Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/scipy/stats/tests/test_discrete_distns.py

@@ -0,0 +1,68 @@
+from scipy.stats import betabinom, hypergeom, bernoulli, boltzmann
+import numpy as np
+from numpy.testing import assert_almost_equal, assert_equal, assert_allclose
+
+
+def test_hypergeom_logpmf():
+    # symmetries test
+    # f(k,N,K,n) = f(n-k,N,N-K,n) = f(K-k,N,K,N-n) = f(k,N,n,K)
+    k = 5
+    N = 50
+    K = 10
+    n = 5
+    logpmf1 = hypergeom.logpmf(k, N, K, n)
+    logpmf2 = hypergeom.logpmf(n - k, N, N - K, n)
+    logpmf3 = hypergeom.logpmf(K - k, N, K, N - n)
+    logpmf4 = hypergeom.logpmf(k, N, n, K)
+    assert_almost_equal(logpmf1, logpmf2, decimal=12)
+    assert_almost_equal(logpmf1, logpmf3, decimal=12)
+    assert_almost_equal(logpmf1, logpmf4, decimal=12)
+
+    # test related distribution
+    # Bernoulli distribution if n = 1
+    k = 1
+    N = 10
+    K = 7
+    n = 1
+    hypergeom_logpmf = hypergeom.logpmf(k, N, K, n)
+    bernoulli_logpmf = bernoulli.logpmf(k, K/N)
+    assert_almost_equal(hypergeom_logpmf, bernoulli_logpmf, decimal=12)
+
+
+def test_boltzmann_upper_bound():
+    k = np.arange(-3, 5)
+
+    N = 1
+    p = boltzmann.pmf(k, 0.123, N)
+    expected = k == 0
+    assert_equal(p, expected)
+
+    lam = np.log(2)
+    N = 3
+    p = boltzmann.pmf(k, lam, N)
+    expected = [0, 0, 0, 4/7, 2/7, 1/7, 0, 0]
+    assert_allclose(p, expected, rtol=1e-13)
+
+    c = boltzmann.cdf(k, lam, N)
+    expected = [0, 0, 0, 4/7, 6/7, 1, 1, 1]
+    assert_allclose(c, expected, rtol=1e-13)
+
+
+def test_betabinom_a_and_b_unity():
+    # test limiting case that betabinom(n, 1, 1) is a discrete uniform
+    # distribution from 0 to n
+    n = 20
+    k = np.arange(n + 1)
+    p = betabinom(n, 1, 1).pmf(k)
+    expected = np.repeat(1 / (n + 1), n + 1)
+    assert_almost_equal(p, expected)
+
+
+def test_betabinom_bernoulli():
+    # test limiting case that betabinom(1, a, b) = bernoulli(a / (a + b))
+    a = 2.3
+    b = 0.63
+    k = np.arange(2)
+    p = betabinom(1, a, b).pmf(k)
+    expected = bernoulli(a / (a + b)).pmf(k)
+    assert_almost_equal(p, expected)