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trying to fix bugs in kerns

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Nicolo Fusi 2013-02-12 17:45:44 +00:00
parent 7fbc6935d9
commit 03c1f77c08
6 changed files with 58 additions and 37 deletions
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29
GPy/kern/kern.py
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@ -6,7 +6,7 @@ import numpy as np
from ..core.parameterised import parameterised
from kernpart import kernpart
import itertools
from product_orthogonal import product_orthogonal
from product_orthogonal import product_orthogonal
class kern(parameterised):
def __init__(self,D,parts=[], input_slices=None):
@ -323,15 +323,22 @@ class kern(parameterised):
slices1, slices2 = self._process_slices(slices1,slices2)
[p.psi2(Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s1,s2,s2]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
# MASSIVE TODO: do something smart for white
# "crossterms"
# "crossterms". Here we are recomputing psi1 for white (we don't need to), but it's
# not really expensive, since it's just a matrix of zeroes.
# psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
# [p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
crossterms = 0.0
# for 3 kernels this returns something like
# [(0,1), (0,2), (1,2)]
# in theory, we should also account for (1,0), (2,0) and so on, but
# the transpose deals exactly with that
# for a,b in itertools.combinations(psi1_matrices, 2):
# tmp = np.multiply(a,b)
# target += tmp[:,None,:] + tmp[:, :,None]
# crossterms += tmp[:,None,:] + tmp[:, :,None]
return target
return target + crossterms
def dpsi2_dtheta(self,partial,partial1,Z,mu,S,slices1=None,slices2=None):
"""Returns shape (N,M,M,Ntheta)"""
@ -339,22 +346,26 @@ class kern(parameterised):
target = np.zeros(self.Nparam)
[p.dpsi2_dtheta(partial[s1,s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,i_s,s1,s2,ps in zip(self.parts,self.input_slices,slices1,slices2,self.param_slices)]
# # "crossterms"
# # 1. get all the psi1 statistics
# psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
# [p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
# partial1 = np.zeros_like(partial1)
# partial1 = np.ones_like(partial1)
# # 2. get all the dpsi1/dtheta gradients
# psi1_gradients = [np.zeros(self.Nparam) for p in self.parts]
# [p.dpsi1_dtheta(partial1[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],psi1g_target[ps]) for p,ps,s1,s2,i_s,psi1g_target in zip(self.parts, self.param_slices,slices1,slices2,self.input_slices,psi1_gradients)]
# # 3. multiply them somehow
# for a,b in itertools.combinations(range(len(psi1_matrices)), 2):
# gne = (psi1_gradients[a][None]*psi1_matrices[b].sum(0)[:,None]).sum(0)
# target += (gne[None] + gne[:, None]).sum(0)
# tmp = (psi1_gradients[a][None, None] * psi1_matrices[b][:,:, None])
# # target += (tmp[None] + tmp[:,None]).sum(0).sum(0).sum(0)
# # gne = (psi1_gradients[a].sum()*psi1_matrices[b].sum())
# # target += gne
# #target += (gne[None] + gne[:, None]).sum(0)
# target += (partial.sum(0)[:,:,None] * (tmp[:, None] + tmp[:,:,None]).sum(0)).sum(0).sum(0)
return target
def dpsi2_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):