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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15"""
16Test nn.probability.distribution.Exponential.
17"""
18import pytest
19
20import mindspore.nn as nn
21import mindspore.nn.probability.distribution as msd
22from mindspore import dtype
23from mindspore import Tensor
24
25
26def test_arguments():
27    """
28    Args passing during initialization.
29    """
30    e = msd.Exponential()
31    assert isinstance(e, msd.Distribution)
32    e = msd.Exponential([0.1, 0.3, 0.5, 1.0], dtype=dtype.float32)
33    assert isinstance(e, msd.Distribution)
34
35def test_type():
36    with pytest.raises(TypeError):
37        msd.Exponential([0.1], dtype=dtype.int32)
38
39def test_name():
40    with pytest.raises(TypeError):
41        msd.Exponential([0.1], name=1.0)
42
43def test_seed():
44    with pytest.raises(TypeError):
45        msd.Exponential([0.1], seed='seed')
46
47def test_rate():
48    """
49    Invalid rate.
50    """
51    with pytest.raises(ValueError):
52        msd.Exponential([-0.1], dtype=dtype.float32)
53    with pytest.raises(ValueError):
54        msd.Exponential([0.0], dtype=dtype.float32)
55
56class ExponentialProb(nn.Cell):
57    """
58    Exponential distribution: initialize with rate.
59    """
60    def __init__(self):
61        super(ExponentialProb, self).__init__()
62        self.e = msd.Exponential(0.5, dtype=dtype.float32)
63
64    def construct(self, value):
65        prob = self.e.prob(value)
66        log_prob = self.e.log_prob(value)
67        cdf = self.e.cdf(value)
68        log_cdf = self.e.log_cdf(value)
69        sf = self.e.survival_function(value)
70        log_sf = self.e.log_survival(value)
71        return prob + log_prob + cdf + log_cdf + sf + log_sf
72
73def test_exponential_prob():
74    """
75    Test probability functions: passing value through construct.
76    """
77    net = ExponentialProb()
78    value = Tensor([0.2, 0.3, 5.0, 2, 3.9], dtype=dtype.float32)
79    ans = net(value)
80    assert isinstance(ans, Tensor)
81
82class ExponentialProb1(nn.Cell):
83    """
84    Exponential distribution: initialize without rate.
85    """
86    def __init__(self):
87        super(ExponentialProb1, self).__init__()
88        self.e = msd.Exponential(dtype=dtype.float32)
89
90    def construct(self, value, rate):
91        prob = self.e.prob(value, rate)
92        log_prob = self.e.log_prob(value, rate)
93        cdf = self.e.cdf(value, rate)
94        log_cdf = self.e.log_cdf(value, rate)
95        sf = self.e.survival_function(value, rate)
96        log_sf = self.e.log_survival(value, rate)
97        return prob + log_prob + cdf + log_cdf + sf + log_sf
98
99def test_exponential_prob1():
100    """
101    Test probability functions: passing value/rate through construct.
102    """
103    net = ExponentialProb1()
104    value = Tensor([0.2, 0.9, 1, 2, 3], dtype=dtype.float32)
105    rate = Tensor([0.5], dtype=dtype.float32)
106    ans = net(value, rate)
107    assert isinstance(ans, Tensor)
108
109class ExponentialKl(nn.Cell):
110    """
111    Test class: kl_loss between Exponential distributions.
112    """
113    def __init__(self):
114        super(ExponentialKl, self).__init__()
115        self.e1 = msd.Exponential(0.7, dtype=dtype.float32)
116        self.e2 = msd.Exponential(dtype=dtype.float32)
117
118    def construct(self, rate_b, rate_a):
119        kl1 = self.e1.kl_loss('Exponential', rate_b)
120        kl2 = self.e2.kl_loss('Exponential', rate_b, rate_a)
121        return kl1 + kl2
122
123def test_kl():
124    """
125    Test kl_loss function.
126    """
127    net = ExponentialKl()
128    rate_b = Tensor([0.3], dtype=dtype.float32)
129    rate_a = Tensor([0.7], dtype=dtype.float32)
130    ans = net(rate_b, rate_a)
131    assert isinstance(ans, Tensor)
132
133class ExponentialCrossEntropy(nn.Cell):
134    """
135    Test class: cross_entropy of Exponential distribution.
136    """
137    def __init__(self):
138        super(ExponentialCrossEntropy, self).__init__()
139        self.e1 = msd.Exponential(0.3, dtype=dtype.float32)
140        self.e2 = msd.Exponential(dtype=dtype.float32)
141
142    def construct(self, rate_b, rate_a):
143        h1 = self.e1.cross_entropy('Exponential', rate_b)
144        h2 = self.e2.cross_entropy('Exponential', rate_b, rate_a)
145        return h1 + h2
146
147def test_cross_entropy():
148    """
149    Test cross_entropy between Exponential distributions.
150    """
151    net = ExponentialCrossEntropy()
152    rate_b = Tensor([0.3], dtype=dtype.float32)
153    rate_a = Tensor([0.7], dtype=dtype.float32)
154    ans = net(rate_b, rate_a)
155    assert isinstance(ans, Tensor)
156
157class ExponentialBasics(nn.Cell):
158    """
159    Test class: basic mean/sd/mode/entropy function.
160    """
161    def __init__(self):
162        super(ExponentialBasics, self).__init__()
163        self.e = msd.Exponential([0.3, 0.5], dtype=dtype.float32)
164
165    def construct(self):
166        mean = self.e.mean()
167        sd = self.e.sd()
168        var = self.e.var()
169        mode = self.e.mode()
170        entropy = self.e.entropy()
171        return mean + sd + var + mode + entropy
172
173def test_bascis():
174    """
175    Test mean/sd/var/mode/entropy functionality of Exponential distribution.
176    """
177    net = ExponentialBasics()
178    ans = net()
179    assert isinstance(ans, Tensor)
180
181
182class ExpConstruct(nn.Cell):
183    """
184    Exponential distribution: going through construct.
185    """
186    def __init__(self):
187        super(ExpConstruct, self).__init__()
188        self.e = msd.Exponential(0.5, dtype=dtype.float32)
189        self.e1 = msd.Exponential(dtype=dtype.float32)
190
191    def construct(self, value, rate):
192        prob = self.e('prob', value)
193        prob1 = self.e('prob', value, rate)
194        prob2 = self.e1('prob', value, rate)
195        return prob + prob1 + prob2
196
197def test_exp_construct():
198    """
199    Test probability function going through construct.
200    """
201    net = ExpConstruct()
202    value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
203    probs = Tensor([0.5], dtype=dtype.float32)
204    ans = net(value, probs)
205    assert isinstance(ans, Tensor)
206