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提交 fb83675c authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Use numpy.allclose for floatX comparisons. Fix tests with floatX=float32

上级 a9f2a795
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正在显示 包含 45 行增加 和 45 行删除
theano/tensor/tests/test_randomstreams.py
浏览文件 @ fb83675c
......@@ -37,8 +37,8 @@ class T_RandomStreams(unittest.TestCase):
numpy_val1 = rng.uniform(size=(2,2))
#print numpy_val0
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_seed_in_initialize(self):
m = Module()
......@@ -58,8 +58,8 @@ class T_RandomStreams(unittest.TestCase):
numpy_val1 = rng.uniform(size=(2,2))
#print numpy_val0
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_seed_fn(self):
m = Module()
......@@ -81,8 +81,8 @@ class T_RandomStreams(unittest.TestCase):
numpy_val1 = rng.uniform(size=(2,2))
#print numpy_val0
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_getitem(self):
......@@ -102,8 +102,8 @@ class T_RandomStreams(unittest.TestCase):
fn_val1 = made.fn()
numpy_val0 = rng.uniform(size=(2,2))
numpy_val1 = rng.uniform(size=(2,2))
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_setitem(self):
......@@ -129,8 +129,8 @@ class T_RandomStreams(unittest.TestCase):
fn_val1 = made.fn()
numpy_val0 = rng.uniform(size=(2,2))
numpy_val1 = rng.uniform(size=(2,2))
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_multiple(self):
M = Module()
......@@ -197,8 +197,8 @@ class T_RandomStreams(unittest.TestCase):
print numpy_val0
print numpy_val1
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_normal(self):
"""Test that RandomStreams.normal generates the same results as numpy"""
......@@ -217,8 +217,8 @@ class T_RandomStreams(unittest.TestCase):
numpy_val0 = rng.normal(-1, 2, size=(2,2))
numpy_val1 = rng.normal(-1, 2, size=(2,2))
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_random_integers(self):
"""Test that RandomStreams.random_integers generates the same results as numpy"""
......@@ -373,8 +373,8 @@ class T_RandomStreams(unittest.TestCase):
val1 = made.f()
numpy_val0 = numpy_rng.uniform()
numpy_val1 = numpy_rng.uniform()
assert numpy.all(val0 == numpy_val0)
assert numpy.all(val1 == numpy_val1)
assert numpy.allclose(val0, numpy_val0)
assert numpy.allclose(val1, numpy_val1)
val2 = made.g()
numpy_val2 = numpy_rng.multinomial(n=1, pvals=[.5, .5])
......@@ -406,16 +406,16 @@ class T_RandomStreams(unittest.TestCase):
val1 = made.f([.9])
numpy_val0 = numpy_rng.uniform(low=[-5, .5, 0, 1], high=1)
numpy_val1 = numpy_rng.uniform(low=[.9], high=1)
assert numpy.all(val0 == numpy_val0)
assert numpy.all(val1 == numpy_val1)
assert numpy.allclose(val0, numpy_val0)
assert numpy.allclose(val1, numpy_val1)
numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))
val0b = made.fb([-4., -2], [-1, 0])
val1b = made.fb([-4.], [-1])
numpy_val0b = numpy_rng.uniform(low=[-4., -2], high=[-1, 0])
numpy_val1b = numpy_rng.uniform(low=[-4.], high=[-1])
assert numpy.all(val0b == numpy_val0b)
assert numpy.all(val1b == numpy_val1b)
assert numpy.allclose(val0b, numpy_val0b)
assert numpy.allclose(val1b, numpy_val1b)
self.assertRaises(ValueError, made.fb, [-4., -2], [-1, 0, 1])
#TODO: do we want that?
#self.assertRaises(ValueError, made.fb, [-4., -2], [-1])
......@@ -425,8 +425,8 @@ class T_RandomStreams(unittest.TestCase):
val1c = made.fc([-4.], [-1], [1])
numpy_val0c = numpy_rng.uniform(low=[-4., -2], high=[-1, 0])
numpy_val1c = numpy_rng.uniform(low=[-4.], high=[-1])
assert numpy.all(val0c == numpy_val0c)
assert numpy.all(val1c == numpy_val1c)
assert numpy.allclose(val0c, numpy_val0c)
assert numpy.allclose(val1c, numpy_val1c)
self.assertRaises(ValueError, made.fc, [-4., -2], [-1, 0], [1])
self.assertRaises(ValueError, made.fc, [-4., -2], [-1, 0], [1,2])
self.assertRaises(ValueError, made.fc, [-4., -2], [-1, 0], [2,1])
......@@ -455,9 +455,9 @@ class T_RandomStreams(unittest.TestCase):
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]])
assert numpy.all(val0 == numpy_val0)
assert numpy.all(val1 == numpy_val1)
assert numpy.all(val2 == numpy_val2)
assert numpy.allclose(val0, numpy_val0)
assert numpy.allclose(val1, numpy_val1)
assert numpy.allclose(val2, numpy_val2)
def test_uniform_vector(self):
m = Module()
......@@ -481,18 +481,18 @@ class T_RandomStreams(unittest.TestCase):
# Arguments of size (3,)
val0 = made.f(low_val, high_val)
numpy_val0 = numpy_rng.uniform(low=low_val, high=high_val)
assert numpy.all(val0 == numpy_val0)
assert numpy.allclose(val0, numpy_val0)
# arguments of size (2,)
val1 = made.f(low_val[:-1], high_val[:-1])
numpy_val1 = numpy_rng.uniform(low=low_val[:-1], high=high_val[:-1])
assert numpy.all(val1 == numpy_val1)
assert numpy.allclose(val1, numpy_val1)
# Specifying the size explicitly
numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))
val2 = made.g(low_val, high_val)
numpy_val2 = numpy_rng.uniform(low=low_val, high=high_val, size=(3,))
assert numpy.all(val2 == numpy_val2)
assert numpy.allclose(val2, numpy_val2)
self.assertRaises(ValueError, made.g, low_val[:-1], high_val[:-1])
def test_binomial_vector(self):
......@@ -553,18 +553,18 @@ class T_RandomStreams(unittest.TestCase):
# Arguments of size (3,)
val0 = made.f(avg_val, std_val)
numpy_val0 = numpy_rng.normal(loc=avg_val, scale=std_val)
assert numpy.all(val0 == numpy_val0)
assert numpy.allclose(val0, numpy_val0)
# arguments of size (2,)
val1 = made.f(avg_val[:-1], std_val[:-1])
numpy_val1 = numpy_rng.normal(loc=avg_val[:-1], scale=std_val[:-1])
assert numpy.all(val1 == numpy_val1)
assert numpy.allclose(val1, numpy_val1)
# Specifying the size explicitly
numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))
val2 = made.g(avg_val, std_val)
numpy_val2 = numpy_rng.normal(loc=avg_val, scale=std_val, size=(3,))
assert numpy.all(val2 == numpy_val2)
assert numpy.allclose(val2, numpy_val2)
self.assertRaises(ValueError, made.g, avg_val[:-1], std_val[:-1])
def test_random_integers_vector(self):
......
theano/tensor/tests/test_shared_randomstreams.py
浏览文件 @ fb83675c
......@@ -49,12 +49,12 @@ class T_SharedRandomStreams(unittest.TestCase):
numpy_val1 = rng.uniform(size=(2,2))
#print numpy_val0
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.allclose(fn_val0, numpy_val0)
print fn_val0
print numpy_val0
print fn_val1
print numpy_val1
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val1, numpy_val1)
def test_seed_fn(self):
random = RandomStreams(234)
......@@ -73,8 +73,8 @@ class T_SharedRandomStreams(unittest.TestCase):
numpy_val1 = rng.uniform(size=(2,2))
#print numpy_val0
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_getitem(self):
......@@ -91,8 +91,8 @@ class T_SharedRandomStreams(unittest.TestCase):
fn_val1 = fn()
numpy_val0 = rng.uniform(size=(2,2))
numpy_val1 = rng.uniform(size=(2,2))
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_setitem(self):
......@@ -109,8 +109,8 @@ class T_SharedRandomStreams(unittest.TestCase):
fn_val1 = fn()
numpy_val0 = rng.uniform(size=(2,2))
numpy_val1 = rng.uniform(size=(2,2))
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_ndim(self):
"""Test that the behaviour of 'ndim' optional parameter"""
......@@ -146,8 +146,8 @@ class T_SharedRandomStreams(unittest.TestCase):
numpy_val0 = rng.uniform(-1, 1, size=(2,2))
numpy_val1 = rng.uniform(-1, 1, size=(2,2))
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_normal(self):
"""Test that RandomStreams.normal generates the same results as numpy"""
......@@ -163,8 +163,8 @@ class T_SharedRandomStreams(unittest.TestCase):
numpy_val0 = rng.normal(-1, 2, size=(2,2))
numpy_val1 = rng.normal(-1, 2, size=(2,2))
assert numpy.all(fn_val0 == numpy_val0)
assert numpy.all(fn_val1 == numpy_val1)
assert numpy.allclose(fn_val0, numpy_val0)
assert numpy.allclose(fn_val1, numpy_val1)
def test_random_integers(self):
"""Test that RandomStreams.random_integers generates the same results as numpy"""
......@@ -343,8 +343,8 @@ class T_SharedRandomStreams(unittest.TestCase):
val1 = f()
numpy_val0 = numpy_rng.uniform()
numpy_val1 = numpy_rng.uniform()
assert numpy.all(val0 == numpy_val0)
assert numpy.all(val1 == numpy_val1)
assert numpy.allclose(val0, numpy_val0)
assert numpy.allclose(val1, numpy_val1)
val2 = g()
numpy_val2 = numpy_rng.multinomial(n=1, pvals=[.5, .5])
......
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