def testLogLogisticSample(self): log_logistic_scale = 1.5 loc = np.log(log_logistic_scale).astype(np.float32) scale = 0.33 dist = tfd.LogLogistic(loc=loc, scale=scale, validate_args=True) samples = self.evaluate(dist.sample(6000, seed=test_util.test_seed())) self.assertAllClose(np.mean(samples), self.evaluate(dist.mean()), atol=0.1) self.assertAllClose(np.std(samples), self.evaluate(dist.stddev()), atol=0.5)
def testLogLogisticMode(self):
log_logistic_scale = np.float32([3., 1.5, 0.75])
loc = np.log(log_logistic_scale)
scale = np.float32([0.4, 0.6, 1.5])
dist = tfd.LogLogistic(loc=loc, scale=scale, validate_args=True)
mode = log_logistic_scale * ((1. - scale) / (1. + scale))**scale
mode[2] = 0.
self.assertAllClose(self.evaluate(dist.mode()), mode)
def testLogLogisticEntropy(self):
log_logistic_scale = np.float32([3., 1.5, 0.75])
loc = np.log(log_logistic_scale)
scale = np.float32([0.4, 0.6, 1.5])
dist = tfd.LogLogistic(loc=loc, scale=scale, validate_args=True)
self.assertAllClose(
self.evaluate(dist.entropy()),
stats.fisk.entropy(loc=0., scale=log_logistic_scale, c=1. / scale))
def testLogLogisticMeanNoNanAllowed(self):
log_logistic_scale = np.float32([3., 1.5, 0.75])
loc = np.log(log_logistic_scale)
scale = np.float32([0.4, 0.6, 1.5])
dist = tfd.LogLogistic(
loc=loc, scale=scale, validate_args=True, allow_nan_stats=False)
with self.assertRaisesOpError('Condition x < y.*'):
self.evaluate(dist.mean())
def testLogLogisticLogSurvivalScaleBatch(self):
log_logistic_scale = 1.5
loc = np.log(log_logistic_scale)
scale = np.array([1.2, 5.1])
dist = tfd.LogLogistic(loc=loc, scale=scale, validate_args=True)
x = np.array([1.0], dtype=np.float32)
logsf = dist.log_survival_function(x)
self.assertAllClose(
self.evaluate(logsf),
stats.fisk.logsf(x, loc=0., scale=log_logistic_scale, c=1. / scale))
def testLogLogisticLogSurvivalLocBatch(self):
log_logistic_scale = [0.42, 1.3]
loc = np.log(log_logistic_scale)
scale = 2.5
dist = tfd.LogLogistic(loc=loc, scale=scale, validate_args=True)
x = np.array([[1e-4, 1.0], [3., 2.0]], dtype=np.float32)
logsf = dist.log_survival_function(x)
self.assertAllClose(
self.evaluate(logsf),
stats.fisk.logsf(x, loc=0., scale=log_logistic_scale, c=1. / scale))
def testLogLogisticLogCDFScaleBatch(self):
log_logistic_scale = [0.75, 2.5]
loc = np.log(log_logistic_scale)
scale = np.array([0.3, 2.1])
dist = tfd.LogLogistic(loc=loc, scale=scale, validate_args=True)
x = np.array([[1e-4, 1.0], [5.0, 2.0]], dtype=np.float32)
log_cdf = dist.log_cdf(x)
self.assertAllClose(
self.evaluate(log_cdf),
stats.fisk.logcdf(x, loc=0., scale=log_logistic_scale, c=1. / scale))
def testLogLogisticCDFLocBatch(self):
log_logistic_scale = [0.5, 1.5]
loc = np.log(log_logistic_scale)
scale = 2.5
dist = tfd.LogLogistic(loc=loc, scale=scale, validate_args=True)
x = np.array([1e-4], dtype=np.float32)
cdf = dist.cdf(x)
self.assertAllClose(
self.evaluate(cdf),
stats.fisk.cdf(x, loc=0., scale=log_logistic_scale, c=1. / scale))
def testLogLogisticVariance(self):
log_logistic_scale = np.float32([3., 1.5, 0.75])
loc = np.log(log_logistic_scale)
scale = np.float32([0.4, 0.3, 0.2])
dist = tfd.LogLogistic(loc=loc, scale=scale, validate_args=True)
# scipy.stats.fisk.var only works on scalars, so we calculate this in a
# loop:
scipy_var = [stats.fisk.var(
loc=0., scale=s, c=1. / c) for (s, c) in zip(log_logistic_scale, scale)]
self.assertAllClose(self.evaluate(dist.variance()), scipy_var)
self.assertAllClose(self.evaluate(dist.stddev()), np.sqrt(scipy_var))
def testLogLogisticLogPDFScaleBatch(self):
log_logistic_scale = [1.5, 2.]
loc = np.log(log_logistic_scale)
scale = np.array([2.5, 5.])
dist = tfd.LogLogistic(loc=loc, scale=scale, validate_args=True)
x = np.array([2.0], dtype=np.float32)
log_pdf = dist.log_prob(x)
self.assertAllClose(
self.evaluate(log_pdf),
stats.fisk.logpdf(x, loc=0., scale=log_logistic_scale, c=1. / scale))
def testSupportBijectorOutsideRange(self):
dist = tfd.LogLogistic(loc=0., scale=0.5, validate_args=True)
with self.assertRaisesOpError('must be greater than or equal to 0'):
dist.experimental_default_event_space_bijector().inverse(
[-4.2, -1e-6, -1.3])
def testAssertValidSample(self):
dist = tfd.LogLogistic(
loc=np.log([1., 1., 4.]), scale=2., validate_args=True)
with self.assertRaisesOpError('Sample must be non-negative.'):
self.evaluate(dist.cdf([3., -0.2, 1.]))
