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提交 2b7ee2ec authored 作者: Frédéric Bastien's avatar Frédéric Bastien 提交者： GitHub
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Merge pull request #5193 from kvmanohar22/numpy_imports

Numpy imports
上级 a3424449 ac4b7a5d
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正在显示 包含 316 行增加 和 319 行删除
theano/sparse/basic.py
浏览文件 @ 2b7ee2ec
...@@ -12,7 +12,7 @@ from __future__ import absolute_import, print_function, division ...@@ -12,7 +12,7 @@ from __future__ import absolute_import, print_function, division
import sys import sys
import numpy import numpy as np
from numpy.lib.stride_tricks import as_strided from numpy.lib.stride_tricks import as_strided
from six import integer_types from six import integer_types
from six.moves import xrange from six.moves import xrange
...@@ -86,11 +86,11 @@ def _is_dense(x): ...@@ -86,11 +86,11 @@ def _is_dense(x):
L{numpy.ndarray}). L{numpy.ndarray}).
""" """
if not isinstance(x, (scipy.sparse.spmatrix, numpy.ndarray)): if not isinstance(x, (scipy.sparse.spmatrix, np.ndarray)):
raise NotImplementedError("this function should only be called on " raise NotImplementedError("this function should only be called on "
"sparse.scipy.sparse.spmatrix or " "sparse.scipy.sparse.spmatrix or "
"numpy.ndarray, not,", x) "numpy.ndarray, not,", x)
return isinstance(x, numpy.ndarray) return isinstance(x, np.ndarray)
# Wrapper type # Wrapper type
...@@ -205,8 +205,8 @@ def sp_zeros_like(x): ...@@ -205,8 +205,8 @@ def sp_zeros_like(x):
# TODO: don't restrict to CSM formats # TODO: don't restrict to CSM formats
_, _, indptr, shape = csm_properties(x) _, _, indptr, shape = csm_properties(x)
return CSM(format=x.format)(data=numpy.array([], dtype=x.type.dtype), return CSM(format=x.format)(data=np.array([], dtype=x.type.dtype),
indices=numpy.array([], dtype='int32'), indices=np.array([], dtype='int32'),
indptr=tensor.zeros_like(indptr), indptr=tensor.zeros_like(indptr),
shape=shape) shape=shape)
...@@ -293,9 +293,9 @@ class _sparse_py_operators: ...@@ -293,9 +293,9 @@ class _sparse_py_operators:
args = args, args = args,
if len(args) == 2: if len(args) == 2:
scalar_arg_1 = (numpy.isscalar(args[0]) or scalar_arg_1 = (np.isscalar(args[0]) or
getattr(args[0], 'type', None) == tensor.iscalar) getattr(args[0], 'type', None) == tensor.iscalar)
scalar_arg_2 = (numpy.isscalar(args[1]) or scalar_arg_2 = (np.isscalar(args[1]) or
getattr(args[1], 'type', None) == tensor.iscalar) getattr(args[1], 'type', None) == tensor.iscalar)
if scalar_arg_1 and scalar_arg_2: if scalar_arg_1 and scalar_arg_2:
ret = get_item_scalar(self, args) ret = get_item_scalar(self, args)
...@@ -554,17 +554,17 @@ class CSM(gof.Op): ...@@ -554,17 +554,17 @@ class CSM(gof.Op):
data = tensor.as_tensor_variable(data) data = tensor.as_tensor_variable(data)
if not isinstance(indices, gof.Variable): if not isinstance(indices, gof.Variable):
indices_ = numpy.asarray(indices) indices_ = np.asarray(indices)
indices_32 = theano._asarray(indices, dtype='int32') indices_32 = theano._asarray(indices, dtype='int32')
assert (indices_ == indices_32).all() assert (indices_ == indices_32).all()
indices = indices_32 indices = indices_32
if not isinstance(indptr, gof.Variable): if not isinstance(indptr, gof.Variable):
indptr_ = numpy.asarray(indptr) indptr_ = np.asarray(indptr)
indptr_32 = theano._asarray(indptr, dtype='int32') indptr_32 = theano._asarray(indptr, dtype='int32')
assert (indptr_ == indptr_32).all() assert (indptr_ == indptr_32).all()
indptr = indptr_32 indptr = indptr_32
if not isinstance(shape, gof.Variable): if not isinstance(shape, gof.Variable):
shape_ = numpy.asarray(shape) shape_ = np.asarray(shape)
shape_32 = theano._asarray(shape, dtype='int32') shape_32 = theano._asarray(shape, dtype='int32')
assert (shape_ == shape_32).all() assert (shape_ == shape_32).all()
shape = shape_32 shape = shape_32
...@@ -606,7 +606,7 @@ class CSM(gof.Op): ...@@ -606,7 +606,7 @@ class CSM(gof.Op):
if self.format == 'csc': if self.format == 'csc':
out[0] = scipy.sparse.csc_matrix((data, indices.copy(), out[0] = scipy.sparse.csc_matrix((data, indices.copy(),
indptr.copy()), indptr.copy()),
numpy.asarray(shape), copy=False) np.asarray(shape), copy=False)
else: else:
assert self.format == 'csr' assert self.format == 'csr'
out[0] = scipy.sparse.csr_matrix((data, indices.copy(), out[0] = scipy.sparse.csr_matrix((data, indices.copy(),
...@@ -729,8 +729,8 @@ class CSMGrad(gof.op.Op): ...@@ -729,8 +729,8 @@ class CSMGrad(gof.op.Op):
else: else:
sp_dim = x_shape[0] sp_dim = x_shape[0]
g_row = numpy.zeros(sp_dim, dtype=g_data.dtype) g_row = np.zeros(sp_dim, dtype=g_data.dtype)
gout_data = numpy.zeros(x_data.shape, dtype=node.outputs[0].dtype) gout_data = np.zeros(x_data.shape, dtype=node.outputs[0].dtype)
for i in range(len(x_indptr) - 1): for i in range(len(x_indptr) - 1):
for j_ptr in range(g_indptr[i], g_indptr[i + 1]): for j_ptr in range(g_indptr[i], g_indptr[i + 1]):
...@@ -1100,7 +1100,7 @@ class GetItem2Lists(gof.op.Op): ...@@ -1100,7 +1100,7 @@ class GetItem2Lists(gof.op.Op):
x = inp[0] x = inp[0]
ind1 = inp[1] ind1 = inp[1]
ind2 = inp[2] ind2 = inp[2]
out[0] = numpy.asarray(x[ind1, ind2]).flatten() out[0] = np.asarray(x[ind1, ind2]).flatten()
""" """
Here scipy returns the corresponding elements in a matrix which isn't Here scipy returns the corresponding elements in a matrix which isn't
what we are aiming for. Using asarray and flatten, out[0] becomes an what we are aiming for. Using asarray and flatten, out[0] becomes an
...@@ -1244,7 +1244,7 @@ class GetItem2d(gof.op.Op): ...@@ -1244,7 +1244,7 @@ class GetItem2d(gof.op.Op):
elif ((isinstance(ind, gof.Variable) and elif ((isinstance(ind, gof.Variable) and
getattr(ind, 'ndim', -1) == 0) or getattr(ind, 'ndim', -1) == 0) or
numpy.isscalar(ind)): np.isscalar(ind)):
raise NotImplementedError( raise NotImplementedError(
'Theano has no sparse vector' + 'Theano has no sparse vector' +
'Use X[a:b, c:d], X[a:b, c:c+1] or X[a:b] instead.') 'Use X[a:b, c:d], X[a:b, c:c+1] or X[a:b] instead.')
...@@ -1653,9 +1653,9 @@ class SpSum(gof.op.Op): ...@@ -1653,9 +1653,9 @@ class SpSum(gof.op.Op):
(x,) = inputs (x,) = inputs
(z,) = outputs (z,) = outputs
if self.axis is None: if self.axis is None:
z[0] = numpy.asarray(x.sum()) z[0] = np.asarray(x.sum())
else: else:
z[0] = numpy.asarray(x.sum(self.axis)).ravel() z[0] = np.asarray(x.sum(self.axis)).ravel()
def grad(self, inputs, gout): def grad(self, inputs, gout):
(x,) = inputs (x,) = inputs
...@@ -2540,7 +2540,7 @@ class __ComparisonOpSD(gof.op.Op): ...@@ -2540,7 +2540,7 @@ class __ComparisonOpSD(gof.op.Op):
assert x.shape == y.shape assert x.shape == y.shape
assert _is_dense(y) assert _is_dense(y)
o = self.comparison(x, y).astype('uint8') o = self.comparison(x, y).astype('uint8')
o = numpy.asarray(o) o = np.asarray(o)
out[0] = o out[0] = o
def infer_shape(self, node, ins_shapes): def infer_shape(self, node, ins_shapes):
...@@ -3382,7 +3382,7 @@ class TrueDot(gof.op.Op): ...@@ -3382,7 +3382,7 @@ class TrueDot(gof.op.Op):
# 'ushort', 'intc', 'uintc', 'longlong', 'ulonglong', 'single', # 'ushort', 'intc', 'uintc', 'longlong', 'ulonglong', 'single',
# 'double', 'longdouble', 'csingle', 'cdouble', 'clongdouble'] # 'double', 'longdouble', 'csingle', 'cdouble', 'clongdouble']
# But ulonglong is uint64 on x86-64, but with a different typenum! # But ulonglong is uint64 on x86-64, but with a different typenum!
if rval.dtype.num != numpy.dtype(str(rval.dtype)).num: if rval.dtype.num != np.dtype(str(rval.dtype)).num:
assert str(rval.dtype) == node.outputs[0].dtype assert str(rval.dtype) == node.outputs[0].dtype
# Create a view with the expected typenum. # Create a view with the expected typenum.
format = node.outputs[0].type.format format = node.outputs[0].type.format
...@@ -3509,7 +3509,7 @@ class StructuredDot(gof.Op): ...@@ -3509,7 +3509,7 @@ class StructuredDot(gof.Op):
# dot of an NxM sparse matrix, with a Mx1 dense matrix, returns vector # dot of an NxM sparse matrix, with a Mx1 dense matrix, returns vector
# not matrix # not matrix
if variable.ndim == 1: if variable.ndim == 1:
variable = numpy.expand_dims(variable, 1) variable = np.expand_dims(variable, 1)
elif variable.ndim != 2: elif variable.ndim != 2:
raise Exception('Output of structured dot should be a matrix ' raise Exception('Output of structured dot should be a matrix '
'(ndim=2)') '(ndim=2)')
...@@ -3622,7 +3622,7 @@ class StructuredDotGradCSC(gof.Op): ...@@ -3622,7 +3622,7 @@ class StructuredDotGradCSC(gof.Op):
def perform(self, node, inputs, outputs): def perform(self, node, inputs, outputs):
(a_indices, a_indptr, b, g_ab) = inputs (a_indices, a_indptr, b, g_ab) = inputs
(out,) = outputs (out,) = outputs
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype) g_a_data = np.zeros(a_indices.shape, dtype=g_ab.dtype)
for j in xrange(len(a_indptr) - 1): for j in xrange(len(a_indptr) - 1):
ind0 = a_indptr[j] ind0 = a_indptr[j]
ind1 = a_indptr[j + 1] ind1 = a_indptr[j + 1]
...@@ -3631,7 +3631,7 @@ class StructuredDotGradCSC(gof.Op): ...@@ -3631,7 +3631,7 @@ class StructuredDotGradCSC(gof.Op):
# Depending on the type of g_ab and b (sparse or dense), # Depending on the type of g_ab and b (sparse or dense),
# the following dot product can result in a scalar or # the following dot product can result in a scalar or
# a (1, 1) sparse matrix. # a (1, 1) sparse matrix.
dot_val = numpy.dot(g_ab[i], b[j].T) dot_val = np.dot(g_ab[i], b[j].T)
if isinstance(dot_val, scipy.sparse.spmatrix): if isinstance(dot_val, scipy.sparse.spmatrix):
dot_val = dot_val[0, 0] dot_val = dot_val[0, 0]
g_a_data[i_idx] = dot_val g_a_data[i_idx] = dot_val
...@@ -3752,7 +3752,7 @@ class StructuredDotGradCSR(gof.Op): ...@@ -3752,7 +3752,7 @@ class StructuredDotGradCSR(gof.Op):
def perform(self, node, inputs, outputs): def perform(self, node, inputs, outputs):
(a_indices, a_indptr, b, g_ab) = inputs (a_indices, a_indptr, b, g_ab) = inputs
(out,) = outputs (out,) = outputs
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype) g_a_data = np.zeros(a_indices.shape, dtype=g_ab.dtype)
for i in xrange(len(a_indptr) - 1): # loop over rows for i in xrange(len(a_indptr) - 1): # loop over rows
ind0 = a_indptr[i] ind0 = a_indptr[i]
ind1 = a_indptr[i + 1] ind1 = a_indptr[i + 1]
...@@ -3763,7 +3763,7 @@ class StructuredDotGradCSR(gof.Op): ...@@ -3763,7 +3763,7 @@ class StructuredDotGradCSR(gof.Op):
# Depending on the type of g_ab and b (sparse or dense), # Depending on the type of g_ab and b (sparse or dense),
# the following dot product can result in a scalar or # the following dot product can result in a scalar or
# a (1, 1) sparse matrix. # a (1, 1) sparse matrix.
dot_val = numpy.dot(g_ab[i], b[j].T) dot_val = np.dot(g_ab[i], b[j].T)
if isinstance(dot_val, scipy.sparse.spmatrix): if isinstance(dot_val, scipy.sparse.spmatrix):
dot_val = dot_val[0, 0] dot_val = dot_val[0, 0]
g_a_data[j_idx] = dot_val g_a_data[j_idx] = dot_val
...@@ -3910,7 +3910,7 @@ class SamplingDot(gof.op.Op): ...@@ -3910,7 +3910,7 @@ class SamplingDot(gof.op.Op):
if not _is_sparse(p): if not _is_sparse(p):
raise TypeError(p) raise TypeError(p)
out[0] = p.__class__(p.multiply(numpy.dot(x, y.T))) out[0] = p.__class__(p.multiply(np.dot(x, y.T)))
def grad(self, inputs, gout): def grad(self, inputs, gout):
(x, y, p) = inputs (x, y, p) = inputs
...@@ -4243,7 +4243,7 @@ class ConstructSparseFromList(gof.Op): ...@@ -4243,7 +4243,7 @@ class ConstructSparseFromList(gof.Op):
out, = out_ out, = out_
rows, cols = values.shape rows, cols = values.shape
assert rows == len(ilist) assert rows == len(ilist)
indptr = numpy.arange(cols + 1) * rows indptr = np.arange(cols + 1) * rows
indices = as_strided(ilist, indices = as_strided(ilist,
strides=(0, ilist.strides[0]), strides=(0, ilist.strides[0]),
shape=(cols, ilist.shape[0])).flatten() shape=(cols, ilist.shape[0])).flatten()
......
theano/sparse/opt.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
import scipy import scipy
import theano import theano
...@@ -879,7 +879,7 @@ local_usmm = gof.opt.PatternSub( ...@@ -879,7 +879,7 @@ local_usmm = gof.opt.PatternSub(
(theano.tensor.sub, 'z', (theano.tensor.sub, 'z',
(theano.tensor.mul, (theano.tensor.mul,
{'pattern': 'alpha', {'pattern': 'alpha',
'constraint': lambda expr: (numpy.all(expr.type.broadcastable) and 'constraint': lambda expr: (np.all(expr.type.broadcastable) and
theano.config.blas.ldflags)}, theano.config.blas.ldflags)},
(sparse._dot, 'x', 'y'))), (sparse._dot, 'x', 'y'))),
(usmm, (theano.tensor.neg, 'alpha'), 'x', 'y', 'z')) (usmm, (theano.tensor.neg, 'alpha'), 'x', 'y', 'z'))
......
theano/sparse/sandbox/sp.py
浏览文件 @ 2b7ee2ec
...@@ -8,7 +8,7 @@ U{http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps}. ...@@ -8,7 +8,7 @@ U{http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps}.
""" """
# COPIED FROM hpu/icml09/sp.py # COPIED FROM hpu/icml09/sp.py
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
import scipy import scipy
from scipy import sparse as scipy_sparse from scipy import sparse as scipy_sparse
from six.moves import xrange from six.moves import xrange
...@@ -81,18 +81,17 @@ class ConvolutionIndices(Op): ...@@ -81,18 +81,17 @@ class ConvolutionIndices(Op):
raise Exception("ws is obsolete and it must be always True") raise Exception("ws is obsolete and it must be always True")
(dx, dy) = strides (dx, dy) = strides
N = numpy
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w) # inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1 # in the first case, default nfeatures to 1
if N.size(inshp) == 2: if np.size(inshp) == 2:
inshp = (1,) + inshp inshp = (1,) + inshp
inshp = N.array(inshp) inshp = np.array(inshp)
kshp = N.array(kshp) kshp = np.array(kshp)
ksize = N.prod(kshp) ksize = np.prod(kshp)
kern = ksize - 1 - N.arange(ksize) kern = ksize - 1 - np.arange(ksize)
# size of output image if doing proper convolution # size of output image if doing proper convolution
# (mode='full',dx=dy=0) outshp is the actual output shape # (mode='full',dx=dy=0) outshp is the actual output shape
...@@ -102,32 +101,32 @@ class ConvolutionIndices(Op): ...@@ -102,32 +101,32 @@ class ConvolutionIndices(Op):
s = -1 s = -1
else: else:
s = 1 s = 1
outshp = N.int64(N.ceil((inshp[1:] + s * kshp - s * 1) \ outshp = np.int64(np.ceil((inshp[1:] + s * kshp - s * 1) \
/ N.array([dy, dx], dtype='float'))) / np.array([dy, dx], dtype='float')))
if any(outshp <= 0): if any(outshp <= 0):
err = 'Invalid kernel', kshp, 'and/or step size', (dx, dy),\ err = 'Invalid kernel', kshp, 'and/or step size', (dx, dy),\
'for given input shape', inshp 'for given input shape', inshp
raise ValueError(err) raise ValueError(err)
outsize = N.prod(outshp) outsize = np.prod(outshp)
insize = N.prod(inshp) insize = np.prod(inshp)
# range of output units over which to iterate # range of output units over which to iterate
if mode == 'valid': if mode == 'valid':
lbound = N.array([kshp[0] - 1, kshp[1] - 1]) lbound = np.array([kshp[0] - 1, kshp[1] - 1])
ubound = lbound + (inshp[1:] - kshp + 1) ubound = lbound + (inshp[1:] - kshp + 1)
else: else:
lbound = N.zeros(2) lbound = np.zeros(2)
ubound = fulloutshp ubound = fulloutshp
# coordinates of image in "fulloutshp" coordinates # coordinates of image in "fulloutshp" coordinates
topleft = N.array([kshp[0] - 1, kshp[1] - 1]) topleft = np.array([kshp[0] - 1, kshp[1] - 1])
# bound when counting the receptive field # bound when counting the receptive field
botright = topleft + inshp[1:] botright = topleft + inshp[1:]
# sparse matrix specifics... # sparse matrix specifics...
if ws: if ws:
spmatshp = (outsize * N.prod(kshp) * inshp[0], insize) spmatshp = (outsize * np.prod(kshp) * inshp[0], insize)
else: else:
spmatshp = (nkern * outsize, insize) spmatshp = (nkern * outsize, insize)
spmat = scipy_sparse.lil_matrix(spmatshp) spmat = scipy_sparse.lil_matrix(spmatshp)
...@@ -152,17 +151,17 @@ class ConvolutionIndices(Op): ...@@ -152,17 +151,17 @@ class ConvolutionIndices(Op):
# FOR EACH OUTPUT PIXEL... # FOR EACH OUTPUT PIXEL...
# loop over output image height # loop over output image height
for oy in N.arange(lbound[0], ubound[0], dy): for oy in np.arange(lbound[0], ubound[0], dy):
# loop over output image width # loop over output image width
for ox in N.arange(lbound[1], ubound[1], dx): for ox in np.arange(lbound[1], ubound[1], dx):
# kern[l] is filter value to apply at (oj,oi) # kern[l] is filter value to apply at (oj,oi)
# for (iy,ix) # for (iy,ix)
l = 0 l = 0
# ... ITERATE OVER INPUT UNITS IN RECEPTIVE FIELD # ... ITERATE OVER INPUT UNITS IN RECEPTIVE FIELD
for ky in oy + N.arange(kshp[0]): for ky in oy + np.arange(kshp[0]):
for kx in ox + N.arange(kshp[1]): for kx in ox + np.arange(kshp[1]):
# verify if we are still within image # verify if we are still within image
# boundaries. Equivalent to # boundaries. Equivalent to
...@@ -173,13 +172,13 @@ class ConvolutionIndices(Op): ...@@ -173,13 +172,13 @@ class ConvolutionIndices(Op):
# convert to "valid" input space # convert to "valid" input space
# coords used to determine column # coords used to determine column
# index to write to in sparse mat # index to write to in sparse mat
iy, ix = N.array((ky, kx)) - topleft iy, ix = np.array((ky, kx)) - topleft
# determine raster-index of input pixel... # determine raster-index of input pixel...
# taking into account multiple # taking into account multiple
# input features # input features
col = iy * inshp[2] + ix + \ col = iy * inshp[2] + ix + \
fmapi * N.prod(inshp[1:]) fmapi * np.prod(inshp[1:])
# convert oy,ox values to output # convert oy,ox values to output
# space coordinates # space coordinates
...@@ -188,7 +187,7 @@ class ConvolutionIndices(Op): ...@@ -188,7 +187,7 @@ class ConvolutionIndices(Op):
else: else:
(y, x) = (oy, ox) - topleft (y, x) = (oy, ox) - topleft
# taking into account step size # taking into account step size
(y, x) = N.array([y, x]) / (dy, dx) (y, x) = np.array([y, x]) / (dy, dx)
# convert to row index of sparse matrix # convert to row index of sparse matrix
if ws: if ws:
...@@ -228,7 +227,7 @@ class ConvolutionIndices(Op): ...@@ -228,7 +227,7 @@ class ConvolutionIndices(Op):
if ws: if ws:
kmap = None kmap = None
else: else:
kmap = N.zeros(ntaps, dtype='int') kmap = np.zeros(ntaps, dtype='int')
k = 0 k = 0
# print 'TEMPORARY BUGFIX: REMOVE !!!' # print 'TEMPORARY BUGFIX: REMOVE !!!'
for j in xrange(spmat.shape[1]): for j in xrange(spmat.shape[1]):
...@@ -259,7 +258,7 @@ class ConvolutionIndices(Op): ...@@ -259,7 +258,7 @@ class ConvolutionIndices(Op):
indices, indptr, spmatshp, outshp = self.evaluate(inshp, kshp) indices, indptr, spmatshp, outshp = self.evaluate(inshp, kshp)
out_indices[0] = indices out_indices[0] = indices
out_indptr[0] = indptr out_indptr[0] = indptr
spmat_shape[0] = numpy.asarray(spmatshp) spmat_shape[0] = np.asarray(spmatshp)
convolution_indices = ConvolutionIndices() convolution_indices = ConvolutionIndices()
...@@ -318,13 +317,12 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None, ...@@ -318,13 +317,12 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None,
:TODO: test for 1D and think of how to do n-d convolutions :TODO: test for 1D and think of how to do n-d convolutions
""" """
N = numpy
# start by computing output dimensions, size, etc # start by computing output dimensions, size, etc
kern_size = N.int64(N.prod(kshp)) kern_size = np.int64(np.prod(kshp))
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w) # inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1 # in the first case, default nfeatures to 1
if N.size(imgshp) == 2: if np.size(imgshp) == 2:
imgshp = (1,) + imgshp imgshp = (1,) + imgshp
# construct indices and index pointers for sparse matrix, which, # construct indices and index pointers for sparse matrix, which,
...@@ -334,12 +332,12 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None, ...@@ -334,12 +332,12 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None,
convolution_indices.conv_eval(imgshp, kshp, step, mode) convolution_indices.conv_eval(imgshp, kshp, step, mode)
# build sparse matrix, then generate stack of image patches # build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(N.ones(indices.size), indices, csc = theano.sparse.CSM(sptype)(np.ones(indices.size), indices,
indptr, spmat_shape) indptr, spmat_shape)
patches = (sparse.structured_dot(csc, images.T)).T patches = (sparse.structured_dot(csc, images.T)).T
# compute output of linear classifier # compute output of linear classifier
pshape = tensor.stack([images.shape[0] * tensor.as_tensor(N.prod(outshp)),\ pshape = tensor.stack([images.shape[0] * tensor.as_tensor(np.prod(outshp)),\
tensor.as_tensor(imgshp[0] * kern_size)]) tensor.as_tensor(imgshp[0] * kern_size)])
patch_stack = tensor.reshape(patches, pshape, ndim=2) patch_stack = tensor.reshape(patches, pshape, ndim=2)
...@@ -354,14 +352,14 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None, ...@@ -354,14 +352,14 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None,
# now to have feature maps in raster order ... # now to have feature maps in raster order ...
# go from bsize*outshp x nkern to bsize x nkern*outshp # go from bsize*outshp x nkern to bsize x nkern*outshp
newshp = tensor.stack([images.shape[0],\ newshp = tensor.stack([images.shape[0],\
tensor.as_tensor(N.prod(outshp)),\ tensor.as_tensor(np.prod(outshp)),\
tensor.as_tensor(nkern)]) tensor.as_tensor(nkern)])
tensout = tensor.reshape(output, newshp, ndim=3) tensout = tensor.reshape(output, newshp, ndim=3)
output = tensor.DimShuffle((False,) * tensout.ndim, (0, 2, 1))(tensout) output = tensor.DimShuffle((False,) * tensout.ndim, (0, 2, 1))(tensout)
if flatten: if flatten:
output = tensor.flatten(output, 2) output = tensor.flatten(output, 2)
return output, N.hstack((nkern, outshp)) return output, np.hstack((nkern, outshp))
def max_pool(images, imgshp, maxpoolshp): def max_pool(images, imgshp, maxpoolshp):
...@@ -380,12 +378,11 @@ def max_pool(images, imgshp, maxpoolshp): ...@@ -380,12 +378,11 @@ def max_pool(images, imgshp, maxpoolshp):
:return: out1, symbolic result (2D tensor) :return: out1, symbolic result (2D tensor)
:return: out2, logical shape of the output :return: out2, logical shape of the output
""" """
N = numpy poolsize = np.int64(np.prod(maxpoolshp))
poolsize = N.int64(N.prod(maxpoolshp))
# imgshp contains either 2 entries (height,width) or 3 (nfeatures,h,w) # imgshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1 # in the first case, default nfeatures to 1
if N.size(imgshp) == 2: if np.size(imgshp) == 2:
imgshp = (1,) + imgshp imgshp = (1,) + imgshp
# construct indices and index pointers for sparse matrix, which, # construct indices and index pointers for sparse matrix, which,
...@@ -401,12 +398,12 @@ def max_pool(images, imgshp, maxpoolshp): ...@@ -401,12 +398,12 @@ def max_pool(images, imgshp, maxpoolshp):
# print 'outshp = ', outshp # print 'outshp = ', outshp
# build sparse matrix, then generate stack of image patches # build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(N.ones(indices.size), indices, csc = theano.sparse.CSM(sptype)(np.ones(indices.size), indices,
indptr, spmat_shape) indptr, spmat_shape)
patches = sparse.structured_dot(csc, images.T).T patches = sparse.structured_dot(csc, images.T).T
pshape = tensor.stack([images.shape[0] *\ pshape = tensor.stack([images.shape[0] *\
tensor.as_tensor(N.prod(outshp)), tensor.as_tensor(np.prod(outshp)),
tensor.as_tensor(imgshp[0]), tensor.as_tensor(imgshp[0]),
tensor.as_tensor(poolsize)]) tensor.as_tensor(poolsize)])
patch_stack = tensor.reshape(patches, pshape, ndim=3) patch_stack = tensor.reshape(patches, pshape, ndim=3)
...@@ -414,7 +411,7 @@ def max_pool(images, imgshp, maxpoolshp): ...@@ -414,7 +411,7 @@ def max_pool(images, imgshp, maxpoolshp):
out1 = tensor.max(patch_stack, axis=2) out1 = tensor.max(patch_stack, axis=2)
pshape = tensor.stack([images.shape[0], pshape = tensor.stack([images.shape[0],
tensor.as_tensor(N.prod(outshp)), tensor.as_tensor(np.prod(outshp)),
tensor.as_tensor(imgshp[0])]) tensor.as_tensor(imgshp[0])])
out2 = tensor.reshape(out1, pshape, ndim=3) out2 = tensor.reshape(out1, pshape, ndim=3)
......
theano/sparse/sandbox/sp2.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
from six.moves import xrange from six.moves import xrange
import theano import theano
import scipy.sparse import scipy.sparse
...@@ -74,7 +74,7 @@ class Poisson(gof.op.Op): ...@@ -74,7 +74,7 @@ class Poisson(gof.op.Op):
assert _is_sparse(x) assert _is_sparse(x)
assert x.format in ["csr", "csc"] assert x.format in ["csr", "csc"]
out[0] = x.copy() out[0] = x.copy()
out[0].data = numpy.asarray(numpy.random.poisson(out[0].data), out[0].data = np.asarray(np.random.poisson(out[0].data),
dtype=x.dtype) dtype=x.dtype)
out[0].eliminate_zeros() out[0].eliminate_zeros()
...@@ -123,7 +123,7 @@ class Binomial(gof.op.Op): ...@@ -123,7 +123,7 @@ class Binomial(gof.op.Op):
def perform(self, node, inputs, outputs): def perform(self, node, inputs, outputs):
(n, p, shape) = inputs (n, p, shape) = inputs
(out,) = outputs (out,) = outputs
binomial = numpy.random.binomial(n, p, size=shape) binomial = np.random.binomial(n, p, size=shape)
csx_matrix = getattr(scipy.sparse, self.format + '_matrix') csx_matrix = getattr(scipy.sparse, self.format + '_matrix')
out[0] = csx_matrix(binomial, dtype=self.dtype) out[0] = csx_matrix(binomial, dtype=self.dtype)
...@@ -195,14 +195,14 @@ class Multinomial(gof.op.Op): ...@@ -195,14 +195,14 @@ class Multinomial(gof.op.Op):
if n.ndim == 0: if n.ndim == 0:
for i in xrange(p.shape[0]): for i in xrange(p.shape[0]):
k, l = p.indptr[i], p.indptr[i + 1] k, l = p.indptr[i], p.indptr[i + 1]
out[0].data[k:l] = numpy.random.multinomial(n, p.data[k:l]) out[0].data[k:l] = np.random.multinomial(n, p.data[k:l])
elif n.ndim == 1: elif n.ndim == 1:
if n.shape[0] != p.shape[0]: if n.shape[0] != p.shape[0]:
raise ValueError('The number of element of n must be ' raise ValueError('The number of element of n must be '
'the same as the number of row of p.') 'the same as the number of row of p.')
for i in xrange(p.shape[0]): for i in xrange(p.shape[0]):
k, l = p.indptr[i], p.indptr[i + 1] k, l = p.indptr[i], p.indptr[i + 1]
out[0].data[k:l] = numpy.random.multinomial(n[i], p.data[k:l]) out[0].data[k:l] = np.random.multinomial(n[i], p.data[k:l])
def grad(self, inputs, outputs_gradients): def grad(self, inputs, outputs_gradients):
comment_n = "No gradient exists for the number of samples in class\ comment_n = "No gradient exists for the number of samples in class\
......
theano/sparse/sandbox/test_sp.py
浏览文件 @ 2b7ee2ec
...@@ -11,7 +11,7 @@ if not theano.sparse.enable_sparse: ...@@ -11,7 +11,7 @@ if not theano.sparse.enable_sparse:
import scipy.sparse import scipy.sparse
from scipy.signal import convolve2d from scipy.signal import convolve2d
import scipy.sparse as sparse import scipy.sparse as sparse
import numpy import numpy as np
from six.moves import xrange from six.moves import xrange
from theano import function, tensor from theano import function, tensor
...@@ -43,8 +43,8 @@ class TestSP(unittest.TestCase): ...@@ -43,8 +43,8 @@ class TestSP(unittest.TestCase):
bias = tensor.dvector() bias = tensor.dvector()
kerns = tensor.dmatrix() kerns = tensor.dmatrix()
input = tensor.dmatrix() input = tensor.dmatrix()
rng = numpy.random.RandomState(3423489) rng = np.random.RandomState(3423489)
filters = rng.randn(nkern, numpy.prod(kshp)) filters = rng.randn(nkern, np.prod(kshp))
biasvals = rng.randn(nkern) biasvals = rng.randn(nkern)
for mode in ('FAST_COMPILE', 'FAST_RUN'): for mode in ('FAST_COMPILE', 'FAST_RUN'):
...@@ -57,12 +57,12 @@ class TestSP(unittest.TestCase): ...@@ -57,12 +57,12 @@ class TestSP(unittest.TestCase):
f = function([kerns, bias, input], output, mode=mode) f = function([kerns, bias, input], output, mode=mode)
# now test with real values # now test with real values
img2d = numpy.arange(bsize * numpy.prod(imshp)).reshape(( \ img2d = np.arange(bsize * np.prod(imshp)).reshape(( \
bsize,) + imshp) bsize,) + imshp)
img1d = img2d.reshape(bsize, -1) img1d = img2d.reshape(bsize, -1)
# create filters (need to be flipped to use convolve2d) # create filters (need to be flipped to use convolve2d)
filtersflipped = numpy.zeros((nkern,) + kshp) filtersflipped = np.zeros((nkern,) + kshp)
for k in range(nkern): for k in range(nkern):
it = reversed(filters[k, :]) it = reversed(filters[k, :])
for i in range(kshp[0]): for i in range(kshp[0]):
...@@ -71,11 +71,11 @@ class TestSP(unittest.TestCase): ...@@ -71,11 +71,11 @@ class TestSP(unittest.TestCase):
# compute output with convolve2d # compute output with convolve2d
if conv_mode == 'valid': if conv_mode == 'valid':
fulloutshp = numpy.array(imshp) - numpy.array(kshp) + 1 fulloutshp = np.array(imshp) - np.array(kshp) + 1
else: else:
fulloutshp = numpy.array(imshp) + numpy.array(kshp) - 1 fulloutshp = np.array(imshp) + np.array(kshp) - 1
ntime1 = time.time() ntime1 = time.time()
refout = numpy.zeros((bsize,)+tuple(fulloutshp)+(nkern,)) refout = np.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
for b in range(bsize): for b in range(bsize):
for n in range(nkern): for n in range(nkern):
refout[b, ..., n] = convolve2d(img2d[b, :, :], refout[b, ..., n] = convolve2d(img2d[b, :, :],
...@@ -88,7 +88,7 @@ class TestSP(unittest.TestCase): ...@@ -88,7 +88,7 @@ class TestSP(unittest.TestCase):
bench1 += biasvals.reshape(1, 1, nkern) bench1 += biasvals.reshape(1, 1, nkern)
# swap the last two dimensions (output needs to be nkern x outshp) # swap the last two dimensions (output needs to be nkern x outshp)
bench1 = numpy.swapaxes(bench1, 1, 2) bench1 = np.swapaxes(bench1, 1, 2)
ttime1 = time.time() ttime1 = time.time()
out1 = f(filters, biasvals, img1d) out1 = f(filters, biasvals, img1d)
ttot += time.time() - ttime1 ttot += time.time() - ttime1
...@@ -101,13 +101,13 @@ class TestSP(unittest.TestCase): ...@@ -101,13 +101,13 @@ class TestSP(unittest.TestCase):
#downprop = function([kerns,input], vis, mode=mode) #downprop = function([kerns,input], vis, mode=mode)
#visval = downprop(filters,img1d) #visval = downprop(filters,img1d)
# test downward propagation -- reference implementation # test downward propagation -- reference implementation
#pshape = (img1d.shape[0],numpy.prod(outshp[1:]),numpy.prod(kshp)) #pshape = (img1d.shape[0],np.prod(outshp[1:]),np.prod(kshp))
#patchstack = numpy.zeros(pshape) #patchstack = np.zeros(pshape)
# for bi in numpy.arange(pshape[0]): # batch index # for bi in np.arange(pshape[0]): # batch index
#abspos = 0 #abspos = 0
# for outy in numpy.arange(outshp[1]): # for outy in np.arange(outshp[1]):
# for outx in numpy.arange(outshp[2]): # for outx in np.arange(outshp[2]):
# for ni in numpy.arange(nkern): # for ni in np.arange(nkern):
# print 'filters[n,:].shape = ', filters[n,:].shape # print 'filters[n,:].shape = ', filters[n,:].shape
# print 'out1[bi,abspos].shape =',out1[bi,abspos].shape # print 'out1[bi,abspos].shape =',out1[bi,abspos].shape
#patchstack[bi,abspos,:] = filters[n,:]*out1[bi,abspos] #patchstack[bi,abspos,:] = filters[n,:]*out1[bi,abspos]
...@@ -115,13 +115,13 @@ class TestSP(unittest.TestCase): ...@@ -115,13 +115,13 @@ class TestSP(unittest.TestCase):
#patchstack = patchstack.reshape(1,-1) #patchstack = patchstack.reshape(1,-1)
# indices, indptr, spmat_shape, sptype, outshp = \ # indices, indptr, spmat_shape, sptype, outshp = \
# sp.convolution_indices.conv_eval(imshp,kshp,ss,conv_mode) # sp.convolution_indices.conv_eval(imshp,kshp,ss,conv_mode)
#spmat = sparse.csc_matrix((numpy.ones_like(indices),indices,indptr),spmat_shape) #spmat = sparse.csc_matrix((np.ones_like(indices),indices,indptr),spmat_shape)
#visref = numpy.dot(patchstack, spmat.todense()) #visref = np.dot(patchstack, spmat.todense())
# print 'visval = ', visval # print 'visval = ', visval
# print 'visref = ', visref # print 'visref = ', visref
#assert numpy.all(visref==visval) #assert np.all(visref==visval)
# print '**** Convolution Profiling Results (',mode,') ****' # print '**** Convolution Profiling Results (',mode,') ****'
...@@ -143,10 +143,10 @@ class TestSP(unittest.TestCase): ...@@ -143,10 +143,10 @@ class TestSP(unittest.TestCase):
# symbolic stuff # symbolic stuff
kerns = [tensor.dmatrix(), tensor.dmatrix()] kerns = [tensor.dmatrix(), tensor.dmatrix()]
input = tensor.dmatrix() input = tensor.dmatrix()
rng = numpy.random.RandomState(3423489) rng = np.random.RandomState(3423489)
# build actual input images # build actual input images
img2d = numpy.arange(bsize*numpy.prod(imshp)).reshape((bsize,)+imshp) img2d = np.arange(bsize*np.prod(imshp)).reshape((bsize,)+imshp)
img1d = img2d.reshape(bsize, -1) img1d = img2d.reshape(bsize, -1)
for mode in ('FAST_COMPILE', 'FAST_RUN'): for mode in ('FAST_COMPILE', 'FAST_RUN'):
...@@ -157,8 +157,8 @@ class TestSP(unittest.TestCase): ...@@ -157,8 +157,8 @@ class TestSP(unittest.TestCase):
nkerns[0], input, imshp, ss[0], mode=conv_mode) nkerns[0], input, imshp, ss[0], mode=conv_mode)
l1propup = function([kerns[0], input], l1hid, mode=mode) l1propup = function([kerns[0], input], l1hid, mode=mode)
#l1kernvals = numpy.random.rand(nkerns[0],numpy.prod(kshp[0])) #l1kernvals = np.random.rand(nkerns[0],np.prod(kshp[0]))
l1kernvals = numpy.arange(nkerns[0]*numpy.prod(kshp[0])).reshape(nkerns[0], numpy.prod(kshp[0])) l1kernvals = np.arange(nkerns[0]*np.prod(kshp[0])).reshape(nkerns[0], np.prod(kshp[0]))
l1hidval = l1propup(l1kernvals, img1d) l1hidval = l1propup(l1kernvals, img1d)
# actual values # actual values
...@@ -166,17 +166,17 @@ class TestSP(unittest.TestCase): ...@@ -166,17 +166,17 @@ class TestSP(unittest.TestCase):
nkerns[1], l1hid, l1shp, ss[1], mode=conv_mode) nkerns[1], l1hid, l1shp, ss[1], mode=conv_mode)
l2propup = function([kerns[1], l1hid], l2hid, mode=mode) l2propup = function([kerns[1], l1hid], l2hid, mode=mode)
#l2kernvals = numpy.random.rand(nkerns[1],numpy.prod(kshp[1])*nkerns[0]) #l2kernvals = np.random.rand(nkerns[1],np.prod(kshp[1])*nkerns[0])
l2kernvals = numpy.arange(nkerns[1]*numpy.prod(kshp[1])*nkerns[0]).reshape(nkerns[1], numpy.prod(kshp[1])*nkerns[0]) l2kernvals = np.arange(nkerns[1]*np.prod(kshp[1])*nkerns[0]).reshape(nkerns[1], np.prod(kshp[1])*nkerns[0])
# for debugging, we bring things back to integers # for debugging, we bring things back to integers
l1hidval = numpy.arange(numpy.size(l1hidval)).reshape(l1hidval.shape) l1hidval = np.arange(np.size(l1hidval)).reshape(l1hidval.shape)
l2hidval = l2propup(l2kernvals, l1hidval) l2hidval = l2propup(l2kernvals, l1hidval)
def test_maxpool(self): def test_maxpool(self):
# generate flatted images # generate flatted images
maxpoolshps = ((2, 2), (3, 3), (4, 4), (5, 5), (6, 6)) maxpoolshps = ((2, 2), (3, 3), (4, 4), (5, 5), (6, 6))
imval = numpy.random.rand(4, 5, 10, 10) imval = np.random.rand(4, 5, 10, 10)
images = tensor.dmatrix() images = tensor.dmatrix()
for maxpoolshp in maxpoolshps: for maxpoolshp in maxpoolshps:
...@@ -187,10 +187,10 @@ class TestSP(unittest.TestCase): ...@@ -187,10 +187,10 @@ class TestSP(unittest.TestCase):
output_val = f(imval.reshape(imval.shape[0], -1)) output_val = f(imval.reshape(imval.shape[0], -1))
# numeric verification # numeric verification
my_output_val = numpy.zeros((imval.shape[0], imval.shape[1], my_output_val = np.zeros((imval.shape[0], imval.shape[1],
imval.shape[2] // maxpoolshp[0], imval.shape[2] // maxpoolshp[0],
imval.shape[3] // maxpoolshp[1])) imval.shape[3] // maxpoolshp[1]))
assert numpy.prod(my_output_val.shape[1:]) == numpy.prod(numpy.r_[imval.shape[1], outshp]) assert np.prod(my_output_val.shape[1:]) == np.prod(np.r_[imval.shape[1], outshp])
for n in range(imval.shape[0]): for n in range(imval.shape[0]):
for k in range(imval.shape[1]): for k in range(imval.shape[1]):
...@@ -198,9 +198,9 @@ class TestSP(unittest.TestCase): ...@@ -198,9 +198,9 @@ class TestSP(unittest.TestCase):
for j in range(imval.shape[3] // maxpoolshp[1]): for j in range(imval.shape[3] // maxpoolshp[1]):
ii, jj = i*maxpoolshp[0], j*maxpoolshp[1] ii, jj = i*maxpoolshp[0], j*maxpoolshp[1]
patch = imval[n, k, ii:ii+maxpoolshp[0], jj:jj+maxpoolshp[1]] patch = imval[n, k, ii:ii+maxpoolshp[0], jj:jj+maxpoolshp[1]]
my_output_val[n, k, i, j] = numpy.max(patch) my_output_val[n, k, i, j] = np.max(patch)
my_output_val = my_output_val.reshape(imval.shape[0], -1) my_output_val = my_output_val.reshape(imval.shape[0], -1)
assert numpy.all(output_val == my_output_val) assert np.all(output_val == my_output_val)
def mp(input): def mp(input):
output, outshp = sp.max_pool(input, imval.shape[1:], maxpoolshp) output, outshp = sp.max_pool(input, imval.shape[1:], maxpoolshp)
......
theano/sparse/sandbox/truedot.py
浏览文件 @ 2b7ee2ec
...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division ...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import unittest import unittest
import theano import theano
import numpy import numpy as np
import scipy.sparse as sp import scipy.sparse as sp
from theano import sparse from theano import sparse
......
theano/sparse/tests/test_basic.py
浏览文件 @ 2b7ee2ec
...@@ -4,7 +4,7 @@ import time ...@@ -4,7 +4,7 @@ import time
import unittest import unittest
from nose.plugins.skip import SkipTest from nose.plugins.skip import SkipTest
import numpy import numpy as np
from six.moves import xrange from six.moves import xrange
try: try:
import scipy.sparse as sp import scipy.sparse as sp
...@@ -83,8 +83,8 @@ def random_lil(shape, dtype, nnz): ...@@ -83,8 +83,8 @@ def random_lil(shape, dtype, nnz):
huge = 2 ** 30 huge = 2 ** 30
for k in range(nnz): for k in range(nnz):
# set non-zeros in random locations (row x, col y) # set non-zeros in random locations (row x, col y)
idx = numpy.random.randint(1, huge+1, size=2) % shape idx = np.random.randint(1, huge+1, size=2) % shape
value = numpy.random.rand() value = np.random.rand()
# if dtype *int*, value will always be zeros! # if dtype *int*, value will always be zeros!
if "int" in dtype: if "int" in dtype:
value = int(value * 100) value = int(value * 100)
...@@ -136,23 +136,23 @@ def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None, ...@@ -136,23 +136,23 @@ def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None,
assert gap[0] >= 0 assert gap[0] >= 0
def _rand(): def _rand():
where = numpy.random.binomial(1, p, size=shape).astype('int8') where = np.random.binomial(1, p, size=shape).astype('int8')
if out_dtype in sparse.discrete_dtypes: if out_dtype in sparse.discrete_dtypes:
if not gap: if not gap:
value = numpy.random.randint(50, size=shape) value = np.random.randint(50, size=shape)
elif len(gap) == 2: elif len(gap) == 2:
value = numpy.random.randint(gap[0], gap[1], size=shape) value = np.random.randint(gap[0], gap[1], size=shape)
else: else:
value = numpy.random.randint(gap[0], size=shape) value = np.random.randint(gap[0], size=shape)
else: else:
if not gap: if not gap:
value = numpy.random.random(shape) value = np.random.random(shape)
elif len(gap) == 2: elif len(gap) == 2:
a, b = gap a, b = gap
value = a + numpy.random.random(shape) * (b - a) value = a + np.random.random(shape) * (b - a)
else: else:
value = numpy.random.random(shape) * gap[0] value = np.random.random(shape) * gap[0]
return (where * value).astype(out_dtype) return (where * value).astype(out_dtype)
variable = [getattr(theano.sparse, format + '_matrix')(dtype=out_dtype) variable = [getattr(theano.sparse, format + '_matrix')(dtype=out_dtype)
...@@ -169,13 +169,13 @@ def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None, ...@@ -169,13 +169,13 @@ def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None,
for idx in range(n): for idx in range(n):
assert data[idx].nnz > 1, ( assert data[idx].nnz > 1, (
"can't make a sparse matrix with explicit 0") "can't make a sparse matrix with explicit 0")
d_idx = numpy.random.randint(data[idx].nnz) d_idx = np.random.randint(data[idx].nnz)
data[idx].data[d_idx] = 0 data[idx].data[d_idx] = 0
# numpy 1.5.0 with scipy 0.9.0 have scipy.sparse.XXX_matrix return # numpy 1.5.0 with scipy 0.9.0 have scipy.sparse.XXX_matrix return
# typenum 10(ulonglong) instead of 8(uint64) event if they are the same! # typenum 10(ulonglong) instead of 8(uint64) event if they are the same!
# Theano don't like ulonglong type_num # Theano don't like ulonglong type_num
dtype = numpy.dtype(out_dtype) # Convert into dtype object. dtype = np.dtype(out_dtype) # Convert into dtype object.
if data[0].dtype.num != dtype.num and dtype.str == data[0].dtype.str: if data[0].dtype.num != dtype.num and dtype.str == data[0].dtype.str:
data[0].data = theano._asarray(data[0].data, out_dtype) data[0].data = theano._asarray(data[0].data, out_dtype)
assert data[0].dtype.num == dtype.num assert data[0].dtype.num == dtype.num
...@@ -423,7 +423,7 @@ class SparseInferShapeTester(utt.InferShapeTester): ...@@ -423,7 +423,7 @@ class SparseInferShapeTester(utt.InferShapeTester):
[x + y], [x + y],
[sp.csr_matrix(random_lil((10, 40), [sp.csr_matrix(random_lil((10, 40),
config.floatX, 3)), config.floatX, 3)),
numpy.random.randn(10, 40).astype(config.floatX)], np.random.randn(10, 40).astype(config.floatX)],
(AddSD, sparse.opt.AddSD_ccode)) (AddSD, sparse.opt.AddSD_ccode))
def test_mul_ss(self): def test_mul_ss(self):
...@@ -444,7 +444,7 @@ class SparseInferShapeTester(utt.InferShapeTester): ...@@ -444,7 +444,7 @@ class SparseInferShapeTester(utt.InferShapeTester):
[x * y], [x * y],
[sp.csr_matrix(random_lil((10, 40), [sp.csr_matrix(random_lil((10, 40),
config.floatX, 3)), config.floatX, 3)),
numpy.random.randn(10, 40).astype(config.floatX)], np.random.randn(10, 40).astype(config.floatX)],
MulSD, excluding=["local_mul_s_d"]) MulSD, excluding=["local_mul_s_d"])
def test_remove0(self): def test_remove0(self):
...@@ -518,7 +518,7 @@ class SparseInferShapeTester(utt.InferShapeTester): ...@@ -518,7 +518,7 @@ class SparseInferShapeTester(utt.InferShapeTester):
x = tensor.matrix() x = tensor.matrix()
self._compile_and_check([x], self._compile_and_check([x],
[csc_from_dense(x)], [csc_from_dense(x)],
[numpy.random.randn(10, 40).astype( [np.random.randn(10, 40).astype(
config.floatX)], config.floatX)],
csc_from_dense.__class__) csc_from_dense.__class__)
...@@ -531,9 +531,9 @@ class SparseInferShapeTester(utt.InferShapeTester): ...@@ -531,9 +531,9 @@ class SparseInferShapeTester(utt.InferShapeTester):
self._compile_and_check( self._compile_and_check(
[x, vals, ilist], [x, vals, ilist],
[out], [out],
[numpy.zeros((40, 10), dtype=config.floatX), [np.zeros((40, 10), dtype=config.floatX),
numpy.random.randn(12, 10).astype(config.floatX), np.random.randn(12, 10).astype(config.floatX),
numpy.random.randint(low=0, high=40, size=(12,))], np.random.randint(low=0, high=40, size=(12,))],
ConstructSparseFromList ConstructSparseFromList
) )
...@@ -565,8 +565,8 @@ class TestConstructSparseFromList(unittest.TestCase): ...@@ -565,8 +565,8 @@ class TestConstructSparseFromList(unittest.TestCase):
assert isinstance(g.owner.op, ConstructSparseFromList) assert isinstance(g.owner.op, ConstructSparseFromList)
# Test the sparse grad # Test the sparse grad
valm = numpy.random.rand(5, 4).astype(config.floatX) valm = np.random.rand(5, 4).astype(config.floatX)
valv = numpy.random.randint(0, 5, 10) valv = np.random.randint(0, 5, 10)
m = theano.tensor.matrix() m = theano.tensor.matrix()
shared_v = theano.shared(valv) shared_v = theano.shared(valv)
...@@ -603,21 +603,21 @@ class T_AddMul(unittest.TestCase): ...@@ -603,21 +603,21 @@ class T_AddMul(unittest.TestCase):
def testMulSS(self): def testMulSS(self):
self._testSS(mul, self._testSS(mul,
numpy.array([[1., 0], [3, 0], [0, 6]]), np.array([[1., 0], [3, 0], [0, 6]]),
numpy.array([[1., 2], [3, 0], [0, 6]])) np.array([[1., 2], [3, 0], [0, 6]]))
def testMulSD(self): def testMulSD(self):
self._testSD(mul, self._testSD(mul,
numpy.array([[1., 0], [3, 0], [0, 6]]), np.array([[1., 0], [3, 0], [0, 6]]),
numpy.array([[1., 2], [3, 0], [0, 6]])) np.array([[1., 2], [3, 0], [0, 6]]))
def testMulDS(self): def testMulDS(self):
self._testDS(mul, self._testDS(mul,
numpy.array([[1., 0], [3, 0], [0, 6]]), np.array([[1., 0], [3, 0], [0, 6]]),
numpy.array([[1., 2], [3, 0], [0, 6]])) np.array([[1., 2], [3, 0], [0, 6]]))
def _testSS(self, op, array1=numpy.array([[1., 0], [3, 0], [0, 6]]), def _testSS(self, op, array1=np.array([[1., 0], [3, 0], [0, 6]]),
array2=numpy.asarray([[0, 2.], [0, 4], [5, 0]])): array2=np.asarray([[0, 2.], [0, 4], [5, 0]])):
for mtype1, mtype2 in product(_mtypes, _mtypes): for mtype1, mtype2 in product(_mtypes, _mtypes):
for dtype1, dtype2 in [('float64', 'int8'), for dtype1, dtype2 in [('float64', 'int8'),
('int8', 'float64'), ('int8', 'float64'),
...@@ -643,19 +643,19 @@ class T_AddMul(unittest.TestCase): ...@@ -643,19 +643,19 @@ class T_AddMul(unittest.TestCase):
val = eval_outputs([apb]) val = eval_outputs([apb])
self.assertTrue(val.shape == (3, 2)) self.assertTrue(val.shape == (3, 2))
if op is add: if op is add:
self.assertTrue(numpy.all(val.todense() == (array1 + array2))) self.assertTrue(np.all(val.todense() == (array1 + array2)))
if dtype1.startswith('float') and dtype2.startswith('float'): if dtype1.startswith('float') and dtype2.startswith('float'):
verify_grad_sparse(op, [a, b], structured=False) verify_grad_sparse(op, [a, b], structured=False)
elif op is mul: elif op is mul:
self.assertTrue(numpy.all(val.todense() self.assertTrue(np.all(val.todense()
== (array1 * array2))) == (array1 * array2)))
if dtype1.startswith('float') and dtype2.startswith('float'): if dtype1.startswith('float') and dtype2.startswith('float'):
verify_grad_sparse(op, [a, b], structured=False) verify_grad_sparse(op, [a, b], structured=False)
def _testSD(self, op, array1=numpy.array([[1., 0], [3, 0], [0, 6]]), def _testSD(self, op, array1=np.array([[1., 0], [3, 0], [0, 6]]),
array2=numpy.asarray([[0, 2.], [0, 4], [5, 0]])): array2=np.asarray([[0, 2.], [0, 4], [5, 0]])):
for mtype in _mtypes: for mtype in _mtypes:
for a in [numpy.array(array1), tensor.as_tensor_variable(array1), for a in [np.array(array1), tensor.as_tensor_variable(array1),
theano.shared(array1)]: theano.shared(array1)]:
for dtype1, dtype2 in [('float64', 'int8'), for dtype1, dtype2 in [('float64', 'int8'),
('int8', 'float64'), ('int8', 'float64'),
...@@ -675,9 +675,9 @@ class T_AddMul(unittest.TestCase): ...@@ -675,9 +675,9 @@ class T_AddMul(unittest.TestCase):
self.assertTrue(val.shape == (3, 2)) self.assertTrue(val.shape == (3, 2))
if op is add: if op is add:
self.assertTrue(_is_dense_variable(apb)) self.assertTrue(_is_dense_variable(apb))
self.assertTrue(numpy.all(val == (array1 + b))) self.assertTrue(np.all(val == (array1 + b)))
ans = numpy.array([[1., 2], [3, 4], [5, 6]]) ans = np.array([[1., 2], [3, 4], [5, 6]])
self.assertTrue(numpy.all(val == ans)) self.assertTrue(np.all(val == ans))
if isinstance(a, theano.Constant): if isinstance(a, theano.Constant):
a = a.data a = a.data
if getattr(a, 'owner', None): if getattr(a, 'owner', None):
...@@ -686,8 +686,8 @@ class T_AddMul(unittest.TestCase): ...@@ -686,8 +686,8 @@ class T_AddMul(unittest.TestCase):
verify_grad_sparse(op, [a, b], structured=True) verify_grad_sparse(op, [a, b], structured=True)
elif op is mul: elif op is mul:
self.assertTrue(_is_sparse_variable(apb)) self.assertTrue(_is_sparse_variable(apb))
self.assertTrue(numpy.all(val.todense() == (b.multiply(array1)))) self.assertTrue(np.all(val.todense() == (b.multiply(array1))))
self.assertTrue(numpy.all(val.todense() == numpy.array( self.assertTrue(np.all(val.todense() == np.array(
[[1, 0], [9, 0], [0, 36]]))) [[1, 0], [9, 0], [0, 36]])))
if isinstance(a, theano.Constant): if isinstance(a, theano.Constant):
a = a.data a = a.data
...@@ -696,10 +696,10 @@ class T_AddMul(unittest.TestCase): ...@@ -696,10 +696,10 @@ class T_AddMul(unittest.TestCase):
if dtype1.startswith('float') and dtype2.startswith('float'): if dtype1.startswith('float') and dtype2.startswith('float'):
verify_grad_sparse(op, [a, b], structured=False) verify_grad_sparse(op, [a, b], structured=False)
def _testDS(self, op, array1=numpy.array([[1., 0], [3, 0], [0, 6]]), def _testDS(self, op, array1=np.array([[1., 0], [3, 0], [0, 6]]),
array2=numpy.asarray([[0, 2.], [0, 4], [5, 0]])): array2=np.asarray([[0, 2.], [0, 4], [5, 0]])):
for mtype in _mtypes: for mtype in _mtypes:
for b in [numpy.asarray(array2), for b in [np.asarray(array2),
tensor.as_tensor_variable(array2), tensor.as_tensor_variable(array2),
theano.shared(array2)]: theano.shared(array2)]:
for dtype1, dtype2 in [('float64', 'int8'), for dtype1, dtype2 in [('float64', 'int8'),
...@@ -718,18 +718,18 @@ class T_AddMul(unittest.TestCase): ...@@ -718,18 +718,18 @@ class T_AddMul(unittest.TestCase):
self.assertTrue(val.shape == (3, 2)) self.assertTrue(val.shape == (3, 2))
if op is add: if op is add:
self.assertTrue(_is_dense_variable(apb)) self.assertTrue(_is_dense_variable(apb))
self.assertTrue(numpy.all(val == (a + array2))) self.assertTrue(np.all(val == (a + array2)))
ans = numpy.array([[1., 2], [3, 4], [5, 6]]) ans = np.array([[1., 2], [3, 4], [5, 6]])
self.assertTrue(numpy.all(val == ans)) self.assertTrue(np.all(val == ans))
if isinstance(b, theano.Constant): if isinstance(b, theano.Constant):
b = b.data b = b.data
if dtype1.startswith('float') and dtype2.startswith('float'): if dtype1.startswith('float') and dtype2.startswith('float'):
verify_grad_sparse(op, [a, b], structured=True) verify_grad_sparse(op, [a, b], structured=True)
elif op is mul: elif op is mul:
self.assertTrue(_is_sparse_variable(apb)) self.assertTrue(_is_sparse_variable(apb))
ans = numpy.array([[1, 0], [9, 0], [0, 36]]) ans = np.array([[1, 0], [9, 0], [0, 36]])
self.assertTrue(numpy.all(val.todense() == (a.multiply(array2)))) self.assertTrue(np.all(val.todense() == (a.multiply(array2))))
self.assertTrue(numpy.all(val.todense() == ans)) self.assertTrue(np.all(val.todense() == ans))
if isinstance(b, theano.Constant): if isinstance(b, theano.Constant):
b = b.data b = b.data
if dtype1.startswith('float') and dtype2.startswith('float'): if dtype1.startswith('float') and dtype2.startswith('float'):
...@@ -742,7 +742,7 @@ class test_comparison(unittest.TestCase): ...@@ -742,7 +742,7 @@ class test_comparison(unittest.TestCase):
# took from tensor basic_test.py # took from tensor basic_test.py
def _rand_ranged(self, min, max, shape): def _rand_ranged(self, min, max, shape):
return numpy.asarray(numpy.random.rand(*shape) * (max - min) + min, return np.asarray(np.random.rand(*shape) * (max - min) + min,
dtype=config.floatX) dtype=config.floatX)
tests = [lambda x, y: x > y, lambda x, y: x < y, tests = [lambda x, y: x > y, lambda x, y: x < y,
...@@ -768,7 +768,7 @@ class test_comparison(unittest.TestCase): ...@@ -768,7 +768,7 @@ class test_comparison(unittest.TestCase):
m1 = scipyType(random_lil((10, 40), config.floatX, 3)) m1 = scipyType(random_lil((10, 40), config.floatX, 3))
m2 = scipyType(random_lil((10, 40), config.floatX, 3)) m2 = scipyType(random_lil((10, 40), config.floatX, 3))
self.assertTrue(numpy.array_equal(f(m1, m2).data, testOp(m1, m2).data)) self.assertTrue(np.array_equal(f(m1, m2).data, testOp(m1, m2).data))
def __generalized_sd_test(self, theanop, symbolicType, testOp, scipyType): def __generalized_sd_test(self, theanop, symbolicType, testOp, scipyType):
...@@ -787,7 +787,7 @@ class test_comparison(unittest.TestCase): ...@@ -787,7 +787,7 @@ class test_comparison(unittest.TestCase):
m1 = scipyType(random_lil((10, 40), config.floatX, 3)) m1 = scipyType(random_lil((10, 40), config.floatX, 3))
m2 = self._rand_ranged(1000, -1000, [10, 40]) m2 = self._rand_ranged(1000, -1000, [10, 40])
self.assertTrue(numpy.array_equal(f(m1, m2).data, testOp(m1, m2).data)) self.assertTrue(np.array_equal(f(m1, m2).data, testOp(m1, m2).data))
def __generalized_ds_test(self, theanop, symbolicType, testOp, scipyType): def __generalized_ds_test(self, theanop, symbolicType, testOp, scipyType):
...@@ -806,7 +806,7 @@ class test_comparison(unittest.TestCase): ...@@ -806,7 +806,7 @@ class test_comparison(unittest.TestCase):
m1 = scipyType(random_lil((10, 40), config.floatX, 3)) m1 = scipyType(random_lil((10, 40), config.floatX, 3))
m2 = self._rand_ranged(1000, -1000, [10, 40]) m2 = self._rand_ranged(1000, -1000, [10, 40])
self.assertTrue(numpy.array_equal(f(m2, m1).data, testOp(m2, m1).data)) self.assertTrue(np.array_equal(f(m2, m1).data, testOp(m2, m1).data))
def test_ss_csr_comparison(self): def test_ss_csr_comparison(self):
...@@ -859,14 +859,14 @@ class test_comparison(unittest.TestCase): ...@@ -859,14 +859,14 @@ class test_comparison(unittest.TestCase):
y = theano.tensor.matrix() y = theano.tensor.matrix()
m1 = sp.csc_matrix((2, 2), dtype=theano.config.floatX) m1 = sp.csc_matrix((2, 2), dtype=theano.config.floatX)
m2 = numpy.asarray([[0, 0], [0, 0]], dtype=theano.config.floatX) m2 = np.asarray([[0, 0], [0, 0]], dtype=theano.config.floatX)
for func in self.testsDic: for func in self.testsDic:
op = func(y, x) op = func(y, x)
f = theano.function([y, x], op) f = theano.function([y, x], op)
self.assertTrue(numpy.array_equal(f(m2, m1), self.assertTrue(np.array_equal(f(m2, m1),
self.testsDic[func](m2, m1))) self.testsDic[func](m2, m1)))
...@@ -876,7 +876,7 @@ class T_conversion(unittest.TestCase): ...@@ -876,7 +876,7 @@ class T_conversion(unittest.TestCase):
if 0: if 0:
def test0(self): def test0(self):
a = tensor.as_tensor_variable(numpy.random.rand(5)) a = tensor.as_tensor_variable(np.random.rand(5))
s = csc_from_dense(a) s = csc_from_dense(a)
val = eval_outputs([s]) val = eval_outputs([s])
self.assertTrue(str(val.dtype) == 'float64') self.assertTrue(str(val.dtype) == 'float64')
...@@ -884,7 +884,7 @@ class T_conversion(unittest.TestCase): ...@@ -884,7 +884,7 @@ class T_conversion(unittest.TestCase):
if 0: if 0:
def test1(self): def test1(self):
a = tensor.as_tensor_variable(numpy.random.rand(5)) a = tensor.as_tensor_variable(np.random.rand(5))
s = csr_from_dense(a) s = csr_from_dense(a)
val = eval_outputs([s]) val = eval_outputs([s])
self.assertTrue(str(val.dtype) == 'float64') self.assertTrue(str(val.dtype) == 'float64')
...@@ -898,7 +898,7 @@ class T_conversion(unittest.TestCase): ...@@ -898,7 +898,7 @@ class T_conversion(unittest.TestCase):
d = dense_from_sparse(s) d = dense_from_sparse(s)
val = eval_outputs([d]) val = eval_outputs([d])
self.assertTrue(str(val.dtype) == s.dtype) self.assertTrue(str(val.dtype) == s.dtype)
self.assertTrue(numpy.all(val[0] == [1, 0, 0, 0, 0])) self.assertTrue(np.all(val[0] == [1, 0, 0, 0, 0]))
def test_todense(self): def test_todense(self):
# call sparse_var.todense() # call sparse_var.todense()
...@@ -908,7 +908,7 @@ class T_conversion(unittest.TestCase): ...@@ -908,7 +908,7 @@ class T_conversion(unittest.TestCase):
d = s.toarray() d = s.toarray()
val = eval_outputs([d]) val = eval_outputs([d])
self.assertTrue(str(val.dtype) == s.dtype) self.assertTrue(str(val.dtype) == s.dtype)
self.assertTrue(numpy.all(val[0] == [1, 0, 0, 0, 0])) self.assertTrue(np.all(val[0] == [1, 0, 0, 0, 0]))
@staticmethod @staticmethod
def check_format_ndim(format, ndim): def check_format_ndim(format, ndim):
...@@ -923,8 +923,8 @@ class T_conversion(unittest.TestCase): ...@@ -923,8 +923,8 @@ class T_conversion(unittest.TestCase):
c = d.sum() c = d.sum()
g = tensor.grad(c, x) g = tensor.grad(c, x)
f = theano.function([x], [s, g]) f = theano.function([x], [s, g])
f(numpy.array(0, dtype=config.floatX, ndmin=ndim)) f(np.array(0, dtype=config.floatX, ndmin=ndim))
f(numpy.array(7, dtype=config.floatX, ndmin=ndim)) f(np.array(7, dtype=config.floatX, ndmin=ndim))
def test_format_ndim(self): def test_format_ndim(self):
for format in 'csc', 'csr': for format in 'csc', 'csr':
...@@ -972,10 +972,10 @@ class test_csm_properties(unittest.TestCase): ...@@ -972,10 +972,10 @@ class test_csm_properties(unittest.TestCase):
data, indices, indptr, shape = f(spmat) data, indices, indptr, shape = f(spmat)
assert numpy.all(data == spmat.data) assert np.all(data == spmat.data)
assert numpy.all(indices == spmat.indices) assert np.all(indices == spmat.indices)
assert numpy.all(indptr == spmat.indptr) assert np.all(indptr == spmat.indptr)
assert numpy.all(shape == spmat.shape) assert np.all(shape == spmat.shape)
class test_csm(unittest.TestCase): class test_csm(unittest.TestCase):
...@@ -991,7 +991,7 @@ class test_csm(unittest.TestCase): ...@@ -991,7 +991,7 @@ class test_csm(unittest.TestCase):
spmat = sp_types[format](random_lil((4, 3), dtype, 3)) spmat = sp_types[format](random_lil((4, 3), dtype, 3))
verify_grad_sparse(lambda x: CSM(format)(x, spmat.indices, verify_grad_sparse(lambda x: CSM(format)(x, spmat.indices,
spmat.indptr, numpy.asarray(spmat.shape, 'int32')), spmat.indptr, np.asarray(spmat.shape, 'int32')),
[spmat.data], structured=True) [spmat.data], structured=True)
def test_csm_sparser(self): def test_csm_sparser(self):
...@@ -1018,7 +1018,7 @@ class test_csm(unittest.TestCase): ...@@ -1018,7 +1018,7 @@ class test_csm(unittest.TestCase):
spmat = sp_types[format](random_lil((4, 3), dtype, 3)) spmat = sp_types[format](random_lil((4, 3), dtype, 3))
res = f(spmat.data, spmat.indices, spmat.indptr, res = f(spmat.data, spmat.indices, spmat.indptr,
numpy.asarray(spmat.shape, 'int32')) np.asarray(spmat.shape, 'int32'))
assert len(spmat.data) == len(res) assert len(spmat.data) == len(res)
...@@ -1063,12 +1063,12 @@ class test_csm(unittest.TestCase): ...@@ -1063,12 +1063,12 @@ class test_csm(unittest.TestCase):
spmat = sp_types[format](random_lil((4, 3), dtype, 3)) spmat = sp_types[format](random_lil((4, 3), dtype, 3))
res = f(spmat.data, spmat.indices, spmat.indptr, res = f(spmat.data, spmat.indices, spmat.indptr,
numpy.asarray(spmat.shape, 'int32')) np.asarray(spmat.shape, 'int32'))
assert numpy.all(res.data == spmat.data) assert np.all(res.data == spmat.data)
assert numpy.all(res.indices == spmat.indices) assert np.all(res.indices == spmat.indices)
assert numpy.all(res.indptr == spmat.indptr) assert np.all(res.indptr == spmat.indptr)
assert numpy.all(res.shape == spmat.shape) assert np.all(res.shape == spmat.shape)
class test_structureddot(unittest.TestCase): class test_structureddot(unittest.TestCase):
...@@ -1082,7 +1082,7 @@ class test_structureddot(unittest.TestCase): ...@@ -1082,7 +1082,7 @@ class test_structureddot(unittest.TestCase):
# allocate a random sparse matrix # allocate a random sparse matrix
spmat = sp.csc_matrix(random_lil((4, 3), 'float32', 3)) spmat = sp.csc_matrix(random_lil((4, 3), 'float32', 3))
mat = numpy.asarray(numpy.random.randn(3, 2), 'float32') mat = np.asarray(np.random.randn(3, 2), 'float32')
verify_grad_sparse(structured_dot, [spmat, mat], structured=True) verify_grad_sparse(structured_dot, [spmat, mat], structured=True)
...@@ -1098,7 +1098,7 @@ class test_structureddot(unittest.TestCase): ...@@ -1098,7 +1098,7 @@ class test_structureddot(unittest.TestCase):
# allocate a random sparse matrix # allocate a random sparse matrix
spmat = sp.csr_matrix(random_lil((4, 3), 'float64', 3)) spmat = sp.csr_matrix(random_lil((4, 3), 'float64', 3))
mat = numpy.asarray(numpy.random.randn(3, 2), 'float64') mat = np.asarray(np.random.randn(3, 2), 'float64')
verify_grad_sparse(structured_dot, [spmat, mat], structured=True) verify_grad_sparse(structured_dot, [spmat, mat], structured=True)
...@@ -1129,8 +1129,8 @@ class test_structureddot(unittest.TestCase): ...@@ -1129,8 +1129,8 @@ class test_structureddot(unittest.TestCase):
# an intc vs. int32 bug. # an intc vs. int32 bug.
# The lil makes an intc on my computer when sparse_dtype # The lil makes an intc on my computer when sparse_dtype
# is int32. # is int32.
spmat.dtype = numpy.dtype(sparse_dtype) spmat.dtype = np.dtype(sparse_dtype)
mat = numpy.asarray(numpy.random.randn(N, K) * 9, mat = np.asarray(np.random.randn(N, K) * 9,
dtype=dense_dtype) dtype=dense_dtype)
# print 'DTYPES', sparse_dtype, dense_dtype # print 'DTYPES', sparse_dtype, dense_dtype
# print 'sym types', a.type, b.type # print 'sym types', a.type, b.type
...@@ -1158,9 +1158,9 @@ class test_structureddot(unittest.TestCase): ...@@ -1158,9 +1158,9 @@ class test_structureddot(unittest.TestCase):
spmat = sp.lil_matrix((4, 6), dtype='int64') spmat = sp.lil_matrix((4, 6), dtype='int64')
for i in range(5): for i in range(5):
# set non-zeros in random locations (row x, col y) # set non-zeros in random locations (row x, col y)
x = numpy.floor(numpy.random.rand() * spmat.shape[0]) x = np.floor(np.random.rand() * spmat.shape[0])
y = numpy.floor(numpy.random.rand() * spmat.shape[1]) y = np.floor(np.random.rand() * spmat.shape[1])
spmat[x, y] = numpy.random.rand() * 10 spmat[x, y] = np.random.rand() * 10
spmat = sp.csc_matrix(spmat) spmat = sp.csc_matrix(spmat)
images = tensor.Tensor(dtype='float32', images = tensor.Tensor(dtype='float32',
...@@ -1179,12 +1179,12 @@ class test_structureddot(unittest.TestCase): ...@@ -1179,12 +1179,12 @@ class test_structureddot(unittest.TestCase):
sdcscpresent = True sdcscpresent = True
assert sdcscpresent assert sdcscpresent
kernvals = numpy.array(spmat.data[:spmat.size]) kernvals = np.array(spmat.data[:spmat.size])
# print 'kdtype', kernvals.dtype, kernvals.shape, # print 'kdtype', kernvals.dtype, kernvals.shape,
# print kernvals.ndim, kernvals.dtype.num # print kernvals.ndim, kernvals.dtype.num
# print 'type of kernvals = ', kernvals.dtype # print 'type of kernvals = ', kernvals.dtype
bsize = 3 bsize = 3
imvals = 1.0 * numpy.array(numpy.arange(bsize * spmat.shape[1]).\ imvals = 1.0 * np.array(np.arange(bsize * spmat.shape[1]).\
reshape(bsize, spmat.shape[1]), reshape(bsize, spmat.shape[1]),
dtype='float32') dtype='float32')
outvals = f(kernvals, imvals) outvals = f(kernvals, imvals)
...@@ -1230,7 +1230,7 @@ class test_structureddot(unittest.TestCase): ...@@ -1230,7 +1230,7 @@ class test_structureddot(unittest.TestCase):
(400, 3000, 200, 6000), (400, 3000, 200, 6000),
]: ]:
spmat = sp.csc_matrix(random_lil((M, N), sparse_dtype, nnz)) spmat = sp.csc_matrix(random_lil((M, N), sparse_dtype, nnz))
mat = numpy.asarray(numpy.random.randn(N, K), dense_dtype) mat = np.asarray(np.random.randn(N, K), dense_dtype)
theano_times = [] theano_times = []
scipy_times = [] scipy_times = []
for i in xrange(5): for i in xrange(5):
...@@ -1243,8 +1243,8 @@ class test_structureddot(unittest.TestCase): ...@@ -1243,8 +1243,8 @@ class test_structureddot(unittest.TestCase):
theano_times.append(t1 - t0) theano_times.append(t1 - t0)
scipy_times.append(t2 - t1) scipy_times.append(t2 - t1)
theano_time = numpy.min(theano_times) theano_time = np.min(theano_times)
scipy_time = numpy.min(scipy_times) scipy_time = np.min(scipy_times)
speedup = scipy_time / theano_time speedup = scipy_time / theano_time
# print scipy_times # print scipy_times
...@@ -1278,7 +1278,7 @@ class test_structureddot(unittest.TestCase): ...@@ -1278,7 +1278,7 @@ class test_structureddot(unittest.TestCase):
(400, 3000, 200, 6000), (400, 3000, 200, 6000),
]: ]:
spmat = sp.csr_matrix(random_lil((M, N), sparse_dtype, nnz)) spmat = sp.csr_matrix(random_lil((M, N), sparse_dtype, nnz))
mat = numpy.asarray(numpy.random.randn(N, K), dense_dtype) mat = np.asarray(np.random.randn(N, K), dense_dtype)
t0 = time.time() t0 = time.time()
theano_result = f(spmat, mat) theano_result = f(spmat, mat)
t1 = time.time() t1 = time.time()
...@@ -1309,18 +1309,18 @@ class DotTests(utt.InferShapeTester): ...@@ -1309,18 +1309,18 @@ class DotTests(utt.InferShapeTester):
utt.seed_rng() utt.seed_rng()
self.x_csr = scipy.sparse.csr_matrix( self.x_csr = scipy.sparse.csr_matrix(
numpy.random.binomial(1, 0.5, x_size), dtype=theano.config.floatX) np.random.binomial(1, 0.5, x_size), dtype=theano.config.floatX)
self.x_csc = scipy.sparse.csc_matrix( self.x_csc = scipy.sparse.csc_matrix(
numpy.random.binomial(1, 0.5, x_size), dtype=theano.config.floatX) np.random.binomial(1, 0.5, x_size), dtype=theano.config.floatX)
self.y = numpy.asarray(numpy.random.uniform(-1, 1, y_size), self.y = np.asarray(np.random.uniform(-1, 1, y_size),
dtype=theano.config.floatX) dtype=theano.config.floatX)
self.y_csr = scipy.sparse.csr_matrix( self.y_csr = scipy.sparse.csr_matrix(
numpy.random.binomial(1, 0.5, y_size), dtype=theano.config.floatX) np.random.binomial(1, 0.5, y_size), dtype=theano.config.floatX)
self.y_csc = scipy.sparse.csc_matrix( self.y_csc = scipy.sparse.csc_matrix(
numpy.random.binomial(1, 0.5, y_size), dtype=theano.config.floatX) np.random.binomial(1, 0.5, y_size), dtype=theano.config.floatX)
self.v_10 = numpy.asarray(numpy.random.uniform(-1, 1, 10), self.v_10 = np.asarray(np.random.uniform(-1, 1, 10),
dtype=theano.config.floatX) dtype=theano.config.floatX)
self.v_100 = numpy.asarray(numpy.random.uniform(-1, 1, 100), self.v_100 = np.asarray(np.random.uniform(-1, 1, 100),
dtype=theano.config.floatX) dtype=theano.config.floatX)
def test_csr_dense(self): def test_csr_dense(self):
...@@ -1386,7 +1386,7 @@ class DotTests(utt.InferShapeTester): ...@@ -1386,7 +1386,7 @@ class DotTests(utt.InferShapeTester):
# Test infer_shape # Test infer_shape
f_a = theano.function([x, y], theano.sparse.dot(x, y).shape) f_a = theano.function([x, y], theano.sparse.dot(x, y).shape)
f_b = lambda x, y: (x * y).shape f_b = lambda x, y: (x * y).shape
assert numpy.all(f_a(vx, vy) == f_b(vx, vy)) assert np.all(f_a(vx, vy) == f_b(vx, vy))
topo = f_a.maker.fgraph.toposort() topo = f_a.maker.fgraph.toposort()
if theano.config.mode != 'FAST_COMPILE': if theano.config.mode != 'FAST_COMPILE':
nb = 0 nb = 0
...@@ -1402,7 +1402,7 @@ class DotTests(utt.InferShapeTester): ...@@ -1402,7 +1402,7 @@ class DotTests(utt.InferShapeTester):
a = sparse.csr_matrix('a', dtype='float32') a = sparse.csr_matrix('a', dtype='float32')
b = cuda.float32_shared_constructor( b = cuda.float32_shared_constructor(
numpy.random.rand(3, 4).astype('float32')) np.random.rand(3, 4).astype('float32'))
d = sparse.dot(a, b) d = sparse.dot(a, b)
f = theano.function([a], d) f = theano.function([a], d)
...@@ -1429,8 +1429,8 @@ class DotTests(utt.InferShapeTester): ...@@ -1429,8 +1429,8 @@ class DotTests(utt.InferShapeTester):
y = m2.reshape(shape=(2, 4, 9), ndim=3) y = m2.reshape(shape=(2, 4, 9), ndim=3)
f = theano.function(inputs=[I, C], outputs=y) f = theano.function(inputs=[I, C], outputs=y)
i = numpy.asarray([[4, 3, 7, 7], [2, 8, 4, 5]], dtype=intX) i = np.asarray([[4, 3, 7, 7], [2, 8, 4, 5]], dtype=intX)
a = numpy.asarray(numpy.random.randint(0, 100, (size, size)), a = np.asarray(np.random.randint(0, 100, (size, size)),
dtype=intX) dtype=intX)
f(i, a) f(i, a)
...@@ -1441,7 +1441,7 @@ class DotTests(utt.InferShapeTester): ...@@ -1441,7 +1441,7 @@ class DotTests(utt.InferShapeTester):
# allocate a random sparse matrix # allocate a random sparse matrix
spmat = sp.csr_matrix(random_lil((4, 3), 'float64', 3)) spmat = sp.csr_matrix(random_lil((4, 3), 'float64', 3))
mat = numpy.asarray(numpy.random.randn(2, 4), 'float64') mat = np.asarray(np.random.randn(2, 4), 'float64')
def buildgraph_T(mat): def buildgraph_T(mat):
return Dot()(mat, spmat) return Dot()(mat, spmat)
...@@ -1456,12 +1456,12 @@ class UsmmTests(unittest.TestCase): ...@@ -1456,12 +1456,12 @@ class UsmmTests(unittest.TestCase):
y_size = (100, 200) y_size = (100, 200)
z_size = (x_size[0], y_size[1]) z_size = (x_size[0], y_size[1])
self.rng = numpy.random.RandomState(seed=utt.fetch_seed()) self.rng = np.random.RandomState(seed=utt.fetch_seed())
self.x = numpy.asarray(self.rng.binomial(1, 0.5, x_size), self.x = np.asarray(self.rng.binomial(1, 0.5, x_size),
dtype=theano.config.floatX) dtype=theano.config.floatX)
self.y = numpy.asarray(self.rng.uniform(-1, 1, y_size), self.y = np.asarray(self.rng.uniform(-1, 1, y_size),
dtype=theano.config.floatX) dtype=theano.config.floatX)
self.z = numpy.asarray(self.rng.uniform(-1, 1, z_size), self.z = np.asarray(self.rng.uniform(-1, 1, z_size),
dtype=theano.config.floatX) dtype=theano.config.floatX)
# this is slow, but it's the only test for the op. # this is slow, but it's the only test for the op.
...@@ -1487,17 +1487,17 @@ class UsmmTests(unittest.TestCase): ...@@ -1487,17 +1487,17 @@ class UsmmTests(unittest.TestCase):
x = mat(format1, 'x', dtype1) x = mat(format1, 'x', dtype1)
y = mat(format2, 'y', dtype2) y = mat(format2, 'y', dtype2)
a = theano.tensor.scalar('a', dtype=dtype3) a = theano.tensor.scalar('a', dtype=dtype3)
z = theano.shared(numpy.asarray(self.z, dtype=dtype4).copy()) z = theano.shared(np.asarray(self.z, dtype=dtype4).copy())
f_b = lambda z, a, x, y: z - a * (x * y) f_b = lambda z, a, x, y: z - a * (x * y)
x_data = numpy.asarray(self.x, dtype=dtype1) x_data = np.asarray(self.x, dtype=dtype1)
if format1 != 'dense': if format1 != 'dense':
x_data = as_sparse_format(x_data, format1) x_data = as_sparse_format(x_data, format1)
y_data = numpy.asarray(self.y, dtype=dtype2) y_data = np.asarray(self.y, dtype=dtype2)
if format2 != 'dense': if format2 != 'dense':
y_data = as_sparse_format(y_data, format2) y_data = as_sparse_format(y_data, format2)
a_data = numpy.asarray(1.5, dtype=dtype3) a_data = np.asarray(1.5, dtype=dtype3)
z_data = numpy.asarray(self.z, dtype=dtype4) z_data = np.asarray(self.z, dtype=dtype4)
f_b_out = f_b(z_data, a_data, x_data, y_data) f_b_out = f_b(z_data, a_data, x_data, y_data)
...@@ -1603,17 +1603,17 @@ class UsmmTests(unittest.TestCase): ...@@ -1603,17 +1603,17 @@ class UsmmTests(unittest.TestCase):
x = mat(format1, 'x', dtype1) x = mat(format1, 'x', dtype1)
y = mat(format2, 'y', dtype2) y = mat(format2, 'y', dtype2)
a = theano.tensor.scalar('a', dtype=dtype3) a = theano.tensor.scalar('a', dtype=dtype3)
z = theano.shared(numpy.asarray(self.z, dtype=dtype4).copy()) z = theano.shared(np.asarray(self.z, dtype=dtype4).copy())
f_b = lambda z, a, x, y: z - a * (x * y) f_b = lambda z, a, x, y: z - a * (x * y)
x_data = numpy.asarray(self.x, dtype=dtype1) x_data = np.asarray(self.x, dtype=dtype1)
if format1 != 'dense': if format1 != 'dense':
x_data = as_sparse_format(x_data, format1) x_data = as_sparse_format(x_data, format1)
y_data = numpy.asarray(self.y, dtype=dtype2) y_data = np.asarray(self.y, dtype=dtype2)
if format2 != 'dense': if format2 != 'dense':
y_data = as_sparse_format(y_data, format2) y_data = as_sparse_format(y_data, format2)
a_data = numpy.asarray(1.5, dtype=dtype3) a_data = np.asarray(1.5, dtype=dtype3)
z_data = numpy.asarray(self.z, dtype=dtype4) z_data = np.asarray(self.z, dtype=dtype4)
f_b_out = f_b(z_data, a_data, x_data, y_data) f_b_out = f_b(z_data, a_data, x_data, y_data)
...@@ -1641,8 +1641,8 @@ class test_zeros_like(unittest.TestCase): ...@@ -1641,8 +1641,8 @@ class test_zeros_like(unittest.TestCase):
def test(self): def test(self):
x = theano.sparse.csr_matrix() x = theano.sparse.csr_matrix()
f = theano.function([x], theano.sparse.sp_zeros_like(x)) f = theano.function([x], theano.sparse.sp_zeros_like(x))
vx = scipy.sparse.csr_matrix(numpy.asarray( vx = scipy.sparse.csr_matrix(np.asarray(
numpy.random.binomial(1, 0.5, (100, 100)), np.random.binomial(1, 0.5, (100, 100)),
dtype=theano.config.floatX)) dtype=theano.config.floatX))
fx = f(vx) fx = f(vx)
...@@ -1666,7 +1666,7 @@ def test_shape(): ...@@ -1666,7 +1666,7 @@ def test_shape():
a = SparseType('csr', dtype=sparse_dtype)() a = SparseType('csr', dtype=sparse_dtype)()
f = theano.function([a], a.shape) f = theano.function([a], a.shape)
assert numpy.all(f(sp.csr_matrix(random_lil((100, 10), sparse_dtype, 3))) assert np.all(f(sp.csr_matrix(random_lil((100, 10), sparse_dtype, 3)))
== (100, 10)) == (100, 10))
if theano.config.mode != 'FAST_COMPILE': if theano.config.mode != 'FAST_COMPILE':
topo = f.maker.fgraph.toposort() topo = f.maker.fgraph.toposort()
...@@ -1765,12 +1765,12 @@ class ColScaleCSCTester(utt.InferShapeTester): ...@@ -1765,12 +1765,12 @@ class ColScaleCSCTester(utt.InferShapeTester):
for format in sparse.sparse_formats: for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format, shape=(8, 10)) variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector()) variable.append(tensor.vector())
data.append(numpy.random.random(10).astype(config.floatX)) data.append(np.random.random(10).astype(config.floatX))
f = theano.function(variable, self.op(*variable)) f = theano.function(variable, self.op(*variable))
tested = f(*data) tested = f(*data)
x, s = data[0].toarray(), data[1][numpy.newaxis, :] x, s = data[0].toarray(), data[1][np.newaxis, :]
expected = x * s expected = x * s
assert tested.format == format assert tested.format == format
...@@ -1781,7 +1781,7 @@ class ColScaleCSCTester(utt.InferShapeTester): ...@@ -1781,7 +1781,7 @@ class ColScaleCSCTester(utt.InferShapeTester):
('csr', sparse.RowScaleCSC)]: ('csr', sparse.RowScaleCSC)]:
variable, data = sparse_random_inputs(format, shape=(8, 10)) variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector()) variable.append(tensor.vector())
data.append(numpy.random.random(10).astype(config.floatX)) data.append(np.random.random(10).astype(config.floatX))
self._compile_and_check(variable, self._compile_and_check(variable,
[self.op(*variable)], [self.op(*variable)],
...@@ -1792,7 +1792,7 @@ class ColScaleCSCTester(utt.InferShapeTester): ...@@ -1792,7 +1792,7 @@ class ColScaleCSCTester(utt.InferShapeTester):
for format in sparse.sparse_formats: for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format, shape=(8, 10)) variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector()) variable.append(tensor.vector())
data.append(numpy.random.random(10).astype(config.floatX)) data.append(np.random.random(10).astype(config.floatX))
verify_grad_sparse(self.op, data, structured=True) verify_grad_sparse(self.op, data, structured=True)
...@@ -1806,12 +1806,12 @@ class RowScaleCSCTester(utt.InferShapeTester): ...@@ -1806,12 +1806,12 @@ class RowScaleCSCTester(utt.InferShapeTester):
for format in sparse.sparse_formats: for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format, shape=(8, 10)) variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector()) variable.append(tensor.vector())
data.append(numpy.random.random(8).astype(config.floatX)) data.append(np.random.random(8).astype(config.floatX))
f = theano.function(variable, self.op(*variable)) f = theano.function(variable, self.op(*variable))
tested = f(*data) tested = f(*data)
x, s = data[0].toarray(), data[1][:, numpy.newaxis] x, s = data[0].toarray(), data[1][:, np.newaxis]
expected = x * s expected = x * s
assert tested.format == format assert tested.format == format
...@@ -1822,7 +1822,7 @@ class RowScaleCSCTester(utt.InferShapeTester): ...@@ -1822,7 +1822,7 @@ class RowScaleCSCTester(utt.InferShapeTester):
('csr', sparse.ColScaleCSC)]: ('csr', sparse.ColScaleCSC)]:
variable, data = sparse_random_inputs(format, shape=(8, 10)) variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector()) variable.append(tensor.vector())
data.append(numpy.random.random(8).astype(config.floatX)) data.append(np.random.random(8).astype(config.floatX))
self._compile_and_check(variable, self._compile_and_check(variable,
[self.op(*variable)], [self.op(*variable)],
...@@ -1833,7 +1833,7 @@ class RowScaleCSCTester(utt.InferShapeTester): ...@@ -1833,7 +1833,7 @@ class RowScaleCSCTester(utt.InferShapeTester):
for format in sparse.sparse_formats: for format in sparse.sparse_formats:
variable, data = sparse_random_inputs(format, shape=(8, 10)) variable, data = sparse_random_inputs(format, shape=(8, 10))
variable.append(tensor.vector()) variable.append(tensor.vector())
data.append(numpy.random.random(8).astype(config.floatX)) data.append(np.random.random(8).astype(config.floatX))
verify_grad_sparse(self.op, data, structured=True) verify_grad_sparse(self.op, data, structured=True)
...@@ -1935,12 +1935,12 @@ class SquareDiagonalTester(utt.InferShapeTester): ...@@ -1935,12 +1935,12 @@ class SquareDiagonalTester(utt.InferShapeTester):
for format in sparse.sparse_formats: for format in sparse.sparse_formats:
for size in range(5, 9): for size in range(5, 9):
variable = [tensor.vector()] variable = [tensor.vector()]
data = [numpy.random.random(size).astype(config.floatX)] data = [np.random.random(size).astype(config.floatX)]
f = theano.function(variable, self.op(*variable)) f = theano.function(variable, self.op(*variable))
tested = f(*data).toarray() tested = f(*data).toarray()
expected = numpy.diag(*data) expected = np.diag(*data)
utt.assert_allclose(expected, tested) utt.assert_allclose(expected, tested)
assert tested.dtype == expected.dtype assert tested.dtype == expected.dtype
assert tested.shape == expected.shape assert tested.shape == expected.shape
...@@ -1949,7 +1949,7 @@ class SquareDiagonalTester(utt.InferShapeTester): ...@@ -1949,7 +1949,7 @@ class SquareDiagonalTester(utt.InferShapeTester):
for format in sparse.sparse_formats: for format in sparse.sparse_formats:
for size in range(5, 9): for size in range(5, 9):
variable = [tensor.vector()] variable = [tensor.vector()]
data = [numpy.random.random(size).astype(config.floatX)] data = [np.random.random(size).astype(config.floatX)]
self._compile_and_check(variable, self._compile_and_check(variable,
[self.op(*variable)], [self.op(*variable)],
...@@ -1960,7 +1960,7 @@ class SquareDiagonalTester(utt.InferShapeTester): ...@@ -1960,7 +1960,7 @@ class SquareDiagonalTester(utt.InferShapeTester):
for format in sparse.sparse_formats: for format in sparse.sparse_formats:
for size in range(5, 9): for size in range(5, 9):
variable = [tensor.vector()] variable = [tensor.vector()]
data = [numpy.random.random(size).astype(config.floatX)] data = [np.random.random(size).astype(config.floatX)]
verify_grad_sparse( verify_grad_sparse(
self.op, self.op,
...@@ -2091,7 +2091,7 @@ class Remove0Tester(utt.InferShapeTester): ...@@ -2091,7 +2091,7 @@ class Remove0Tester(utt.InferShapeTester):
assert target.has_sorted_indices assert target.has_sorted_indices
def test_infer_shape(self): def test_infer_shape(self):
mat = (numpy.arange(12) + 1).reshape((4, 3)) mat = (np.arange(12) + 1).reshape((4, 3))
mat[0, 1] = mat[1, 0] = mat[2, 2] = 0 mat[0, 1] = mat[1, 0] = mat[2, 2] = 0
x_csc = theano.sparse.csc_matrix(dtype=theano.config.floatX) x_csc = theano.sparse.csc_matrix(dtype=theano.config.floatX)
...@@ -2109,7 +2109,7 @@ class Remove0Tester(utt.InferShapeTester): ...@@ -2109,7 +2109,7 @@ class Remove0Tester(utt.InferShapeTester):
self.op_class) self.op_class)
def test_grad(self): def test_grad(self):
mat = (numpy.arange(9) + 1).reshape((3, 3)) mat = (np.arange(9) + 1).reshape((3, 3))
mat[0, 1] = mat[1, 0] = mat[2, 2] = 0 mat[0, 1] = mat[1, 0] = mat[2, 2] = 0
mat_csc = sp.csc_matrix(mat, dtype=theano.config.floatX) mat_csc = sp.csc_matrix(mat, dtype=theano.config.floatX)
...@@ -2121,7 +2121,7 @@ class Remove0Tester(utt.InferShapeTester): ...@@ -2121,7 +2121,7 @@ class Remove0Tester(utt.InferShapeTester):
class Test_getitem(unittest.TestCase): class Test_getitem(unittest.TestCase):
def setUp(self): def setUp(self):
self.rng = numpy.random.RandomState(utt.fetch_seed()) self.rng = np.random.RandomState(utt.fetch_seed())
def test_GetItemList(self): def test_GetItemList(self):
...@@ -2152,7 +2152,7 @@ class Test_getitem(unittest.TestCase): ...@@ -2152,7 +2152,7 @@ class Test_getitem(unittest.TestCase):
def test_get_item_list_grad(self): def test_get_item_list_grad(self):
op = theano.sparse.basic.GetItemList() op = theano.sparse.basic.GetItemList()
def op_with_fixed_index(x): def op_with_fixed_index(x):
return op(x, index=numpy.asarray([0, 1])) return op(x, index=np.asarray([0, 1]))
x, x_val = sparse_random_inputs("csr", (4, 5)) x, x_val = sparse_random_inputs("csr", (4, 5))
...@@ -2174,8 +2174,8 @@ class Test_getitem(unittest.TestCase): ...@@ -2174,8 +2174,8 @@ class Test_getitem(unittest.TestCase):
t_geta = fa(A[0]) t_geta = fa(A[0])
t_getb = fb(B[0]) t_getb = fb(B[0])
s_geta = numpy.asarray(scipy.sparse.csr_matrix(A[0])[[0, 0, 1, 3], [0, 1, 2, 4]]) s_geta = np.asarray(scipy.sparse.csr_matrix(A[0])[[0, 0, 1, 3], [0, 1, 2, 4]])
s_getb = numpy.asarray(scipy.sparse.csc_matrix(B[0])[[0, 0, 1, 3], [0, 1, 2, 4]]) s_getb = np.asarray(scipy.sparse.csc_matrix(B[0])[[0, 0, 1, 3], [0, 1, 2, 4]])
utt.assert_allclose(t_geta, s_geta) utt.assert_allclose(t_geta, s_geta)
utt.assert_allclose(t_getb, s_getb) utt.assert_allclose(t_getb, s_getb)
...@@ -2194,7 +2194,7 @@ class Test_getitem(unittest.TestCase): ...@@ -2194,7 +2194,7 @@ class Test_getitem(unittest.TestCase):
def test_get_item_2lists_grad(self): def test_get_item_2lists_grad(self):
op = theano.sparse.basic.GetItem2Lists() op = theano.sparse.basic.GetItem2Lists()
def op_with_fixed_index(x): def op_with_fixed_index(x):
return op(x, ind1=numpy.asarray([0, 1]), ind2=numpy.asarray([2, 3])) return op(x, ind1=np.asarray([0, 1]), ind2=np.asarray([2, 3]))
x, x_val = sparse_random_inputs("csr", (4, 5)) x, x_val = sparse_random_inputs("csr", (4, 5))
...@@ -2241,7 +2241,7 @@ class Test_getitem(unittest.TestCase): ...@@ -2241,7 +2241,7 @@ class Test_getitem(unittest.TestCase):
r1 = f1(vx, m, n, p, q) r1 = f1(vx, m, n, p, q)
t1 = vx[m:n, p:q] t1 = vx[m:n, p:q]
assert r1.shape == t1.shape assert r1.shape == t1.shape
assert numpy.all(t1.toarray() == r1.toarray()) assert np.all(t1.toarray() == r1.toarray())
""" """
Important: based on a discussion with both Fred and James Important: based on a discussion with both Fred and James
...@@ -2254,25 +2254,25 @@ class Test_getitem(unittest.TestCase): ...@@ -2254,25 +2254,25 @@ class Test_getitem(unittest.TestCase):
r2 = f2(vx, m, n, p) r2 = f2(vx, m, n, p)
t2 = vx[m:n, p] t2 = vx[m:n, p]
assert r2.shape == t2.shape assert r2.shape == t2.shape
assert numpy.all(t2.toarray() == r2.toarray()) assert np.all(t2.toarray() == r2.toarray())
f3 = theano.function([x, a, b, c], x[a, b:c]) f3 = theano.function([x, a, b, c], x[a, b:c])
r3 = f3(vx, m, n, p) r3 = f3(vx, m, n, p)
t3 = vx[m, n:p] t3 = vx[m, n:p]
assert r3.shape == t3.shape assert r3.shape == t3.shape
assert numpy.all(t3.toarray() == r3.toarray()) assert np.all(t3.toarray() == r3.toarray())
f5 = theano.function([x], x[1:2,3]) f5 = theano.function([x], x[1:2,3])
r5 = f5(vx) r5 = f5(vx)
t5 = vx[1:2, 3] t5 = vx[1:2, 3]
assert r5.shape == t5.shape assert r5.shape == t5.shape
assert numpy.all(r5.toarray() == t5.toarray()) assert np.all(r5.toarray() == t5.toarray())
f7 = theano.function([x], x[50]) f7 = theano.function([x], x[50])
r7 = f7(vx) r7 = f7(vx)
t7 = vx[50] t7 = vx[50]
assert r7.shape == t7.shape assert r7.shape == t7.shape
assert numpy.all(r7.toarray() == t7.toarray()) assert np.all(r7.toarray() == t7.toarray())
""" """
if is_supported_version: if is_supported_version:
f4 = theano.function([x, a, b, e], x[a:b:e]) f4 = theano.function([x, a, b, e], x[a:b:e])
...@@ -2283,7 +2283,7 @@ class Test_getitem(unittest.TestCase): ...@@ -2283,7 +2283,7 @@ class Test_getitem(unittest.TestCase):
r4 = f4(vx, m, n) r4 = f4(vx, m, n)
t4 = vx[m:n] t4 = vx[m:n]
assert r4.shape == t4.shape assert r4.shape == t4.shape
assert numpy.all(t4.toarray() == r4.toarray()) assert np.all(t4.toarray() == r4.toarray())
#----------------------------------------------------------- #-----------------------------------------------------------
# test cases using int indexing instead of theano variable # test cases using int indexing instead of theano variable
...@@ -2291,7 +2291,7 @@ class Test_getitem(unittest.TestCase): ...@@ -2291,7 +2291,7 @@ class Test_getitem(unittest.TestCase):
r6 = f6(vx) r6 = f6(vx)
t6 = vx[1:10:j, 10:20:k] t6 = vx[1:10:j, 10:20:k]
assert r6.shape == t6.shape assert r6.shape == t6.shape
assert numpy.all(r6.toarray() == t6.toarray()) assert np.all(r6.toarray() == t6.toarray())
#---------------------------------------------------------- #----------------------------------------------------------
# test cases with indexing both with theano variable and int # test cases with indexing both with theano variable and int
...@@ -2304,13 +2304,13 @@ class Test_getitem(unittest.TestCase): ...@@ -2304,13 +2304,13 @@ class Test_getitem(unittest.TestCase):
r8 = f8(vx, m, n) r8 = f8(vx, m, n)
t8 = vx[m:n, 10:20] t8 = vx[m:n, 10:20]
assert r8.shape == t8.shape assert r8.shape == t8.shape
assert numpy.all(r8.toarray() == t8.toarray()) assert np.all(r8.toarray() == t8.toarray())
f9 = theano.function([x, a, b], x[1:a:j, 1:b:k]) f9 = theano.function([x, a, b], x[1:a:j, 1:b:k])
r9 = f9(vx, p, q) r9 = f9(vx, p, q)
t9 = vx[1:p:j, 1:q:k] t9 = vx[1:p:j, 1:q:k]
assert r9.shape == t9.shape assert r9.shape == t9.shape
assert numpy.all(r9.toarray() == t9.toarray()) assert np.all(r9.toarray() == t9.toarray())
#----------------------------------------------------------- #-----------------------------------------------------------
# Test mixing None and variables # Test mixing None and variables
...@@ -2318,13 +2318,13 @@ class Test_getitem(unittest.TestCase): ...@@ -2318,13 +2318,13 @@ class Test_getitem(unittest.TestCase):
r10 = f10(vx, p, q) r10 = f10(vx, p, q)
t10 = vx[:p, :q] t10 = vx[:p, :q]
assert r10.shape == t10.shape assert r10.shape == t10.shape
assert numpy.all(r10.toarray() == t10.toarray()) assert np.all(r10.toarray() == t10.toarray())
f11 = theano.function([x, a], x[:, a:]) f11 = theano.function([x, a], x[:, a:])
r11 = f11(vx, p) r11 = f11(vx, p)
t11 = vx[:, p:] t11 = vx[:, p:]
assert r11.shape == t11.shape assert r11.shape == t11.shape
assert numpy.all(r11.toarray() == t11.toarray()) assert np.all(r11.toarray() == t11.toarray())
# Test that is work with shared variable # Test that is work with shared variable
sx = theano.shared(vx) sx = theano.shared(vx)
...@@ -2332,7 +2332,7 @@ class Test_getitem(unittest.TestCase): ...@@ -2332,7 +2332,7 @@ class Test_getitem(unittest.TestCase):
r12 = f12(p) r12 = f12(p)
t12 = vx[:, p:] t12 = vx[:, p:]
assert r12.shape == t12.shape assert r12.shape == t12.shape
assert numpy.all(r12.toarray() == t12.toarray()) assert np.all(r12.toarray() == t12.toarray())
#------------------------------------------------------------ #------------------------------------------------------------
# Invalid things # Invalid things
...@@ -2381,25 +2381,25 @@ class Test_getitem(unittest.TestCase): ...@@ -2381,25 +2381,25 @@ class Test_getitem(unittest.TestCase):
r1 = f1(vx, 10, 10) r1 = f1(vx, 10, 10)
t1 = vx[10, 10] t1 = vx[10, 10]
assert r1.shape == t1.shape assert r1.shape == t1.shape
assert numpy.all(t1 == r1) assert np.all(t1 == r1)
f2 = theano.function([x, a], x[50, a]) f2 = theano.function([x, a], x[50, a])
r2 = f2(vx, m) r2 = f2(vx, m)
t2 = vx[50, m] t2 = vx[50, m]
assert r2.shape == t2.shape assert r2.shape == t2.shape
assert numpy.all(t2 == r2) assert np.all(t2 == r2)
f3 = theano.function([x, a], x[a, 50]) f3 = theano.function([x, a], x[a, 50])
r3 = f3(vx, m) r3 = f3(vx, m)
t3 = vx[m, 50] t3 = vx[m, 50]
assert r3.shape == t3.shape assert r3.shape == t3.shape
assert numpy.all(t3 == r3) assert np.all(t3 == r3)
f4 = theano.function([x], x[50, 42]) f4 = theano.function([x], x[50, 42])
r4 = f4(vx) r4 = f4(vx)
t4 = vx[m, n] t4 = vx[m, n]
assert r3.shape == t3.shape assert r3.shape == t3.shape
assert numpy.all(t4 == r4) assert np.all(t4 == r4)
# Test that is work with shared variable # Test that is work with shared variable
sx = theano.shared(vx) sx = theano.shared(vx)
...@@ -2407,7 +2407,7 @@ class Test_getitem(unittest.TestCase): ...@@ -2407,7 +2407,7 @@ class Test_getitem(unittest.TestCase):
r1 = f1(10, 10) r1 = f1(10, 10)
t1 = vx[10, 10] t1 = vx[10, 10]
assert r1.shape == t1.shape assert r1.shape == t1.shape
assert numpy.all(t1 == r1) assert np.all(t1 == r1)
class CastTester(utt.InferShapeTester): class CastTester(utt.InferShapeTester):
...@@ -2573,8 +2573,8 @@ class AddSSDataTester(utt.InferShapeTester): ...@@ -2573,8 +2573,8 @@ class AddSSDataTester(utt.InferShapeTester):
for format in sparse.sparse_formats: for format in sparse.sparse_formats:
variable = getattr(theano.sparse, format + '_matrix') variable = getattr(theano.sparse, format + '_matrix')
rand = numpy.array( rand = np.array(
numpy.random.randint(1, 4, size=(3, 4)) - 1, np.random.randint(1, 4, size=(3, 4)) - 1,
dtype=theano.config.floatX) dtype=theano.config.floatX)
constant = as_sparse_format(rand, format) constant = as_sparse_format(rand, format)
...@@ -2834,7 +2834,7 @@ def structure_function(f, index=0): ...@@ -2834,7 +2834,7 @@ def structure_function(f, index=0):
StructuredSigmoidTester = elemwise_checker( StructuredSigmoidTester = elemwise_checker(
sparse.structured_sigmoid, sparse.structured_sigmoid,
structure_function(lambda x: 1.0 / (1.0 + numpy.exp(-x))), structure_function(lambda x: 1.0 / (1.0 + np.exp(-x))),
test_dtypes=[m for m in sparse.all_dtypes test_dtypes=[m for m in sparse.all_dtypes
if (not m in sparse.complex_dtypes and if (not m in sparse.complex_dtypes and
not m.startswith('uint'))], not m.startswith('uint'))],
...@@ -2843,83 +2843,83 @@ StructuredSigmoidTester = elemwise_checker( ...@@ -2843,83 +2843,83 @@ StructuredSigmoidTester = elemwise_checker(
StructuredExpTester = elemwise_checker( StructuredExpTester = elemwise_checker(
sparse.structured_exp, sparse.structured_exp,
structure_function(numpy.exp), structure_function(np.exp),
name='StructuredExpTester') name='StructuredExpTester')
StructuredLogTester = elemwise_checker( StructuredLogTester = elemwise_checker(
sparse.structured_log, sparse.structured_log,
structure_function(numpy.log), structure_function(np.log),
gap=(0.5, 10), gap=(0.5, 10),
name='StructuredLogTester') name='StructuredLogTester')
StructuredPowTester = elemwise_checker( StructuredPowTester = elemwise_checker(
lambda x: sparse.structured_pow(x, 2), lambda x: sparse.structured_pow(x, 2),
structure_function(lambda x: numpy.power(x, 2)), structure_function(lambda x: np.power(x, 2)),
name='StructuredPowTester') name='StructuredPowTester')
StructuredMinimumTester = elemwise_checker( StructuredMinimumTester = elemwise_checker(
lambda x: structured_minimum(x, 2), lambda x: structured_minimum(x, 2),
structure_function(lambda x: numpy.minimum(x, 2)), structure_function(lambda x: np.minimum(x, 2)),
name='StructuredMinimumTester') name='StructuredMinimumTester')
StructuredMaximumTester = elemwise_checker( StructuredMaximumTester = elemwise_checker(
lambda x: structured_maximum(x, 2), lambda x: structured_maximum(x, 2),
structure_function(lambda x: numpy.maximum(x, 2)), structure_function(lambda x: np.maximum(x, 2)),
name='StructuredMaximumTester') name='StructuredMaximumTester')
StructuredAddTester = elemwise_checker( StructuredAddTester = elemwise_checker(
lambda x: structured_add(x, 2), lambda x: structured_add(x, 2),
structure_function(lambda x: numpy.add(x, 2)), structure_function(lambda x: np.add(x, 2)),
name='StructuredAddTester') name='StructuredAddTester')
SinTester = elemwise_checker( SinTester = elemwise_checker(
sparse.sin, sparse.sin,
numpy.sin) np.sin)
TanTester = elemwise_checker( TanTester = elemwise_checker(
sparse.tan, sparse.tan,
numpy.tan, np.tan,
gap=(-1, 1)) gap=(-1, 1))
ArcsinTester = elemwise_checker( ArcsinTester = elemwise_checker(
sparse.arcsin, sparse.arcsin,
numpy.arcsin, np.arcsin,
gap=(-1, 1), gap=(-1, 1),
gap_grad=(-0.99, 0.99)) gap_grad=(-0.99, 0.99))
ArctanTester = elemwise_checker( ArctanTester = elemwise_checker(
sparse.arctan, sparse.arctan,
numpy.arctan) np.arctan)
SinhTester = elemwise_checker( SinhTester = elemwise_checker(
sparse.sinh, sparse.sinh,
numpy.sinh) np.sinh)
ArcsinhTester = elemwise_checker( ArcsinhTester = elemwise_checker(
sparse.arcsinh, sparse.arcsinh,
numpy.arcsinh, np.arcsinh,
gap=(-1, 1)) gap=(-1, 1))
TanhTester = elemwise_checker( TanhTester = elemwise_checker(
sparse.tanh, sparse.tanh,
numpy.tanh, np.tanh,
gap=(-1, 1)) gap=(-1, 1))
ArctanhTester = elemwise_checker( ArctanhTester = elemwise_checker(
sparse.arctanh, sparse.arctanh,
numpy.arctanh, np.arctanh,
gap=(-0.9, 1), gap=(-0.9, 1),
gap_grad=(-0.9, 0.95)) gap_grad=(-0.9, 0.95))
RintTester = elemwise_checker( RintTester = elemwise_checker(
sparse.rint, sparse.rint,
numpy.rint, np.rint,
grad_test=False, grad_test=False,
test_dtypes=sparse.float_dtypes) test_dtypes=sparse.float_dtypes)
SgnTester = elemwise_checker( SgnTester = elemwise_checker(
sparse.sgn, sparse.sgn,
numpy.sign, np.sign,
grad_test=False, grad_test=False,
test_dtypes=[m for m in sparse.all_dtypes test_dtypes=[m for m in sparse.all_dtypes
if (not m in sparse.complex_dtypes and if (not m in sparse.complex_dtypes and
...@@ -2927,43 +2927,43 @@ SgnTester = elemwise_checker( ...@@ -2927,43 +2927,43 @@ SgnTester = elemwise_checker(
CeilTester = elemwise_checker( CeilTester = elemwise_checker(
sparse.ceil, sparse.ceil,
numpy.ceil, np.ceil,
grad_test=False, grad_test=False,
test_dtypes=[m for m in sparse.all_dtypes test_dtypes=[m for m in sparse.all_dtypes
if not m in sparse.complex_dtypes]) if not m in sparse.complex_dtypes])
FloorTester = elemwise_checker( FloorTester = elemwise_checker(
sparse.floor, sparse.floor,
numpy.floor, np.floor,
grad_test=False, grad_test=False,
test_dtypes=[m for m in sparse.all_dtypes test_dtypes=[m for m in sparse.all_dtypes
if not m in sparse.complex_dtypes]) if not m in sparse.complex_dtypes])
Log1pTester = elemwise_checker( Log1pTester = elemwise_checker(
sparse.log1p, sparse.log1p,
numpy.log1p, np.log1p,
gap=(0.5, 10)) gap=(0.5, 10))
Expm1Tester = elemwise_checker( Expm1Tester = elemwise_checker(
sparse.expm1, sparse.expm1,
numpy.expm1) np.expm1)
Deg2radTester = elemwise_checker( Deg2radTester = elemwise_checker(
sparse.deg2rad, sparse.deg2rad,
numpy.deg2rad, np.deg2rad,
test_dtypes=[m for m in sparse.all_dtypes test_dtypes=[m for m in sparse.all_dtypes
if not m in sparse.complex_dtypes]) if not m in sparse.complex_dtypes])
Rad2degTester = elemwise_checker( Rad2degTester = elemwise_checker(
sparse.rad2deg, sparse.rad2deg,
numpy.rad2deg, np.rad2deg,
test_dtypes=[m for m in sparse.all_dtypes test_dtypes=[m for m in sparse.all_dtypes
if not m in sparse.complex_dtypes]) if not m in sparse.complex_dtypes])
TruncTester = elemwise_checker( TruncTester = elemwise_checker(
sparse.trunc, sparse.trunc,
numpy.trunc, np.trunc,
test_dtypes=[m for m in sparse.all_dtypes test_dtypes=[m for m in sparse.all_dtypes
if not m in sparse.complex_dtypes]) if not m in sparse.complex_dtypes])
...@@ -2974,12 +2974,12 @@ SqrTester = elemwise_checker( ...@@ -2974,12 +2974,12 @@ SqrTester = elemwise_checker(
SqrtTester = elemwise_checker( SqrtTester = elemwise_checker(
sparse.sqrt, sparse.sqrt,
numpy.sqrt, np.sqrt,
gap=(0, 10)) gap=(0, 10))
ConjTester = elemwise_checker( ConjTester = elemwise_checker(
sparse.conj, sparse.conj,
numpy.conj, np.conj,
grad_test=False) grad_test=False)
...@@ -2994,7 +2994,7 @@ class MulSVTester(unittest.TestCase): ...@@ -2994,7 +2994,7 @@ class MulSVTester(unittest.TestCase):
for format in ['csr', 'csc']: for format in ['csr', 'csc']:
for dtype in ['float32', 'float64']: for dtype in ['float32', 'float64']:
spmat = sp_types[format](random_lil((4, 3), dtype, 3)) spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype) mat = np.asarray(np.random.rand(3), dtype=dtype)
verify_grad_sparse(mul_s_v, verify_grad_sparse(mul_s_v,
[spmat, mat], [spmat, mat],
...@@ -3011,7 +3011,7 @@ class MulSVTester(unittest.TestCase): ...@@ -3011,7 +3011,7 @@ class MulSVTester(unittest.TestCase):
f = theano.function([x, y], mul_s_v(x, y)) f = theano.function([x, y], mul_s_v(x, y))
spmat = sp_types[format](random_lil((4, 3), dtype, 3)) spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype) mat = np.asarray(np.random.rand(3), dtype=dtype)
out = f(spmat, mat) out = f(spmat, mat)
...@@ -3029,7 +3029,7 @@ class StructuredAddSVTester(unittest.TestCase): ...@@ -3029,7 +3029,7 @@ class StructuredAddSVTester(unittest.TestCase):
for format in ['csr', 'csc']: for format in ['csr', 'csc']:
for dtype in ['float32', 'float64']: for dtype in ['float32', 'float64']:
spmat = sp_types[format](random_lil((4, 3), dtype, 3)) spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype) mat = np.asarray(np.random.rand(3), dtype=dtype)
verify_grad_sparse(structured_add_s_v, verify_grad_sparse(structured_add_s_v,
[spmat, mat], [spmat, mat],
...@@ -3047,8 +3047,8 @@ class StructuredAddSVTester(unittest.TestCase): ...@@ -3047,8 +3047,8 @@ class StructuredAddSVTester(unittest.TestCase):
spmat = sp_types[format](random_lil((4, 3), dtype, 3)) spmat = sp_types[format](random_lil((4, 3), dtype, 3))
spones = spmat.copy() spones = spmat.copy()
spones.data = numpy.ones_like(spones.data) spones.data = np.ones_like(spones.data)
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype) mat = np.asarray(np.random.rand(3), dtype=dtype)
out = f(spmat, mat) out = f(spmat, mat)
...@@ -3076,7 +3076,7 @@ class TrueDotTester(utt.InferShapeTester): ...@@ -3076,7 +3076,7 @@ class TrueDotTester(utt.InferShapeTester):
tested = f(*data) tested = f(*data)
x, y = [m.toarray() for m in data] x, y = [m.toarray() for m in data]
expected = numpy.dot(x, y) expected = np.dot(x, y)
assert tested.format == format assert tested.format == format
assert tested.dtype == expected.dtype assert tested.dtype == expected.dtype
...@@ -3098,7 +3098,7 @@ class TrueDotTester(utt.InferShapeTester): ...@@ -3098,7 +3098,7 @@ class TrueDotTester(utt.InferShapeTester):
f = theano.function(variable, self.op(*variable)) f = theano.function(variable, self.op(*variable))
tested = f(*data) tested = f(*data)
expected = numpy.dot(data[0].toarray(), data[1]) expected = np.dot(data[0].toarray(), data[1])
assert tested.format == format assert tested.format == format
assert tested.dtype == expected.dtype assert tested.dtype == expected.dtype
...@@ -3146,11 +3146,11 @@ class SamplingDotTester(utt.InferShapeTester): ...@@ -3146,11 +3146,11 @@ class SamplingDotTester(utt.InferShapeTester):
x = [tensor.matrix() for t in range(2)] x = [tensor.matrix() for t in range(2)]
x.append(sparse.csr_matrix()) x.append(sparse.csr_matrix())
# unsquare shape # unsquare shape
a = [numpy.array(numpy.random.randint(1, 6, size=(4, 3)) - 1, a = [np.array(np.random.randint(1, 6, size=(4, 3)) - 1,
dtype=theano.config.floatX), dtype=theano.config.floatX),
numpy.array(numpy.random.randint(1, 6, size=(5, 3)) - 1, np.array(np.random.randint(1, 6, size=(5, 3)) - 1,
dtype=theano.config.floatX), dtype=theano.config.floatX),
numpy.array(numpy.random.randint(1, 3, size=(4, 5)) - 1, np.array(np.random.randint(1, 3, size=(4, 5)) - 1,
dtype=theano.config.floatX) dtype=theano.config.floatX)
] ]
a[2] = sp.csr_matrix(a[2]) a[2] = sp.csr_matrix(a[2])
...@@ -3166,7 +3166,7 @@ class SamplingDotTester(utt.InferShapeTester): ...@@ -3166,7 +3166,7 @@ class SamplingDotTester(utt.InferShapeTester):
tested = f(*self.a) tested = f(*self.a)
x, y, p = self.a x, y, p = self.a
expected = p.multiply(numpy.dot(x, y.T)) expected = p.multiply(np.dot(x, y.T))
utt.assert_allclose(as_ndarray(expected), tested.toarray()) utt.assert_allclose(as_ndarray(expected), tested.toarray())
assert tested.format == 'csr' assert tested.format == 'csr'
...@@ -3198,7 +3198,7 @@ test_shared_options = theano.tensor.tests.test_sharedvar.makeSharedTester( ...@@ -3198,7 +3198,7 @@ test_shared_options = theano.tensor.tests.test_sharedvar.makeSharedTester(
internal_type_=scipy.sparse.csc_matrix, internal_type_=scipy.sparse.csc_matrix,
test_internal_type_=scipy.sparse.issparse, test_internal_type_=scipy.sparse.issparse,
theano_fct_=lambda a: dense_from_sparse(a * 2.), theano_fct_=lambda a: dense_from_sparse(a * 2.),
ref_fct_=lambda a: numpy.asarray((a * 2).todense()), ref_fct_=lambda a: np.asarray((a * 2).todense()),
cast_value_=scipy.sparse.csr_matrix, cast_value_=scipy.sparse.csr_matrix,
name='test_shared_options', name='test_shared_options',
) )
......
theano/sparse/tests/test_opt.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest from nose.plugins.skip import SkipTest
import numpy import numpy as np
try: try:
import scipy.sparse as sp import scipy.sparse as sp
import scipy.sparse import scipy.sparse
...@@ -157,14 +157,14 @@ def test_local_dense_from_sparse_sparse_from_dense(): ...@@ -157,14 +157,14 @@ def test_local_dense_from_sparse_sparse_from_dense():
def test_sd_csc(): def test_sd_csc():
A = sp.rand(4, 5, density=0.60, format='csc', dtype=numpy.float32) A = sp.rand(4, 5, density=0.60, format='csc', dtype=np.float32)
b = numpy.random.rand(5,2).astype(numpy.float32) b = np.random.rand(5,2).astype(np.float32)
target = A*b target = A*b
a_val = theano.tensor.as_tensor_variable(A.data) a_val = theano.tensor.as_tensor_variable(A.data)
a_ind = theano.tensor.as_tensor_variable(A.indices) a_ind = theano.tensor.as_tensor_variable(A.indices)
a_ptr = theano.tensor.as_tensor_variable(A.indptr) a_ptr = theano.tensor.as_tensor_variable(A.indptr)
nrows = theano.tensor.as_tensor_variable(numpy.int32(A.shape[0])) nrows = theano.tensor.as_tensor_variable(np.int32(A.shape[0]))
b = theano.tensor.as_tensor_variable(b) b = theano.tensor.as_tensor_variable(b)
res = theano.sparse.opt.sd_csc(a_val, a_ind, a_ptr, nrows, b).eval() res = theano.sparse.opt.sd_csc(a_val, a_ind, a_ptr, nrows, b).eval()
......
theano/sparse/tests/test_sp2.py
浏览文件 @ 2b7ee2ec
...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division ...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import unittest import unittest
from nose.plugins.skip import SkipTest from nose.plugins.skip import SkipTest
import numpy import numpy as np
try: try:
import scipy.sparse as sp import scipy.sparse as sp
except ImportError: except ImportError:
...@@ -30,7 +30,7 @@ class PoissonTester(utt.InferShapeTester): ...@@ -30,7 +30,7 @@ class PoissonTester(utt.InferShapeTester):
for format in sparse.sparse_formats: for format in sparse.sparse_formats:
variable = getattr(theano.sparse, format + '_matrix') variable = getattr(theano.sparse, format + '_matrix')
rand = numpy.array(numpy.random.randint(1, 4, size=(3, 4)) - 1, rand = np.array(np.random.randint(1, 4, size=(3, 4)) - 1,
dtype=theano.config.floatX) dtype=theano.config.floatX)
x[format] = variable() x[format] = variable()
...@@ -50,7 +50,7 @@ class PoissonTester(utt.InferShapeTester): ...@@ -50,7 +50,7 @@ class PoissonTester(utt.InferShapeTester):
assert tested.format == format assert tested.format == format
assert tested.dtype == self.a[format].dtype assert tested.dtype == self.a[format].dtype
assert numpy.allclose(numpy.floor(tested.data), tested.data) assert np.allclose(np.floor(tested.data), tested.data)
assert tested.shape == self.a[format].shape assert tested.shape == self.a[format].shape
def test_infer_shape(self): def test_infer_shape(self):
...@@ -67,7 +67,7 @@ class BinomialTester(utt.InferShapeTester): ...@@ -67,7 +67,7 @@ class BinomialTester(utt.InferShapeTester):
shape = tensor.lvector() shape = tensor.lvector()
_n = 5 _n = 5
_p = .25 _p = .25
_shape = numpy.asarray([3, 5], dtype='int64') _shape = np.asarray([3, 5], dtype='int64')
inputs = [n, p, shape] inputs = [n, p, shape]
_inputs = [_n, _p, _shape] _inputs = [_n, _p, _shape]
...@@ -88,7 +88,7 @@ class BinomialTester(utt.InferShapeTester): ...@@ -88,7 +88,7 @@ class BinomialTester(utt.InferShapeTester):
assert tested.shape == tuple(self._shape) assert tested.shape == tuple(self._shape)
assert tested.format == sp_format assert tested.format == sp_format
assert tested.dtype == o_type assert tested.dtype == o_type
assert numpy.allclose(numpy.floor(tested.todense()), assert np.allclose(np.floor(tested.todense()),
tested.todense()) tested.todense())
def test_infer_shape(self): def test_infer_shape(self):
...@@ -103,7 +103,7 @@ class BinomialTester(utt.InferShapeTester): ...@@ -103,7 +103,7 @@ class BinomialTester(utt.InferShapeTester):
class MultinomialTester(utt.InferShapeTester): class MultinomialTester(utt.InferShapeTester):
p = sparse.csr_matrix() p = sparse.csr_matrix()
_p = sp.csr_matrix(numpy.asarray([[0.0, 0.5, 0.0, 0.5], _p = sp.csr_matrix(np.asarray([[0.0, 0.5, 0.0, 0.5],
[0.1, 0.2, 0.3, 0.4], [0.1, 0.2, 0.3, 0.4],
[0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0],
[0.3, 0.3, 0.0, 0.4]], [0.3, 0.3, 0.0, 0.4]],
...@@ -120,16 +120,16 @@ class MultinomialTester(utt.InferShapeTester): ...@@ -120,16 +120,16 @@ class MultinomialTester(utt.InferShapeTester):
_n = 5 _n = 5
tested = f(self._p, _n) tested = f(self._p, _n)
assert tested.shape == self._p.shape assert tested.shape == self._p.shape
assert numpy.allclose(numpy.floor(tested.todense()), tested.todense()) assert np.allclose(np.floor(tested.todense()), tested.todense())
assert tested[2, 1] == _n assert tested[2, 1] == _n
n = tensor.lvector() n = tensor.lvector()
f = theano.function([self.p, n], multinomial(n, self.p)) f = theano.function([self.p, n], multinomial(n, self.p))
_n = numpy.asarray([1, 2, 3, 4], dtype='int64') _n = np.asarray([1, 2, 3, 4], dtype='int64')
tested = f(self._p, _n) tested = f(self._p, _n)
assert tested.shape == self._p.shape assert tested.shape == self._p.shape
assert numpy.allclose(numpy.floor(tested.todense()), tested.todense()) assert np.allclose(np.floor(tested.todense()), tested.todense())
assert tested[2, 1] == _n[2] assert tested[2, 1] == _n[2]
def test_infer_shape(self): def test_infer_shape(self):
......
theano/sparse/tests/test_utils.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest from nose.plugins.skip import SkipTest
import numpy import numpy as np
import theano.sparse import theano.sparse
if not theano.sparse.enable_sparse: if not theano.sparse.enable_sparse:
raise SkipTest('Optional package sparse disabled') raise SkipTest('Optional package sparse disabled')
...@@ -11,21 +11,21 @@ from theano.sparse.tests.test_basic import as_sparse_format ...@@ -11,21 +11,21 @@ from theano.sparse.tests.test_basic import as_sparse_format
def test_hash_from_sparse(): def test_hash_from_sparse():
hashs = [] hashs = []
rng = numpy.random.rand(5, 5) rng = np.random.rand(5, 5)
for format in ['csc', 'csr']: for format in ['csc', 'csr']:
rng = as_sparse_format(rng, format) rng = as_sparse_format(rng, format)
for data in [[[-2]], [[-1]], [[0]], [[1]], [[2]], for data in [[[-2]], [[-1]], [[0]], [[1]], [[2]],
numpy.zeros((1, 5)), numpy.zeros((1, 6)), np.zeros((1, 5)), np.zeros((1, 6)),
# Data buffer empty but different shapes # Data buffer empty but different shapes
# numpy.zeros((1, 0)), numpy.zeros((2, 0)), # np.zeros((1, 0)), np.zeros((2, 0)),
# Same data buffer and shapes but different strides # Same data buffer and shapes but different strides
numpy.arange(25).reshape(5, 5), np.arange(25).reshape(5, 5),
numpy.arange(25).reshape(5, 5).T, np.arange(25).reshape(5, 5).T,
# Same data buffer, shapes and strides # Same data buffer, shapes and strides
# but different dtypes # but different dtypes
numpy.zeros((5, 5), dtype="uint32"), np.zeros((5, 5), dtype="uint32"),
numpy.zeros((5, 5), dtype="int32"), np.zeros((5, 5), dtype="int32"),
# Test slice # Test slice
rng, rng[1:], rng[:4], rng[1:3], rng, rng[1:], rng[:4], rng[1:3],
# Don't test step as they are not supported by sparse # Don't test step as they are not supported by sparse
......
theano/sparse/type.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
try: try:
import scipy.sparse import scipy.sparse
imported_scipy = True imported_scipy = True
...@@ -20,7 +20,7 @@ def _is_sparse(x): ...@@ -20,7 +20,7 @@ def _is_sparse(x):
True iff x is a L{scipy.sparse.spmatrix} (and not a L{numpy.ndarray}). True iff x is a L{scipy.sparse.spmatrix} (and not a L{numpy.ndarray}).
""" """
if not isinstance(x, (scipy.sparse.spmatrix, numpy.ndarray, tuple, list)): if not isinstance(x, (scipy.sparse.spmatrix, np.ndarray, tuple, list)):
raise NotImplementedError("this function should only be called on " raise NotImplementedError("this function should only be called on "
"sparse.scipy.sparse.spmatrix or " "sparse.scipy.sparse.spmatrix or "
"numpy.ndarray, not,", x) "numpy.ndarray, not,", x)
...@@ -107,12 +107,12 @@ class SparseType(gof.Type): ...@@ -107,12 +107,12 @@ class SparseType(gof.Type):
return (SparseType.may_share_memory(a, b.data) or return (SparseType.may_share_memory(a, b.data) or
SparseType.may_share_memory(a, b.indices) or SparseType.may_share_memory(a, b.indices) or
SparseType.may_share_memory(a, b.indptr)) SparseType.may_share_memory(a, b.indptr))
if _is_sparse(b) and isinstance(a, numpy.ndarray): if _is_sparse(b) and isinstance(a, np.ndarray):
a, b = b, a a, b = b, a
if _is_sparse(a) and isinstance(b, numpy.ndarray): if _is_sparse(a) and isinstance(b, np.ndarray):
if (numpy.may_share_memory(a.data, b) or if (np.may_share_memory(a.data, b) or
numpy.may_share_memory(a.indices, b) or np.may_share_memory(a.indices, b) or
numpy.may_share_memory(a.indptr, b)): np.may_share_memory(a.indptr, b)):
# currently we can't share memory with a.shape as it is a tuple # currently we can't share memory with a.shape as it is a tuple
return True return True
return False return False
...@@ -168,8 +168,8 @@ class SparseType(gof.Type): ...@@ -168,8 +168,8 @@ class SparseType(gof.Type):
obj.indices.size, obj.indptr.size, obj.nnz) obj.indices.size, obj.indptr.size, obj.nnz)
def get_size(self, shape_info): def get_size(self, shape_info):
return (shape_info[1] * numpy.dtype(self.dtype).itemsize + return (shape_info[1] * np.dtype(self.dtype).itemsize +
(shape_info[2] + shape_info[3]) * numpy.dtype('int32').itemsize) (shape_info[2] + shape_info[3]) * np.dtype('int32').itemsize)
# Register SparseType's C code for ViewOp. # Register SparseType's C code for ViewOp.
theano.compile.register_view_op_c_code( theano.compile.register_view_op_c_code(
......
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