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massive merge of the debug branch

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This commit is contained in:
Nicolo Fusi 2013-01-30 15:30:54 +00:00
commit 8a3e10700d
13 changed files with 327 additions and 109 deletions
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43
GPy/kern/kern.py
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@ -6,7 +6,7 @@ import numpy as np
from ..core.parameterised import parameterised
from functools import partial
from kernpart import kernpart
import itertools
class kern(parameterised):
def __init__(self,D,parts=[], input_slices=None):
@ -259,29 +259,56 @@ class kern(parameterised):
:Z: np.ndarray of inducing inputs (M x Q)
: mu, S: np.ndarrays of means and variacnes (each N x Q)
:returns psi2: np.ndarray (N,M,M,Q) """
target = np.zeros((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)
[p.psi2(Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[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
def dpsi2_dtheta(self,partial,Z,mu,S,slices1=None,slices2=None):
def dpsi2_dtheta(self,partial,partial1,Z,mu,S,slices1=None,slices2=None):
"""Returns shape (N,M,M,Ntheta)"""
slices1, slices2 = self._process_slices(slices1,slices2)
target = np.zeros(self.Nparam)
[p.dpsi2_dtheta(partial[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)]
partial1 = np.zeros_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)
return target
def dpsi2_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):
slices1, slices2 = self._process_slices(slices1,slices2)
target = np.zeros_like(Z)
[p.dpsi2_dZ(partial[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
def dpsi2_dmuS(self,Z,mu,S,slices1=None,slices2=None):
def dpsi2_dmuS(self,partial,Z,mu,S,slices1=None,slices2=None):
"""return shapes are N,M,M,Q"""
slices1, slices2 = self._process_slices(slices1,slices2)
target_mu, target_S = np.zeros((2,mu.shape[0],mu.shape[1]))
[p.dpsi2_dmuS(partial[s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target_mu[s1,i_s],target_S[s1,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
[p.dpsi2_dmuS(partial[s1,s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target_mu[s1,i_s],target_S[s1,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
#TODO: there are some extra terms to compute here!
return target_mu, target_S