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提交 0372f4a5 authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Use randint() instead of random_integers() in the tests.

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正在显示 包含 65 行增加 和 60 行删除
theano/sparse/tests/test_basic.py
浏览文件 @ 0372f4a5
......@@ -82,7 +82,7 @@ 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.random_integers(huge, size=2) % shape
idx = numpy.random.randint(1, huge+1, size=2) % shape
value = numpy.random.rand()
# if dtype *int*, value will always be zeros!
if "int" in dtype:
......@@ -484,7 +484,7 @@ class TestConstructSparseFromList(unittest.TestCase):
# Test the sparse grad
valm = numpy.random.rand(5, 4).astype(config.floatX)
valv = numpy.random.random_integers(0, 4, 10)
valv = numpy.random.randint(0, 5, 10)
m = theano.tensor.matrix()
shared_v = theano.shared(valv)
......@@ -2492,7 +2492,7 @@ class AddSSDataTester(utt.InferShapeTester):
variable = getattr(theano.sparse, format + '_matrix')
rand = numpy.array(
numpy.random.random_integers(3, size=(3, 4)) - 1,
numpy.random.randint(1, 4, size=(3, 4)) - 1,
dtype=theano.config.floatX)
constant = as_sparse_format(rand, format)
......@@ -3064,11 +3064,11 @@ class SamplingDotTester(utt.InferShapeTester):
x = [tensor.matrix() for t in range(2)]
x.append(sparse.csr_matrix())
# unsquare shape
a = [numpy.array(numpy.random.random_integers(5, size=(4, 3)) - 1,
a = [numpy.array(numpy.random.randint(1, 6, size=(4, 3)) - 1,
dtype=theano.config.floatX),
numpy.array(numpy.random.random_integers(5, size=(5, 3)) - 1,
numpy.array(numpy.random.randint(1, 6, size=(5, 3)) - 1,
dtype=theano.config.floatX),
numpy.array(numpy.random.random_integers(2, size=(4, 5)) - 1,
numpy.array(numpy.random.randint(1, 3, size=(4, 5)) - 1,
dtype=theano.config.floatX)
]
a[2] = sp.csr_matrix(a[2])
......
theano/sparse/tests/test_sp2.py
浏览文件 @ 0372f4a5
......@@ -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.random_integers(3, size=(3, 4)) - 1,
rand = numpy.array(numpy.random.randint(1, 4, size=(3, 4)) - 1,
dtype=theano.config.floatX)
x[format] = variable()
......
theano/tensor/raw_random.py
浏览文件 @ 0372f4a5
......@@ -551,6 +551,8 @@ def random_integers_helper(random_state, low, high, size):
This is a generalization of numpy.random.random_integers to the case where
low and high are tensors.
Since random_integers is deprecated it calls randint() instead.
"""
# Figure out the output shape
if size is not None:
......@@ -587,7 +589,7 @@ def random_integers_helper(random_state, low, high, size):
high.shape)
# Iterate over these indices, drawing one sample at a time from numpy
for oi, li, hi in zip(*broadcast_ind):
out[oi] = random_state.random_integers(low=low[li], high=high[hi])
out[oi] = random_state.randint(low=low[li], high=high[hi]+1)
return out
......
theano/tensor/tests/test_basic.py
浏览文件 @ 0372f4a5
......@@ -561,7 +561,7 @@ def rand_nonzero(shape, eps=3e-4):
def randint(*shape):
return numpy.random.random_integers(-5, 5, shape)
return numpy.random.randint(-5, 6, shape)
def randuint(*shape):
return numpy.array(numpy.random.randint(5, size=shape), dtype=numpy.uint32)
......@@ -577,7 +577,7 @@ def randcomplex_nonzero(shape, eps=1e-4):
def randint_nonzero(*shape):
r = numpy.random.random_integers(-5, 4, shape)
r = numpy.random.randint(-5, 5, shape)
return r + (r == 0) * 5
......@@ -587,7 +587,7 @@ def rand_ranged(min, max, shape):
def randint_ranged(min, max, shape):
return numpy.random.random_integers(min, max, shape)
return numpy.random.randint(min, max+1, shape)
def randc128_ranged(min, max, shape):
......@@ -7535,17 +7535,17 @@ class TestInferShape(utt.InferShapeTester):
[adtens4_bro_val], Rebroadcast)
# Alloc
randint = numpy.random.random_integers
randint = numpy.random.randint
adscal = dscalar()
aiscal = lscalar()
biscal = lscalar()
ciscal = lscalar()
discal = lscalar()
adscal_val = rand()
aiscal_val = randint(3, 5, size=())
biscal_val = randint(3, 5, size=())
ciscal_val = randint(3, 5, size=())
discal_val = randint(3, 5, size=())
aiscal_val = randint(3, 6, size=())
biscal_val = randint(3, 6, size=())
ciscal_val = randint(3, 6, size=())
discal_val = randint(3, 6, size=())
self._compile_and_check([adscal, aiscal, biscal, ciscal, discal],
[Alloc()(adscal, aiscal, biscal, ciscal, discal)],
[adscal_val, aiscal_val, biscal_val,
......@@ -8000,8 +8000,7 @@ class T_Choose(utt.InferShapeTester):
a = tensor.vector(dtype='int32')
b = tensor.matrix(dtype='float32')
A = numpy.asarray(numpy.random.random_integers(0, 3, 4),
dtype='int32')
A = numpy.random.randint(0, 4, 4, dtype='int32')
B = numpy.asarray(numpy.random.rand(4, 4), dtype='float32')
for m in self.modes:
......@@ -8048,8 +8047,7 @@ class T_Choose(utt.InferShapeTester):
b = tensor.tensor3(dtype='float32')
c = tensor.tensor3(dtype='float32')
A = numpy.asarray(numpy.random.random_integers(0, 1, (2, 1, 1)),
dtype='int32')
A = numpy.random.randint(0, 2, (2, 1, 1), dtype='int32')
B = numpy.asarray(numpy.random.rand(1, 6, 1), dtype='float32')
C = numpy.asarray(numpy.random.rand(1, 1, 5), dtype='float32')
......
theano/tensor/tests/test_extra_ops.py
浏览文件 @ 0372f4a5
......@@ -245,7 +245,7 @@ class TestBinCountOp(utt.InferShapeTester):
'uint8', 'uint16', 'uint32', 'uint64'):
x = T.vector('x', dtype=dtype)
a = np.random.random_integers(50, size=(25)).astype(dtype)
a = np.random.randint(1, 51, size=(25)).astype(dtype)
weights = np.random.random((25,)).astype(config.floatX)
f1 = theano.function([x], bincount(x))
......@@ -281,7 +281,7 @@ class TestBinCountOp(utt.InferShapeTester):
self.assertRaises(TypeError, BinCountOp(), x)
else:
a = np.random.random_integers(50, size=(25)).astype(dtype)
a = np.random.randint(1, 51, size=(25)).astype(dtype)
weights = np.random.random((25,)).astype(config.floatX)
f1 = theano.function([x], BinCountOp()(x, weights=None))
......@@ -316,29 +316,29 @@ class TestBinCountOp(utt.InferShapeTester):
else:
self._compile_and_check([x],
[BinCountOp()(x, None)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
[np.random.randint(
1, 51, size=(25,)).astype(dtype)],
self.op_class)
weights = np.random.random((25,)).astype(config.floatX)
self._compile_and_check([x],
[BinCountOp()(x, weights=weights)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
[np.random.randint(
1, 51, size=(25,)).astype(dtype)],
self.op_class)
if not numpy_16:
continue
self._compile_and_check([x],
[BinCountOp(minlength=60)(x, weights=weights)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
[np.random.randint(
1, 51, size=(25,)).astype(dtype)],
self.op_class)
self._compile_and_check([x],
[BinCountOp(minlength=5)(x, weights=weights)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
[np.random.randint(
1, 51, size=(25,)).astype(dtype)],
self.op_class)
......@@ -525,11 +525,11 @@ class TestRepeatOp(utt.InferShapeTester):
r_var = T.vector(dtype=dtype)
if axis is None:
r = np.random.random_integers(
5, size=a.size).astype(dtype)
r = np.random.randint(
1, 6, size=a.size).astype(dtype)
else:
r = np.random.random_integers(
5, size=(10,)).astype(dtype)
r = np.random.randint(
1, 6, size=(10,)).astype(dtype)
if dtype in self.numpy_unsupported_dtypes and r_var.ndim == 1:
self.assertRaises(TypeError,
......@@ -541,8 +541,8 @@ class TestRepeatOp(utt.InferShapeTester):
f(a, r))
# check when r is a list of single integer, e.g. [3].
r = np.random.random_integers(
10, size=()).astype(dtype) + 2
r = np.random.randint(
1, 11, size=()).astype(dtype) + 2
f = theano.function([x],
repeat(x, [r], axis=axis))
assert np.allclose(np.repeat(a, r, axis=axis),
......@@ -553,7 +553,7 @@ class TestRepeatOp(utt.InferShapeTester):
# check when r is theano tensortype that broadcastable is (True,)
r_var = theano.tensor.TensorType(broadcastable=(True,),
dtype=dtype)()
r = np.random.random_integers(5, size=(1,)).astype(dtype)
r = np.random.randint(1, 6, size=(1,)).astype(dtype)
f = theano.function([x, r_var],
repeat(x, r_var, axis=axis))
assert np.allclose(np.repeat(a, r[0], axis=axis),
......@@ -583,14 +583,14 @@ class TestRepeatOp(utt.InferShapeTester):
r_var = T.vector(dtype=dtype)
if axis is None:
r = np.random.random_integers(
5, size=a.size).astype(dtype)
r = np.random.randint(
1, 6, size=a.size).astype(dtype)
elif a.size > 0:
r = np.random.random_integers(
5, size=a.shape[axis]).astype(dtype)
r = np.random.randint(
1, 6, size=a.shape[axis]).astype(dtype)
else:
r = np.random.random_integers(
5, size=(10,)).astype(dtype)
r = np.random.randint(
1, 6, size=(10,)).astype(dtype)
self._compile_and_check(
[x, r_var],
......@@ -625,7 +625,7 @@ class TestBartlett(utt.InferShapeTester):
def test_perform(self):
x = tensor.lscalar()
f = function([x], self.op(x))
M = numpy.random.random_integers(3, 50, size=())
M = numpy.random.randint(3, 51, size=())
assert numpy.allclose(f(M), numpy.bartlett(M))
assert numpy.allclose(f(0), numpy.bartlett(0))
assert numpy.allclose(f(-1), numpy.bartlett(-1))
......@@ -635,7 +635,7 @@ class TestBartlett(utt.InferShapeTester):
def test_infer_shape(self):
x = tensor.lscalar()
self._compile_and_check([x], [self.op(x)],
[numpy.random.random_integers(3, 50, size=())],
[numpy.random.randint(3, 51, size=())],
self.op_class)
self._compile_and_check([x], [self.op(x)], [0], self.op_class)
self._compile_and_check([x], [self.op(x)], [1], self.op_class)
......
theano/tensor/tests/test_raw_random.py
浏览文件 @ 0372f4a5
......@@ -378,8 +378,10 @@ class T_random_function(utt.InferShapeTester):
self.assertTrue(numpy.allclose(val1, numpy_val1))
def test_random_integers(self):
"""Test that raw_random.random_integers generates the same
results as numpy."""
# Test that raw_random.random_integers generates the same
# results as numpy. We use randint() for comparison since
# random_integers() is deprecated.
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
# Use non-default parameters, and larger dimensions because of
......@@ -395,8 +397,8 @@ class T_random_function(utt.InferShapeTester):
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.random_integers(-3, 16, size=(11, 8))
numpy_val1 = numpy_rng.random_integers(-3, 16, size=(11, 8))
numpy_val0 = numpy_rng.randint(-3, 17, size=(11, 8))
numpy_val1 = numpy_rng.randint(-3, 17, size=(11, 8))
self.assertTrue(numpy.allclose(val0, numpy_val0))
self.assertTrue(numpy.allclose(val1, numpy_val1))
......@@ -845,13 +847,13 @@ class T_random_function(utt.InferShapeTester):
# Arguments of size (3,)
rng0, val0 = f(rng, low_val, high_val)
numpy_val0 = numpy.asarray([numpy_rng.random_integers(low=lv, high=hv)
numpy_val0 = numpy.asarray([numpy_rng.randint(low=lv, high=hv+1)
for lv, hv in zip(low_val, high_val)])
assert numpy.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, low_val[:-1], high_val[:-1])
numpy_val1 = numpy.asarray([numpy_rng.random_integers(low=lv, high=hv)
numpy_val1 = numpy.asarray([numpy_rng.randint(low=lv, high=hv+1)
for lv, hv in zip(low_val[:-1], high_val[:-1])])
assert numpy.all(val1 == numpy_val1)
......@@ -860,7 +862,7 @@ class T_random_function(utt.InferShapeTester):
random_integers(rng_R, low=low, high=high, size=(3,)),
accept_inplace=True)
rng2, val2 = g(rng1, low_val, high_val)
numpy_val2 = numpy.asarray([numpy_rng.random_integers(low=lv, high=hv)
numpy_val2 = numpy.asarray([numpy_rng.randint(low=lv, high=hv+1)
for lv, hv in zip(low_val, high_val)])
assert numpy.all(val2 == numpy_val2)
self.assertRaises(ValueError, g, rng2, low_val[:-1], high_val[:-1])
......
theano/tensor/tests/test_shared_randomstreams.py
浏览文件 @ 0372f4a5
......@@ -170,7 +170,10 @@ class T_SharedRandomStreams(unittest.TestCase):
assert numpy.allclose(fn_val1, numpy_val1)
def test_random_integers(self):
"""Test that RandomStreams.random_integers generates the same results as numpy"""
# Test that RandomStreams.random_integers generates the same
# results as numpy. We use randint() for numpy since
# random_integers() is deprecated.
# Check over two calls to see if the random state is correctly updated.
random = RandomStreams(utt.fetch_seed())
fn = function([], random.random_integers((20, 20), -5, 5))
......@@ -179,8 +182,8 @@ class T_SharedRandomStreams(unittest.TestCase):
rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
rng = numpy.random.RandomState(int(rng_seed)) # int() is for 32bit
numpy_val0 = rng.random_integers(-5, 5, size=(20, 20))
numpy_val1 = rng.random_integers(-5, 5, size=(20, 20))
numpy_val0 = rng.randint(-5, 6, size=(20, 20))
numpy_val1 = rng.randint(-5, 6, size=(20, 20))
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
......@@ -610,21 +613,21 @@ class T_SharedRandomStreams(unittest.TestCase):
# Arguments of size (3,)
val0 = f(low_val, high_val)
numpy_val0 = numpy.asarray([numpy_rng.random_integers(low=lv, high=hv)
numpy_val0 = numpy.asarray([numpy_rng.randint(low=lv, high=hv+1)
for lv, hv in zip(low_val, high_val)])
assert numpy.all(val0 == numpy_val0)
# arguments of size (2,)
val1 = f(low_val[:-1], high_val[:-1])
numpy_val1 = numpy.asarray([numpy_rng.random_integers(low=lv, high=hv)
numpy_val1 = numpy.asarray([numpy_rng.randint(low=lv, high=hv+1)
for lv, hv in zip(low_val[:-1], high_val[:-1])])
assert numpy.all(val1 == numpy_val1)
# Specifying the size explicitly
g = function([low, high], random.random_integers(low=low, high=high, size=(3,)))
g = function([low, high], random.randint(low=low, high=high+1, size=(3,)))
val2 = g(low_val, high_val)
numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))
numpy_val2 = numpy.asarray([numpy_rng.random_integers(low=lv, high=hv)
numpy_val2 = numpy.asarray([numpy_rng.randint(low=lv, high=hv+1)
for lv, hv in zip(low_val, high_val)])
assert numpy.all(val2 == numpy_val2)
self.assertRaises(ValueError, g, low_val[:-1], high_val[:-1])
......
theano/typed_list/tests/test_basic.py
浏览文件 @ 0372f4a5
......@@ -34,7 +34,7 @@ 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.random_integers(huge, size=2) % shape
idx = numpy.random.randint(1, huge+1, size=2) % shape
value = numpy.random.rand()
# if dtype *int*, value will always be zeros!
if "int" in dtype:
......
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