def test_betaprime(): alpha = Symbol("alpha", positive=True) betap = Symbol("beta", positive=True) X = BetaPrime('x', alpha, betap) assert density(X)(x) == x**(alpha - 1)*(x + 1)**(-alpha - betap)/beta(alpha, betap) alpha = Symbol("alpha", positive=False) raises(ValueError, lambda: BetaPrime('x', alpha, betap)) alpha = Symbol("alpha", positive=True) betap = Symbol("beta", positive=False) raises(ValueError, lambda: BetaPrime('x', alpha, betap))
def test_betaprime(): alpha = Symbol("alpha", positive=True) betap = Symbol("beta", positive=True) X = BetaPrime('x', alpha, betap) assert density(X)(x) == x**(alpha - 1) * (x + 1)**(-alpha - betap) / beta( alpha, betap)
def test_betaprime(): alpha = Symbol("alpha", positive=True) beta = Symbol("beta", positive=True) X = BetaPrime('x', alpha, beta) assert density(X)(x) == (x**(alpha - 1)*(x + 1)**(-alpha - beta) *gamma(alpha + beta)/(gamma(alpha)*gamma(beta)))
def test_betaprime(): alpha = Symbol("alpha", positive=True) betap = Symbol("beta", positive=True) X = BetaPrime('x', alpha, betap) assert density(X)(x) == x**(alpha - 1)*(x + 1)**(-alpha - betap)/beta(alpha, betap) alpha = Symbol("alpha", nonpositive=True) raises(ValueError, lambda: BetaPrime('x', alpha, betap)) alpha = Symbol("alpha", positive=True) betap = Symbol("beta", nonpositive=True) raises(ValueError, lambda: BetaPrime('x', alpha, betap)) X = BetaPrime('x', 1, 1) assert median(X) == FiniteSet(1)
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_betaprime(): alpha = Symbol("alpha", positive=True) beta = Symbol("beta", positive=True) x = Symbol("x") X = BetaPrime(alpha, beta, symbol=x) assert density(X) == (Lambda( _x, _x**(alpha - 1) * (_x + 1)**(-alpha - beta) * gamma(alpha + beta) / (gamma(alpha) * gamma(beta))))
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 numsamples = 5 scipy = import_module('scipy') if not scipy: skip('Scipy is not installed. Abort tests for _sample_scipy.') else: with ignore_warnings( UserWarning ): ### TODO: Restore tests once warnings are removed g_sample = list( sample(Gamma("G", 2, 7), size=size, numsamples=numsamples)) assert len(g_sample) == numsamples for X in distribs_scipy: samps = next(sample(X, size=size, library='scipy')) samps2 = next(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