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broken commit, working on cross terms for psi statistics

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Nicolo Fusi 2013-01-29 17:23:51 +00:00
parent 10c774e84e
commit 6959a905dc
1 changed files with 25 additions and 1 deletions
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26
GPy/kern/kern.py
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@ -6,7 +6,7 @@ import numpy as np
from ..core.parameterised import parameterised from ..core.parameterised import parameterised
from functools import partial from functools import partial
from kernpart import kernpart from kernpart import kernpart
import itertools
class kern(parameterised): class kern(parameterised):
def __init__(self,D,parts=[], input_slices=None): def __init__(self,D,parts=[], input_slices=None):
@ -262,6 +262,15 @@ class kern(parameterised):
target = np.zeros((mu.shape[0],Z.shape[0],Z.shape[0])) target = np.zeros((mu.shape[0],Z.shape[0],Z.shape[0]))
slices1, slices2 = self._process_slices(slices1,slices2) 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)] [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"
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)]
for a,b in itertools.combinations(psi1_matrices, 2):
tmp = np.multiply(a,b)
target += tmp[:,None,:] + tmp[:, :,None]
return target return target
def dpsi2_dtheta(self,partial,Z,mu,S,slices1=None,slices2=None): def dpsi2_dtheta(self,partial,Z,mu,S,slices1=None,slices2=None):
@ -269,12 +278,27 @@ class kern(parameterised):
slices1, slices2 = self._process_slices(slices1,slices2) slices1, slices2 = self._process_slices(slices1,slices2)
target = np.zeros(self.Nparam) 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)] [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)]
# 2. get all the dpsi1/dtheta gradients
psi1_gradients = [np.zeros(self.Nparam) for p in self.parts]
[p.dpsi1_dtheta(partial[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,ps,s1,s2,i_s in zip(self.parts, self.param_slices,slices1,slices2,self.input_slices)]
# 3. multiply them somehow
for a,b in itertools.combinations(range(len(psi1_matrices)), 2):
psi2_cross = np.multiply(psi1, psi1_grad) # some newaxis of this
target += psi2_cross[:,None,:] + psi2_cross[:, :,None]
return target return target
def dpsi2_dZ(self,partial,Z,mu,S,slices1=None,slices2=None): def dpsi2_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):
slices1, slices2 = self._process_slices(slices1,slices2) slices1, slices2 = self._process_slices(slices1,slices2)
target = np.zeros_like(Z) target = np.zeros_like(Z)
[p.dpsi2_dZ(partial[s1,s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s2,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)] [p.dpsi2_dZ(partial[s1,s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s2,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
return target return target
def dpsi2_dmuS(self,partial,Z,mu,S,slices1=None,slices2=None): def dpsi2_dmuS(self,partial,Z,mu,S,slices1=None,slices2=None):