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提交 d147486a authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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上级 996d48b1 67dc8d70
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正在显示 包含 97 行增加 和 3 行删除
theano/tensor/tests/test_basic.py
浏览文件 @ d147486a
......@@ -1795,28 +1795,38 @@ class TestInversePermutation(unittest.TestCase):
utt.seed_rng()
def test_dim1(self):
"""Test the inversion of one permutation (int vector)"""
p = ivector()
inv = inverse_permutation(p)
f_inverse = function([p], inv)
# Generate a random permutation
rng = numpy.random.RandomState(utt.fetch_seed())
p_val = rng.permutation(10)
inv_val = f_inverse(p_val)
# Check that the inverse of the inverse is the original permutation
assert numpy.all(f_inverse(inv_val) == p_val)
# Check that permutation(inverse) == inverse(permutation) = identity
assert numpy.all(p_val[inv_val] == numpy.arange(10))
assert numpy.all(inv_val[p_val] == numpy.arange(10))
def test_dim2(self):
"""Test the inversion of several permutation at a time"""
# Each row of p is a different permutation to inverse
p = imatrix()
inv = inverse_permutation(p)
f_inverse = function([p], inv)
rng = numpy.random.RandomState(utt.fetch_seed())
# Generate 10 random permutations
p_val = numpy.asarray([rng.permutation(10) for i in range(7)])
inv_val = f_inverse(p_val)
# Check that the inverse of the inverse is the original permutation list
assert numpy.all(f_inverse(inv_val) == p_val)
# Check that, for each permutation,
# permutation(inverse) == inverse(permutation) = identity
for p_row, i_row in zip(p_val, inv_val):
assert numpy.all(p_row[i_row] == numpy.arange(10))
assert numpy.all(i_row[p_row] == numpy.arange(10))
......@@ -1827,6 +1837,7 @@ class TestReorderRowElements(unittest.TestCase):
utt.seed_rng()
def test_1_1(self):
"""Test ReorderRowElements(vector, vector)"""
input = vector()
p = ivector()
out = reorder_row_elements(input, p)
......@@ -1837,15 +1848,18 @@ class TestReorderRowElements(unittest.TestCase):
p_val = rng.permutation(5)
out_val = reorder(input_val, p_val)
# Should be equivalent to advanced indexing
out_bis = input_val[p_val]
assert numpy.all(out_val == out_bis)
# Verify gradient
def reorder_fixed(s_input):
"""Auxiliary op defined to get rid of gradient wrt p_val"""
return reorder_row_elements(s_input, p_val)
utt.verify_grad(reorder_fixed, [input_val])
def test_2_1(self):
"""Test broadcasting in ReorderRowElements(matrix, vector)"""
input = matrix()
p = ivector()
out = reorder_row_elements(input, p)
......@@ -1856,15 +1870,18 @@ class TestReorderRowElements(unittest.TestCase):
p_val = rng.permutation(5)
out_val = reorder(input_val, p_val)
# The same permutation should be applied to every row of the input matrix.
out_bis = numpy.asarray([row[p_val] for row in input_val])
assert numpy.all(out_val == out_bis)
# Verify gradient
def reorder_fixed(s_input):
"""Auxiliary op defined to get rid of gradient wrt p_val"""
return reorder_row_elements(s_input, p_val)
utt.verify_grad(reorder_fixed, [input_val])
def test_2_2(self):
"""Test ReorderRowElements(matrix, matrix)"""
input = matrix()
p = imatrix()
out = reorder_row_elements(input, p)
......@@ -1875,11 +1892,14 @@ class TestReorderRowElements(unittest.TestCase):
p_val = numpy.asarray([rng.permutation(5) for i in range(3)])
out_val = reorder(input_val, p_val)
# Each row of p contains a permutation to apply to the corresponding
# row of input
out_bis = numpy.asarray([i_row[p_row] for i_row, p_row in zip(input_val, p_val)])
assert numpy.all(out_val == out_bis)
# Verify gradient
def reorder_fixed(s_input):
"""Auxiliary op defined to get rid of gradient wrt p_val"""
return reorder_row_elements(s_input, p_val)
utt.verify_grad(reorder_fixed, [input_val])
......
theano/tensor/tests/test_randomstreams.py
浏览文件 @ d147486a
......@@ -139,12 +139,18 @@ class T_RandomStreams(unittest.TestCase):
assert m.random is m.m2.random
def test_ndim(self):
"""Test that the behaviour of 'ndim' optional parameter"""
# 'ndim' is an optional integer parameter, specifying the length
# of the 'shape', placed as first argument.
# ndim not specified, OK
m1 = Module()
m1.random = RandomStreams(234)
m1.fn = Method([], m1.random.uniform((2,2)))
made1 = m1.make()
made1.random.initialize()
# ndim specified, consistent with shape, OK
m2 = Module()
m2.random = RandomStreams(234)
m2.fn = Method([], m2.random.uniform(2, (2,2)))
......@@ -155,6 +161,7 @@ class T_RandomStreams(unittest.TestCase):
val2 = made2.fn()
assert numpy.all(val1 == val2)
# ndim specified, inconsistent with shape, should raise ValueError
m3 = Module()
m3.random = RandomStreams(234)
m3.fn = Method([], m3.random.uniform(1, (2,2)))
......@@ -163,6 +170,8 @@ class T_RandomStreams(unittest.TestCase):
self.assertRaises(ValueError, made3.fn)
def test_uniform(self):
"""Test that RandomStreams.uniform generates the same results as numpy"""
# Check over two calls to see if the random state is correctly updated.
m = Module()
m.random = RandomStreams(234)
m.fn = Method([], m.random.uniform((2,2), -1, 1))
......@@ -186,6 +195,8 @@ class T_RandomStreams(unittest.TestCase):
assert numpy.all(fn_val1 == numpy_val1)
def test_normal(self):
"""Test that RandomStreams.normal generates the same results as numpy"""
# Check over two calls to see if the random state is correctly updated.
m = Module()
m.random = RandomStreams(234)
m.fn = Method([], m.random.normal((2,2), -1, 2))
......@@ -204,6 +215,8 @@ class T_RandomStreams(unittest.TestCase):
assert numpy.all(fn_val1 == numpy_val1)
def test_random_integers(self):
"""Test that RandomStreams.random_integers generates the same results as numpy"""
# Check over two calls to see if the random state is correctly updated.
m = Module()
m.random = RandomStreams(234)
m.fn = Method([], m.random.random_integers((20,20), -5, 5))
......@@ -222,6 +235,8 @@ class T_RandomStreams(unittest.TestCase):
assert numpy.all(fn_val1 == numpy_val1)
def test_permutation(self):
"""Test that RandomStreams.uniform generates the same results as numpy"""
# Check over two calls to see if the random state is correctly updated.
m = Module()
m.random = RandomStreams(234)
m.fn = Method([], m.random.permutation((20,), 10))
......@@ -233,6 +248,8 @@ class T_RandomStreams(unittest.TestCase):
rng_seed = numpy.random.RandomState(234).randint(2**30)
rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
# rng.permutation outputs one vector at a time, so we iterate.
numpy_val0 = numpy.asarray([rng.permutation(10) for i in range(20)])
numpy_val1 = numpy.asarray([rng.permutation(10) for i in range(20)])
......@@ -240,6 +257,8 @@ class T_RandomStreams(unittest.TestCase):
assert numpy.all(fn_val1 == numpy_val1)
def test_multinomial(self):
"""Test that RandomStreams.multinomial generates the same results as numpy"""
# Check over two calls to see if the random state is correctly updated.
m = Module()
m.random = RandomStreams(234)
m.fn = Method([], m.random.multinomial((20,20), 1, [0.1]*10))
......@@ -258,6 +277,12 @@ class T_RandomStreams(unittest.TestCase):
assert numpy.all(fn_val1 == numpy_val1)
def test_shuffle_row_elements(self):
"""Test that RandomStreams.shuffle_row_elements generates the right results"""
# Check over two calls to see if the random state is correctly updated.
# On matrices, for each row, the elements of that row should be shuffled.
# Note that this differs from numpy.random.shuffle, where all the elements
# of the matrix are shuffled.
mm = Module()
mm.random = RandomStreams(234)
m_input = tensor.dmatrix()
......@@ -288,6 +313,8 @@ class T_RandomStreams(unittest.TestCase):
assert numpy.all(numpy_mval0 == fn_mval0)
assert numpy.all(numpy_mval1 == fn_mval1)
# On vectors, the behaviour is the same as numpy.random.shuffle,
# except that it does not work in place, but returns a shuffled vector.
vm = Module()
vm.random = RandomStreams(234)
v_input = tensor.dvector()
......@@ -305,6 +332,8 @@ class T_RandomStreams(unittest.TestCase):
print numpy_vval
assert numpy.all(numpy_vval == fn_vval)
# Trying to shuffle a vector with function that should shuffle
# matrices, or vice versa, raises a TypeError
self.assertRaises(TypeError, vmade.f, in_mval)
self.assertRaises(TypeError, mmade.f, in_vval)
......
theano/tensor/tests/test_raw_random.py
浏览文件 @ d147486a
......@@ -95,9 +95,15 @@ class T_random_function(unittest.TestCase):
rf2 = random_function(numpy.random.RandomState.uniform, 'float64', -2.0, 2.0)
rng_R = random_state_type()
# ndim is an optional argument indicating the length of the 'shape'
# ndim not specified, OK
post_out4, out4 = rf2(rng_R, (4,))
# ndim specified, consistent with shape, OK
post_out1_4, out1_4 = rf2(rng_R, 1, (4,))
post_out2_4_4, out2_4_4= rf2(rng_R, 2, (4, 4))
# ndim specified, but not compatible with shape
post_out2_4, out2_4 = rf2(rng_R, 2, (4,))
f_ok = compile.function(
......@@ -109,17 +115,24 @@ class T_random_function(unittest.TestCase):
[out2_4],
accept_inplace=True)
# The correct cases should execute properly
o4, o1_4, o2_4_4 = f_ok()
# Check the sanity of the answers
self.assertTrue(numpy.allclose(o4, o1_4))
self.assertTrue(numpy.allclose(o4, o2_4_4[0]))
# The incorrect case should raise ValueError
self.assertRaises(ValueError, f_no)
def test_random_function_ndim_added(self):
"""Test that random_function helper function accepts ndim_added as keyword argument"""
# On a uniform distribution, ndim_added=-1 means that the shape
# provided should be one dimension bigger, and its last value
# will be ignored
# 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.
uni_1 = random_function(numpy.random.RandomState.uniform, 'float64', -2.0, 2.0, ndim_added=1)
uni_0 = random_function(numpy.random.RandomState.uniform, 'float64', -2.0, 2.0, ndim_added=0)
uni_m1 = random_function(numpy.random.RandomState.uniform, 'float64', -2.0, 2.0, ndim_added=-1)
......@@ -164,7 +177,10 @@ class T_random_function(unittest.TestCase):
self.assertTrue(numpy.allclose(u01, u02[0]))
def test_uniform(self):
"""Test that raw_random.uniform 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
post_r, out = uniform(rng_R, (4,), -2.0, 2.0)
f = compile.function(
......@@ -184,7 +200,11 @@ class T_random_function(unittest.TestCase):
self.assertTrue(numpy.allclose(val1, numpy_val1))
def test_binomial(self):
"""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)
f = compile.function(
......@@ -204,7 +224,10 @@ class T_random_function(unittest.TestCase):
self.assertTrue(numpy.all(val1 == numpy_val1))
def test_normal(self):
"""Test that raw_random.normal 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
post_r, out = normal(rng_R, (2,3), 4.0, 2.0)
f = compile.function(
......@@ -224,7 +247,11 @@ class T_random_function(unittest.TestCase):
self.assertTrue(numpy.allclose(val1, numpy_val1))
def test_random_integers(self):
"""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)
f = compile.function(
......@@ -244,6 +271,13 @@ class T_random_function(unittest.TestCase):
self.assertTrue(numpy.allclose(val1, numpy_val1))
def test_permutation_helper(self):
"""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)
rng_R = random_state_type()
post_r, out = rf(rng_R, (7,), 8)
......@@ -255,6 +289,8 @@ class T_random_function(unittest.TestCase):
numpy_rng = numpy.random.RandomState(55)
val0 = f()
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)])
print val0
......@@ -264,6 +300,8 @@ class T_random_function(unittest.TestCase):
self.assertTrue(numpy.all(val0 == numpy_val0))
self.assertTrue(numpy.all(val1 == numpy_val1))
# This call lacks "ndim_added=1", so ndim_added defaults to 0.
# A ValueError should be raised.
rf0 = RandomFunction(permutation_helper, tensor.imatrix, 8)
post_r0, out0 = rf0(rng_R, (7,), 8)
f0 = compile.function(
......@@ -271,6 +309,7 @@ class T_random_function(unittest.TestCase):
[out0], accept_inplace=True)
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)
post_r2, out2 = rf2(rng_R, (7,), 8)
f2 = compile.function(
......@@ -279,6 +318,7 @@ class T_random_function(unittest.TestCase):
self.assertRaises(ValueError, f2)
def test_permutation(self):
"""Test that raw_random.permutation generates the same results as numpy."""
rng_R = random_state_type()
post_r, out = permutation(rng_R, (9,), 6)
f = compile.function(
......@@ -286,6 +326,9 @@ class T_random_function(unittest.TestCase):
[out], accept_inplace=True)
numpy_rng = numpy.random.RandomState(55)
# Check over two calls to see if the random state is correctly updated.
# numpy_rng.permutation outputs one vector at a time,
# 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)])
......@@ -298,6 +341,8 @@ class T_random_function(unittest.TestCase):
self.assertTrue(numpy.all(val1 == numpy_val1))
def test_multinomial(self):
"""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)
......
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