def testLaplaceNonPositiveInitializationParamsRaises(self):
with self.test_session():
loc_v = constant_op.constant(0.0, name="loc")
scale_v = constant_op.constant(-1.0, name="scale")
laplace = laplace_lib.Laplace(loc=loc_v,
scale=scale_v,
validate_args=True)
with self.assertRaisesOpError("scale"):
laplace.mean().eval()
loc_v = constant_op.constant(1.0, name="loc")
scale_v = constant_op.constant(0.0, name="scale")
laplace = laplace_lib.Laplace(loc=loc_v,
scale=scale_v,
validate_args=True)
with self.assertRaisesOpError("scale"):
laplace.mean().eval()
def testLaplaceSampleMultiDimensional(self):
with session.Session():
loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100
scale_v = np.array([np.arange(1, 11,
dtype=np.float32)]).T # 10 x 1
laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
n = 10000
samples = laplace.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n, 10, 100))
self.assertEqual(sample_values.shape, (n, 10, 100))
zeros = np.zeros_like(loc_v + scale_v) # 10 x 100
loc_bc = loc_v + zeros
scale_bc = scale_v + zeros
self.assertAllClose(sample_values.mean(axis=0),
stats.laplace.mean(loc_bc, scale=scale_bc),
rtol=0.35,
atol=0.)
self.assertAllClose(sample_values.var(axis=0),
stats.laplace.var(loc_bc, scale=scale_bc),
rtol=0.10,
atol=0.)
fails = 0
trials = 0
for ai, a in enumerate(np.reshape(loc_v, [-1])):
for bi, b in enumerate(np.reshape(scale_v, [-1])):
s = sample_values[:, bi, ai]
trials += 1
fails += 0 if self._kstest(a, b, s) else 1
self.assertLess(fails, trials * 0.03)
def testLaplacePdfOfSampleMultiDims(self):
with session.Session() as sess:
laplace = laplace_lib.Laplace(loc=[7., 11.], scale=[[5.], [6.]])
num = 50000
samples = laplace.sample(num, seed=137)
pdfs = laplace.prob(samples)
sample_vals, pdf_vals = sess.run([samples, pdfs])
self.assertEqual(samples.get_shape(), (num, 2, 2))
self.assertEqual(pdfs.get_shape(), (num, 2, 2))
self.assertAllClose(stats.laplace.mean([[7., 11.], [7., 11.]],
scale=np.array([[5., 5.],
[6., 6.]])),
sample_vals.mean(axis=0),
rtol=0.05,
atol=0.)
self.assertAllClose(stats.laplace.var([[7., 11.], [7., 11.]],
scale=np.array([[5., 5.],
[6., 6.]])),
sample_vals.var(axis=0),
rtol=0.05,
atol=0.)
self._assertIntegral(sample_vals[:, 0, 0],
pdf_vals[:, 0, 0],
err=0.02)
self._assertIntegral(sample_vals[:, 0, 1],
pdf_vals[:, 0, 1],
err=0.02)
self._assertIntegral(sample_vals[:, 1, 0],
pdf_vals[:, 1, 0],
err=0.02)
self._assertIntegral(sample_vals[:, 1, 1],
pdf_vals[:, 1, 1],
err=0.02)
def testLaplaceMode(self):
with self.test_session():
loc_v = np.array([0.5, 3.0, 2.5])
scale_v = np.array([1.0, 4.0, 5.0])
laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
self.assertEqual(laplace.mode().get_shape(), (3, ))
self.assertAllClose(laplace.mode().eval(), loc_v)
def testLaplaceEntropy(self):
with self.test_session():
loc_v = np.array([1.0, 3.0, 2.5])
scale_v = np.array([1.0, 4.0, 5.0])
expected_entropy = stats.laplace.entropy(loc_v, scale=scale_v)
laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
self.assertEqual(laplace.entropy().get_shape(), (3, ))
self.assertAllClose(laplace.entropy().eval(), expected_entropy)
def testLaplaceVariance(self):
with self.test_session():
loc_v = np.array([1.0, 3.0, 2.5])
scale_v = np.array([1.0, 4.0, 5.0])
laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
expected_variances = stats.laplace.var(loc_v, scale=scale_v)
self.assertEqual(laplace.variance().get_shape(), (3, ))
self.assertAllClose(laplace.variance().eval(), expected_variances)
def testLaplaceShape(self):
with self.test_session():
loc = constant_op.constant([3.0] * 5)
scale = constant_op.constant(11.0)
laplace = laplace_lib.Laplace(loc=loc, scale=scale)
self.assertEqual(laplace.batch_shape_tensor().eval(), (5, ))
self.assertEqual(laplace.batch_shape,
tensor_shape.TensorShape([5]))
self.assertAllEqual(laplace.event_shape_tensor().eval(), [])
self.assertEqual(laplace.event_shape, tensor_shape.TensorShape([]))
def testLaplaceLogSurvivalFunction(self):
with self.test_session():
batch_size = 6
loc = constant_op.constant([2.0] * batch_size)
scale = constant_op.constant([3.0] * batch_size)
loc_v = 2.0
scale_v = 3.0
x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32)
laplace = laplace_lib.Laplace(loc=loc, scale=scale)
expected_sf = stats.laplace.logsf(x, loc_v, scale=scale_v)
sf = laplace.log_survival_function(x)
self.assertEqual(sf.get_shape(), (6, ))
self.assertAllClose(sf.eval(), expected_sf)
def testLaplaceCDF(self):
with self.test_session():
batch_size = 6
loc = constant_op.constant([2.0] * batch_size)
scale = constant_op.constant([3.0] * batch_size)
loc_v = 2.0
scale_v = 3.0
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
laplace = laplace_lib.Laplace(loc=loc, scale=scale)
expected_cdf = stats.laplace.cdf(x, loc_v, scale=scale_v)
cdf = laplace.cdf(x)
self.assertEqual(cdf.get_shape(), (6, ))
self.assertAllClose(cdf.eval(), expected_cdf)
def testLaplaceLogPDFMultidimensionalBroadcasting(self):
with self.test_session():
batch_size = 6
loc = constant_op.constant([[2.0, 4.0]] * batch_size)
scale = constant_op.constant(3.0)
loc_v = np.array([2.0, 4.0])
scale_v = 3.0
x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T
laplace = laplace_lib.Laplace(loc=loc, scale=scale)
expected_log_pdf = stats.laplace.logpdf(x, loc_v, scale=scale_v)
log_pdf = laplace.log_prob(x)
log_pdf_values = log_pdf.eval()
self.assertEqual(log_pdf.get_shape(), (6, 2))
self.assertAllClose(log_pdf_values, expected_log_pdf)
pdf = laplace.prob(x)
pdf_values = pdf.eval()
self.assertEqual(pdf.get_shape(), (6, 2))
self.assertAllClose(pdf_values, np.exp(expected_log_pdf))
def testLaplaceSample(self):
with session.Session():
loc_v = 4.0
scale_v = 3.0
loc = constant_op.constant(loc_v)
scale = constant_op.constant(scale_v)
n = 100000
laplace = laplace_lib.Laplace(loc=loc, scale=scale)
samples = laplace.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n, ))
self.assertEqual(sample_values.shape, (n, ))
self.assertAllClose(sample_values.mean(),
stats.laplace.mean(loc_v, scale=scale_v),
rtol=0.05,
atol=0.)
self.assertAllClose(sample_values.var(),
stats.laplace.var(loc_v, scale=scale_v),
rtol=0.05,
atol=0.)
self.assertTrue(self._kstest(loc_v, scale_v, sample_values))
