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