def testExponentialEntropy(self):
with session.Session():
lam_v = np.array([1.0, 4.0, 2.5])
expected_entropy = stats.expon.entropy(scale=1 / lam_v)
exponential = exponential_lib.Exponential(rate=lam_v)
self.assertEqual(exponential.entropy().get_shape(), (3, ))
self.assertAllClose(exponential.entropy().eval(), expected_entropy)
def testExponentialMean(self):
with session.Session():
lam_v = np.array([1.0, 4.0, 2.5])
expected_mean = stats.expon.mean(scale=1 / lam_v)
exponential = exponential_lib.Exponential(lam=lam_v)
self.assertEqual(exponential.mean().get_shape(), (3, ))
self.assertAllClose(exponential.mean().eval(), expected_mean)
def testExponentialCDF(self):
with session.Session():
batch_size = 6
lam = constant_op.constant([2.0] * batch_size)
lam_v = 2.0
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
exponential = exponential_lib.Exponential(rate=lam)
expected_cdf = stats.expon.cdf(x, scale=1 / lam_v)
cdf = exponential.cdf(x)
self.assertEqual(cdf.get_shape(), (6, ))
self.assertAllClose(cdf.eval(), expected_cdf)
def testExponentialSample(self):
with self.test_session():
lam = constant_op.constant([3.0, 4.0])
lam_v = [3.0, 4.0]
n = constant_op.constant(100000)
exponential = exponential_lib.Exponential(rate=lam)
samples = exponential.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(sample_values.shape, (100000, 2))
self.assertFalse(np.any(sample_values < 0.0))
for i in range(2):
self.assertLess(
stats.kstest(sample_values[:, i],
stats.expon(scale=1.0 / lam_v[i]).cdf)[0],
0.01)
def testExponentialLogPDF(self):
with session.Session():
batch_size = 6
lam = constant_op.constant([2.0] * batch_size)
lam_v = 2.0
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
exponential = exponential_lib.Exponential(lam=lam)
expected_log_pdf = stats.expon.logpdf(x, scale=1 / lam_v)
log_pdf = exponential.log_pdf(x)
self.assertEqual(log_pdf.get_shape(), (6, ))
self.assertAllClose(log_pdf.eval(), expected_log_pdf)
pdf = exponential.pdf(x)
self.assertEqual(pdf.get_shape(), (6, ))
self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf))
def testExponentialSampleMultiDimensional(self):
with self.test_session():
batch_size = 2
lam_v = [3.0, 22.0]
lam = constant_op.constant([lam_v] * batch_size)
exponential = exponential_lib.Exponential(rate=lam)
n = 100000
samples = exponential.sample(n, seed=138)
self.assertEqual(samples.get_shape(), (n, batch_size, 2))
sample_values = samples.eval()
self.assertFalse(np.any(sample_values < 0.0))
for i in range(2):
self.assertLess(
stats.kstest(sample_values[:, 0, i],
stats.expon(scale=1.0 / lam_v[i]).cdf)[0],
0.01)
self.assertLess(
stats.kstest(sample_values[:, 1, i],
stats.expon(scale=1.0 / lam_v[i]).cdf)[0],
0.01)