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322 lines
8.2 KiB
322 lines
8.2 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 Geometric 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 Geometric 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.Geometric(0.7, dtype=dtype.int32)
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def construct(self, x_):
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return self.g.prob(x_)
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def test_pmf():
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"""
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Test pmf.
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"""
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geom_benchmark = stats.geom(0.7)
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expect_pmf = geom_benchmark.pmf([0, 1, 2, 3, 4]).astype(np.float32)
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pdf = Prob()
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x_ = Tensor(np.array([-1, 0, 1, 2, 3]
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).astype(np.float32), dtype=dtype.float32)
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output = pdf(x_)
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_pmf) < tol).all()
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class LogProb(nn.Cell):
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"""
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Test class: log probability of Geometric 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.Geometric(0.7, dtype=dtype.int32)
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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_pmf.
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"""
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geom_benchmark = stats.geom(0.7)
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expect_logpmf = geom_benchmark.logpmf([1, 2, 3, 4, 5]).astype(np.float32)
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logprob = LogProb()
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x_ = Tensor(np.array([0, 1, 2, 3, 4]).astype(
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np.int32), dtype=dtype.float32)
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output = logprob(x_)
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_logpmf) < tol).all()
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class KL(nn.Cell):
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"""
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Test class: kl_loss between Geometric distributions.
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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.Geometric(0.7, dtype=dtype.int32)
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def construct(self, x_):
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return self.g.kl_loss('Geometric', x_)
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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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probs1_a = 0.7
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probs1_b = 0.5
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probs0_a = 1 - probs1_a
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probs0_b = 1 - probs1_b
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expect_kl_loss = np.log(probs1_a / probs1_b) + \
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(probs0_a / probs1_a) * np.log(probs0_a / probs0_b)
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kl_loss = KL()
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output = kl_loss(Tensor([probs1_b], dtype=dtype.float32))
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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 Geometric 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.Geometric([0.5, 0.5], dtype=dtype.int32)
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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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expect_mean = [1.0, 1.0]
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expect_sd = np.sqrt(np.array([0.5, 0.5]) / np.square(np.array([0.5, 0.5])))
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expect_mode = [0.0, 0.0]
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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(sd.asnumpy() - expect_sd) < tol).all()
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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: log probability of bernoulli 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.Geometric([0.7, 0.5], seed=seed, dtype=dtype.int32)
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self.shape = shape
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def construct(self, probs=None):
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return self.g.sample(self.shape, probs)
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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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sample = Sampling(shape)
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output = sample()
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assert output.shape == (2, 3, 2)
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class CDF(nn.Cell):
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"""
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Test class: cdf of Geometric 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.Geometric(0.7, dtype=dtype.int32)
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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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geom_benchmark = stats.geom(0.7)
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expect_cdf = geom_benchmark.cdf([0, 1, 2, 3, 4]).astype(np.float32)
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x_ = Tensor(np.array([-1, 0, 1, 2, 3]
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).astype(np.int32), dtype=dtype.float32)
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cdf = CDF()
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output = cdf(x_)
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tol = 1e-6
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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 Geometric 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.Geometric(0.7, dtype=dtype.int32)
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def construct(self, x_):
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return self.g.log_cdf(x_)
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def test_logcdf():
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"""
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Test log_cdf.
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"""
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geom_benchmark = stats.geom(0.7)
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expect_logcdf = geom_benchmark.logcdf([1, 2, 3, 4, 5]).astype(np.float32)
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x_ = Tensor(np.array([0, 1, 2, 3, 4]).astype(
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np.int32), dtype=dtype.float32)
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logcdf = LogCDF()
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output = logcdf(x_)
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tol = 1e-6
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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: survial function of Geometric 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.Geometric(0.7, dtype=dtype.int32)
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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 survival function.
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"""
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geom_benchmark = stats.geom(0.7)
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expect_survival = geom_benchmark.sf([0, 1, 2, 3, 4]).astype(np.float32)
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x_ = Tensor(np.array([-1, 0, 1, 2, 3]
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).astype(np.int32), dtype=dtype.float32)
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sf = SF()
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output = sf(x_)
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tol = 1e-6
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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 survial function of Geometric 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.Geometric(0.7, dtype=dtype.int32)
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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 function.
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"""
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geom_benchmark = stats.geom(0.7)
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expect_logsurvival = geom_benchmark.logsf(
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[0, 1, 2, 3, 4]).astype(np.float32)
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x_ = Tensor(np.array([-1, 0, 1, 2, 3]
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).astype(np.float32), dtype=dtype.float32)
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log_sf = LogSF()
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output = log_sf(x_)
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tol = 5e-6
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assert (np.abs(output.asnumpy() - expect_logsurvival) < tol).all()
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class EntropyH(nn.Cell):
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"""
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Test class: entropy of Geometric 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.Geometric(0.7, dtype=dtype.int32)
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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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geom_benchmark = stats.geom(0.7)
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expect_entropy = geom_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 Geometric 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.Geometric(0.7, dtype=dtype.int32)
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def construct(self, x_):
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entropy = self.g.entropy()
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kl_loss = self.g.kl_loss('Geometric', x_)
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h_sum_kl = entropy + kl_loss
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ans = self.g.cross_entropy('Geometric', x_)
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return h_sum_kl - ans
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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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prob = Tensor([0.5], dtype=dtype.float32)
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diff = cross_entropy(prob)
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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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