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from sympy import exp, S, sqrt, pi, symbols, Product, gamma, Dummy
from sympy.matrices import Determinant, Matrix, Trace, MatrixSymbol, MatrixSet
from sympy.stats import density, sample
from sympy.stats.matrix_distributions import (MatrixGammaDistribution,
MatrixGamma, MatrixPSpace, Wishart, MatrixNormal)
from sympy.testing.pytest import raises, skip, ignore_warnings
from sympy.external import import_module
def test_MatrixPSpace():
M = MatrixGammaDistribution(1, 2, [[2, 1], [1, 2]])
MP = MatrixPSpace('M', M, 2, 2)
assert MP.distribution == M
raises(ValueError, lambda: MatrixPSpace('M', M, 1.2, 2))
def test_MatrixGamma():
M = MatrixGamma('M', 1, 2, [[1, 0], [0, 1]])
assert M.pspace.distribution.set == MatrixSet(2, 2, S.Reals)
assert isinstance(density(M), MatrixGammaDistribution)
X = MatrixSymbol('X', 2, 2)
num = exp(Trace(Matrix([[-S(1)/2, 0], [0, -S(1)/2]])*X))
assert density(M)(X).doit() == num/(4*pi*sqrt(Determinant(X)))
assert density(M)([[2, 1], [1, 2]]).doit() == sqrt(3)*exp(-2)/(12*pi)
X = MatrixSymbol('X', 1, 2)
Y = MatrixSymbol('Y', 1, 2)
assert density(M)([X, Y]).doit() == exp(-X[0, 0]/2 - Y[0, 1]/2)/(4*pi*sqrt(
X[0, 0]*Y[0, 1] - X[0, 1]*Y[0, 0]))
# symbolic
a, b = symbols('a b', positive=True)
d = symbols('d', positive=True, integer=True)
Y = MatrixSymbol('Y', d, d)
Z = MatrixSymbol('Z', 2, 2)
SM = MatrixSymbol('SM', d, d)
M2 = MatrixGamma('M2', a, b, SM)
M3 = MatrixGamma('M3', 2, 3, [[2, 1], [1, 2]])
k = Dummy('k')
exprd = pi**(-d*(d - 1)/4)*b**(-a*d)*exp(Trace((-1/b)*SM**(-1)*Y)
)*Determinant(SM)**(-a)*Determinant(Y)**(a - d/2 - S(1)/2)/Product(
gamma(-k/2 + a + S(1)/2), (k, 1, d))
assert density(M2)(Y).dummy_eq(exprd)
raises(NotImplementedError, lambda: density(M3 + M)(Z))
raises(ValueError, lambda: density(M)(1))
raises(ValueError, lambda: MatrixGamma('M', -1, 2, [[1, 0], [0, 1]]))
raises(ValueError, lambda: MatrixGamma('M', -1, -2, [[1, 0], [0, 1]]))
raises(ValueError, lambda: MatrixGamma('M', -1, 2, [[1, 0], [2, 1]]))
raises(ValueError, lambda: MatrixGamma('M', -1, 2, [[1, 0], [0]]))
def test_Wishart():
W = Wishart('W', 5, [[1, 0], [0, 1]])
assert W.pspace.distribution.set == MatrixSet(2, 2, S.Reals)
X = MatrixSymbol('X', 2, 2)
term1 = exp(Trace(Matrix([[-S(1)/2, 0], [0, -S(1)/2]])*X))
assert density(W)(X).doit() == term1 * Determinant(X)/(24*pi)
assert density(W)([[2, 1], [1, 2]]).doit() == exp(-2)/(8*pi)
n = symbols('n', positive=True)
d = symbols('d', positive=True, integer=True)
Y = MatrixSymbol('Y', d, d)
SM = MatrixSymbol('SM', d, d)
W = Wishart('W', n, SM)
k = Dummy('k')
exprd = 2**(-d*n/2)*pi**(-d*(d - 1)/4)*exp(Trace(-(S(1)/2)*SM**(-1)*Y)
)*Determinant(SM)**(-n/2)*Determinant(Y)**(
-d/2 + n/2 - S(1)/2)/Product(gamma(-k/2 + n/2 + S(1)/2), (k, 1, d))
assert density(W)(Y).dummy_eq(exprd)
raises(ValueError, lambda: density(W)(1))
raises(ValueError, lambda: Wishart('W', -1, [[1, 0], [0, 1]]))
raises(ValueError, lambda: Wishart('W', -1, [[1, 0], [2, 1]]))
raises(ValueError, lambda: Wishart('W', 2, [[1, 0], [0]]))
def test_MatrixNormal():
M = MatrixNormal('M', [[5, 6]], [4], [[2, 1], [1, 2]])
assert M.pspace.distribution.set == MatrixSet(1, 2, S.Reals)
X = MatrixSymbol('X', 1, 2)
term1 = exp(-Trace(Matrix([[ S(2)/3, -S(1)/3], [-S(1)/3, S(2)/3]])*(
Matrix([[-5], [-6]]) + X.T)*Matrix([[1/4]])*(Matrix([[-5, -6]]) + X))/2)
assert density(M)(X).doit() == term1/(24*pi)
assert density(M)([[7, 8]]).doit() == exp(-S(1)/3)/(24*pi)
d, n = symbols('d n', positive=True, integer=True)
SM2 = MatrixSymbol('SM2', d, d)
SM1 = MatrixSymbol('SM1', n, n)
LM = MatrixSymbol('LM', n, d)
Y = MatrixSymbol('Y', n, d)
M = MatrixNormal('M', LM, SM1, SM2)
exprd = 4*(2*pi)**(-d*n/2)*exp(-Trace(SM2**(-1)*(-LM.T + Y.T)*SM1**(-1)*(-LM + Y)
)/2)*Determinant(SM1)**(-d)*Determinant(SM2)**(-n)
assert density(M)(Y).doit() == exprd
raises(ValueError, lambda: density(M)(1))
raises(ValueError, lambda: MatrixNormal('M', [1, 2], [[1, 0], [0, 1]], [[1, 0], [2, 1]]))
raises(ValueError, lambda: MatrixNormal('M', [1, 2], [[1, 0], [2, 1]], [[1, 0], [0, 1]]))
raises(ValueError, lambda: MatrixNormal('M', [1, 2], [[1, 0], [0, 1]], [[1, 0], [0, 1]]))
raises(ValueError, lambda: MatrixNormal('M', [1, 2], [[1, 0], [2]], [[1, 0], [0, 1]]))
raises(ValueError, lambda: MatrixNormal('M', [1, 2], [[1, 0], [2, 1]], [[1, 0], [0]]))
raises(ValueError, lambda: MatrixNormal('M', [[1, 2]], [[1, 0], [0, 1]], [[1, 0]]))
raises(ValueError, lambda: MatrixNormal('M', [[1, 2]], [1], [[1, 0]]))
def test_sample_scipy():
distribs_scipy = [
MatrixNormal('M', [[5, 6]], [4], [[2, 1], [1, 2]]),
Wishart('W', 5, [[1, 0], [0, 1]])
]
size = 5
scipy = import_module('scipy')
if not scipy:
skip('Scipy not installed. Abort tests for _sample_scipy.')
else:
with ignore_warnings(UserWarning): ### TODO: Restore tests once warnings are removed
for X in distribs_scipy:
samps = next(sample(X, size=size))
for sam in samps:
assert Matrix(sam) in X.pspace.distribution.set
M = MatrixGamma('M', 1, 2, [[1, 0], [0, 1]])
raises(NotImplementedError, lambda: next(sample(M, size=3)))
def test_sample_pymc3():
distribs_pymc3 = [
MatrixNormal('M', [[5, 6], [3, 4]], [[1, 0], [0, 1]], [[2, 1], [1, 2]]),
Wishart('W', 7, [[2, 1], [1, 2]])
]
size = 3
pymc3 = import_module('pymc3')
if not pymc3:
skip('PyMC3 is not installed. Abort tests for _sample_pymc3.')
else:
with ignore_warnings(UserWarning): ### TODO: Restore tests once warnings are removed
for X in distribs_pymc3:
samps = next(sample(X, size=size, library='pymc3'))
for sam in samps:
assert Matrix(sam) in X.pspace.distribution.set
M = MatrixGamma('M', 1, 2, [[1, 0], [0, 1]])
raises(NotImplementedError, lambda: next(sample(M, size=3)))
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