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from sympy.stats import Expectation, Normal, Variance, Covariance
from sympy.testing.pytest import raises
from sympy import symbols, MatrixSymbol, Matrix, ZeroMatrix, ShapeError
from sympy.stats.rv import RandomMatrixSymbol
from sympy.stats.symbolic_multivariate_probability import (ExpectationMatrix,
                            VarianceMatrix, CrossCovarianceMatrix)

j, k = symbols("j,k")

A = MatrixSymbol("A", k, k)
B = MatrixSymbol("B", k, k)
C = MatrixSymbol("C", k, k)
D = MatrixSymbol("D", k, k)

a = MatrixSymbol("a", k, 1)
b = MatrixSymbol("b", k, 1)

A2 = MatrixSymbol("A2", 2, 2)
B2 = MatrixSymbol("B2", 2, 2)

X = RandomMatrixSymbol("X", k, 1)
Y = RandomMatrixSymbol("Y", k, 1)
Z = RandomMatrixSymbol("Z", k, 1)
W = RandomMatrixSymbol("W", k, 1)

R = RandomMatrixSymbol("R", k, k)

X2 = RandomMatrixSymbol("X2", 2, 1)

normal = Normal("normal", 0, 1)

m1 = Matrix([
    [1, j*Normal("normal2", 2, 1)],
    [normal, 0]
])

def test_multivariate_expectation():
    expr = Expectation(a)
    assert expr == Expectation(a) == ExpectationMatrix(a)
    assert expr.expand() == a

    expr = Expectation(X)
    assert expr == Expectation(X) == ExpectationMatrix(X)
    assert expr.shape == (k, 1)
    assert expr.rows == k
    assert expr.cols == 1
    assert isinstance(expr, ExpectationMatrix)

    expr = Expectation(A*X + b)
    assert expr == ExpectationMatrix(A*X + b)
    assert expr.expand() == A*ExpectationMatrix(X) + b
    assert isinstance(expr, ExpectationMatrix)
    assert expr.shape == (k, 1)

    expr = Expectation(m1*X2)
    assert expr.expand() == expr

    expr = Expectation(A2*m1*B2*X2)
    assert expr.args[0].args == (A2, m1, B2, X2)
    assert expr.expand() == A2*ExpectationMatrix(m1*B2*X2)

    expr = Expectation((X + Y)*(X - Y).T)
    assert expr.expand() == ExpectationMatrix(X*X.T) - ExpectationMatrix(X*Y.T) +\
                ExpectationMatrix(Y*X.T) - ExpectationMatrix(Y*Y.T)

    expr = Expectation(A*X + B*Y)
    assert expr.expand() == A*ExpectationMatrix(X) + B*ExpectationMatrix(Y)

    assert Expectation(m1).doit() == Matrix([[1, 2*j], [0, 0]])

    x1 = Matrix([
    [Normal('N11', 11, 1), Normal('N12', 12, 1)],
    [Normal('N21', 21, 1), Normal('N22', 22, 1)]
    ])
    x2 = Matrix([
    [Normal('M11', 1, 1), Normal('M12', 2, 1)],
    [Normal('M21', 3, 1), Normal('M22', 4, 1)]
    ])

    assert Expectation(Expectation(x1 + x2)).doit(deep=False) == ExpectationMatrix(x1 + x2)
    assert Expectation(Expectation(x1 + x2)).doit() == Matrix([[12, 14], [24, 26]])


def test_multivariate_variance():
    raises(ShapeError, lambda: Variance(A))

    expr = Variance(a)  # type: VarianceMatrix
    assert expr == Variance(a) == VarianceMatrix(a)
    assert expr.expand() == ZeroMatrix(k, k)
    expr = Variance(a.T)
    assert expr == Variance(a.T) == VarianceMatrix(a.T)
    assert expr.expand() == ZeroMatrix(k, k)

    expr = Variance(X)
    assert expr == Variance(X) == VarianceMatrix(X)
    assert expr.shape == (k, k)
    assert expr.rows == k
    assert expr.cols == k
    assert isinstance(expr, VarianceMatrix)

    expr = Variance(A*X)
    assert expr == VarianceMatrix(A*X)
    assert expr.expand() == A*VarianceMatrix(X)*A.T
    assert isinstance(expr, VarianceMatrix)
    assert expr.shape == (k, k)

    expr = Variance(A*B*X)
    assert expr.expand() == A*B*VarianceMatrix(X)*B.T*A.T

    expr = Variance(m1*X2)
    assert expr.expand() == expr

    expr = Variance(A2*m1*B2*X2)
    assert expr.args[0].args == (A2, m1, B2, X2)
    assert expr.expand() == expr

    expr = Variance(A*X + B*Y)
    assert expr.expand() == 2*A*CrossCovarianceMatrix(X, Y)*B.T +\
                    A*VarianceMatrix(X)*A.T + B*VarianceMatrix(Y)*B.T

def test_multivariate_crosscovariance():
    raises(ShapeError, lambda: Covariance(X, Y.T))
    raises(ShapeError, lambda: Covariance(X, A))


    expr = Covariance(a.T, b.T)
    assert expr.shape == (1, 1)
    assert expr.expand() == ZeroMatrix(1, 1)

    expr = Covariance(a, b)
    assert expr == Covariance(a, b) == CrossCovarianceMatrix(a, b)
    assert expr.expand() == ZeroMatrix(k, k)
    assert expr.shape == (k, k)
    assert expr.rows == k
    assert expr.cols == k
    assert isinstance(expr, CrossCovarianceMatrix)

    expr = Covariance(A*X + a, b)
    assert expr.expand() == ZeroMatrix(k, k)

    expr = Covariance(X, Y)
    assert isinstance(expr, CrossCovarianceMatrix)
    assert expr.expand() == expr

    expr = Covariance(X, X)
    assert isinstance(expr, CrossCovarianceMatrix)
    assert expr.expand() == VarianceMatrix(X)

    expr = Covariance(X + Y, Z)
    assert isinstance(expr, CrossCovarianceMatrix)
    assert expr.expand() == CrossCovarianceMatrix(X, Z) + CrossCovarianceMatrix(Y, Z)

    expr = Covariance(A*X , Y)
    assert isinstance(expr, CrossCovarianceMatrix)
    assert expr.expand() == A*CrossCovarianceMatrix(X, Y)

    expr = Covariance(X , B*Y)
    assert isinstance(expr, CrossCovarianceMatrix)
    assert expr.expand() == CrossCovarianceMatrix(X, Y)*B.T

    expr = Covariance(A*X + a, B.T*Y + b)
    assert isinstance(expr, CrossCovarianceMatrix)
    assert expr.expand() == A*CrossCovarianceMatrix(X, Y)*B

    expr = Covariance(A*X + B*Y + a, C.T*Z + D.T*W + b)
    assert isinstance(expr, CrossCovarianceMatrix)
    assert expr.expand() == A*CrossCovarianceMatrix(X, W)*D + A*CrossCovarianceMatrix(X, Z)*C \
        + B*CrossCovarianceMatrix(Y, W)*D + B*CrossCovarianceMatrix(Y, Z)*C

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