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246 lines
8.5 KiB
246 lines
8.5 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""test cases for Beta distribution"""
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import numpy as np
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from scipy import stats
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from scipy import special
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.nn.probability.distribution as msd
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from mindspore import Tensor
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from mindspore import dtype
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Prob(nn.Cell):
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"""
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Test class: probability of Beta distribution.
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"""
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def __init__(self):
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super(Prob, self).__init__()
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self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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def construct(self, x_):
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return self.b.prob(x_)
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def test_pdf():
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"""
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Test pdf.
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"""
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beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
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expect_pdf = beta_benchmark.pdf([0.25, 0.75]).astype(np.float32)
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pdf = Prob()
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output = pdf(Tensor([0.25, 0.75], dtype=dtype.float32))
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
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class LogProb(nn.Cell):
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"""
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Test class: log probability of Beta distribution.
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"""
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def __init__(self):
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super(LogProb, self).__init__()
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self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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def construct(self, x_):
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return self.b.log_prob(x_)
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def test_log_likelihood():
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"""
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Test log_pdf.
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"""
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beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
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expect_logpdf = beta_benchmark.logpdf([0.25, 0.75]).astype(np.float32)
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logprob = LogProb()
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output = logprob(Tensor([0.25, 0.75], dtype=dtype.float32))
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
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class KL(nn.Cell):
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"""
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Test class: kl_loss of Beta distribution.
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"""
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def __init__(self):
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super(KL, self).__init__()
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self.b = msd.Beta(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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def construct(self, x_, y_):
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return self.b.kl_loss('Beta', x_, y_)
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def test_kl_loss():
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"""
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Test kl_loss.
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"""
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concentration1_a = np.array([3.0]).astype(np.float32)
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concentration0_a = np.array([4.0]).astype(np.float32)
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concentration1_b = np.array([1.0]).astype(np.float32)
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concentration0_b = np.array([1.0]).astype(np.float32)
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total_concentration_a = concentration1_a + concentration0_a
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total_concentration_b = concentration1_b + concentration0_b
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log_normalization_a = np.log(special.beta(concentration1_a, concentration0_a))
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log_normalization_b = np.log(special.beta(concentration1_b, concentration0_b))
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expect_kl_loss = (log_normalization_b - log_normalization_a) \
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- (special.digamma(concentration1_a) * (concentration1_b - concentration1_a)) \
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- (special.digamma(concentration0_a) * (concentration0_b - concentration0_a)) \
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+ (special.digamma(total_concentration_a) * (total_concentration_b - total_concentration_a))
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kl_loss = KL()
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concentration1 = Tensor(concentration1_b, dtype=dtype.float32)
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concentration0 = Tensor(concentration0_b, dtype=dtype.float32)
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output = kl_loss(concentration1, concentration0)
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
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class Basics(nn.Cell):
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"""
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Test class: mean/sd/mode of Beta distribution.
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"""
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def __init__(self):
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super(Basics, self).__init__()
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self.b = msd.Beta(np.array([3.0]), np.array([3.0]), dtype=dtype.float32)
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def construct(self):
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return self.b.mean(), self.b.sd(), self.b.mode()
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def test_basics():
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"""
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Test mean/standard deviation/mode.
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"""
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basics = Basics()
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mean, sd, mode = basics()
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beta_benchmark = stats.beta(np.array([3.0]), np.array([3.0]))
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expect_mean = beta_benchmark.mean().astype(np.float32)
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expect_sd = beta_benchmark.std().astype(np.float32)
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expect_mode = [0.5]
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tol = 1e-6
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assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
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assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()
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assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
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class Sampling(nn.Cell):
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"""
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Test class: sample of Beta distribution.
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"""
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def __init__(self, shape, seed=0):
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super(Sampling, self).__init__()
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self.b = msd.Beta(np.array([3.0]), np.array([1.0]), seed=seed, dtype=dtype.float32)
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self.shape = shape
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def construct(self, concentration1=None, concentration0=None):
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return self.b.sample(self.shape, concentration1, concentration0)
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def test_sample():
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"""
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Test sample.
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"""
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shape = (2, 3)
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seed = 10
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concentration1 = Tensor([2.0], dtype=dtype.float32)
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concentration0 = Tensor([2.0, 2.0, 2.0], dtype=dtype.float32)
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sample = Sampling(shape, seed=seed)
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output = sample(concentration1, concentration0)
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assert output.shape == (2, 3, 3)
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class EntropyH(nn.Cell):
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"""
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Test class: entropy of Beta distribution.
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"""
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def __init__(self):
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super(EntropyH, self).__init__()
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self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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def construct(self):
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return self.b.entropy()
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def test_entropy():
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"""
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Test entropy.
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"""
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beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
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expect_entropy = beta_benchmark.entropy().astype(np.float32)
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entropy = EntropyH()
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output = entropy()
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
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class CrossEntropy(nn.Cell):
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"""
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Test class: cross entropy between Beta distributions.
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"""
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def __init__(self):
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super(CrossEntropy, self).__init__()
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self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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def construct(self, x_, y_):
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entropy = self.b.entropy()
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kl_loss = self.b.kl_loss('Beta', x_, y_)
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h_sum_kl = entropy + kl_loss
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cross_entropy = self.b.cross_entropy('Beta', x_, y_)
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return h_sum_kl - cross_entropy
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def test_cross_entropy():
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"""
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Test cross_entropy.
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"""
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cross_entropy = CrossEntropy()
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concentration1 = Tensor([3.0], dtype=dtype.float32)
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concentration0 = Tensor([2.0], dtype=dtype.float32)
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diff = cross_entropy(concentration1, concentration0)
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tol = 1e-6
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assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()
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class Net(nn.Cell):
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"""
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Test class: expand single distribution instance to multiple graphs
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by specifying the attributes.
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"""
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def __init__(self):
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super(Net, self).__init__()
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self.beta = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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def construct(self, x_, y_):
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kl = self.beta.kl_loss('Beta', x_, y_)
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prob = self.beta.prob(kl)
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return prob
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def test_multiple_graphs():
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"""
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Test multiple graphs case.
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"""
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prob = Net()
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concentration1_a = np.array([3.0]).astype(np.float32)
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concentration0_a = np.array([1.0]).astype(np.float32)
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concentration1_b = np.array([2.0]).astype(np.float32)
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concentration0_b = np.array([1.0]).astype(np.float32)
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ans = prob(Tensor(concentration1_b), Tensor(concentration0_b))
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total_concentration_a = concentration1_a + concentration0_a
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total_concentration_b = concentration1_b + concentration0_b
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log_normalization_a = np.log(special.beta(concentration1_a, concentration0_a))
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log_normalization_b = np.log(special.beta(concentration1_b, concentration0_b))
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expect_kl_loss = (log_normalization_b - log_normalization_a) \
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- (special.digamma(concentration1_a) * (concentration1_b - concentration1_a)) \
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- (special.digamma(concentration0_a) * (concentration0_b - concentration0_a)) \
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+ (special.digamma(total_concentration_a) * (total_concentration_b - total_concentration_a))
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beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
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expect_prob = beta_benchmark.pdf(expect_kl_loss).astype(np.float32)
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expect_prob) < tol).all()
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