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added support for sparse matrices

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Nicolo Fusi 2013-02-06 14:46:23 +00:00
parent 6959751149
commit 71e461a780
1 changed files with 22 additions and 10 deletions
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28
GPy/inference/SGD.py
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@ -1,4 +1,6 @@
import numpy as np
import scipy as sp
import scipy.sparse
from optimization import Optimizer
from scipy import linalg, optimize
import copy
@ -123,13 +125,19 @@ class opt_SGD(Optimizer):
else:
raise NotImplementedError
def step_with_missing_data(self, f_fp, X, step, shapes):
def step_with_missing_data(self, f_fp, X, step, shapes, sparse_matrix):
N, Q = X.shape
if not sparse_matrix:
samples = self.non_null_samples(self.model.Y)
j = self.subset_parameter_vector(self.x_opt, samples, shapes)
self.model.N = samples.sum()
self.model.X = X[samples]
self.model.Y = self.model.Y[samples]
else:
samples = self.model.Y.nonzero()[0]
self.model.N = len(samples)
self.model.Y = np.asarray(self.model.Y[samples].todense(), dtype = np.float64)
j = self.subset_parameter_vector(self.x_opt, samples, shapes)
self.model.X = X[samples]
# self.model.Y -= self.model.Y.mean() # <----------------- WARNING!!!!
# self.model.Y /= self.model.Y.std()
model_name = self.model.__class__.__name__
@ -158,6 +166,9 @@ class opt_SGD(Optimizer):
N, Q = self.model.X.shape
D = self.model.Y.shape[1]
self.trace = []
sparse_matrix = sp.sparse.issparse(self.model.Y)
missing_data = True
if not sparse_matrix:
missing_data = self.check_for_missing(self.model.Y)
self.model.Youter = None # this is probably not very efficient
self.model.YYT = None
@ -178,13 +189,14 @@ class opt_SGD(Optimizer):
for j in features:
count += 1
self.model.D = len(j)
self.model.Y = Y[:, j]
self.model.Y = Y[:, j:j+1]
# self.model.trYYT = np.sum(np.square(self.model.Y))
if missing_data:
if self.model.Y.std() == 0.0 or self.model.Y.shape[0] == 0:
continue
if missing_data or sparse_matrix:
# if self.model.Y.std() == 0.0 or self.model.Y.shape[0] == 0: <--- not sure about this
# continue
shapes = self.get_param_shapes(N, Q)
f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes)
f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes, sparse_matrix)
else:
Nj = N
momentum_term = self.momentum * step # compute momentum using update(t-1)