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提交 47de332b authored 作者: Eric Larsen's avatar Eric Larsen
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corrections to file layout

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正在显示 包含 117 行增加 和 86 行删除
theano/tensor/tests/test_raw_random.py
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......@@ -12,6 +12,7 @@ from theano import tensor
from theano import compile, config, gof
class T_random_function(utt.InferShapeTester):
def setUp(self):
utt.seed_rng()
......@@ -39,21 +40,23 @@ class T_random_function(utt.InferShapeTester):
assert numpy.all(f_0 == f_1)
def test_inplace_norun(self):
rf = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector, inplace=True)
rf = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector,
inplace=True)
assert rf.inplace
assert getattr(rf, 'destroy_map', {}) != {}
def test_args(self):
"""Test that arguments to RandomFunction are honored"""
rf2 = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector)
rf4 = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector, inplace=True)
rf4 = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector,
inplace=True)
rng_R = random_state_type()
# use make_node to override some of the self.args
post_r2, out2 = rf2(rng_R, (4,), -2, 2) # NOT INPLACE
post_r4, out4 = rf4(rng_R, (4,), -4, 4) # INPLACE
post_r2_4, out2_4 = rf2(rng_R, (4,), -4.0, 2) # NOT INPLACE
post_r2_4_4, out2_4_4 = rf2(rng_R, (4,), -4.0, 4.0) # NOT INPLACE
post_r2, out2 = rf2(rng_R, (4,), -2, 2) # NOT INPLACE
post_r4, out4 = rf4(rng_R, (4,), -4, 4) # INPLACE
post_r2_4, out2_4 = rf2(rng_R, (4, ), -4.0, 2) # NOT INPLACE
post_r2_4_4, out2_4_4 = rf2(rng_R, (4, ), -4.0, 4.0) # NOT INPLACE
# configure out4 to be computed inplace
# The update expression means that the random state rng_R will
......@@ -80,7 +83,7 @@ class T_random_function(utt.InferShapeTester):
#print f2_4_4b
# setting bounds is same as multiplying by 2
assert numpy.allclose(f2*2, f4), (f2, f4)
assert numpy.allclose(f2 * 2, f4), (f2, f4)
# retrieving from non-inplace generator
# is same as inplace one for first call
......@@ -105,10 +108,11 @@ class T_random_function(utt.InferShapeTester):
update=post_r2,
mutable=True)],
out2,
mode='FAST_RUN') #DEBUG_MODE can't pass the id-based test below
mode='FAST_RUN') # DEBUG_MODE can't pass the id-based
# test below
# test that the RandomState object stays the same from function call to function call,
# but that the values returned change from call to call.
# test that the RandomState object stays the same from function call to
# function call, but that the values returned change from call to call.
id0 = id(f[rng_R])
val0 = f()
......@@ -119,7 +123,8 @@ class T_random_function(utt.InferShapeTester):
assert not numpy.allclose(val0, val1)
def test_no_inplace(self):
"""Test that when not running inplace, the RandomState is not updated"""
"""Test that when not running inplace, the RandomState is'
' not updated"""
rf = RandomFunction('uniform', tensor.dvector)
rng_R = random_state_type()
......@@ -136,9 +141,9 @@ class T_random_function(utt.InferShapeTester):
f2 = compile.function(
[compile.In(rng_R,
value = rng,
update = post_r,
mutable = False)],
value=rng,
update=post_r,
mutable=False)],
[post_r, out])
rng2, val2 = f2()
# rng should be in a fresh state
......@@ -161,11 +166,11 @@ class T_random_function(utt.InferShapeTester):
# ndim is an optional argument indicating the length of the 'shape'
# ndim not specified, OK
post_out4, out4 = uniform(rng_R, (4,))
post_out4, out4 = uniform(rng_R, (4,))
# ndim specified, consistent with shape, OK
post_out1_4, out1_4 = uniform(rng_R, (4,), ndim=1)
post_out2_4_4, out2_4_4= uniform(rng_R, (4, 4), ndim=2)
post_out1_4, out1_4 = uniform(rng_R, (4, ), ndim=1)
post_out2_4_4, out2_4_4 = uniform(rng_R, (4, 4), ndim=2)
# ndim specified, but not compatible with shape
self.assertRaises(ValueError, uniform, rng_R, (4,), ndim=2)
......@@ -207,7 +212,7 @@ class T_random_function(utt.InferShapeTester):
broadcastable=(True, True, True, False))()
post_out2, out2 = uniform(rng_R, size=None, ndim=2, low=low, high=high)
self.assertEqual(out2.ndim, 4)
self.assertEqual(out2.broadcastable, (True,False,True,False))
self.assertEqual(out2.broadcastable, (True, False, True, False))
g = compile.function(
[low,
......@@ -218,10 +223,10 @@ class T_random_function(utt.InferShapeTester):
mutable=True)],
[out2],
accept_inplace=True)
low_v = [[[3]],[[4]],[[-5]]]
low_v = [[[3]], [[4]], [[-5]]]
high_v = [[[[5, 8]]]]
o2, = g(low_v, high_v)
self.assertEqual(o2.shape, (1,3,1,2))
self.assertEqual(o2.shape, (1, 3, 1, 2))
def test_random_function_noshape_noargs(self):
'''Test if random_function helper works without args or shape'''
......@@ -231,14 +236,16 @@ class T_random_function(utt.InferShapeTester):
self.assertRaises(TypeError, permutation, rng_R, size=None, ndim=2)
def test_random_function_ndim_added(self):
"""Test that random_function helper function accepts ndim_added as keyword argument"""
"""Test that random_function helper function accepts ndim_added as'
' keyword argument"""
# If using numpy's uniform distribution, ndim_added should be 0,
# because the shape provided as argument is the output shape.
# Specifying a different ndim_added will change the Op's output ndim,
# so numpy.uniform will produce a result of incorrect shape,
# and a ValueError should be raised.
def ndim_added_deco(ndim_added):
def randomfunction(random_state, size=(), low=0.0, high=0.0, ndim=None):
def randomfunction(random_state, size=(), low=0.0, high=0.0,
ndim=None):
ndim, size, bcast = raw_random._infer_ndim_bcast(ndim, size)
if ndim_added < 0:
bcast = bcast[:ndim_added]
......@@ -246,7 +253,8 @@ class T_random_function(utt.InferShapeTester):
bcast = bcast + ((False,) * ndim_added)
assert len(bcast) == ndim + ndim_added
op = RandomFunction('uniform',
tensor.TensorType(dtype='float64', broadcastable=bcast),
tensor.TensorType(dtype='float64',
broadcastable=bcast),
ndim_added=ndim_added)
return op(random_state, size, low, high)
return randomfunction
......@@ -258,11 +266,11 @@ class T_random_function(utt.InferShapeTester):
rng_R = random_state_type()
p_uni11, uni11 = uni_1(rng_R, size=(4,))
p_uni12, uni12 = uni_1(rng_R, size=(3,4))
p_uni12, uni12 = uni_1(rng_R, size=(3, 4))
p_uni01, uni01 = uni_0(rng_R, size=(4,))
p_uni02, uni02 = uni_0(rng_R, size=(3,4))
p_uni02, uni02 = uni_0(rng_R, size=(3, 4))
p_unim11, unim11 = uni_m1(rng_R, size=(4,))
p_unim12, unim12 = uni_m1(rng_R, size=(3,4))
p_unim12, unim12 = uni_m1(rng_R, size=(3, 4))
self.assertEqual(uni11.ndim, 2)
self.assertEqual(uni12.ndim, 3)
......@@ -331,12 +339,13 @@ class T_random_function(utt.InferShapeTester):
self.assertTrue(numpy.allclose(val1, numpy_val1))
def test_binomial(self):
"""Test that raw_random.binomial generates the same results as numpy."""
"""Test that raw_random.binomial generates the same results'
' as numpy."""
# 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
# the integer nature of the result
post_r, bin = binomial(rng_R, (7,12), 5, 0.8)
post_r, bin = binomial(rng_R, (7, 12), 5, 0.8)
f = compile.function(
[compile.In(rng_R,
......@@ -347,8 +356,8 @@ class T_random_function(utt.InferShapeTester):
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.binomial(5, 0.8, size=(7,12))
numpy_val1 = numpy_rng.binomial(5, 0.8, size=(7,12))
numpy_val0 = numpy_rng.binomial(5, 0.8, size=(7, 12))
numpy_val1 = numpy_rng.binomial(5, 0.8, size=(7, 12))
print val0
print numpy_val0
print val1
......@@ -361,7 +370,7 @@ class T_random_function(utt.InferShapeTester):
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
# Use non-default parameters
post_r, out = normal(rng_R, (2,3), 4.0, 2.0)
post_r, out = normal(rng_R, (2, 3), 4.0, 2.0)
f = compile.function(
[compile.In(rng_R,
......@@ -372,8 +381,8 @@ class T_random_function(utt.InferShapeTester):
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.normal(4.0, 2.0, size=(2,3))
numpy_val1 = numpy_rng.normal(4.0, 2.0, size=(2,3))
numpy_val0 = numpy_rng.normal(4.0, 2.0, size=(2, 3))
numpy_val1 = numpy_rng.normal(4.0, 2.0, size=(2, 3))
print val0
print numpy_val0
print val1
......@@ -382,12 +391,13 @@ 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."""
# 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
# the integer nature of the result
post_r, out = random_integers(rng_R, (11,8), -3, 16)
post_r, out = random_integers(rng_R, (11, 8), -3, 16)
f = compile.function(
[compile.In(rng_R,
......@@ -398,8 +408,8 @@ 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.random_integers(-3, 16, size=(11, 8))
numpy_val1 = numpy_rng.random_integers(-3, 16, size=(11, 8))
print val0
print numpy_val0
print val1
......@@ -408,14 +418,16 @@ class T_random_function(utt.InferShapeTester):
self.assertTrue(numpy.allclose(val1, numpy_val1))
def test_permutation_helper(self):
"""Test that raw_random.permutation_helper generates the same results as numpy,
"""Test that raw_random.permutation_helper generates the same'
' results as numpy,
and that the 'ndim_added' keyword behaves correctly."""
# permutation_helper needs "ndim_added=1", because its output
# is one dimension more than its "shape" argument (and there's
# no way to determine that automatically).
# Check the working case, over two calls to see if the random
# state is correctly updated.
rf = RandomFunction(permutation_helper, tensor.imatrix, 8, ndim_added=1)
rf = RandomFunction(permutation_helper, tensor.imatrix, 8,
ndim_added=1)
rng_R = random_state_type()
post_r, out = rf(rng_R, (7,), 8)
......@@ -430,8 +442,10 @@ class T_random_function(utt.InferShapeTester):
val1 = f()
# numpy_rng.permutation outputs one vector at a time,
# so we call it iteratively to generate all the samples.
numpy_val0 = numpy.asarray([numpy_rng.permutation(8) for i in range(7)])
numpy_val1 = numpy.asarray([numpy_rng.permutation(8) for i in range(7)])
numpy_val0 = numpy.asarray([numpy_rng.permutation(8)
for i in range(7)])
numpy_val1 = numpy.asarray([numpy_rng.permutation(8)
for i in range(7)])
print val0
print numpy_val0
print val1
......@@ -451,7 +465,8 @@ class T_random_function(utt.InferShapeTester):
self.assertRaises(ValueError, f0)
# Here, ndim_added is 2 instead of 1. A ValueError should be raised.
rf2 = RandomFunction(permutation_helper, tensor.imatrix, 8, ndim_added=2)
rf2 = RandomFunction(permutation_helper, tensor.imatrix, 8,
ndim_added=2)
post_r2, out2 = rf2(rng_R, (7,), 8)
f2 = compile.function(
[compile.In(rng_R,
......@@ -461,7 +476,8 @@ class T_random_function(utt.InferShapeTester):
self.assertRaises(ValueError, f2)
def test_permutation(self):
"""Test that raw_random.permutation generates the same results as numpy."""
"""Test that raw_random.permutation generates the same'
' results as numpy."""
rng_R = random_state_type()
post_r, out = permutation(rng_R, size=(9,), n=6)
print 'OUT NDIM', out.ndim
......@@ -477,8 +493,10 @@ class T_random_function(utt.InferShapeTester):
# so we call it iteratively to generate all the samples.
val0 = f()
val1 = f()
numpy_val0 = numpy.asarray([numpy_rng.permutation(6) for i in range(9)])
numpy_val1 = numpy.asarray([numpy_rng.permutation(6) for i in range(9)])
numpy_val0 = numpy.asarray([numpy_rng.permutation(6)
for i in range(9)])
numpy_val1 = numpy.asarray([numpy_rng.permutation(6)
for i in range(9)])
print val0
print numpy_val0
print val1
......@@ -487,10 +505,11 @@ class T_random_function(utt.InferShapeTester):
self.assertTrue(numpy.all(val1 == numpy_val1))
def test_multinomial(self):
"""Test that raw_random.multinomial generates the same results as numpy."""
"""Test that raw_random.multinomial generates the same'
' results as numpy."""
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
post_r, out = multinomial(rng_R, (7,3), 6, [0.2]*5)
post_r, out = multinomial(rng_R, (7, 3), 6, [0.2] * 5)
f = compile.function(
[compile.In(rng_R,
......@@ -501,8 +520,8 @@ class T_random_function(utt.InferShapeTester):
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
val0, = f()
val1, = f()
numpy_val0 = numpy_rng.multinomial(6, [0.2]*5, (7,3))
numpy_val1 = numpy_rng.multinomial(6, [0.2]*5, (7,3))
numpy_val0 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
numpy_val1 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
print val0
print numpy_val0
print val1
......@@ -510,9 +529,8 @@ class T_random_function(utt.InferShapeTester):
self.assertTrue(numpy.all(val0 == numpy_val0))
self.assertTrue(numpy.all(val1 == numpy_val1))
self.assertTrue(val0.shape == (7,3,5))
self.assertTrue(val1.shape == (7,3,5))
self.assertTrue(val0.shape == (7, 3, 5))
self.assertTrue(val1.shape == (7, 3, 5))
def test_symbolic_shape(self):
rng_R = random_state_type()
......@@ -521,11 +539,11 @@ class T_random_function(utt.InferShapeTester):
f = compile.function([rng_R, shape], out)
rng_state0 = numpy.random.RandomState(utt.fetch_seed())
assert f(rng_state0, [2,3]).shape == (2,3)
assert f(rng_state0, [4,8]).shape == (4,8)
assert f(rng_state0, [2, 3]).shape == (2, 3)
assert f(rng_state0, [4, 8]).shape == (4, 8)
self.assertRaises(ValueError, f, rng_state0, [4])
self.assertRaises(ValueError, f, rng_state0, [4,3,4,5])
self.assertRaises(ValueError, f, rng_state0, [4, 3, 4, 5])
def test_default_shape(self):
rng_R = random_state_type()
......@@ -536,8 +554,10 @@ class T_random_function(utt.InferShapeTester):
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0)
post1, val1 = f(post0)
numpy_val0 = numpy.asarray(numpy_rng.uniform(), dtype=theano.config.floatX)
numpy_val1 = numpy.asarray(numpy_rng.uniform(), dtype=theano.config.floatX)
numpy_val0 = numpy.asarray(numpy_rng.uniform(),
dtype=theano.config.floatX)
numpy_val1 = numpy.asarray(numpy_rng.uniform(),
dtype=theano.config.floatX)
assert numpy.all(val0 == numpy_val0)
assert numpy.all(val1 == numpy_val1)
......@@ -573,7 +593,8 @@ class T_random_function(utt.InferShapeTester):
high = tensor.vector()
post_rb, outb = uniform(rng_R, low=low, high=high)
assert outb.ndim == 1
fb = compile.function([rng_R, low, high], [post_rb, outb], accept_inplace=True)
fb = compile.function([rng_R, low, high], [post_rb, outb],
accept_inplace=True)
post0b, val0b = fb(post1, [-4., -2], [-1, 0])
post1b, val1b = fb(post0b, [-4.], [-1])
......@@ -587,7 +608,8 @@ class T_random_function(utt.InferShapeTester):
size = tensor.lvector()
post_rc, outc = uniform(rng_R, low=low, high=high, size=size, ndim=1)
fc = compile.function([rng_R, low, high, size], [post_rc, outc], accept_inplace=True)
fc = compile.function([rng_R, low, high, size], [post_rc, outc],
accept_inplace=True)
post0c, val0c = fc(post1b, [-4., -2], [-1, 0], [2])
post1c, val1c = fc(post0c, [-4.], [-1], [1])
numpy_val0c = as_floatX(numpy_rng.uniform(low=[-4., -2], high=[-1, 0]))
......@@ -595,8 +617,8 @@ class T_random_function(utt.InferShapeTester):
assert numpy.all(val0c == numpy_val0c)
assert numpy.all(val1c == numpy_val1c)
self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1, 0], [1])
self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1, 0], [1,2])
self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1, 0], [2,1])
self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1, 0], [1, 2])
self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1, 0], [2, 1])
self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1], [1])
#TODO: do we want that?
#self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1], [2])
......@@ -607,7 +629,8 @@ class T_random_function(utt.InferShapeTester):
high = tensor.dcol()
post_r, out = uniform(rng_R, low=low, high=high)
assert out.ndim == 2
f = compile.function([rng_R, low, high], [post_r, out], accept_inplace=True)
f = compile.function([rng_R, low, high], [post_r, out],
accept_inplace=True)
rng_state0 = numpy.random.RandomState(utt.fetch_seed())
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
......@@ -617,7 +640,8 @@ class T_random_function(utt.InferShapeTester):
numpy_val0 = numpy_rng.uniform(low=[-5, .5, 0, 1], high=[1.])
numpy_val1 = numpy_rng.uniform(low=[.9], high=[[1.], [1.1], [1.5]])
numpy_val2 = numpy_rng.uniform(low=[-5, .5, 0, 1], high=[[1.], [1.1], [1.5]])
numpy_val2 = numpy_rng.uniform(low=[-5, .5, 0, 1],
high=[[1.], [1.1], [1.5]])
assert numpy.all(val0 == numpy_val0), (val0, numpy_val0)
assert numpy.all(val1 == numpy_val1)
......@@ -629,7 +653,8 @@ class T_random_function(utt.InferShapeTester):
high = tensor.vector()
post_r, out = uniform(rng_R, low=low, high=high)
assert out.ndim == 1
f = compile.function([rng_R, low, high], [post_r, out], accept_inplace=True)
f = compile.function([rng_R, low, high], [post_r, out],
accept_inplace=True)
def as_floatX(thing):
return numpy.asarray(thing, dtype=theano.config.floatX)
......@@ -645,7 +670,8 @@ class T_random_function(utt.InferShapeTester):
# arguments of size (2,)
rng1, val1 = f(rng0, low_val[:-1], high_val[:-1])
numpy_val1 = as_floatX(numpy_rng.uniform(low=low_val[:-1], high=high_val[:-1]))
numpy_val1 = as_floatX(numpy_rng.uniform(low=low_val[:-1],
high=high_val[:-1]))
assert numpy.all(val1 == numpy_val1)
# Specifying the size explicitly
......@@ -653,7 +679,8 @@ class T_random_function(utt.InferShapeTester):
uniform(rng_R, low=low, high=high, size=(3,)),
accept_inplace=True)
rng2, val2 = g(rng1, low_val, high_val)
numpy_val2 = as_floatX(numpy_rng.uniform(low=low_val, high=high_val, size=(3,)))
numpy_val2 = as_floatX(numpy_rng.uniform(low=low_val, high=high_val,
size=(3,)))
assert numpy.all(val2 == numpy_val2)
self.assertRaises(ValueError, g, rng2, low_val[:-1], high_val[:-1])
......@@ -663,7 +690,8 @@ class T_random_function(utt.InferShapeTester):
prob = tensor.vector()
post_r, out = binomial(rng_R, n=n, p=prob)
assert out.ndim == 1
f = compile.function([rng_R, n, prob], [post_r, out], accept_inplace=True)
f = compile.function([rng_R, n, prob], [post_r, out],
accept_inplace=True)
n_val = [1, 2, 3]
prob_val = numpy.asarray([.1, .2, .3], dtype=config.floatX)
......@@ -695,7 +723,8 @@ class T_random_function(utt.InferShapeTester):
std = tensor.vector()
post_r, out = normal(rng_R, avg=avg, std=std)
assert out.ndim == 1
f = compile.function([rng_R, avg, std], [post_r, out], accept_inplace=True)
f = compile.function([rng_R, avg, std], [post_r, out],
accept_inplace=True)
def as_floatX(thing):
return numpy.asarray(thing, dtype=theano.config.floatX)
......@@ -713,7 +742,8 @@ class T_random_function(utt.InferShapeTester):
# arguments of size (2,)
rng1, val1 = f(rng0, avg_val[:-1], std_val[:-1])
numpy_val1 = numpy.asarray(numpy_rng.normal(loc=avg_val[:-1], scale=std_val[:-1]),
numpy_val1 = numpy.asarray(numpy_rng.normal(loc=avg_val[:-1],
scale=std_val[:-1]),
dtype=theano.config.floatX)
assert numpy.all(val1 == numpy_val1)
......@@ -722,7 +752,8 @@ class T_random_function(utt.InferShapeTester):
normal(rng_R, avg=avg, std=std, size=(3,)),
accept_inplace=True)
rng2, val2 = g(rng1, avg_val, std_val)
numpy_val2 = numpy.asarray(numpy_rng.normal(loc=avg_val, scale=std_val, size=(3,)),
numpy_val2 = numpy.asarray(numpy_rng.normal(loc=avg_val, scale=std_val,
size=(3,)),
dtype=theano.config.floatX)
assert numpy.all(val2 == numpy_val2)
self.assertRaises(ValueError, g, rng2, avg_val[:-1], std_val[:-1])
......@@ -733,7 +764,8 @@ class T_random_function(utt.InferShapeTester):
high = tensor.lvector()
post_r, out = random_integers(rng_R, low=low, high=high)
assert out.ndim == 1
f = compile.function([rng_R, low, high], [post_r, out], accept_inplace=True)
f = compile.function([rng_R, low, high], [post_r, out],
accept_inplace=True)
low_val = [100, 200, 300]
high_val = [110, 220, 330]
......@@ -771,7 +803,8 @@ class T_random_function(utt.InferShapeTester):
pvals = tensor.matrix()
post_r, out = multinomial(rng_R, n=n, pvals=pvals)
assert out.ndim == 2
f = compile.function([rng_R, n, pvals], [post_r, out], accept_inplace=True)
f = compile.function([rng_R, n, pvals], [post_r, out],
accept_inplace=True)
n_val = [1, 2, 3]
pvals_val = [[.1, .9], [.2, .8], [.3, .7]]
......@@ -801,18 +834,18 @@ class T_random_function(utt.InferShapeTester):
assert numpy.all(val2 == numpy_val2)
self.assertRaises(ValueError, g, rng2, n_val[:-1], pvals_val[:-1])
def test_multinomial_tensor3_a(self):
# Test the examples given in the multinomial documentation regarding
# tensor3 objects
rng_R = random_state_type()
n = 9
pvals = tensor.dtensor3()
post_r, out = multinomial(rng_R, n=n, pvals=pvals, size=(1,-1))
post_r, out = multinomial(rng_R, n=n, pvals=pvals, size=(1, -1))
assert out.ndim == 3
assert out.broadcastable==(True, False, False)
assert out.broadcastable == (True, False, False)
f = compile.function([rng_R, pvals], [post_r, out], accept_inplace=True)
f = compile.function([rng_R, pvals], [post_r, out],
accept_inplace=True)
rng = numpy.random.RandomState(utt.fetch_seed())
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
......@@ -821,7 +854,7 @@ class T_random_function(utt.InferShapeTester):
assert pvals_val.shape == (1, 3, 2)
new_rng, draw = f(rng, pvals_val)
assert draw.shape==(1,3,2)
assert draw.shape == (1, 3, 2)
assert numpy.allclose(draw.sum(axis=2), 9)
def test_multinomial_tensor3_b(self):
......@@ -830,11 +863,12 @@ class T_random_function(utt.InferShapeTester):
rng_R = random_state_type()
n = 9
pvals = tensor.dtensor3()
post_r, out = multinomial(rng_R, n=n, pvals=pvals, size=(10, 1,-1))
post_r, out = multinomial(rng_R, n=n, pvals=pvals, size=(10, 1, -1))
assert out.ndim == 4
assert out.broadcastable==(False, True, False, False)
assert out.broadcastable == (False, True, False, False)
f = compile.function([rng_R, pvals], [post_r, out], accept_inplace=True)
f = compile.function([rng_R, pvals], [post_r, out],
accept_inplace=True)
rng = numpy.random.RandomState(utt.fetch_seed())
numpy_rng = numpy.random.RandomState(utt.fetch_seed())
......@@ -843,15 +877,15 @@ class T_random_function(utt.InferShapeTester):
assert pvals_val.shape == (1, 3, 2)
out_rng, draw = f(rng, pvals_val)
assert draw.shape==(10,1,3,2)
assert draw.shape == (10, 1, 3, 2)
assert numpy.allclose(draw.sum(axis=3), 9)
def test_dtype(self):
rng_R = random_state_type()
low = tensor.lscalar()
high = tensor.lscalar()
post_r, out = random_integers(rng_R, low=low, high=high, size=(20,), dtype='int8')
post_r, out = random_integers(rng_R, low=low, high=high, size=(20, ),
dtype='int8')
assert out.dtype == 'int8'
f = compile.function([rng_R, low, high], [post_r, out])
......@@ -863,7 +897,6 @@ class T_random_function(utt.InferShapeTester):
assert val1.dtype == 'int8'
assert numpy.all(abs(val1) <= 1)
def test_dtype_normal_uniform_687(self):
# Regression test for #687.
rng_R = random_state_type()
......@@ -874,11 +907,9 @@ class T_random_function(utt.InferShapeTester):
dtype='float32')[1].dtype == 'float32'
def setUp(self):
super(T_random_function, self).setUp()
def test_infer_shape(self):
rng_R = random_state_type()
rng_R_val = numpy.random.RandomState(utt.fetch_seed())
......
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