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kern params adapted: Nparams > num_params and fixes of input_dim

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Max Zwiessele 2013-06-05 16:14:30 +01:00
parent aa7fd122ca
commit 0490861099
42 changed files with 480 additions and 502 deletions
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29
GPy/kern/kern.py
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@ -4,23 +4,22 @@
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):
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 input_dim: The dimensioality of the kernel's input space
: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
@ -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):
@ -327,8 +326,8 @@ 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
@ -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
@ -379,7 +378,7 @@ 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
@ -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")