradients
if dimension == 1:
X = self.X1D
Y = self.Y1D
else:
X = self.X2D
Y = self.Y2D
#Get model type (GP_regression, GP_sparse_regression, etc)
# Get model type (GPRegression, SparseGPRegression, etc)
model_fit = getattr(GPy.models, model_type)
noise = GPy.kern.white(dimension)
@ -42,114 +42,114 @@ class GradientTests(unittest.TestCase):
# contrain all parameters to be positive
self.assertTrue(m.checkgrad())
def test_gp_regression_rbf_1d(self):
def test_GPRegression_rbf_1d(self):
''' Testing the GP regression with rbf kernel with white kernel on 1d data '''
rbf = GPy.kern.rbf(1)
self.check_model_with_white(rbf, model_type='GP_regression', dimension=1)
self.check_model_with_white(rbf, model_type='GPRegression', dimension=1)
def test_GP_regression_rbf_2D(self):
def test_GPRegression_rbf_2D(self):
''' Testing the GP regression with rbf and white kernel on 2d data '''
rbf = GPy.kern.rbf(2)
self.check_model_with_white(rbf, model_type='GP_regression', dimension=2)
self.check_model_with_white(rbf, model_type='GPRegression', dimension=2)
def test_GP_regression_rbf_ARD_2D(self):
def test_GPRegression_rbf_ARD_2D(self):
''' Testing the GP regression with rbf and white kernel on 2d data '''
k = GPy.kern.rbf(2,ARD=True)
self.check_model_with_white(k, model_type='GP_regression', dimension=2)
k = GPy.kern.rbf(2, ARD=True)
self.check_model_with_white(k, model_type='GPRegression', dimension=2)
def test_GP_regression_matern52_1D(self):
def test_GPRegression_matern52_1D(self):
''' Testing the GP regression with matern52 kernel on 1d data '''
matern52 = GPy.kern.Matern52(1)
self.check_model_with_white(matern52, model_type='GP_regression', dimension=1)
self.check_model_with_white(matern52, model_type='GPRegression', dimension=1)
def test_GP_regression_matern52_2D(self):
def test_GPRegression_matern52_2D(self):
''' Testing the GP regression with matern52 kernel on 2d data '''
matern52 = GPy.kern.Matern52(2)
self.check_model_with_white(matern52, model_type='GP_regression', dimension=2)
self.check_model_with_white(matern52, model_type='GPRegression', dimension=2)
def test_GP_regression_matern52_ARD_2D(self):
def test_GPRegression_matern52_ARD_2D(self):
''' Testing the GP regression with matern52 kernel on 2d data '''
matern52 = GPy.kern.Matern52(2,ARD=True)
self.check_model_with_white(matern52, model_type='GP_regression', dimension=2)
matern52 = GPy.kern.Matern52(2, ARD=True)
self.check_model_with_white(matern52, model_type='GPRegression', dimension=2)
def test_GP_regression_matern32_1D(self):
def test_GPRegression_matern32_1D(self):
''' Testing the GP regression with matern32 kernel on 1d data '''
matern32 = GPy.kern.Matern32(1)
self.check_model_with_white(matern32, model_type='GP_regression', dimension=1)
self.check_model_with_white(matern32, model_type='GPRegression', dimension=1)
def test_GP_regression_matern32_2D(self):
def test_GPRegression_matern32_2D(self):
''' Testing the GP regression with matern32 kernel on 2d data '''
matern32 = GPy.kern.Matern32(2)
self.check_model_with_white(matern32, model_type='GP_regression', dimension=2)
self.check_model_with_white(matern32, model_type='GPRegression', dimension=2)
def test_GP_regression_matern32_ARD_2D(self):
def test_GPRegression_matern32_ARD_2D(self):
''' Testing the GP regression with matern32 kernel on 2d data '''
matern32 = GPy.kern.Matern32(2,ARD=True)
self.check_model_with_white(matern32, model_type='GP_regression', dimension=2)
matern32 = GPy.kern.Matern32(2, ARD=True)
self.check_model_with_white(matern32, model_type='GPRegression', dimension=2)
def test_GP_regression_exponential_1D(self):
def test_GPRegression_exponential_1D(self):
''' Testing the GP regression with exponential kernel on 1d data '''
exponential = GPy.kern.exponential(1)
self.check_model_with_white(exponential, model_type='GP_regression', dimension=1)
self.check_model_with_white(exponential, model_type='GPRegression', dimension=1)
def test_GP_regression_exponential_2D(self):
def test_GPRegression_exponential_2D(self):
''' Testing the GP regression with exponential kernel on 2d data '''
exponential = GPy.kern.exponential(2)
self.check_model_with_white(exponential, model_type='GP_regression', dimension=2)
self.check_model_with_white(exponential, model_type='GPRegression', dimension=2)
def test_GP_regression_exponential_ARD_2D(self):
def test_GPRegression_exponential_ARD_2D(self):
''' Testing the GP regression with exponential kernel on 2d data '''
exponential = GPy.kern.exponential(2,ARD=True)
self.check_model_with_white(exponential, model_type='GP_regression', dimension=2)
exponential = GPy.kern.exponential(2, ARD=True)
self.check_model_with_white(exponential, model_type='GPRegression', dimension=2)
def test_GP_regression_bias_kern_1D(self):
def test_GPRegression_bias_kern_1D(self):
''' Testing the GP regression with bias kernel on 1d data '''
bias = GPy.kern.bias(1)
self.check_model_with_white(bias, model_type='GP_regression', dimension=1)
self.check_model_with_white(bias, model_type='GPRegression', dimension=1)
def test_GP_regression_bias_kern_2D(self):
def test_GPRegression_bias_kern_2D(self):
''' Testing the GP regression with bias kernel on 2d data '''
bias = GPy.kern.bias(2)
self.check_model_with_white(bias, model_type='GP_regression', dimension=2)
self.check_model_with_white(bias, model_type='GPRegression', dimension=2)
def test_GP_regression_linear_kern_1D_ARD(self):
def test_GPRegression_linear_kern_1D_ARD(self):
''' Testing the GP regression with linear kernel on 1d data '''
linear = GPy.kern.linear(1,ARD=True)
self.check_model_with_white(linear, model_type='GP_regression', dimension=1)
linear = GPy.kern.linear(1, ARD=True)
self.check_model_with_white(linear, model_type='GPRegression', dimension=1)
def test_GP_regression_linear_kern_2D_ARD(self):
def test_GPRegression_linear_kern_2D_ARD(self):
''' Testing the GP regression with linear kernel on 2d data '''
linear = GPy.kern.linear(2,ARD=True)
self.check_model_with_white(linear, model_type='GP_regression', dimension=2)
linear = GPy.kern.linear(2, ARD=True)
self.check_model_with_white(linear, model_type='GPRegression', dimension=2)
def test_GP_regression_linear_kern_1D(self):
def test_GPRegression_linear_kern_1D(self):
''' Testing the GP regression with linear kernel on 1d data '''
linear = GPy.kern.linear(1)
self.check_model_with_white(linear, model_type='GP_regression', dimension=1)
self.check_model_with_white(linear, model_type='GPRegression', dimension=1)
def test_GP_regression_linear_kern_2D(self):
def test_GPRegression_linear_kern_2D(self):
''' Testing the GP regression with linear kernel on 2d data '''
linear = GPy.kern.linear(2)
self.check_model_with_white(linear, model_type='GP_regression', dimension=2)
self.check_model_with_white(linear, model_type='GPRegression', dimension=2)
def test_sparse_GP_regression_rbf_white_kern_1d(self):
def test_SparseGPRegression_rbf_white_kern_1d(self):
''' Testing the sparse GP regression with rbf kernel with white kernel on 1d data '''
rbf = GPy.kern.rbf(1)
self.check_model_with_white(rbf, model_type='sparse_GP_regression', dimension=1)
self.check_model_with_white(rbf, model_type='SparseGPRegression', dimension=1)
def test_sparse_GP_regression_rbf_white_kern_2D(self):
def test_SparseGPRegression_rbf_white_kern_2D(self):
''' Testing the sparse GP regression with rbf and white kernel on 2d data '''
rbf = GPy.kern.rbf(2)
self.check_model_with_white(rbf, model_type='sparse_GP_regression', dimension=2)
self.check_model_with_white(rbf, model_type='SparseGPRegression', dimension=2)
def test_GPLVM_rbf_bias_white_kern_2D(self):
""" Testing GPLVM with rbf + bias and white kernel """
N, input_dim, D = 50, 1, 2
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim, 0.5, 0.9*np.ones((1,))) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05)
k = GPy.kern.rbf(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
m = GPy.models.GPLVM(Y, input_dim, kernel = k)
Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
m = GPy.models.GPLVM(Y, input_dim, kernel=k)
m.ensure_default_constraints()
self.assertTrue(m.checkgrad())
@ -159,43 +159,46 @@ class GradientTests(unittest.TestCase):
X = np.random.rand(N, input_dim)
k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
m = GPy.models.GPLVM(Y, input_dim, init = 'PCA', kernel = k)
Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
m = GPy.models.GPLVM(Y, input_dim, init='PCA', kernel=k)
m.ensure_default_constraints()
self.assertTrue(m.checkgrad())
def test_GP_EP_probit(self):
N = 20
X = np.hstack([np.random.normal(5,2,N/2),np.random.normal(10,2,N/2)])[:,None]
Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None]
Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None]
kernel = GPy.kern.rbf(1)
distribution = GPy.likelihoods.likelihood_functions.binomial()
distribution = GPy.likelihoods.likelihood_functions.Binomial()
likelihood = GPy.likelihoods.EP(Y, distribution)
m = GPy.core.GP(X, likelihood, kernel)
m.ensure_default_constraints()
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
#self.assertTrue(m.EPEM)
# self.assertTrue(m.EPEM)
def test_sparse_EP_DTC_probit(self):
N = 20
X = np.hstack([np.random.normal(5,2,N/2),np.random.normal(10,2,N/2)])[:,None]
Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
Z = np.linspace(0,15,4)[:,None]
X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None]
Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None]
Z = np.linspace(0, 15, 4)[:, None]
kernel = GPy.kern.rbf(1)
distribution = GPy.likelihoods.likelihood_functions.binomial()
distribution = GPy.likelihoods.likelihood_functions.Binomial()
likelihood = GPy.likelihoods.EP(Y, distribution)
m = GPy.core.sparse_GP(X, likelihood, kernel,Z)
m = GPy.core.SparseGP(X, likelihood, kernel, Z)
m.ensure_default_constraints()
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
def test_generalized_FITC(self):
N = 20
X = np.hstack([np.random.rand(N/2)+1,np.random.rand(N/2)-1])[:,None]
X = np.hstack([np.random.rand(N / 2) + 1, np.random.rand(N / 2) - 1])[:, None]
k = GPy.kern.rbf(1) + GPy.kern.white(1)
Y = np.hstack([np.ones(N/2),-np.ones(N/2)])[:,None]
likelihood = GPy.inference.likelihoods.binomial(Y)
distribution = GPy.likelihoods.likelihood_functions.Binomial()
likelihood = GPy.likelihoods.EP(Y, distribution)
#likelihood = GPy.inference.likelihoods.Binomial(Y)
m = GPy.models.generalized_FITC(X,likelihood,k,inducing=4)
m.constrain_positive('(var|len)')
m.approximate_likelihood()

4
GPy/util/datasets.py
View file

@ -22,7 +22,7 @@ def fetch_dataset(resource, save_name = None, save_file = True, messages = True)
print "Downloading resource: " , resource, " ... ",
response = url.urlopen(resource)
# TODO: Some error checking...
# ...
# ...
html = response.read()
response.close()
if save_file:
@ -33,8 +33,6 @@ def fetch_dataset(resource, save_name = None, save_file = True, messages = True)
if messages:
print "Done!"
return html
def della_gatta_TRP63_gene_expression(gene_number=None):
mat_data = scipy.io.loadmat(os.path.join(data_path, 'DellaGattadata.mat'))

177
GPy/util/linalg.py
View file

@ -1,87 +1,80 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
#tdot function courtesy of Ian Murray:
# tdot function courtesy of Ian Murray:
# Iain Murray, April 2013. iain contactable via iainmurray.net
# http://homepages.inf.ed.ac.uk/imurray2/code/tdot/tdot.py
import numpy as np
from scipy import linalg, optimize, weave
import pylab as pb
import Tango
import sys
import re
import pdb
import cPickle
from scipy import linalg, weave
import types
import ctypes
from ctypes import byref, c_char, c_int, c_double # TODO
#import scipy.lib.lapack
import scipy as sp
# import scipy.lib.lapack
try:
_blaslib = ctypes.cdll.LoadLibrary(np.core._dotblas.__file__)
_blaslib = ctypes.cdll.LoadLibrary(np.core._dotblas.__file__) # @UndefinedVariable
_blas_available = True
except:
_blas_available = False
def trace_dot(a,b):
def trace_dot(a, b):
"""
efficiently compute the trace of the matrix product of a and b
"""
return np.sum(a*b)
return np.sum(a * b)
def mdot(*args):
"""Multiply all the arguments using matrix product rules.
The output is equivalent to multiplying the arguments one by one
from left to right using dot().
Precedence can be controlled by creating tuples of arguments,
for instance mdot(a,((b,c),d)) multiplies a (a*((b*c)*d)).
Note that this means the output of dot(a,b) and mdot(a,b) will differ if
a or b is a pure tuple of numbers.
"""
if len(args)==1:
return args[0]
elif len(args)==2:
return _mdot_r(args[0],args[1])
else:
return _mdot_r(args[:-1],args[-1])
"""Multiply all the arguments using matrix product rules.
The output is equivalent to multiplying the arguments one by one
from left to right using dot().
Precedence can be controlled by creating tuples of arguments,
for instance mdot(a,((b,c),d)) multiplies a (a*((b*c)*d)).
Note that this means the output of dot(a,b) and mdot(a,b) will differ if
a or b is a pure tuple of numbers.
"""
if len(args) == 1:
return args[0]
elif len(args) == 2:
return _mdot_r(args[0], args[1])
else:
return _mdot_r(args[:-1], args[-1])
def _mdot_r(a,b):
"""Recursive helper for mdot"""
if type(a)==types.TupleType:
if len(a)>1:
a = mdot(*a)
else:
a = a[0]
if type(b)==types.TupleType:
if len(b)>1:
b = mdot(*b)
else:
b = b[0]
return np.dot(a,b)
def _mdot_r(a, b):
"""Recursive helper for mdot"""
if type(a) == types.TupleType:
if len(a) > 1:
a = mdot(*a)
else:
a = a[0]
if type(b) == types.TupleType:
if len(b) > 1:
b = mdot(*b)
else:
b = b[0]
return np.dot(a, b)
def jitchol(A,maxtries=5):
def jitchol(A, maxtries=5):
A = np.asfortranarray(A)
L,info = linalg.lapack.flapack.dpotrf(A,lower=1)
if info ==0:
L, info = linalg.lapack.flapack.dpotrf(A, lower=1)
if info == 0:
return L
else:
diagA = np.diag(A)
if np.any(diagA<0.):
if np.any(diagA < 0.):
raise linalg.LinAlgError, "not pd: negative diagonal elements"
jitter= diagA.mean()*1e-6
for i in range(1,maxtries+1):
jitter = diagA.mean() * 1e-6
for i in range(1, maxtries + 1):
print 'Warning: adding jitter of {:.10e}'.format(jitter)
try:
return linalg.cholesky(A+np.eye(A.shape[0]).T*jitter, lower = True)
return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
except:
jitter *= 10
raise linalg.LinAlgError,"not positive definite, even with jitter."
raise linalg.LinAlgError, "not positive definite, even with jitter."
def jitchol_old(A,maxtries=5):
def jitchol_old(A, maxtries=5):
"""
:param A : An almost pd square matrix
@ -93,20 +86,20 @@ def jitchol_old(A,maxtries=5):
np.allclose(sp.linalg.cholesky(XXT, lower = True), np.triu(sp.linalg.cho_factor(XXT)[0]).T)
"""
try:
return linalg.cholesky(A, lower = True)
return linalg.cholesky(A, lower=True)
except linalg.LinAlgError:
diagA = np.diag(A)
if np.any(diagA<0.):
if np.any(diagA < 0.):
raise linalg.LinAlgError, "not pd: negative diagonal elements"
jitter= diagA.mean()*1e-6
for i in range(1,maxtries+1):
jitter = diagA.mean() * 1e-6
for i in range(1, maxtries + 1):
print '\rWarning: adding jitter of {:.10e} '.format(jitter),
try:
return linalg.cholesky(A+np.eye(A.shape[0]).T*jitter, lower = True)
return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
except:
jitter *= 10
raise linalg.LinAlgError,"not positive definite, even with jitter."
raise linalg.LinAlgError, "not positive definite, even with jitter."
def pdinv(A, *args):
"""
@ -125,7 +118,7 @@ def pdinv(A, *args):
logdet = 2.*np.sum(np.log(np.diag(L)))
Li = chol_inv(L)
Ai, _ = linalg.lapack.flapack.dpotri(L)
#Ai = np.tril(Ai) + np.tril(Ai,-1).T
# Ai = np.tril(Ai) + np.tril(Ai,-1).T
symmetrify(Ai)
return Ai, L, Li, logdet
@ -140,7 +133,7 @@ def chol_inv(L):
"""
return linalg.lapack.flapack.dtrtri(L, lower = True)[0]
return linalg.lapack.flapack.dtrtri(L, lower=True)[0]
def multiple_pdinv(A):
@ -155,11 +148,11 @@ def multiple_pdinv(A):
hld: 0.5* the log of the determinants of A
"""
N = A.shape[-1]
chols = [jitchol(A[:,:,i]) for i in range(N)]
chols = [jitchol(A[:, :, i]) for i in range(N)]
halflogdets = [np.sum(np.log(np.diag(L[0]))) for L in chols]
invs = [linalg.lapack.flapack.dpotri(L[0],True)[0] for L in chols]
invs = [np.triu(I)+np.triu(I,1).T for I in invs]
return np.dstack(invs),np.array(halflogdets)
invs = [linalg.lapack.flapack.dpotri(L[0], True)[0] for L in chols]
invs = [np.triu(I) + np.triu(I, 1).T for I in invs]
return np.dstack(invs), np.array(halflogdets)
def PCA(Y, input_dim):
@ -179,18 +172,18 @@ def PCA(Y, input_dim):
if not np.allclose(Y.mean(axis=0), 0.0):
print "Y is not zero mean, centering it locally (GPy.util.linalg.PCA)"
#Y -= Y.mean(axis=0)
# Y -= Y.mean(axis=0)
Z = linalg.svd(Y-Y.mean(axis=0), full_matrices = False)
[X, W] = [Z[0][:,0:input_dim], np.dot(np.diag(Z[1]), Z[2]).T[:,0:input_dim]]
Z = linalg.svd(Y - Y.mean(axis=0), full_matrices=False)
[X, W] = [Z[0][:, 0:input_dim], np.dot(np.diag(Z[1]), Z[2]).T[:, 0:input_dim]]
v = X.std(axis=0)
X /= v;
W *= v;
return X, W.T
def tdot_numpy(mat,out=None):
return np.dot(mat,mat.T,out)
def tdot_numpy(mat, out=None):
return np.dot(mat, mat.T, out)
def tdot_blas(mat, out=None):
"""returns np.dot(mat, mat.T), but faster for large 2D arrays of doubles."""
@ -198,16 +191,16 @@ def tdot_blas(mat, out=None):
return np.dot(mat, mat.T)
nn = mat.shape[0]
if out is None:
out = np.zeros((nn,nn))
out = np.zeros((nn, nn))
else:
assert(out.dtype == 'float64')
assert(out.shape == (nn,nn))
assert(out.shape == (nn, nn))
# FIXME: should allow non-contiguous out, and copy output into it:
assert(8 in out.strides)
# zeroing needed because of dumb way I copy across triangular answer
out[:] = 0.0
## Call to DSYRK from BLAS
# # Call to DSYRK from BLAS
# If already in Fortran order (rare), and has the right sorts of strides I
# could avoid the copy. I also thought swapping to cblas API would allow use
# of C order. However, I tried that and had errors with large matrices:
@ -226,17 +219,17 @@ def tdot_blas(mat, out=None):
_blaslib.dsyrk_(byref(UPLO), byref(TRANS), byref(N), byref(K),
byref(ALPHA), A, byref(LDA), byref(BETA), C, byref(LDC))
symmetrify(out,upper=True)
symmetrify(out, upper=True)
return out
def tdot(*args, **kwargs):
if _blas_available:
return tdot_blas(*args,**kwargs)
return tdot_blas(*args, **kwargs)
else:
return tdot_numpy(*args,**kwargs)
return tdot_numpy(*args, **kwargs)
def DSYR_blas(A,x,alpha=1.):
def DSYR_blas(A, x, alpha=1.):
"""
Performs a symmetric rank-1 update operation:
A <- A + alpha * np.dot(x,x.T)
@ -256,9 +249,9 @@ def DSYR_blas(A,x,alpha=1.):
INCX = c_int(1)
_blaslib.dsyr_(byref(UPLO), byref(N), byref(ALPHA),
x_, byref(INCX), A_, byref(LDA))
symmetrify(A,upper=True)
symmetrify(A, upper=True)
def DSYR_numpy(A,x,alpha=1.):
def DSYR_numpy(A, x, alpha=1.):
"""
Performs a symmetric rank-1 update operation:
A <- A + alpha * np.dot(x,x.T)
@ -269,23 +262,23 @@ def DSYR_numpy(A,x,alpha=1.):
:param x: Nx1 np.array
:param alpha: scalar
"""
A += alpha*np.dot(x[:,None],x[None,:])
A += alpha * np.dot(x[:, None], x[None, :])
def DSYR(*args, **kwargs):
if _blas_available:
return DSYR_blas(*args,**kwargs)
return DSYR_blas(*args, **kwargs)
else:
return DSYR_numpy(*args,**kwargs)
return DSYR_numpy(*args, **kwargs)
def symmetrify(A,upper=False):
def symmetrify(A, upper=False):
"""
Take the square matrix A and make it symmetrical by copting elements from the lower half to the upper
works IN PLACE.
"""
N,M = A.shape
assert N==M
N, M = A.shape
assert N == M
c_contig_code = """
int iN;
for (int i=1; i<N; i++){
@ -305,13 +298,13 @@ def symmetrify(A,upper=False):
}
"""
if A.flags['C_CONTIGUOUS'] and upper:
weave.inline(f_contig_code,['A','N'], extra_compile_args=['-O3'])
weave.inline(f_contig_code, ['A', 'N'], extra_compile_args=['-O3'])
elif A.flags['C_CONTIGUOUS'] and not upper:
weave.inline(c_contig_code,['A','N'], extra_compile_args=['-O3'])
weave.inline(c_contig_code, ['A', 'N'], extra_compile_args=['-O3'])
elif A.flags['F_CONTIGUOUS'] and upper:
weave.inline(c_contig_code,['A','N'], extra_compile_args=['-O3'])
weave.inline(c_contig_code, ['A', 'N'], extra_compile_args=['-O3'])
elif A.flags['F_CONTIGUOUS'] and not upper:
weave.inline(f_contig_code,['A','N'], extra_compile_args=['-O3'])
weave.inline(f_contig_code, ['A', 'N'], extra_compile_args=['-O3'])
else:
if upper:
tmp = np.tril(A.T)
@ -319,15 +312,15 @@ def symmetrify(A,upper=False):
tmp = np.tril(A)
A[:] = 0.0
A += tmp
A += np.tril(tmp,-1).T
A += np.tril(tmp, -1).T
def symmetrify_murray(A):
A += A.T
nn = A.shape[0]
A[[range(nn),range(nn)]] /= 2.0
A[[range(nn), range(nn)]] /= 2.0
def cholupdate(L,x):
def cholupdate(L, x):
"""
update the LOWER cholesky factor of a pd matrix IN PLACE
@ -337,7 +330,7 @@ def cholupdate(L,x):
support_code = """
#include <math.h>
"""
code="""
code = """
double r,c,s;
int j,i;
for(j=0; j<N; j++){
@ -353,11 +346,11 @@ def cholupdate(L,x):
"""
x = x.copy()
N = x.size
weave.inline(code, support_code=support_code, arg_names=['N','L','x'], type_converters=weave.converters.blitz)
weave.inline(code, support_code=support_code, arg_names=['N', 'L', 'x'], type_converters=weave.converters.blitz)
def backsub_both_sides(L, X,transpose='left'):
def backsub_both_sides(L, X, transpose='left'):
""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
if transpose=='left':
if transpose == 'left':
tmp, _ = linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
return linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
else:

34
GPy/util/plot_latent.py
View file

@ -2,9 +2,9 @@ import pylab as pb
import numpy as np
from .. import util
def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40):
def plot_latent(Model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40):
"""
:param labels: a np.array of size model.N containing labels for the points (can be number, strings, etc)
:param labels: a np.array of size Model.N containing labels for the points (can be number, strings, etc)
:param resolution: the resolution of the grid on which to evaluate the predictive variance
"""
if ax is None:
@ -12,26 +12,26 @@ def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None,
util.plot.Tango.reset()
if labels is None:
labels = np.ones(model.N)
labels = np.ones(Model.N)
if which_indices is None:
if model.input_dim==1:
if Model.input_dim==1:
input_1 = 0
input_2 = None
if model.input_dim==2:
if Model.input_dim==2:
input_1, input_2 = 0,1
else:
try:
input_1, input_2 = np.argsort(model.input_sensitivity())[:2]
input_1, input_2 = np.argsort(Model.input_sensitivity())[:2]
except:
raise ValueError, "cannot Atomatically determine which dimensions to plot, please pass 'which_indices'"
else:
input_1, input_2 = which_indices
#first, plot the output variance as a function of the latent space
Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(model.X[:,[input_1, input_2]],resolution=resolution)
Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(Model.X[:,[input_1, input_2]],resolution=resolution)
Xtest_full = np.zeros((Xtest.shape[0], Model.X.shape[1]))
Xtest_full[:, :2] = Xtest
mu, var, low, up = model.predict(Xtest_full)
mu, var, low, up = Model.predict(Xtest_full)
var = var[:, :1]
ax.imshow(var.reshape(resolution, resolution).T,
extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear',origin='lower')
@ -55,12 +55,12 @@ def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None,
m = marker
index = np.nonzero(labels==ul)[0]
if model.input_dim==1:
x = model.X[index,input_1]
if Model.input_dim==1:
x = Model.X[index,input_1]
y = np.zeros(index.size)
else:
x = model.X[index,input_1]
y = model.X[index,input_2]
x = Model.X[index,input_1]
y = Model.X[index,input_2]
ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
ax.set_xlabel('latent dimension %i'%input_1)
@ -76,16 +76,16 @@ def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None,
return ax
def plot_latent_indices(model, which_indices=None, *args, **kwargs):
def plot_latent_indices(Model, which_indices=None, *args, **kwargs):
if which_indices is None:
try:
input_1, input_2 = np.argsort(model.input_sensitivity())[:2]
input_1, input_2 = np.argsort(Model.input_sensitivity())[:2]
except:
raise ValueError, "cannot Automatically determine which dimensions to plot, please pass 'which_indices'"
else:
input_1, input_2 = which_indices
ax = plot_latent(model, which_indices=[input_1, input_2], *args, **kwargs)
ax = plot_latent(Model, which_indices=[input_1, input_2], *args, **kwargs)
# TODO: Here test if there are inducing points...
ax.plot(model.Z[:, input_1], model.Z[:, input_2], '^w')
ax.plot(Model.Z[:, input_1], Model.Z[:, input_2], '^w')
return ax

48
GPy/util/visualize.py
View file

@ -43,16 +43,16 @@ class vector_show(data_show):
class lvm(data_show):
def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0,1]):
"""Visualize a latent variable model
def __init__(self, vals, Model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0,1]):
"""Visualize a latent variable Model
:param model: the latent variable model to visualize.
:param Model: the latent variable Model to visualize.
:param data_visualize: the object used to visualize the data which has been modelled.
:type data_visualize: visualize.data_show type.
:param latent_axes: the axes where the latent visualization should be plotted.
"""
if vals == None:
vals = model.X[0]
vals = Model.X[0]
data_show.__init__(self, vals, axes=latent_axes)
@ -68,13 +68,13 @@ class lvm(data_show):
self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
self.data_visualize = data_visualize
self.model = model
self.Model = Model
self.latent_axes = latent_axes
self.sense_axes = sense_axes
self.called = False
self.move_on = False
self.latent_index = latent_index
self.latent_dim = model.input_dim
self.latent_dim = Model.input_dim
# The red cross which shows current latent point.
self.latent_values = vals
@ -85,7 +85,7 @@ class lvm(data_show):
def modify(self, vals):
"""When latent values are modified update the latent representation and ulso update the output visualization."""
self.vals = vals.copy()
y = self.model.predict(self.vals)[0]
y = self.Model.predict(self.vals)[0]
self.data_visualize.modify(y)
self.latent_handle.set_data(self.vals[self.latent_index[0]], self.vals[self.latent_index[1]])
self.axes.figure.canvas.draw()
@ -113,15 +113,15 @@ class lvm(data_show):
# A click in the bar chart axis for selection a dimension.
if self.sense_axes != None:
self.sense_axes.cla()
self.sense_axes.bar(np.arange(self.model.input_dim),1./self.model.input_sensitivity(),color='b')
self.sense_axes.bar(np.arange(self.Model.input_dim),1./self.Model.input_sensitivity(),color='b')
if self.latent_index[1] == self.latent_index[0]:
self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='y')
self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='y')
self.sense_axes.bar(np.array(self.latent_index[0]),1./self.Model.input_sensitivity()[self.latent_index[0]],color='y')
self.sense_axes.bar(np.array(self.latent_index[1]),1./self.Model.input_sensitivity()[self.latent_index[1]],color='y')
else:
self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='g')
self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='r')
self.sense_axes.bar(np.array(self.latent_index[0]),1./self.Model.input_sensitivity()[self.latent_index[0]],color='g')
self.sense_axes.bar(np.array(self.latent_index[1]),1./self.Model.input_sensitivity()[self.latent_index[1]],color='r')
self.sense_axes.figure.canvas.draw()
@ -131,21 +131,21 @@ class lvm_subplots(lvm):
latent_axes is a np array of dimension np.ceil(input_dim/2),
one for each pair of the latent dimensions.
"""
def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None):
self.nplots = int(np.ceil(model.input_dim/2.))+1
def __init__(self, vals, Model, data_visualize, latent_axes=None, sense_axes=None):
self.nplots = int(np.ceil(Model.input_dim/2.))+1
assert len(latent_axes)==self.nplots
if vals==None:
vals = model.X[0, :]
vals = Model.X[0, :]
self.latent_values = vals
for i, axis in enumerate(latent_axes):
if i == self.nplots-1:
if self.nplots*2!=model.input_dim:
if self.nplots*2!=Model.input_dim:
latent_index = [i*2, i*2]
lvm.__init__(self, self.latent_vals, model, data_visualize, axis, sense_axes, latent_index=latent_index)
lvm.__init__(self, self.latent_vals, Model, data_visualize, axis, sense_axes, latent_index=latent_index)
else:
latent_index = [i*2, i*2+1]
lvm.__init__(self, self.latent_vals, model, data_visualize, axis, latent_index=latent_index)
lvm.__init__(self, self.latent_vals, Model, data_visualize, axis, latent_index=latent_index)
@ -158,7 +158,7 @@ class lvm_dimselect(lvm):
GPy.examples.dimensionality_reduction.BGPVLM_oil()
"""
def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1]):
def __init__(self, vals, Model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1]):
if latent_axes==None and sense_axes==None:
self.fig,(latent_axes,self.sense_axes) = plt.subplots(1,2)
elif sense_axes==None:
@ -167,14 +167,14 @@ class lvm_dimselect(lvm):
else:
self.sense_axes = sense_axes
lvm.__init__(self,vals,model,data_visualize,latent_axes,sense_axes,latent_index)
lvm.__init__(self,vals,Model,data_visualize,latent_axes,sense_axes,latent_index)
print "use left and right mouse butons to select dimensions"
def on_click(self, event):
if event.inaxes==self.sense_axes:
new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.input_dim-1))
new_index = max(0,min(int(np.round(event.xdata-0.5)),self.Model.input_dim-1))
if event.button == 1:
# Make it red if and y-axis (red=port=left) if it is a left button click
self.latent_index[1] = new_index
@ -185,7 +185,7 @@ class lvm_dimselect(lvm):
self.show_sensitivities()
self.latent_axes.cla()
self.model.plot_latent(which_indices=self.latent_index,
self.Model.plot_latent(which_indices=self.latent_index,
ax=self.latent_axes)
self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
self.modify(self.latent_values)
@ -199,7 +199,7 @@ class lvm_dimselect(lvm):
def on_leave(self,event):
latent_values = self.latent_values.copy()
y = self.model.predict(latent_values[None,:])[0]
y = self.Model.predict(latent_values[None,:])[0]
self.data_visualize.modify(y)
@ -221,7 +221,7 @@ class image_show(data_show):
if not self.palette == []: # Can just show the image (self.set_image() took care of setting the palette)
self.handle = self.axes.imshow(self.vals, interpolation='nearest')
else: # Use a boring gray map.
self.handle = self.axes.imshow(self.vals, cmap=plt.cm.gray, interpolation='nearest')
self.handle = self.axes.imshow(self.vals, cmap=plt.cm.gray, interpolation='nearest') # @UndefinedVariable
plt.show()
def modify(self, vals):