def test_entropy(self):
with paddle.fluid.dygraph.guard(self.place):
np.testing.assert_allclose(
self._paddle_beta.entropy(),
scipy.stats.beta.entropy(self.alpha, self.beta),
rtol=RTOL.get(str(self._paddle_beta.alpha.numpy().dtype)),
atol=ATOL.get(str(self._paddle_beta.alpha.numpy().dtype)))
def test_entropy(self):
with paddle.fluid.dygraph.guard(self.place):
np.testing.assert_allclose(
self._paddle_diric.entropy(),
scipy.stats.dirichlet.entropy(self.concentration),
rtol=RTOL.get(str(self.concentration.dtype)),
atol=ATOL.get(str(self.concentration.dtype)))
def test_log_prob(self):
value = [np.random.rand(*self._paddle_beta.alpha.shape)]
for v in value:
with paddle.fluid.dygraph.guard(self.place):
np.testing.assert_allclose(
self._paddle_beta.log_prob(paddle.to_tensor(v)),
scipy.stats.beta.logpdf(v, self.alpha, self.beta),
rtol=RTOL.get(str(self._paddle_beta.alpha.numpy().dtype)),
atol=ATOL.get(str(self._paddle_beta.alpha.numpy().dtype)))
def test_variance(self):
with paddle.static.program_guard(self.program):
[out] = self.executor.run(self.program,
feed=self.feeds,
fetch_list=[self._paddle_diric.variance])
np.testing.assert_allclose(
out,
scipy.stats.dirichlet.var(self.concentration),
rtol=RTOL.get(str(self.concentration.dtype)),
atol=ATOL.get(str(self.concentration.dtype)))
def test_mean(self):
with paddle.static.program_guard(self.program):
[mean] = self.executor.run(self.program,
feed=self.feeds,
fetch_list=[self._paddle_beta.mean])
np.testing.assert_allclose(mean,
scipy.stats.beta.mean(
self.alpha, self.beta),
rtol=RTOL.get(str(self.alpha.dtype)),
atol=ATOL.get(str(self.alpha.dtype)))
def test_log_prob(self):
value = [np.random.rand(*self.concentration.shape)]
value = [v / v.sum() for v in value]
for v in value:
with paddle.fluid.dygraph.guard(self.place):
np.testing.assert_allclose(
self._paddle_diric.log_prob(paddle.to_tensor(v)),
scipy.stats.dirichlet.logpdf(v, self.concentration),
rtol=RTOL.get(str(self.concentration.dtype)),
atol=ATOL.get(str(self.concentration.dtype)))
def test_log_prob(self):
with paddle.static.program_guard(self.program):
random_number = np.random.rand(*self.concentration.shape)
random_number = random_number / random_number.sum()
feeds = dict(self.feeds, value=random_number)
value = paddle.static.data('value', random_number.shape,
random_number.dtype)
out = self._paddle_diric.log_prob(value)
[out] = self.executor.run(self.program,
feed=feeds,
fetch_list=[out])
np.testing.assert_allclose(
out,
scipy.stats.dirichlet.logpdf(random_number, self.concentration),
rtol=RTOL.get(str(self.concentration.dtype)),
atol=ATOL.get(str(self.concentration.dtype)))
def test_log_prob(self):
with paddle.static.program_guard(self.program):
value = paddle.static.data('value', self._paddle_beta.alpha.shape,
self._paddle_beta.alpha.dtype)
prob = self._paddle_beta.log_prob(value)
random_number = np.random.rand(*self._paddle_beta.alpha.shape)
feeds = dict(self.feeds, value=random_number)
[prob] = self.executor.run(self.program,
feed=feeds,
fetch_list=[prob])
np.testing.assert_allclose(prob,
scipy.stats.beta.logpdf(
random_number, self.alpha,
self.beta),
rtol=RTOL.get(str(self.alpha.dtype)),
atol=ATOL.get(str(self.alpha.dtype)))