def testCovariance(self):
vex = tfd.VectorExponentialDiag(loc=tf.ones([2, 3], dtype=tf.float32),
validate_args=True)
self.assertAllClose(np.diag(np.ones([3], dtype=np.float32)),
self.evaluate(vex.covariance()))
vex = tfd.VectorExponentialDiag(loc=tf.ones([3], dtype=tf.float32),
scale_identity_multiplier=[3., 2.],
validate_args=True)
self.assertAllEqual([2], vex.batch_shape)
self.assertAllEqual([3], vex.event_shape)
self.assertAllClose(
np.array([[[3., 0, 0], [0, 3, 0], [0, 0, 3]],
[[2, 0, 0], [0, 2, 0], [0, 0, 2]]])**2.,
self.evaluate(vex.covariance()))
vex = tfd.VectorExponentialDiag(loc=tf.ones([3], dtype=tf.float32),
scale_diag=[[3., 2, 1], [4, 5, 6]],
validate_args=True)
self.assertAllEqual([2], vex.batch_shape)
self.assertAllEqual([3], vex.event_shape)
self.assertAllClose(
np.array([[[3., 0, 0], [0, 2, 0], [0, 0, 1]],
[[4, 0, 0], [0, 5, 0], [0, 0, 6]]])**2.,
self.evaluate(vex.covariance()))
def testStddev(self):
vex = tfd.VectorExponentialDiag(loc=tf.zeros([2, 3], dtype=tf.float32))
self.assertAllClose(np.ones([3], dtype=np.float32),
self.evaluate(vex.stddev()))
vex = tfd.VectorExponentialDiag(loc=tf.zeros([3], dtype=tf.float32),
scale_identity_multiplier=[3., 2.])
self.assertAllClose(np.array([[3., 3, 3], [2., 2, 2]]),
self.evaluate(vex.stddev()))
vex = tfd.VectorExponentialDiag(loc=tf.zeros([3], dtype=tf.float32),
scale_diag=[[3., 2, 1], [4, 5, 6]])
self.assertAllClose(np.array([[3., 2, 1], [4., 5, 6]]),
self.evaluate(vex.stddev()))
def testSampleWithBroadcastScale(self):
# mu corresponds to a 2-batch of 3-variate normals
mu = np.zeros([2, 3])
# diag corresponds to no batches of 3-variate normals
diag = np.ones([3])
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
mean = dist.mean()
self.assertAllEqual([2, 3], mean.shape)
self.assertAllClose(mu + diag, self.evaluate(mean))
n = int(1e4)
samps = self.evaluate(dist.sample(n, seed=test_util.test_seed()))
samps_centered = samps - samps.mean(axis=0)
cov_mat = self.evaluate(tf.linalg.diag(diag))**2
sample_cov = np.matmul(samps_centered.transpose([1, 2, 0]),
samps_centered.transpose([1, 0, 2])) / n
self.assertAllClose(mu + diag,
samps.mean(axis=0),
atol=0.10,
rtol=0.05)
self.assertAllClose([cov_mat, cov_mat],
sample_cov,
atol=0.10,
rtol=0.05)
def testSupportBijectorOutsideRange(self):
vex = tfd.VectorExponentialDiag(loc=tf.ones([3], dtype=tf.float32),
scale_diag=[[3., 2., 1.], [4., 5.,
6.]],
validate_args=True)
x = np.array([[-8.3, -0.4, -1e-6]])
bijector_inverse_x = vex.experimental_default_event_space_bijector(
).inverse(x)
self.assertAllNan(self.evaluate(bijector_inverse_x))
def testVariance(self):
vex = tfd.VectorExponentialDiag(loc=tf.zeros([2, 3], dtype=tf.float32),
validate_args=True)
self.assertAllClose(np.ones([3], dtype=np.float32),
self.evaluate(vex.variance()))
vex = tfd.VectorExponentialDiag(loc=tf.ones([3], dtype=tf.float32),
scale_identity_multiplier=[3., 2.],
validate_args=True)
self.assertAllClose(
np.array([[3., 3, 3], [2., 2, 2]])**2.,
self.evaluate(vex.variance()))
vex = tfd.VectorExponentialDiag(loc=tf.ones([3], dtype=tf.float32),
scale_diag=[[3., 2, 1], [4., 5, 6]],
validate_args=True)
self.assertAllClose(
np.array([[3., 2, 1], [4., 5, 6]])**2.,
self.evaluate(vex.variance()))
def testSample(self):
mu = np.array([-2., 1])
diag = np.array([1., -2])
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
samps = self.evaluate(dist.sample(int(1e4), seed=test_util.test_seed()))
cov_mat = self.evaluate(tf.linalg.diag(diag))**2
self.assertAllClose(
[-2 + 1, 1. - 2], samps.mean(axis=0), atol=0., rtol=0.05)
self.assertAllClose(cov_mat, np.cov(samps.T), atol=0.05, rtol=0.05)
def testSingularScaleRaises(self):
mu = [-1., 1]
diag = [1., 0]
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
with self.assertRaisesOpError('Singular'):
self.evaluate(dist.sample(seed=test_util.test_seed()))
def testAssertValidSample(self):
v = tfd.VectorExponentialDiag(loc=[4., 5.], validate_args=True)
with self.assertRaisesOpError(
'Sample is not contained in the support.'):
self.evaluate(v.log_prob([3., 4.]))
def testMeanWithBroadcastLoc(self):
mu = [-1.]
diag = [1., -5]
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
self.assertAllEqual([-1. + 1, -1. - 5], self.evaluate(dist.mean()))
def testMode(self):
mu = [-1.]
diag = [1., -5]
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
self.assertAllEqual([-1., -1.], self.evaluate(dist.mode()))
def testMean(self):
mu = [-1., 1]
diag = [1., -5]
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
self.assertAllEqual([-1. + 1., 1. - 5.], self.evaluate(dist.mean()))
def testVectorParams(self):
mu = [-1.]
diag = [-5.]
dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)
self.assertAllEqual([3, 1],
dist.sample(3, seed=test_util.test_seed()).shape)
def testScalarParams(self):
mu = -1.
diag = -5.
with self.assertRaisesRegexp(ValueError, 'at least 1 dimension'):
tfd.VectorExponentialDiag(mu, diag)
