mirror of
https://github.com/SheffieldML/GPy.git
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Merge branch 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
Ricardo
commit
fc34cedde5
15 changed files with 394 additions and 445 deletions
8
.gitignore
vendored
8
.gitignore
vendored
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@ -39,3 +39,11 @@ nosetests.xml
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#bfgs optimiser leaves this lying around
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iterate.dat
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# Nosetests #
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#############
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*.noseids
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# git merge files #
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###################
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*.orig
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@ -251,7 +251,18 @@ class parameterised(object):
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def _set_params_transformed(self, x):
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""" takes the vector x, which is then modified (by untying, reparameterising or inserting fixed values), and then call self._set_params"""
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self._set_params(self._untransform_params(x))
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def _untransform_params(self,x):
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"""
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The transformation required for _set_params_transformed.
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This moves the vector x seen by the optimiser (unconstrained) to the
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valid parameter vector seen by the model
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Note:
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- This function is separate from _set_params_transformed for downstream flexibility
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"""
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# work out how many places are fixed, and where they are. tricky logic!
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fix_places = self.fixed_indices + [t[1:] for t in self.tied_indices]
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if len(fix_places):
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@ -272,7 +283,8 @@ class parameterised(object):
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[np.put(xx,i,t.f(xx[i])) for i,t in zip(self.constrained_indices, self.constraints)]
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if hasattr(self,'debug'):
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stop
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self._set_params(xx)
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return xx
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def _get_param_names_transformed(self):
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"""
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@ -39,23 +39,29 @@ class logexp(transformation):
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return '(+ve)'
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class logexp_clipped(transformation):
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def __init__(self):
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max_bound = 1e300
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min_bound = 1e-10
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log_max_bound = np.log(max_bound)
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log_min_bound = np.log(min_bound)
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def __init__(self, lower=1e-6):
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self.domain = 'positive'
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self.lower = lower
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def f(self, x):
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f = np.log(1. + np.exp(x))
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exp = np.exp(np.clip(x, self.log_min_bound, self.log_max_bound))
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f = np.log(1. + exp)
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return f
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def finv(self, f):
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return np.log(np.exp(f) - 1.)
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return np.log(np.exp(np.clip(f, self.min_bound, self.max_bound)) - 1.)
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def gradfactor(self, f):
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ef = np.exp(f)
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gf = (ef - 1.) / ef
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return np.where(f < 1e-6, 0, gf)
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def initialize(self,f):
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if np.any(f<0.):
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return np.where(f < self.lower, 0, gf)
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def initialize(self, f):
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if np.any(f < 0.):
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print "Warning: changing parameters to satisfy constraints"
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return np.abs(f)
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def __str__(self):
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return '(+ve)'
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return '(+ve_c)'
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class exponent(transformation):
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def __init__(self):
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@ -67,7 +73,7 @@ class exponent(transformation):
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def gradfactor(self, f):
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return f
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def initialize(self, f):
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if np.any(f<0.):
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if np.any(f < 0.):
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print "Warning: changing parameters to satisfy constraints"
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return np.abs(f)
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def __str__(self):
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@ -83,7 +89,7 @@ class negative_exponent(transformation):
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def gradfactor(self, f):
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return f
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def initialize(self, f):
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if np.any(f>0.):
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if np.any(f > 0.):
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print "Warning: changing parameters to satisfy constraints"
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return -np.abs(f)
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def __str__(self):
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@ -114,11 +120,11 @@ class logistic(transformation):
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def finv(self, f):
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return np.log(np.clip(f - self.lower, 1e-10, np.inf) / np.clip(self.upper - f, 1e-10, np.inf))
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def gradfactor(self, f):
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return (f-self.lower)*(self.upper-f)/self.difference
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def initialize(self,f):
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if np.any(np.logical_or(f<self.lower,f>self.upper)):
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return (f - self.lower) * (self.upper - f) / self.difference
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def initialize(self, f):
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if np.any(np.logical_or(f < self.lower, f > self.upper)):
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print "Warning: changing parameters to satisfy constraints"
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return np.where(np.logical_or(f<self.lower,f>self.upper),self.f(f*0.),f)
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return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(f * 0.), f)
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def __str__(self):
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return '({},{})'.format(self.lower, self.upper)
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@ -2,13 +2,11 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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import pylab as pb
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from matplotlib import pyplot as plt, pyplot
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from matplotlib import pyplot as plt
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import GPy
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from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
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from GPy.util.datasets import simulation_BGPLVM
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from GPy.core.transformations import square, logexp_clipped
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from GPy.util.datasets import swiss_roll_generated
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default_seed = np.random.seed(123344)
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@ -47,10 +45,11 @@ def BGPLVM(seed=default_seed):
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def GPLVM_oil_100(optimize=True):
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data = GPy.util.datasets.oil_100()
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Y = data['X']
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# create simple GP model
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kernel = GPy.kern.rbf(6, ARD=True) + GPy.kern.bias(6)
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m = GPy.models.GPLVM(data['X'], 6, kernel=kernel)
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m = GPy.models.GPLVM(Y, 6, kernel=kernel)
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m.data_labels = data['Y'].argmax(axis=1)
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# optimize
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@ -63,27 +62,83 @@ def GPLVM_oil_100(optimize=True):
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m.plot_latent(labels=m.data_labels)
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return m
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def BGPLVM_oil(optimize=True, N=100, Q=10, M=20, max_f_eval=300, plot=False):
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def swiss_roll(optimize=True, N=1000, M=15, Q=4, sigma=.2, plot=False):
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from GPy.util.datasets import swiss_roll
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from GPy.core.transformations import logexp_clipped
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data = swiss_roll_generated(N=N, sigma=sigma)
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Y = data['Y']
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Y -= Y.mean()
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Y /= Y.std()
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t = data['t']
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c = data['colors']
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try:
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from sklearn.manifold.isomap import Isomap
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iso = Isomap().fit(Y)
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X = iso.embedding_
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if Q > 2:
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X = np.hstack((X, np.random.randn(N, Q - 2)))
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except ImportError:
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X = np.random.randn(N, Q)
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if plot:
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from mpl_toolkits import mplot3d
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import pylab
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fig = pylab.figure("Swiss Roll Data")
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ax = fig.add_subplot(121, projection='3d')
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ax.scatter(*Y.T, c=c)
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ax.set_title("Swiss Roll")
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ax = fig.add_subplot(122)
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ax.scatter(*X.T[:2], c=c)
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ax.set_title("Initialization")
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var = .5
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S = (var * np.ones_like(X) + np.clip(np.random.randn(N, Q) * var ** 2,
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- (1 - var),
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(1 - var))) + .001
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Z = np.random.permutation(X)[:M]
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kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
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m = Bayesian_GPLVM(Y, Q, X=X, X_variance=S, M=M, Z=Z, kernel=kernel)
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m.data_colors = c
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m.data_t = t
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m.constrain('variance|length', logexp_clipped())
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m['lengthscale'] = 1. # X.var(0).max() / X.var(0)
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m['noise'] = Y.var() / 100.
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m.ensure_default_constraints()
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if optimize:
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m.optimize('scg', messages=1)
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return m
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def BGPLVM_oil(optimize=True, N=100, Q=5, M=25, max_f_eval=4e3, plot=False, **k):
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np.random.seed(0)
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data = GPy.util.datasets.oil()
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from GPy.core.transformations import logexp_clipped
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# create simple GP model
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kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
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Y = data['X'][:N]
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=kernel, M=M)
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Yn = Y - Y.mean(0)
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Yn /= Yn.std(0)
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m = GPy.models.Bayesian_GPLVM(Yn, Q, kernel=kernel, M=M, **k)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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m.constrain('variance', logexp_clipped())
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m.constrain('length', logexp_clipped())
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m['lengt'] = 100.
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m.constrain('variance|leng', logexp_clipped())
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m['lengt'] = m.X.var(0).max() / m.X.var(0)
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m['noise'] = Yn.var() / 100.
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m.ensure_default_constraints()
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# optimize
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if optimize:
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m.unconstrain('noise'); m.constrain_fixed('noise', Y.var() / 100.)
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m.optimize('scg', messages=1, max_f_eval=150)
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m.unconstrain('noise')
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m.constrain('noise', logexp_clipped())
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m.optimize('scg', messages=1, max_f_eval=max_f_eval)
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if plot:
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@ -95,11 +150,6 @@ def BGPLVM_oil(optimize=True, N=100, Q=10, M=20, max_f_eval=300, plot=False):
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lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :].copy(), m, data_show, latent_axes=latent_axes) # , sense_axes=sense_axes)
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raw_input('Press enter to finish')
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plt.close('all')
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# # plot
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# print(m)
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# m.plot_latent(labels=m.data_labels)
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# pb.figure()
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# pb.bar(np.arange(m.kern.D), 1. / m.input_sensitivity())
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return m
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def oil_100():
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@ -115,6 +165,8 @@ def oil_100():
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# m.plot_latent(labels=data['Y'].argmax(axis=1))
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return m
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def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
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x = np.linspace(0, 4 * np.pi, N)[:, None]
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s1 = np.vectorize(lambda x: np.sin(x))
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@ -127,15 +179,6 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
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s3 = s3(x)
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sS = sS(x)
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# s1 -= s1.mean()
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# s2 -= s2.mean()
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# s3 -= s3.mean()
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# sS -= sS.mean()
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# s1 /= .5 * (np.abs(s1).max() - np.abs(s1).min())
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# s2 /= .5 * (np.abs(s2).max() - np.abs(s2).min())
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# s3 /= .5 * (np.abs(s3).max() - np.abs(s3).min())
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# sS /= .5 * (np.abs(sS).max() - np.abs(sS).min())
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S1 = np.hstack([s1, sS])
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S2 = np.hstack([s2, sS])
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S3 = np.hstack([s3, sS])
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|
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@ -155,16 +198,17 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
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Y2 /= Y2.std(0)
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Y3 /= Y3.std(0)
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slist = [s1, s2, s3, sS]
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slist = [sS, s1, s2, s3]
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slist_names = ["sS", "s1", "s2", "s3"]
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Ylist = [Y1, Y2, Y3]
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if plot_sim:
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import pylab
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import itertools
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fig = pylab.figure("MRD Simulation", figsize=(8, 6))
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fig = pylab.figure("MRD Simulation Data", figsize=(8, 6))
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fig.clf()
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ax = fig.add_subplot(2, 1, 1)
|
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labls = sorted(filter(lambda x: x.startswith("s"), locals()))
|
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labls = slist_names
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for S, lab in itertools.izip(slist, labls):
|
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ax.plot(S, label=lab)
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ax.legend()
|
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|
|
@ -178,6 +222,7 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
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return slist, [S1, S2, S3], Ylist
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|
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def bgplvm_simulation_matlab_compare():
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from GPy.util.datasets import simulation_BGPLVM
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sim_data = simulation_BGPLVM()
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Y = sim_data['Y']
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S = sim_data['S']
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|
|
@ -187,7 +232,6 @@ def bgplvm_simulation_matlab_compare():
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from GPy.models import mrd
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from GPy import kern
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reload(mrd); reload(kern)
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# k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
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k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
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m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
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# X=mu,
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|
|
@ -197,24 +241,14 @@ def bgplvm_simulation_matlab_compare():
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m.auto_scale_factor = True
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m['noise'] = Y.var() / 100.
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m['linear_variance'] = .01
|
||||
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||||
# lscstr = 'X_variance'
|
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# m[lscstr] = .01
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# m.unconstrain(lscstr); m.constrain_fixed(lscstr, .1)
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||||
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# cstr = 'white'
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# m.unconstrain(cstr); m.constrain_bounded(cstr, .01, 1.)
|
||||
|
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# cstr = 'noise'
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# m.unconstrain(cstr); m.constrain_bounded(cstr, .01, 1.)
|
||||
return m
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|
||||
def bgplvm_simulation(burnin='scg', plot_sim=False,
|
||||
max_burnin=100, true_X=False,
|
||||
do_opt=True,
|
||||
max_f_eval=1000):
|
||||
D1, D2, D3, N, M, Q = 15, 8, 8, 350, 3, 6
|
||||
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
|
||||
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)
|
||||
|
||||
from GPy.models import mrd
|
||||
from GPy import kern
|
||||
|
|
@ -224,154 +258,60 @@ def bgplvm_simulation(burnin='scg', plot_sim=False,
|
|||
Y = Ylist[0]
|
||||
|
||||
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
|
||||
# k = kern.white(Q, .00001) + kern.bias(Q)
|
||||
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k, _debug=True)
|
||||
# m.set('noise',)
|
||||
m.constrain('variance', logexp_clipped())
|
||||
|
||||
m.ensure_default_constraints()
|
||||
m.constrain('variance|noise', logexp_clipped())
|
||||
# m.ensure_default_constraints()
|
||||
m['noise'] = Y.var() / 100.
|
||||
m['linear_variance'] = .001
|
||||
# m.auto_scale_factor = True
|
||||
# m.scale_factor = 1.
|
||||
m['linear_variance'] = .01
|
||||
|
||||
|
||||
if burnin:
|
||||
print "initializing beta"
|
||||
cstr = "noise"
|
||||
m.unconstrain(cstr); m.constrain_fixed(cstr, Y.var() / 70.)
|
||||
m.optimize(burnin, messages=1, max_f_eval=max_burnin)
|
||||
|
||||
print "releasing beta"
|
||||
cstr = "noise"
|
||||
m.unconstrain(cstr); m.constrain_positive(cstr)
|
||||
|
||||
if true_X:
|
||||
true_X = np.hstack((slist[0], slist[3], 0. * np.ones((N, Q - 2))))
|
||||
m.set('X_\d', true_X)
|
||||
m.constrain_fixed("X_\d")
|
||||
|
||||
cstr = 'X_variance'
|
||||
# m.unconstrain(cstr), m.constrain_fixed(cstr, .0001)
|
||||
m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-7, .1)
|
||||
|
||||
# cstr = 'X_variance'
|
||||
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-3, 1.)
|
||||
|
||||
# m['X_var'] = np.ones(N * Q) * .5 + np.random.randn(N * Q) * .01
|
||||
|
||||
# cstr = "iip"
|
||||
# m.unconstrain(cstr); m.constrain_fixed(cstr)
|
||||
|
||||
# cstr = 'variance'
|
||||
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-10, 1.)
|
||||
# cstr = 'X_\d'
|
||||
# m.unconstrain(cstr), m.constrain_bounded(cstr, -10., 10.)
|
||||
#
|
||||
# cstr = 'noise'
|
||||
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-5, 1.)
|
||||
#
|
||||
# cstr = 'white'
|
||||
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-6, 1.)
|
||||
#
|
||||
# cstr = 'linear_variance'
|
||||
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-10, 10.)
|
||||
|
||||
# cstr = 'variance'
|
||||
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-10, 10.)
|
||||
|
||||
# np.seterr(all='call')
|
||||
# def ipdbonerr(errtype, flags):
|
||||
# import ipdb; ipdb.set_trace()
|
||||
# np.seterrcall(ipdbonerr)
|
||||
|
||||
if do_opt and burnin:
|
||||
try:
|
||||
m.optimize(burnin, messages=1, max_f_eval=max_f_eval)
|
||||
except:
|
||||
pass
|
||||
finally:
|
||||
return m
|
||||
if optimize:
|
||||
print "Optimizing model:"
|
||||
m.optimize('scg', max_iters=max_f_eval, max_f_eval=max_f_eval, messages=True)
|
||||
if plot:
|
||||
import pylab
|
||||
m.plot_X_1d()
|
||||
pylab.figure(); pylab.axis(); m.kern.plot_ARD()
|
||||
return m
|
||||
|
||||
def mrd_simulation(plot_sim=False):
|
||||
# num = 2
|
||||
# ard1 = np.array([1., 1, 0, 0], dtype=float)
|
||||
# ard2 = np.array([0., 1, 1, 0], dtype=float)
|
||||
# ard1[ard1 == 0] = 1E-10
|
||||
# ard2[ard2 == 0] = 1E-10
|
||||
|
||||
# ard1i = 1. / ard1
|
||||
# ard2i = 1. / ard2
|
||||
|
||||
# k = GPy.kern.rbf(Q, ARD=True, lengthscale=ard1i) + GPy.kern.bias(Q, 0) + GPy.kern.white(Q, 0.0001)
|
||||
# Y1 = np.random.multivariate_normal(np.zeros(N), k.K(X), D1).T
|
||||
# Y1 -= Y1.mean(0)
|
||||
#
|
||||
# k = GPy.kern.rbf(Q, ARD=True, lengthscale=ard2i) + GPy.kern.bias(Q, 0) + GPy.kern.white(Q, 0.0001)
|
||||
# Y2 = np.random.multivariate_normal(np.zeros(N), k.K(X), D2).T
|
||||
# Y2 -= Y2.mean(0)
|
||||
# make_params = lambda ard: np.hstack([[1], ard, [1, .3]])
|
||||
D1, D2, D3, N, M, Q = 150, 250, 300, 700, 3, 7
|
||||
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
|
||||
|
||||
from GPy.models import mrd
|
||||
from GPy import kern
|
||||
from GPy.core.transformations import logexp_clipped
|
||||
|
||||
reload(mrd); reload(kern)
|
||||
|
||||
# k = kern.rbf(2, ARD=True) + kern.bias(2) + kern.white(2)
|
||||
# Y1 = np.random.multivariate_normal(np.zeros(N), k.K(S1), D1).T
|
||||
# Y2 = np.random.multivariate_normal(np.zeros(N), k.K(S2), D2).T
|
||||
# Y3 = np.random.multivariate_normal(np.zeros(N), k.K(S3), D3).T
|
||||
|
||||
# Ylist = Ylist[0:2]
|
||||
|
||||
# k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q)
|
||||
|
||||
k = kern.linear(Q, [0.01] * Q, True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
|
||||
m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, initx="concat", initz='permute', _debug=False)
|
||||
m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, initx="concat", initz='permute')
|
||||
|
||||
for i, Y in enumerate(Ylist):
|
||||
m['{}_noise'.format(i + 1)] = Y.var() / 100.
|
||||
|
||||
m.constrain('variance', logexp_clipped())
|
||||
m.constrain('variance|noise', logexp_clipped())
|
||||
m.ensure_default_constraints()
|
||||
# m.auto_scale_factor = True
|
||||
|
||||
# cstr = 'variance'
|
||||
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-12, 1.)
|
||||
#
|
||||
# cstr = 'linear_variance'
|
||||
# m.unconstrain(cstr), m.constrain_positive(cstr)
|
||||
|
||||
print "initializing beta"
|
||||
cstr = "noise"
|
||||
m.unconstrain(cstr); m.constrain_fixed(cstr)
|
||||
m.optimize('scg', messages=1, max_f_eval=2e3, gtol=100)
|
||||
|
||||
print "releasing beta"
|
||||
cstr = "noise"
|
||||
m.unconstrain(cstr); m.constrain(cstr, logexp_clipped())
|
||||
|
||||
# np.seterr(all='call')
|
||||
# def ipdbonerr(errtype, flags):
|
||||
# import ipdb; ipdb.set_trace()
|
||||
# np.seterrcall(ipdbonerr)
|
||||
|
||||
return m # , mtest
|
||||
|
||||
def mrd_silhouette():
|
||||
|
||||
pass
|
||||
return m
|
||||
|
||||
def brendan_faces():
|
||||
from GPy import kern
|
||||
data = GPy.util.datasets.brendan_faces()
|
||||
Y = data['Y'][0:-1:10, :]
|
||||
m = GPy.models.GPLVM(data['Y'], 2)
|
||||
Q = 2
|
||||
# Y = data['Y'][0:-1:2, :]
|
||||
Y = data['Y']
|
||||
Yn = Y - Y.mean()
|
||||
Yn /= Yn.std()
|
||||
|
||||
m = GPy.models.GPLVM(Yn, Q)#, M=Y.shape[0]/4)
|
||||
|
||||
# optimize
|
||||
# m.constrain_fixed('white', 1e-2)
|
||||
# m.constrain_bounded('noise', 1e-6, 10)
|
||||
m.constrain('rbf', GPy.core.transformations.logexp_clipped())
|
||||
|
||||
m.ensure_default_constraints()
|
||||
m.optimize(messages=1, max_f_eval=10000)
|
||||
m.optimize('scg', messages=1, max_f_eval=10000)
|
||||
|
||||
ax = m.plot_latent()
|
||||
y = m.likelihood.Y[0, :]
|
||||
|
|
|
|||
|
|
@ -36,7 +36,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
|
|||
Returns
|
||||
x the optimal value for x
|
||||
flog : a list of all the objective values
|
||||
|
||||
function_eval number of fn evaluations
|
||||
status: string describing convergence status
|
||||
"""
|
||||
if xtol is None:
|
||||
xtol = 1e-6
|
||||
|
|
@ -111,7 +112,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
|
|||
iteration += 1
|
||||
if display:
|
||||
print '\r',
|
||||
print 'i: {0:>5g} f:{1:> 12e} b:{2:> 12e} |g|:{3:> 12e}'.format(iteration, fnow, beta, current_grad),
|
||||
print 'Iter: {0:>0{mi}g} Obj:{1:> 12e} Scale:{2:> 12e} |g|:{3:> 12e}'.format(iteration, float(fnow), float(beta), float(current_grad), mi=len(str(maxiters))),
|
||||
# print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
|
||||
sys.stdout.flush()
|
||||
|
||||
|
|
@ -130,7 +131,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
|
|||
# If the gradient is zero then we are done.
|
||||
if current_grad <= gtol:
|
||||
status = 'converged'
|
||||
return x, flog, function_eval, status
|
||||
break
|
||||
# return x, flog, function_eval, status
|
||||
|
||||
# Adjust beta according to comparison ratio.
|
||||
if Delta < 0.25:
|
||||
|
|
@ -147,9 +149,11 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
|
|||
elif success:
|
||||
gamma = np.dot(gradold - gradnew, gradnew) / (mu)
|
||||
d = gamma * d - gradnew
|
||||
|
||||
else:
|
||||
# If we get here, then we haven't terminated in the given number of
|
||||
# iterations.
|
||||
status = "maxiter exceeded"
|
||||
|
||||
if display:
|
||||
print ""
|
||||
return x, flog, function_eval, status
|
||||
|
|
|
|||
|
|
@ -1,146 +0,0 @@
|
|||
#Copyright I. Nabney, N.Lawrence and James Hensman (1996 - 2012)
|
||||
|
||||
#Scaled Conjuagte Gradients, originally in Matlab as part of the Netlab toolbox by I. Nabney, converted to python N. Lawrence and given a pythonic interface by James Hensman
|
||||
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT
|
||||
# HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
|
||||
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT
|
||||
# NOT LIMITED TO, THE IMPLIED WARRANTIES OF
|
||||
# MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
||||
# REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY
|
||||
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES
|
||||
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
|
||||
# OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
|
||||
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
|
||||
# HOWEVER CAUSED AND ON ANY THEORY OF
|
||||
# LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
|
||||
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
# POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
|
||||
import numpy as np
|
||||
import sys
|
||||
|
||||
def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xtol=1e-6, ftol=1e-6):
|
||||
"""
|
||||
Optimisation through Scaled Conjugate Gradients (SCG)
|
||||
|
||||
f: the objective function
|
||||
gradf : the gradient function (should return a 1D np.ndarray)
|
||||
x : the initial condition
|
||||
|
||||
Returns
|
||||
x the optimal value for x
|
||||
flog : a list of all the objective values
|
||||
|
||||
"""
|
||||
|
||||
sigma0 = 1.0e-4
|
||||
fold = f(x, *optargs) # Initial function value.
|
||||
function_eval = 1
|
||||
fnow = fold
|
||||
gradnew = gradf(x, *optargs) # Initial gradient.
|
||||
gradold = gradnew.copy()
|
||||
d = -gradnew # Initial search direction.
|
||||
success = True # Force calculation of directional derivs.
|
||||
nsuccess = 0 # nsuccess counts number of successes.
|
||||
beta = 1.0 # Initial scale parameter.
|
||||
betamin = 1.0e-15 # Lower bound on scale.
|
||||
betamax = 1.0e100 # Upper bound on scale.
|
||||
status = "Not converged"
|
||||
|
||||
flog = [fold]
|
||||
|
||||
iteration = 0
|
||||
|
||||
# Main optimization loop.
|
||||
while iteration < maxiters:
|
||||
|
||||
# Calculate first and second directional derivatives.
|
||||
if success:
|
||||
mu = np.dot(d, gradnew)
|
||||
if mu >= 0:
|
||||
d = -gradnew
|
||||
mu = np.dot(d, gradnew)
|
||||
kappa = np.dot(d, d)
|
||||
sigma = sigma0/np.sqrt(kappa)
|
||||
xplus = x + sigma*d
|
||||
gplus = gradf(xplus, *optargs)
|
||||
theta = np.dot(d, (gplus - gradnew))/sigma
|
||||
|
||||
# Increase effective curvature and evaluate step size alpha.
|
||||
delta = theta + beta*kappa
|
||||
if delta <= 0:
|
||||
delta = beta*kappa
|
||||
beta = beta - theta/kappa
|
||||
|
||||
alpha = - mu/delta
|
||||
|
||||
# Calculate the comparison ratio.
|
||||
xnew = x + alpha*d
|
||||
fnew = f(xnew, *optargs)
|
||||
function_eval += 1
|
||||
|
||||
if function_eval >= max_f_eval:
|
||||
status = "Maximum number of function evaluations exceeded"
|
||||
return x, flog, function_eval, status
|
||||
|
||||
Delta = 2.*(fnew - fold)/(alpha*mu)
|
||||
if Delta >= 0.:
|
||||
success = True
|
||||
nsuccess += 1
|
||||
x = xnew
|
||||
fnow = fnew
|
||||
else:
|
||||
success = False
|
||||
fnow = fold
|
||||
|
||||
# Store relevant variables
|
||||
flog.append(fnow) # Current function value
|
||||
|
||||
iteration += 1
|
||||
if display:
|
||||
print '\r',
|
||||
print 'Iteration: {0:>5g} Objective:{1:> 12e} Scale:{2:> 12e}'.format(iteration, fnow, beta),
|
||||
# print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
|
||||
sys.stdout.flush()
|
||||
|
||||
if success:
|
||||
# Test for termination
|
||||
if (np.max(np.abs(alpha*d)) < xtol) or (np.abs(fnew-fold) < ftol):
|
||||
status='converged'
|
||||
return x, flog, function_eval, status
|
||||
|
||||
else:
|
||||
# Update variables for new position
|
||||
fold = fnew
|
||||
gradold = gradnew
|
||||
gradnew = gradf(x, *optargs)
|
||||
# If the gradient is zero then we are done.
|
||||
if np.dot(gradnew,gradnew) == 0:
|
||||
return x, flog, function_eval, status
|
||||
|
||||
# Adjust beta according to comparison ratio.
|
||||
if Delta < 0.25:
|
||||
beta = min(4.0*beta, betamax)
|
||||
if Delta > 0.75:
|
||||
beta = max(0.5*beta, betamin)
|
||||
|
||||
# Update search direction using Polak-Ribiere formula, or re-start
|
||||
# in direction of negative gradient after nparams steps.
|
||||
if nsuccess == x.size:
|
||||
d = -gradnew
|
||||
nsuccess = 0
|
||||
elif success:
|
||||
gamma = np.dot(gradold - gradnew,gradnew)/(mu)
|
||||
d = gamma*d - gradnew
|
||||
|
||||
# If we get here, then we haven't terminated in the given number of
|
||||
# iterations.
|
||||
status = "maxiter exceeded"
|
||||
|
||||
return x, flog, function_eval, status
|
||||
|
|
@ -61,12 +61,12 @@ class kern(parameterised):
|
|||
|
||||
ax.bar(np.arange(len(ard_params)) - 0.4, ard_params)
|
||||
ax.set_xticks(np.arange(len(ard_params)))
|
||||
ax.set_xticklabels([r"${}$".format(i + 1) for i in range(len(ard_params))])
|
||||
ax.set_xticklabels([r"${}$".format(i) for i in range(len(ard_params))])
|
||||
return ax
|
||||
|
||||
def _transform_gradients(self, g):
|
||||
x = self._get_params()
|
||||
[np.put(x,i,x*t.gradfactor(x[i])) for i,t in zip(self.constrained_indices, self.constraints)]
|
||||
[np.put(x, i, x * t.gradfactor(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]
|
||||
[np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]
|
||||
if len(self.tied_indices) or len(self.fixed_indices):
|
||||
to_remove = np.hstack((self.fixed_indices + [t[1:] for t in self.tied_indices]))
|
||||
|
|
@ -88,7 +88,7 @@ class kern(parameterised):
|
|||
"""
|
||||
return self.add(other)
|
||||
|
||||
def add(self, other,tensor=False):
|
||||
def add(self, other, tensor=False):
|
||||
"""
|
||||
Add another kernel to this one. Both kernels are defined on the same _space_
|
||||
:param other: the other kernel to be added
|
||||
|
|
@ -103,7 +103,7 @@ class kern(parameterised):
|
|||
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.Nparam 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_values = self.fixed_values + other.fixed_values
|
||||
|
|
@ -113,7 +113,7 @@ class kern(parameterised):
|
|||
assert self.D == other.D
|
||||
newkern = kern(self.D, 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.Nparam 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_values = self.fixed_values + other.fixed_values
|
||||
|
|
@ -126,7 +126,7 @@ class kern(parameterised):
|
|||
"""
|
||||
return self.prod(other)
|
||||
|
||||
def prod(self, other,tensor=False):
|
||||
def prod(self, other, tensor=False):
|
||||
"""
|
||||
multiply two kernels (either on the same space, or on the tensor product of the input space)
|
||||
:param other: the other kernel to be added
|
||||
|
|
@ -136,12 +136,12 @@ class kern(parameterised):
|
|||
K2 = other.copy()
|
||||
|
||||
slices = []
|
||||
for sl1, sl2 in itertools.product(K1.input_slices,K2.input_slices):
|
||||
s1, s2 = [False]*K1.D, [False]*K2.D
|
||||
for sl1, sl2 in itertools.product(K1.input_slices, K2.input_slices):
|
||||
s1, s2 = [False] * K1.D, [False] * K2.D
|
||||
s1[sl1], s2[sl2] = [True], [True]
|
||||
slices += [s1+s2]
|
||||
slices += [s1 + s2]
|
||||
|
||||
newkernparts = [prod(k1, k2,tensor) for k1, k2 in itertools.product(K1.parts, K2.parts)]
|
||||
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)
|
||||
|
|
@ -176,8 +176,8 @@ class kern(parameterised):
|
|||
prev_constr_ind = [K1.constrained_indices] + [K1.Nparam + 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_values = K1.fixed_values + K2.fixed_values
|
||||
# prev_constr_fix = K1.fixed_indices + [arr + K1.Nparam for arr in K2.fixed_indices]
|
||||
# prev_constr_fix_values = K1.fixed_values + K2.fixed_values
|
||||
|
||||
# follow the previous ties
|
||||
for arr in prev_ties:
|
||||
|
|
@ -189,8 +189,8 @@ class kern(parameterised):
|
|||
index = np.where(index_param == i)[0]
|
||||
if index.size > 1:
|
||||
self.tie_params(index)
|
||||
for i,t in zip(prev_constr_ind,prev_constr):
|
||||
self.constrain(np.where(index_param == i)[0],t)
|
||||
for i, t in zip(prev_constr_ind, prev_constr):
|
||||
self.constrain(np.where(index_param == i)[0], t)
|
||||
|
||||
def _get_params(self):
|
||||
return np.hstack([p._get_params() for p in self.parts])
|
||||
|
|
@ -208,15 +208,15 @@ class kern(parameterised):
|
|||
return sum([[name + '_' + n for n in k._get_param_names()] for name, k in zip(names, self.parts)], [])
|
||||
|
||||
def K(self, X, X2=None, which_parts='all'):
|
||||
if which_parts=='all':
|
||||
which_parts = [True]*self.Nparts
|
||||
if which_parts == 'all':
|
||||
which_parts = [True] * self.Nparts
|
||||
assert X.shape[1] == self.D
|
||||
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]
|
||||
else:
|
||||
target = np.zeros((X.shape[0], X2.shape[0]))
|
||||
[p.K(X[:, i_s], X2[:,i_s], target=target) for p, i_s, part_i_used in zip(self.parts, self.input_slices, which_parts) if part_i_used]
|
||||
[p.K(X[:, i_s], X2[:, i_s], target=target) for p, i_s, part_i_used in zip(self.parts, self.input_slices, which_parts) if part_i_used]
|
||||
return target
|
||||
|
||||
def dK_dtheta(self, dL_dK, X, X2=None):
|
||||
|
|
@ -248,8 +248,8 @@ class kern(parameterised):
|
|||
return target
|
||||
|
||||
def Kdiag(self, X, which_parts='all'):
|
||||
if which_parts=='all':
|
||||
which_parts = [True]*self.Nparts
|
||||
if which_parts == 'all':
|
||||
which_parts = [True] * self.Nparts
|
||||
assert X.shape[1] == self.D
|
||||
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]
|
||||
|
|
@ -270,22 +270,22 @@ class kern(parameterised):
|
|||
|
||||
def psi0(self, Z, mu, S):
|
||||
target = np.zeros(mu.shape[0])
|
||||
[p.psi0(Z[:,i_s], mu[:,i_s], S[:,i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
|
||||
[p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
|
||||
return target
|
||||
|
||||
def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S):
|
||||
target = np.zeros(self.Nparam)
|
||||
[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)]
|
||||
[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)
|
||||
|
||||
def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S):
|
||||
target_mu, target_S = np.zeros_like(mu), np.zeros_like(S)
|
||||
[p.dpsi0_dmuS(dL_dpsi0, 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)]
|
||||
[p.dpsi0_dmuS(dL_dpsi0, 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 psi1(self, Z, mu, S):
|
||||
target = np.zeros((mu.shape[0], Z.shape[0]))
|
||||
[p.psi1(Z[:,i_s], mu[:,i_s], S[:,i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
|
||||
[p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
|
||||
return target
|
||||
|
||||
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S):
|
||||
|
|
@ -314,7 +314,7 @@ class kern(parameterised):
|
|||
[p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s], target) for p, i_s in zip(self.parts, self.input_slices)]
|
||||
|
||||
# compute the "cross" terms
|
||||
#TODO: input_slices needed
|
||||
# TODO: input_slices needed
|
||||
for p1, p2 in itertools.combinations(self.parts, 2):
|
||||
# white doesn;t combine with anything
|
||||
if p1.name == 'white' or p2.name == 'white':
|
||||
|
|
@ -389,7 +389,7 @@ class kern(parameterised):
|
|||
[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)]
|
||||
|
||||
# compute the "cross" terms
|
||||
#TODO: we need input_slices here.
|
||||
# TODO: we need input_slices here.
|
||||
for p1, p2 in itertools.combinations(self.parts, 2):
|
||||
# white doesn;t combine with anything
|
||||
if p1.name == 'white' or p2.name == 'white':
|
||||
|
|
@ -419,7 +419,7 @@ class kern(parameterised):
|
|||
[p.dpsi2_dmuS(dL_dpsi2, 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)]
|
||||
|
||||
# compute the "cross" terms
|
||||
#TODO: we need input_slices here.
|
||||
# TODO: we need input_slices here.
|
||||
for p1, p2 in itertools.combinations(self.parts, 2):
|
||||
# white doesn;t combine with anything
|
||||
if p1.name == 'white' or p2.name == 'white':
|
||||
|
|
|
|||
|
|
@ -53,10 +53,10 @@ class Gaussian(likelihood):
|
|||
def _set_params(self, x):
|
||||
x = float(x)
|
||||
if self._variance != x:
|
||||
self._variance = x
|
||||
self.covariance_matrix = np.eye(self.N) * self._variance
|
||||
self.precision = 1. / self._variance
|
||||
self.precision = 1. / x
|
||||
self.covariance_matrix = np.eye(self.N) * x
|
||||
self.V = (self.precision) * self.Y
|
||||
self._variance = x
|
||||
|
||||
def predictive_values(self, mu, var, full_cov):
|
||||
"""
|
||||
|
|
@ -69,6 +69,7 @@ class Gaussian(likelihood):
|
|||
# Note. for D>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.
|
||||
true_var = (var + np.eye(var.shape[0]) * self._variance) * self._scale ** 2
|
||||
_5pc = mean - 2.*np.sqrt(np.diag(true_var))
|
||||
_95pc = mean + 2.*np.sqrt(np.diag(true_var))
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
|
||||
"""
|
||||
def __init__(self, Y, Q, X=None, X_variance=None, init='PCA', M=10,
|
||||
Z=None, kernel=None, oldpsave=5, _debug=False,
|
||||
Z=None, kernel=None, oldpsave=10, _debug=False,
|
||||
**kwargs):
|
||||
if X == None:
|
||||
X = self.initialise_latent(init, Q, Y)
|
||||
|
|
@ -87,19 +87,19 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
return x
|
||||
|
||||
def _set_params(self, x, save_old=True, save_count=0):
|
||||
try:
|
||||
# try:
|
||||
N, Q = self.N, self.Q
|
||||
self.X = x[:self.X.size].reshape(N, Q).copy()
|
||||
self.X_variance = x[(N * Q):(2 * N * Q)].reshape(N, Q).copy()
|
||||
sparse_GP._set_params(self, x[(2 * N * Q):])
|
||||
self.oldps = x
|
||||
except (LinAlgError, FloatingPointError, ZeroDivisionError):
|
||||
print "\rWARNING: Caught LinAlgError, continueing without setting "
|
||||
if self._debug:
|
||||
self._savederrors.append(self.f_call)
|
||||
if save_count > 10:
|
||||
raise
|
||||
self._set_params(self.oldps[-1], save_old=False, save_count=save_count + 1)
|
||||
# self.oldps = x
|
||||
# except (LinAlgError, FloatingPointError, ZeroDivisionError):
|
||||
# print "\rWARNING: Caught LinAlgError, continueing without setting "
|
||||
# if self._debug:
|
||||
# self._savederrors.append(self.f_call)
|
||||
# if save_count > 10:
|
||||
# raise
|
||||
# self._set_params(self.oldps[-1], save_old=False, save_count=save_count + 1)
|
||||
|
||||
def dKL_dmuS(self):
|
||||
dKL_dS = (1. - (1. / (self.X_variance))) * 0.5
|
||||
|
|
@ -167,8 +167,12 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
# d_dmu = (dL_dmu).flatten()
|
||||
# d_dS = (dL_dS).flatten()
|
||||
# ========================
|
||||
dbound_dmuS = np.hstack((d_dmu, d_dS))
|
||||
return np.hstack((dbound_dmuS.flatten(), sparse_GP._log_likelihood_gradients(self)))
|
||||
self.dbound_dmuS = np.hstack((d_dmu, d_dS))
|
||||
self.dbound_dZtheta = sparse_GP._log_likelihood_gradients(self)
|
||||
return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
|
||||
|
||||
def _log_likelihood_normal_gradients(self):
|
||||
Si, _, _, _ = pdinv(self.X_variance)
|
||||
|
||||
def plot_latent(self, which_indices=None, *args, **kwargs):
|
||||
|
||||
|
|
@ -204,19 +208,21 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
else:
|
||||
colors = iter(colors)
|
||||
plots = []
|
||||
x = np.arange(self.X.shape[0])
|
||||
for i in range(self.X.shape[1]):
|
||||
if axes is None:
|
||||
ax = fig.add_subplot(self.X.shape[1], 1, i + 1)
|
||||
else:
|
||||
ax = axes[i]
|
||||
ax.plot(self.X, c='k', alpha=.3)
|
||||
plots.extend(ax.plot(self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
|
||||
ax.fill_between(np.arange(self.X.shape[0]),
|
||||
plots.extend(ax.plot(x, self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
|
||||
ax.fill_between(x,
|
||||
self.X.T[i] - 2 * np.sqrt(self.X_variance.T[i]),
|
||||
self.X.T[i] + 2 * np.sqrt(self.X_variance.T[i]),
|
||||
facecolor=plots[-1].get_color(),
|
||||
alpha=.3)
|
||||
ax.legend(borderaxespad=0.)
|
||||
ax.set_xlim(x.min(), x.max())
|
||||
if i < self.X.shape[1] - 1:
|
||||
ax.set_xticklabels('')
|
||||
pylab.draw()
|
||||
|
|
@ -263,7 +269,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
|
||||
param_dict = dict(self._savedparams)
|
||||
gradient_dict = dict(self._savedgradients)
|
||||
kmm_dict = dict(self._savedpsiKmm)
|
||||
# kmm_dict = dict(self._savedpsiKmm)
|
||||
iters = np.array(param_dict.keys())
|
||||
ABCD_dict = np.array(self._savedABCD)
|
||||
self.showing = 0
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ class GPLVM(GP):
|
|||
if X is None:
|
||||
X = self.initialise_latent(init, Q, Y)
|
||||
if kernel is None:
|
||||
kernel = kern.rbf(Q) + kern.bias(Q)
|
||||
kernel = kern.rbf(Q, ARD=Q>1) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
|
||||
likelihood = Gaussian(Y, normalize=normalize_Y)
|
||||
GP.__init__(self, X, likelihood, kernel, **kwargs)
|
||||
|
||||
|
|
|
|||
|
|
@ -76,7 +76,7 @@ class sparse_GP(GP):
|
|||
# psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1) / sf2)).sum(0)
|
||||
psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1))).sum(0)
|
||||
evals, evecs = linalg.eigh(psi2_beta_scaled)
|
||||
clipped_evals = np.clip(evals, 0., 1e15) # TODO: make clipping configurable
|
||||
clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable
|
||||
if not np.allclose(evals, clipped_evals):
|
||||
print "Warning: clipping posterior eigenvalues"
|
||||
tmp = evecs * np.sqrt(clipped_evals)
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ import numpy as np
|
|||
import GPy
|
||||
import scipy.sparse
|
||||
import scipy.io
|
||||
import cPickle as pickle
|
||||
data_path = os.path.join(os.path.dirname(__file__), 'datasets')
|
||||
default_seed = 10000
|
||||
|
||||
|
|
@ -96,16 +97,29 @@ def stick():
|
|||
lbls = 'connect'
|
||||
return {'Y': Y, 'connect' : connect, 'info': "Stick man data from Ohio."}
|
||||
|
||||
def swiss_roll_generated(N=1000, sigma=0.0):
|
||||
with open(os.path.join(data_path, 'swiss_roll.pickle')) as f:
|
||||
data = pickle.load(f)
|
||||
Na = data['Y'].shape[0]
|
||||
perm = np.random.permutation(np.r_[:Na])[:N]
|
||||
Y = data['Y'][perm, :]
|
||||
t = data['t'][perm]
|
||||
c = data['colors'][perm, :]
|
||||
so = np.argsort(t)
|
||||
Y = Y[so, :]
|
||||
t = t[so]
|
||||
c = c[so, :]
|
||||
return {'Y':Y, 't':t, 'colors':c}
|
||||
|
||||
def swiss_roll_1000():
|
||||
mat_data = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data'))
|
||||
Y = mat_data['X_data'][:, 0:1000].transpose()
|
||||
return {'Y': Y, 'info': "Subsample of the swiss roll data extracting only the first 1000 values."}
|
||||
|
||||
def swiss_roll():
|
||||
def swiss_roll(N=3000):
|
||||
mat_data = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data.mat'))
|
||||
Y = mat_data['X_data'][:, 0:3000].transpose()
|
||||
return {'Y': Y, 'info': "The first 3,000 points from the swiss roll data of Tennenbaum, de Silva and Langford (2001)."}
|
||||
Y = mat_data['X_data'][:, 0:N].transpose()
|
||||
return {'Y': Y, 'X': mat_data['X_data'], 'info': "The first 3,000 points from the swiss roll data of Tennenbaum, de Silva and Langford (2001)."}
|
||||
|
||||
def toy_rbf_1d(seed=default_seed):
|
||||
np.random.seed(seed=seed)
|
||||
|
|
@ -274,7 +288,7 @@ def cmu_mocap(subject, train_motions, test_motions=[], sample_every=4):
|
|||
end_ind += temp_Y[i].shape[0]
|
||||
Y[start_ind:end_ind, :] = temp_Y[i]
|
||||
lbls[start_ind:end_ind, :] = temp_lbls[i]
|
||||
if len(test_motions)>0:
|
||||
if len(test_motions) > 0:
|
||||
temp_Ytest = []
|
||||
temp_lblstest = []
|
||||
|
||||
|
|
@ -304,7 +318,7 @@ def cmu_mocap(subject, train_motions, test_motions=[], sample_every=4):
|
|||
for motion in train_motions:
|
||||
info += motion + ', '
|
||||
info = info[:-2]
|
||||
if len(test_motions)>0:
|
||||
if len(test_motions) > 0:
|
||||
info += '. Test motions: '
|
||||
for motion in test_motions:
|
||||
info += motion + ', '
|
||||
|
|
|
|||
75
GPy/util/datasets/swiss_roll.pickle
Normal file
75
GPy/util/datasets/swiss_roll.pickle
Normal file
File diff suppressed because one or more lines are too long
|
|
@ -44,7 +44,7 @@ class vector_show(data_show):
|
|||
|
||||
|
||||
class lvm(data_show):
|
||||
def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]):
|
||||
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.
|
||||
|
|
@ -71,7 +71,7 @@ class lvm(data_show):
|
|||
self.data_visualize = data_visualize
|
||||
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
|
||||
|
|
@ -81,10 +81,12 @@ class lvm(data_show):
|
|||
self.latent_values = vals
|
||||
self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
|
||||
self.modify(vals)
|
||||
self.show_sensitivities()
|
||||
|
||||
def modify(self, vals):
|
||||
"""When latent values are modified update the latent representation and ulso update the output visualization."""
|
||||
y = self.model.predict(vals)[0]
|
||||
print y
|
||||
self.data_visualize.modify(y)
|
||||
self.latent_handle.set_data(vals[self.latent_index[0]], vals[self.latent_index[1]])
|
||||
self.axes.figure.canvas.draw()
|
||||
|
|
@ -99,6 +101,7 @@ class lvm(data_show):
|
|||
if event.inaxes!=self.latent_axes: return
|
||||
self.move_on = not self.move_on
|
||||
self.called = True
|
||||
|
||||
def on_move(self, event):
|
||||
if event.inaxes!=self.latent_axes: return
|
||||
if self.called and self.move_on:
|
||||
|
|
@ -107,38 +110,9 @@ class lvm(data_show):
|
|||
self.latent_values[self.latent_index[1]]=event.ydata
|
||||
self.modify(self.latent_values)
|
||||
|
||||
class lvm_subplots(lvm):
|
||||
"""
|
||||
latent_axes is a np array of dimension np.ceil(Q/2) + 1,
|
||||
one for each pair of the axes, and the last one for the sensitiity bar chart
|
||||
"""
|
||||
def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]):
|
||||
lvm.__init__(self, vals, model,data_visualize,latent_axes,[0,1])
|
||||
self.nplots = int(np.ceil(model.Q/2.))+1
|
||||
lvm.__init__(self,model,data_visualize,latent_axes,latent_index)
|
||||
self.latent_values = np.zeros(2*np.ceil(self.model.Q/2.)) # possibly an extra dimension on this
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assert latent_axes.size == self.nplots
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class lvm_dimselect(lvm):
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"""
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A visualizer for latent variable models which allows selection of the latent dimensions to use by clicking on a bar chart of their length scales.
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"""
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def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1]):
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if latent_axes==None and sense_axes==None:
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self.fig,(latent_axes,self.sense_axes) = plt.subplots(1,2)
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elif sense_axes==None:
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fig=plt.figure()
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self.sense_axes = fig.add_subplot(111)
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else:
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self.sense_axes = sense_axes
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lvm.__init__(self,vals,model,data_visualize,latent_axes,latent_index)
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self.show_sensitivities()
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print "use left and right mouse butons to select dimensions"
|
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|
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def show_sensitivities(self):
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# A click in the bar chart axis for selection a dimension.
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if self.sense_axes != None:
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self.sense_axes.cla()
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self.sense_axes.bar(np.arange(self.model.Q),1./self.model.input_sensitivity(),color='b')
|
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|
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|
|
@ -152,6 +126,52 @@ class lvm_dimselect(lvm):
|
|||
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self.sense_axes.figure.canvas.draw()
|
||||
|
||||
|
||||
class lvm_subplots(lvm):
|
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"""
|
||||
latent_axes is a np array of dimension np.ceil(Q/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.Q/2.))+1
|
||||
assert len(latent_axes)==self.nplots
|
||||
if vals==None:
|
||||
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.Q:
|
||||
latent_index = [i*2, i*2]
|
||||
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)
|
||||
|
||||
|
||||
|
||||
class lvm_dimselect(lvm):
|
||||
"""
|
||||
A visualizer for latent variable models which allows selection of the latent dimensions to use by clicking on a bar chart of their length scales.
|
||||
|
||||
For an example of the visualizer's use try:
|
||||
|
||||
GPy.examples.dimensionality_reduction.BGPVLM_oil()
|
||||
|
||||
"""
|
||||
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:
|
||||
fig=plt.figure()
|
||||
self.sense_axes = fig.add_subplot(111)
|
||||
else:
|
||||
self.sense_axes = sense_axes
|
||||
|
||||
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:
|
||||
|
|
@ -177,12 +197,6 @@ class lvm_dimselect(lvm):
|
|||
self.called = True
|
||||
|
||||
|
||||
def on_move(self, event):
|
||||
if event.inaxes!=self.latent_axes: return
|
||||
if self.called and self.move_on:
|
||||
self.latent_values[self.latent_index[0]]=event.xdata
|
||||
self.latent_values[self.latent_index[1]]=event.ydata
|
||||
self.modify(self.latent_values)
|
||||
|
||||
def on_leave(self,event):
|
||||
latent_values = self.latent_values.copy()
|
||||
|
|
@ -193,7 +207,7 @@ class lvm_dimselect(lvm):
|
|||
|
||||
class image_show(data_show):
|
||||
"""Show a data vector as an image."""
|
||||
def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False, palette=[], presetMean = 0., presetSTD = -1., selectImage = 0):
|
||||
def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False, palette=[], presetMean = 0., presetSTD = -1., selectImage=0):
|
||||
data_show.__init__(self, vals, axes)
|
||||
self.dimensions = dimensions
|
||||
self.transpose = transpose
|
||||
|
|
@ -214,15 +228,30 @@ class image_show(data_show):
|
|||
def modify(self, vals):
|
||||
self.set_image(vals)
|
||||
self.handle.set_array(self.vals)
|
||||
self.axes.figure.canvas.draw() # Teo - original line: plt.show()
|
||||
self.axes.figure.canvas.draw()
|
||||
|
||||
def set_image(self, vals):
|
||||
dim = self.dimensions[0] * self.dimensions[1]
|
||||
nImg = np.sqrt(vals[0,].size/dim)
|
||||
if nImg > 1 and nImg.is_integer(): # Show a mosaic of images
|
||||
nImg = np.int(nImg)
|
||||
self.vals = np.zeros((self.dimensions[0]*nImg, self.dimensions[1]*nImg))
|
||||
for iR in range(nImg):
|
||||
for iC in range(nImg):
|
||||
currImgId = iR*nImg + iC
|
||||
currImg = np.reshape(vals[0,dim*currImgId+np.array(range(dim))], self.dimensions, order='F')
|
||||
firstRow = iR*self.dimensions[0]
|
||||
lastRow = (iR+1)*self.dimensions[0]
|
||||
firstCol = iC*self.dimensions[1]
|
||||
lastCol = (iC+1)*self.dimensions[1]
|
||||
self.vals[firstRow:lastRow, firstCol:lastCol] = currImg
|
||||
|
||||
else:
|
||||
self.vals = np.reshape(vals[0,dim*self.selectImage+np.array(range(dim))], self.dimensions, order='F')
|
||||
if self.transpose:
|
||||
self.vals = self.vals.T.copy()
|
||||
if not self.scale:
|
||||
self.vals = self.vals
|
||||
# if not self.scale:
|
||||
# self.vals = self.vals
|
||||
if self.invert:
|
||||
self.vals = -self.vals
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue