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283 lines
8.5 KiB
283 lines
8.5 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 Cauchy distribution"""
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import numpy as np
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from scipy import stats
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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 Cauchy 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.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.c.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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cauchy_benchmark = stats.cauchy(np.array([3.0]), np.array([[2.0], [4.0]]))
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expect_pdf = cauchy_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 Cauchy 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.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.c.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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cauchy_benchmark = stats.cauchy(np.array([3.0]), np.array([[2.0], [4.0]]))
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expect_logpdf = cauchy_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 Cauchy 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.c = msd.Cauchy(np.array([3.]), np.array([4.]), dtype=dtype.float32)
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def construct(self, mu, s):
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return self.c.kl_loss('Cauchy', mu, s)
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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_b = np.array([0.]).astype(np.float32)
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scale_b = np.array([1.]).astype(np.float32)
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loc_a = np.array([3.0]).astype(np.float32)
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scale_a = np.array([4.0]).astype(np.float32)
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sum_square = np.square(scale_a + scale_b)
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square_diff = np.square(loc_a - loc_b)
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expect_kl_loss = np.log(sum_square + square_diff) - \
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np.log(4.0 * scale_a * scale_b)
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kl_loss = KL()
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loc = Tensor(loc_b, dtype=dtype.float32)
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scale = Tensor(scale_b, dtype=dtype.float32)
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output = kl_loss(loc, scale)
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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: mode of Cauchy 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.c = msd.Cauchy(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
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def construct(self):
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return self.c.mode()
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def test_basics():
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"""
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Test mode.
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"""
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basics = Basics()
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mode = basics()
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expect_mode = np.array([3.0, 3.0])
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tol = 1e-6
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assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()
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class Sampling(nn.Cell):
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"""
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Test class: sample of Cauchy 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.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
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self.shape = shape
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def construct(self, mean=None, sd=None):
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return self.c.sample(self.shape, mean, sd)
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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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mean = Tensor([2.0], dtype=dtype.float32)
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sd = 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(mean, sd)
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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 Cauchy 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.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.c.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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cauchy_benchmark = stats.cauchy(np.array([3.0]), np.array([[2.0], [4.0]]))
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expect_cdf = cauchy_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 Cauchy 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.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.c.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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cauchy_benchmark = stats.cauchy(np.array([3.0]), np.array([[2.0], [4.0]]))
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expect_logcdf = cauchy_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 = 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 Cauchy 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.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.c.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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cauchy_benchmark = stats.cauchy(np.array([3.0]), np.array([[2.0], [4.0]]))
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expect_survival = cauchy_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 Cauchy 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.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.c.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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cauchy_benchmark = stats.cauchy(np.array([3.0]), np.array([[2.0], [4.0]]))
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expect_log_survival = cauchy_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 = 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 Cauchy 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.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self):
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return self.c.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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expect_entropy = np.log(4 * np.pi * np.array([[2.0], [4.0]]))
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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 Cauchy 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.c = msd.Cauchy(np.array([3.]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self, mu, s):
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entropy = self.c.entropy()
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kl_loss = self.c.kl_loss('Cauchy', mu, s)
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h_sum_kl = entropy + kl_loss
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cross_entropy = self.c.cross_entropy('Cauchy', mu, s)
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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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mean = Tensor([1.0], dtype=dtype.float32)
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sd = Tensor([1.0], dtype=dtype.float32)
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diff = cross_entropy(mean, sd)
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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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