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329 lines
10 KiB
329 lines
10 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 Gamma 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 Gamma 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.g = msd.Gamma(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.g.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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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_pdf = gamma_benchmark.pdf([1.0, 2.0]).astype(np.float32)
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pdf = Prob()
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output = pdf(Tensor([1.0, 2.0], 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 Gamma 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.g = msd.Gamma(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.g.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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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_logpdf = gamma_benchmark.logpdf([1.0, 2.0]).astype(np.float32)
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logprob = LogProb()
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output = logprob(Tensor([1.0, 2.0], 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 Gamma 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.g = msd.Gamma(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.g.kl_loss('Gamma', 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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concentration_a = np.array([3.0]).astype(np.float32)
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rate_a = np.array([4.0]).astype(np.float32)
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concentration_b = np.array([1.0]).astype(np.float32)
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rate_b = np.array([1.0]).astype(np.float32)
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expect_kl_loss = (concentration_a - concentration_b) * special.digamma(concentration_a) \
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+ special.gammaln(concentration_b) - special.gammaln(concentration_a) \
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+ concentration_b * np.log(rate_a) - concentration_b * np.log(rate_b) \
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+ concentration_a * (rate_b / rate_a - 1.)
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kl_loss = KL()
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concentration = Tensor(concentration_b, dtype=dtype.float32)
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rate = Tensor(rate_b, dtype=dtype.float32)
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output = kl_loss(concentration, rate)
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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 Gamma 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.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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def construct(self):
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return self.g.mean(), self.g.sd(), self.g.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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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_mean = gamma_benchmark.mean().astype(np.float32)
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expect_sd = gamma_benchmark.std().astype(np.float32)
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expect_mode = [2.0]
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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 Gamma 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.g = msd.Gamma(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, concentration=None, rate=None):
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return self.g.sample(self.shape, concentration, rate)
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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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concentration = Tensor([2.0], dtype=dtype.float32)
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rate = 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(concentration, rate)
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assert output.shape == (2, 3, 3)
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class CDF(nn.Cell):
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"""
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Test class: cdf of Gamma distribution.
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"""
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def __init__(self):
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super(CDF, self).__init__()
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self.g = msd.Gamma(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.g.cdf(x_)
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def test_cdf():
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"""
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Test cdf.
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"""
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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_cdf = gamma_benchmark.cdf([2.0]).astype(np.float32)
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cdf = CDF()
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output = cdf(Tensor([2.0], dtype=dtype.float32))
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tol = 2e-5
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assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
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class LogCDF(nn.Cell):
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"""
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Test class: log_cdf of Mormal distribution.
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"""
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def __init__(self):
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super(LogCDF, self).__init__()
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self.g = msd.Gamma(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.g.log_cdf(x_)
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def test_log_cdf():
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"""
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Test log cdf.
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"""
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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_logcdf = gamma_benchmark.logcdf([2.0]).astype(np.float32)
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logcdf = LogCDF()
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output = logcdf(Tensor([2.0], dtype=dtype.float32))
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tol = 5e-5
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assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
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class SF(nn.Cell):
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"""
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Test class: survival function of Gamma distribution.
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"""
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def __init__(self):
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super(SF, self).__init__()
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self.g = msd.Gamma(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.g.survival_function(x_)
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def test_survival():
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"""
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Test log_survival.
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"""
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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_survival = gamma_benchmark.sf([2.0]).astype(np.float32)
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survival_function = SF()
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output = survival_function(Tensor([2.0], dtype=dtype.float32))
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tol = 2e-5
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assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
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class LogSF(nn.Cell):
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"""
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Test class: log survival function of Gamma distribution.
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"""
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def __init__(self):
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super(LogSF, self).__init__()
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self.g = msd.Gamma(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.g.log_survival(x_)
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def test_log_survival():
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"""
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Test log_survival.
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"""
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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_log_survival = gamma_benchmark.logsf([2.0]).astype(np.float32)
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log_survival = LogSF()
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output = log_survival(Tensor([2.0], dtype=dtype.float32))
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tol = 2e-5
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assert (np.abs(output.asnumpy() - expect_log_survival) < tol).all()
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class EntropyH(nn.Cell):
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"""
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Test class: entropy of Gamma 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.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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def construct(self):
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return self.g.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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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_entropy = gamma_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 Gamma 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.g = msd.Gamma(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.g.entropy()
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kl_loss = self.g.kl_loss('Gamma', x_, y_)
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h_sum_kl = entropy + kl_loss
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cross_entropy = self.g.cross_entropy('Gamma', 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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concentration = Tensor([3.0], dtype=dtype.float32)
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rate = Tensor([2.0], dtype=dtype.float32)
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diff = cross_entropy(concentration, rate)
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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.get_flags = msd.Gamma(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.g.kl_loss('Gamma', x_, y_)
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prob = self.g.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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concentration_a = np.array([3.0]).astype(np.float32)
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rate_a = np.array([1.0]).astype(np.float32)
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concentration_b = np.array([2.0]).astype(np.float32)
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rate_b = np.array([1.0]).astype(np.float32)
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ans = prob(Tensor(concentration_b), Tensor(rate_b))
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expect_kl_loss = (concentration_a - concentration_b) * special.digamma(concentration_a) \
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+ special.gammaln(concentration_b) - special.gammaln(concentration_a) \
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+ concentration_b * np.log(rate_a) - concentration_b * np.log(rate_b) \
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+ concentration_a * (rate_b / rate_a - 1.)
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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_prob = gamma_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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