add read me

venv/lib/python3.12/site-packages/sympy/stats/sampling/tests/test_sample_continuous_rv.py

@@ -0,0 +1,181 @@
+from sympy.core.numbers import oo
+from sympy.core.symbol import Symbol
+from sympy.functions.elementary.exponential import exp
+from sympy.sets.sets import Interval
+from sympy.external import import_module
+from sympy.stats import Beta, Chi, Normal, Gamma, Exponential, LogNormal, Pareto, ChiSquared, Uniform, sample, \
+    BetaPrime, Cauchy, GammaInverse, GaussianInverse, StudentT, Weibull, density, ContinuousRV, FDistribution, \
+    Gumbel, Laplace, Logistic, Rayleigh, Triangular
+from sympy.testing.pytest import skip, raises
+
+
+def test_sample_numpy():
+    distribs_numpy = [
+        Beta("B", 1, 1),
+        Normal("N", 0, 1),
+        Gamma("G", 2, 7),
+        Exponential("E", 2),
+        LogNormal("LN", 0, 1),
+        Pareto("P", 1, 1),
+        ChiSquared("CS", 2),
+        Uniform("U", 0, 1),
+        FDistribution("FD", 1, 2),
+        Gumbel("GB", 1, 2),
+        Laplace("L", 1, 2),
+        Logistic("LO", 1, 2),
+        Rayleigh("R", 1),
+        Triangular("T", 1, 2, 2),
+    ]
+    size = 3
+    numpy = import_module('numpy')
+    if not numpy:
+        skip('Numpy is not installed. Abort tests for _sample_numpy.')
+    else:
+        for X in distribs_numpy:
+            samps = sample(X, size=size, library='numpy')
+            for sam in samps:
+                assert sam in X.pspace.domain.set
+        raises(NotImplementedError,
+               lambda: sample(Chi("C", 1), library='numpy'))
+    raises(NotImplementedError,
+           lambda: Chi("C", 1).pspace.distribution.sample(library='tensorflow'))
+
+
+def test_sample_scipy():
+    distribs_scipy = [
+        Beta("B", 1, 1),
+        BetaPrime("BP", 1, 1),
+        Cauchy("C", 1, 1),
+        Chi("C", 1),
+        Normal("N", 0, 1),
+        Gamma("G", 2, 7),
+        GammaInverse("GI", 1, 1),
+        GaussianInverse("GUI", 1, 1),
+        Exponential("E", 2),
+        LogNormal("LN", 0, 1),
+        Pareto("P", 1, 1),
+        StudentT("S", 2),
+        ChiSquared("CS", 2),
+        Uniform("U", 0, 1)
+    ]
+    size = 3
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy is not installed. Abort tests for _sample_scipy.')
+    else:
+        for X in distribs_scipy:
+            samps = sample(X, size=size, library='scipy')
+            samps2 = sample(X, size=(2, 2), library='scipy')
+            for sam in samps:
+                assert sam in X.pspace.domain.set
+            for i in range(2):
+                for j in range(2):
+                    assert samps2[i][j] in X.pspace.domain.set
+
+
+def test_sample_pymc():
+    distribs_pymc = [
+        Beta("B", 1, 1),
+        Cauchy("C", 1, 1),
+        Normal("N", 0, 1),
+        Gamma("G", 2, 7),
+        GaussianInverse("GI", 1, 1),
+        Exponential("E", 2),
+        LogNormal("LN", 0, 1),
+        Pareto("P", 1, 1),
+        ChiSquared("CS", 2),
+        Uniform("U", 0, 1)
+    ]
+    size = 3
+    pymc = import_module('pymc')
+    if not pymc:
+        skip('PyMC is not installed. Abort tests for _sample_pymc.')
+    else:
+        for X in distribs_pymc:
+            samps = sample(X, size=size, library='pymc')
+            for sam in samps:
+                assert sam in X.pspace.domain.set
+        raises(NotImplementedError,
+               lambda: sample(Chi("C", 1), library='pymc'))
+
+
+def test_sampling_gamma_inverse():
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy not installed. Abort tests for sampling of gamma inverse.')
+    X = GammaInverse("x", 1, 1)
+    assert sample(X) in X.pspace.domain.set
+
+
+def test_lognormal_sampling():
+    # Right now, only density function and sampling works
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy is not installed. Abort tests')
+    for i in range(3):
+        X = LogNormal('x', i, 1)
+        assert sample(X) in X.pspace.domain.set
+
+    size = 5
+    samps = sample(X, size=size)
+    for samp in samps:
+        assert samp in X.pspace.domain.set
+
+
+def test_sampling_gaussian_inverse():
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy not installed. Abort tests for sampling of Gaussian inverse.')
+    X = GaussianInverse("x", 1, 1)
+    assert sample(X, library='scipy') in X.pspace.domain.set
+
+
+def test_prefab_sampling():
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy is not installed. Abort tests')
+    N = Normal('X', 0, 1)
+    L = LogNormal('L', 0, 1)
+    E = Exponential('Ex', 1)
+    P = Pareto('P', 1, 3)
+    W = Weibull('W', 1, 1)
+    U = Uniform('U', 0, 1)
+    B = Beta('B', 2, 5)
+    G = Gamma('G', 1, 3)
+
+    variables = [N, L, E, P, W, U, B, G]
+    niter = 10
+    size = 5
+    for var in variables:
+        for _ in range(niter):
+            assert sample(var) in var.pspace.domain.set
+            samps = sample(var, size=size)
+            for samp in samps:
+                assert samp in var.pspace.domain.set
+
+
+def test_sample_continuous():
+    z = Symbol('z')
+    Z = ContinuousRV(z, exp(-z), set=Interval(0, oo))
+    assert density(Z)(-1) == 0
+
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy is not installed. Abort tests')
+    assert sample(Z) in Z.pspace.domain.set
+    sym, val = list(Z.pspace.sample().items())[0]
+    assert sym == Z and val in Interval(0, oo)
+
+    libraries = ['scipy', 'numpy', 'pymc']
+    for lib in libraries:
+        try:
+            imported_lib = import_module(lib)
+            if imported_lib:
+                s0, s1, s2 = [], [], []
+                s0 = sample(Z, size=10, library=lib, seed=0)
+                s1 = sample(Z, size=10, library=lib, seed=0)
+                s2 = sample(Z, size=10, library=lib, seed=1)
+                assert all(s0 == s1)
+                assert all(s1 != s2)
+        except NotImplementedError:
+            continue

venv/lib/python3.12/site-packages/sympy/stats/sampling/tests/test_sample_discrete_rv.py

@@ -0,0 +1,109 @@
+from sympy.core.singleton import S
+from sympy.core.symbol import Symbol
+from sympy.external import import_module
+from sympy.stats import (
+    Geometric,
+    Poisson,
+    Zeta,
+    sample,
+    Skellam,
+    Logarithmic,
+    NegativeBinomial,
+    YuleSimon,
+    DiscreteRV,
+)
+from sympy.testing.pytest import skip, raises, slow
+
+
+def test_sample_numpy():
+    distribs_numpy = [
+        Geometric('G', 0.5),
+        Poisson('P', 1),
+        Zeta('Z', 2)
+    ]
+    size = 3
+    numpy = import_module('numpy')
+    if not numpy:
+        skip('Numpy is not installed. Abort tests for _sample_numpy.')
+    else:
+        for X in distribs_numpy:
+            samps = sample(X, size=size, library='numpy')
+            for sam in samps:
+                assert sam in X.pspace.domain.set
+        raises(NotImplementedError,
+               lambda: sample(Skellam('S', 1, 1), library='numpy'))
+    raises(NotImplementedError,
+           lambda: Skellam('S', 1, 1).pspace.distribution.sample(library='tensorflow'))
+
+
+def test_sample_scipy():
+    p = S(2)/3
+    x = Symbol('x', integer=True, positive=True)
+    pdf = p*(1 - p)**(x - 1) # pdf of Geometric Distribution
+    distribs_scipy = [
+        DiscreteRV(x, pdf, set=S.Naturals),
+        Geometric('G', 0.5),
+        Logarithmic('L', 0.5),
+        NegativeBinomial('N', 5, 0.4),
+        Poisson('P', 1),
+        Skellam('S', 1, 1),
+        YuleSimon('Y', 1),
+        Zeta('Z', 2)
+    ]
+    size = 3
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy is not installed. Abort tests for _sample_scipy.')
+    else:
+        for X in distribs_scipy:
+            samps = sample(X, size=size, library='scipy')
+            samps2 = sample(X, size=(2, 2), library='scipy')
+            for sam in samps:
+                assert sam in X.pspace.domain.set
+            for i in range(2):
+                for j in range(2):
+                    assert samps2[i][j] in X.pspace.domain.set
+
+
+def test_sample_pymc():
+    distribs_pymc = [
+        Geometric('G', 0.5),
+        Poisson('P', 1),
+        NegativeBinomial('N', 5, 0.4)
+    ]
+    size = 3
+    pymc = import_module('pymc')
+    if not pymc:
+        skip('PyMC is not installed. Abort tests for _sample_pymc.')
+    else:
+        for X in distribs_pymc:
+            samps = sample(X, size=size, library='pymc')
+            for sam in samps:
+                assert sam in X.pspace.domain.set
+        raises(NotImplementedError,
+               lambda: sample(Skellam('S', 1, 1), library='pymc'))
+
+@slow
+def test_sample_discrete():
+    X = Geometric('X', S.Half)
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy not installed. Abort tests')
+    assert sample(X) in X.pspace.domain.set
+    samps = sample(X, size=2) # This takes long time if ran without scipy
+    for samp in samps:
+        assert samp in X.pspace.domain.set
+
+    libraries = ['scipy', 'numpy', 'pymc']
+    for lib in libraries:
+        try:
+            imported_lib = import_module(lib)
+            if imported_lib:
+                s0, s1, s2 = [], [], []
+                s0 = sample(X, size=10, library=lib, seed=0)
+                s1 = sample(X, size=10, library=lib, seed=0)
+                s2 = sample(X, size=10, library=lib, seed=1)
+                assert all(s0 == s1)
+                assert not all(s1 == s2)
+        except NotImplementedError:
+            continue

venv/lib/python3.12/site-packages/sympy/stats/sampling/tests/test_sample_finite_rv.py

@@ -0,0 +1,94 @@
+from sympy.core.numbers import Rational
+from sympy.core.singleton import S
+from sympy.external import import_module
+from sympy.stats import Binomial, sample, Die, FiniteRV, DiscreteUniform, Bernoulli, BetaBinomial, Hypergeometric, \
+    Rademacher
+from sympy.testing.pytest import skip, raises
+
+def test_given_sample():
+    X = Die('X', 6)
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy is not installed. Abort tests')
+    assert sample(X, X > 5) == 6
+
+def test_sample_numpy():
+    distribs_numpy = [
+        Binomial("B", 5, 0.4),
+        Hypergeometric("H", 2, 1, 1)
+    ]
+    size = 3
+    numpy = import_module('numpy')
+    if not numpy:
+        skip('Numpy is not installed. Abort tests for _sample_numpy.')
+    else:
+        for X in distribs_numpy:
+            samps = sample(X, size=size, library='numpy')
+            for sam in samps:
+                assert sam in X.pspace.domain.set
+        raises(NotImplementedError,
+               lambda: sample(Die("D"), library='numpy'))
+    raises(NotImplementedError,
+           lambda: Die("D").pspace.sample(library='tensorflow'))
+
+
+def test_sample_scipy():
+    distribs_scipy = [
+        FiniteRV('F', {1: S.Half, 2: Rational(1, 4), 3: Rational(1, 4)}),
+        DiscreteUniform("Y", list(range(5))),
+        Die("D"),
+        Bernoulli("Be", 0.3),
+        Binomial("Bi", 5, 0.4),
+        BetaBinomial("Bb", 2, 1, 1),
+        Hypergeometric("H", 1, 1, 1),
+        Rademacher("R")
+    ]
+
+    size = 3
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy not installed. Abort tests for _sample_scipy.')
+    else:
+        for X in distribs_scipy:
+            samps = sample(X, size=size)
+            samps2 = sample(X, size=(2, 2))
+            for sam in samps:
+                assert sam in X.pspace.domain.set
+            for i in range(2):
+                for j in range(2):
+                    assert samps2[i][j] in X.pspace.domain.set
+
+
+def test_sample_pymc():
+    distribs_pymc = [
+        Bernoulli('B', 0.2),
+        Binomial('N', 5, 0.4)
+    ]
+    size = 3
+    pymc = import_module('pymc')
+    if not pymc:
+        skip('PyMC is not installed. Abort tests for _sample_pymc.')
+    else:
+        for X in distribs_pymc:
+            samps = sample(X, size=size, library='pymc')
+            for sam in samps:
+                assert sam in X.pspace.domain.set
+        raises(NotImplementedError,
+                          lambda: (sample(Die("D"), library='pymc')))
+
+
+def test_sample_seed():
+    F = FiniteRV('F', {1: S.Half, 2: Rational(1, 4), 3: Rational(1, 4)})
+    size = 10
+    libraries = ['scipy', 'numpy', 'pymc']
+    for lib in libraries:
+        try:
+            imported_lib = import_module(lib)
+            if imported_lib:
+                s0 = sample(F, size=size, library=lib, seed=0)
+                s1 = sample(F, size=size, library=lib, seed=0)
+                s2 = sample(F, size=size, library=lib, seed=1)
+                assert all(s0 == s1)
+                assert not all(s1 == s2)
+        except NotImplementedError:
+            continue