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提交 ac4b7a5d authored 作者: kvmanohar22's avatar kvmanohar22
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modified numpy imports to one common form

上级 86d21acd
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正在显示 包含 209 行增加 和 209 行删除
theano/sparse/tests/test_basic.py
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......@@ -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 @ ac4b7a5d
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 @ ac4b7a5d
......@@ -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 @ ac4b7a5d
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
......
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