81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
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# scipy-yli: Helpful SciPy utilities and recipes
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# Copyright © 2022 Lee Yingtong Li (RunasSudo)
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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import numpy as np
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import yli
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def test_beta_ratio_vs_jsaffer_pdf():
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"""Compare beta_ratio.pdf with result from https://github.com/jsaffer/beta_quotient_distribution"""
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# Define the example beta distribution
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a1, b1, a2, b2 = 3, 6, 12, 7
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# Compute PDF
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x = np.linspace(0, 2, 100)
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dist = yli.beta_ratio(a1, b1, a2, b2)
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y = dist.pdf(x)
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# Compare with expected values from jsaffer implementation
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expected = np.load('beta_ratio_vs_jsaffer.npy', allow_pickle=False)[0]
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# Allow 1e-10 tolerance
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diff = np.abs(y - expected)
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assert (diff < 1e-10).all()
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def test_beta_ratio_vs_jsaffer_cdf():
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"""Compare beta_ratio.cdf with result from https://github.com/jsaffer/beta_quotient_distribution"""
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# Define the example beta distribution
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a1, b1, a2, b2 = 3, 6, 12, 7
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# Compute PDF
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x = np.linspace(0, 2, 100)
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dist = yli.beta_ratio(a1, b1, a2, b2)
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y = dist.cdf(x)
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# Compare with expected values from jsaffer implementation
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expected = np.load('beta_ratio_vs_jsaffer.npy', allow_pickle=False)[1]
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# Allow 1e-10 tolerance
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diff = np.abs(y - expected)
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assert (diff < 1e-10).all()
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def _gen_beta_ratio_vs_jsaffer():
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"""Generate beta_ratio_vs_jsaffer.npy for test_beta_ratio_vs_jsaffer_pdf/cdf"""
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import beta_quotient_distribution
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a1, b1, a2, b2 = 3, 6, 12, 7
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x = np.linspace(0, 2, 100)
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y1 = np.vectorize(lambda w: float(beta_quotient_distribution.pdf_bb_ratio(a1, a2, b1, b2, w)))(x)
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y2 = np.vectorize(lambda w: float(beta_quotient_distribution.cdf_bb_ratio(a1, a2, b1, b2, w)))(x)
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np.save('beta_ratio_vs_jsaffer.npy', np.array([y1, y2]), allow_pickle=False)
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def test_beta_ratio_mean_vs_empirical():
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"""Compare beta_ratio.mean (via beta_ratio._munp) with empirical mean"""
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# Define the example beta distribution
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dist = yli.beta_ratio(3, 6, 12, 7)
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# Compute empirical mean
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sample = dist.rvs(1000, random_state=31415)
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# Allow 0.01 tolerance
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assert np.abs(dist.mean() - sample.mean()) < 0.01
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