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304 lines
9.5 KiB
304 lines
9.5 KiB
# Copyright 2019 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 Gumbel 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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.gum.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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loc = np.array([0.0]).astype(np.float32)
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scale = np.array([[1.0], [2.0]]).astype(np.float32)
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gumbel_benchmark = stats.gumbel_r(loc, scale)
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value = np.array([1.0, 2.0]).astype(np.float32)
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expect_pdf = gumbel_benchmark.pdf(value).astype(np.float32)
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pdf = Prob()
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output = pdf(Tensor(value, 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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.gum.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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loc = np.array([0.0]).astype(np.float32)
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scale = np.array([[1.0], [2.0]]).astype(np.float32)
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gumbel_benchmark = stats.gumbel_r(loc, scale)
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expect_logpdf = gumbel_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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([1.0, 2.0]), dtype=dtype.float32)
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def construct(self, loc_b, scale_b):
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return self.gum.kl_loss('Gumbel', loc_b, scale_b)
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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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loc = np.array([0.0]).astype(np.float32)
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scale = np.array([1.0, 2.0]).astype(np.float32)
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loc_b = np.array([1.0]).astype(np.float32)
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scale_b = np.array([1.0, 2.0]).astype(np.float32)
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expect_kl_loss = np.log(scale_b) - np.log(scale) +\
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np.euler_gamma * (scale / scale_b - 1.) +\
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np.expm1((loc_b - loc) / scale_b + special.loggamma(scale / scale_b + 1.))
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kl_loss = KL()
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loc_b = Tensor(loc_b, dtype=dtype.float32)
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scale_b = Tensor(scale_b, dtype=dtype.float32)
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output = kl_loss(loc_b, scale_b)
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tol = 1e-5
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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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
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def construct(self):
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return self.gum.mean(), self.gum.sd(), self.gum.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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loc = np.array([0.0]).astype(np.float32)
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scale = np.array([[1.0], [2.0]]).astype(np.float32)
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gumbel_benchmark = stats.gumbel_r(loc, scale)
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expect_mean = gumbel_benchmark.mean().astype(np.float32)
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expect_sd = gumbel_benchmark.std().astype(np.float32)
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expect_mode = np.array([[0.0], [0.0]]).astype(np.float32)
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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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([1.0, 2.0, 3.0]), dtype=dtype.float32, seed=seed)
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self.shape = shape
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def construct(self):
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return self.gum.sample(self.shape)
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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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sample = Sampling(shape, seed=seed)
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output = sample()
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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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.gum.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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loc = np.array([0.0]).astype(np.float32)
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scale = np.array([[1.0], [2.0]]).astype(np.float32)
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gumbel_benchmark = stats.gumbel_r(loc, scale)
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expect_cdf = gumbel_benchmark.cdf([1.0, 2.0]).astype(np.float32)
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cdf = CDF()
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output = cdf(Tensor([1.0, 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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.gum.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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loc = np.array([0.0]).astype(np.float32)
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scale = np.array([[1.0], [2.0]]).astype(np.float32)
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gumbel_benchmark = stats.gumbel_r(loc, scale)
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expect_logcdf = gumbel_benchmark.logcdf([1.0, 2.0]).astype(np.float32)
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logcdf = LogCDF()
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output = logcdf(Tensor([1.0, 2.0], dtype=dtype.float32))
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tol = 1e-4
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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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.gum.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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loc = np.array([0.0]).astype(np.float32)
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scale = np.array([[1.0], [2.0]]).astype(np.float32)
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gumbel_benchmark = stats.gumbel_r(loc, scale)
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expect_survival = gumbel_benchmark.sf([1.0, 2.0]).astype(np.float32)
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survival_function = SF()
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output = survival_function(Tensor([1.0, 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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.gum.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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loc = np.array([0.0]).astype(np.float32)
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scale = np.array([[1.0], [2.0]]).astype(np.float32)
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gumbel_benchmark = stats.gumbel_r(loc, scale)
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expect_log_survival = gumbel_benchmark.logsf([1.0, 2.0]).astype(np.float32)
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log_survival = LogSF()
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output = log_survival(Tensor([1.0, 2.0], dtype=dtype.float32))
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tol = 5e-4
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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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
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def construct(self):
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return self.gum.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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loc = np.array([0.0]).astype(np.float32)
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scale = np.array([[1.0], [2.0]]).astype(np.float32)
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gumbel_benchmark = stats.gumbel_r(loc, scale)
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expect_entropy = gumbel_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 Gumbel 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.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
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def construct(self, x_, y_):
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entropy = self.gum.entropy()
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kl_loss = self.gum.kl_loss('Gumbel', x_, y_)
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h_sum_kl = entropy + kl_loss
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cross_entropy = self.gum.cross_entropy('Gumbel', 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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loc = Tensor([1.0], dtype=dtype.float32)
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scale = Tensor([1.0], dtype=dtype.float32)
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diff = cross_entropy(loc, scale)
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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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