def testGeometricLogPmf_validate_args(self):
with self.cached_session():
batch_size = 6
probs = constant_op.constant([.9] * batch_size)
x = array_ops.placeholder(dtypes.float32, shape=[6])
feed_dict = {x: [2.5, 3.2, 4.3, 5.1, 6., 7.]}
geom = geometric.Geometric(probs=probs, validate_args=True)
with self.assertRaisesOpError("Condition x == y"):
log_prob = geom.log_prob(x)
log_prob.eval(feed_dict=feed_dict)
with self.assertRaisesOpError("Condition x >= 0"):
log_prob = geom.log_prob(np.array([-1.], dtype=np.float32))
log_prob.eval()
geom = geometric.Geometric(probs=probs)
log_prob = geom.log_prob(x)
self.assertEqual([
6,
], log_prob.get_shape())
pmf = geom.prob(x)
self.assertEqual([
6,
], pmf.get_shape())
def testInvalidP(self):
invalid_ps = [-.01, -0.01, -2.]
with self.cached_session():
with self.assertRaisesOpError("Condition x >= 0"):
geom = geometric.Geometric(probs=invalid_ps,
validate_args=True)
geom.probs.eval()
invalid_ps = [1.1, 3., 5.]
with self.cached_session():
with self.assertRaisesOpError("Condition x <= y"):
geom = geometric.Geometric(probs=invalid_ps,
validate_args=True)
geom.probs.eval()
def testGeometricSampleMultiDimensional(self):
with self.cached_session():
batch_size = 2
probs_v = [.3, .9]
probs = constant_op.constant([probs_v] * batch_size)
geom = geometric.Geometric(probs=probs)
n = 400000
samples = geom.sample(n, seed=12345)
self.assertEqual([n, batch_size, 2], samples.get_shape())
sample_values = samples.eval()
self.assertFalse(np.any(sample_values < 0.0))
for i in range(2):
self.assertAllClose(sample_values[:, 0, i].mean(),
stats.geom.mean(probs_v[i], loc=-1),
rtol=.02)
self.assertAllClose(sample_values[:, 0, i].var(),
stats.geom.var(probs_v[i], loc=-1),
rtol=.02)
self.assertAllClose(sample_values[:, 1, i].mean(),
stats.geom.mean(probs_v[i], loc=-1),
rtol=.02)
self.assertAllClose(sample_values[:, 1, i].var(),
stats.geom.var(probs_v[i], loc=-1),
rtol=.02)
def testGeometricStddev(self):
with self.cached_session():
probs_v = np.array([.1, .3, .25])
geom = geometric.Geometric(probs=probs_v)
expected_stddevs = stats.geom.std(probs_v, loc=-1)
self.assertEqual([3], geom.stddev().get_shape())
self.assertAllClose(geom.stddev().eval(), expected_stddevs)
def testGeometricVariance(self):
with self.cached_session():
probs_v = np.array([.1, .3, .25])
geom = geometric.Geometric(probs=probs_v)
expected_vars = stats.geom.var(probs_v, loc=-1)
self.assertEqual([3], geom.variance().get_shape())
self.assertAllClose(expected_vars, geom.variance().eval())
def testGeometricEntropy(self):
with self.cached_session():
probs_v = np.array([.1, .3, .25], dtype=np.float32)
geom = geometric.Geometric(probs=probs_v)
expected_entropy = stats.geom.entropy(probs_v, loc=-1)
self.assertEqual([3], geom.entropy().get_shape())
self.assertAllClose(expected_entropy, geom.entropy().eval())
def testGeometricMode(self):
with self.cached_session():
probs_v = np.array([.1, .3, .25])
geom = geometric.Geometric(probs=probs_v)
self.assertEqual([
3,
],
geom.mode().get_shape())
self.assertAllClose([0.] * 3, geom.mode().eval())
def testGeometricShape(self):
with self.cached_session():
probs = constant_op.constant([.1] * 5)
geom = geometric.Geometric(probs=probs)
self.assertEqual([
5,
],
geom.batch_shape_tensor().eval())
self.assertAllEqual([], geom.event_shape_tensor().eval())
self.assertEqual(tensor_shape.TensorShape([5]), geom.batch_shape)
self.assertEqual(tensor_shape.TensorShape([]), geom.event_shape)
def testGeometricCDF(self):
with self.cached_session():
batch_size = 6
probs = constant_op.constant([[.2, .4, .5]] * batch_size)
probs_v = np.array([.2, .4, .5])
x = np.array([[2., 3., 4., 5.5, 6., 7.]], dtype=np.float32).T
geom = geometric.Geometric(probs=probs)
expected_cdf = stats.geom.cdf(x, probs_v, loc=-1)
cdf = geom.cdf(x)
self.assertEqual([6, 3], cdf.get_shape())
self.assertAllClose(expected_cdf, cdf.eval())
def testGeometricLogPmfMultidimensional(self):
with self.cached_session():
batch_size = 6
probs = constant_op.constant([[.2, .3, .5]] * batch_size)
probs_v = np.array([.2, .3, .5])
x = np.array([[2., 3., 4., 5., 6., 7.]], dtype=np.float32).T
geom = geometric.Geometric(probs=probs)
expected_log_prob = stats.geom.logpmf(x, probs_v, loc=-1)
log_prob = geom.log_prob(x)
log_prob_values = log_prob.eval()
self.assertEqual([6, 3], log_prob.get_shape())
self.assertAllClose(expected_log_prob, log_prob_values)
pmf = geom.prob(x)
pmf_values = pmf.eval()
self.assertEqual([6, 3], pmf.get_shape())
self.assertAllClose(np.exp(expected_log_prob), pmf_values)
def testGeometricAtBoundary(self):
with self.cached_session():
geom = geometric.Geometric(probs=1., validate_args=True)
x = np.array([0., 2., 3., 4., 5., 6., 7.], dtype=np.float32)
expected_log_prob = stats.geom.logpmf(x, [1.], loc=-1)
# Scipy incorrectly returns nan.
expected_log_prob[np.isnan(expected_log_prob)] = 0.
log_prob = geom.log_prob(x)
self.assertEqual([
7,
], log_prob.get_shape())
self.assertAllClose(expected_log_prob, log_prob.eval())
pmf = geom.prob(x)
self.assertEqual([
7,
], pmf.get_shape())
self.assertAllClose(np.exp(expected_log_prob), pmf.eval())
expected_log_cdf = stats.geom.logcdf(x, 1., loc=-1)
log_cdf = geom.log_cdf(x)
self.assertEqual([
7,
], log_cdf.get_shape())
self.assertAllClose(expected_log_cdf, log_cdf.eval())
cdf = geom.cdf(x)
self.assertEqual([
7,
], cdf.get_shape())
self.assertAllClose(np.exp(expected_log_cdf), cdf.eval())
expected_mean = stats.geom.mean(1., loc=-1)
self.assertEqual([], geom.mean().get_shape())
self.assertAllClose(expected_mean, geom.mean().eval())
expected_variance = stats.geom.var(1., loc=-1)
self.assertEqual([], geom.variance().get_shape())
self.assertAllClose(expected_variance, geom.variance().eval())
with self.assertRaisesOpError("Entropy is undefined"):
geom.entropy().eval()
def testGeomLogPmf(self):
with self.cached_session():
batch_size = 6
probs = constant_op.constant([.2] * batch_size)
probs_v = .2
x = np.array([2., 3., 4., 5., 6., 7.], dtype=np.float32)
geom = geometric.Geometric(probs=probs)
expected_log_prob = stats.geom.logpmf(x, probs_v, loc=-1)
log_prob = geom.log_prob(x)
self.assertEqual([
6,
], log_prob.get_shape())
self.assertAllClose(expected_log_prob, log_prob.eval())
pmf = geom.prob(x)
self.assertEqual([
6,
], pmf.get_shape())
self.assertAllClose(np.exp(expected_log_prob), pmf.eval())
def testGeometricSample(self):
with self.cached_session():
probs_v = [.3, .9]
probs = constant_op.constant(probs_v)
n = constant_op.constant(100000)
geom = geometric.Geometric(probs=probs)
samples = geom.sample(n, seed=12345)
self.assertEqual([100000, 2], samples.get_shape())
sample_values = samples.eval()
self.assertFalse(np.any(sample_values < 0.0))
for i in range(2):
self.assertAllClose(sample_values[:, i].mean(),
stats.geom.mean(probs_v[i], loc=-1),
rtol=.02)
self.assertAllClose(sample_values[:, i].var(),
stats.geom.var(probs_v[i], loc=-1),
rtol=.02)