def test_Identity():
A = MatrixSymbol('A', n, m)
In = Identity(n)
Im = Identity(m)
assert A*Im == A
assert In*A == A
assert transpose(In) == In
assert In.inverse() == In
assert In.conjugate() == In
def test_Identity():
n, m = symbols("n m", integer=True)
A = MatrixSymbol("A", n, m)
In = Identity(n)
Im = Identity(m)
assert A * Im == A
assert In * A == A
assert Transpose(In) == In
assert Inverse(In) == In
assert In.conjugate() == In
def test_Identity():
A = MatrixSymbol('A', n, m)
i, j = symbols('i j')
In = Identity(n)
Im = Identity(m)
assert A*Im == A
assert In*A == A
assert transpose(In) == In
assert In.inverse() == In
assert In.conjugate() == In
assert In[i, j] != 0
assert Sum(In[i, j], (i, 0, n-1), (j, 0, n-1)).subs(n,3).doit() == 3
assert Sum(Sum(In[i, j], (i, 0, n-1)), (j, 0, n-1)).subs(n,3).doit() == 3
def test_factor_expand():
A = MatrixSymbol("A", n, n)
B = MatrixSymbol("B", n, n)
expr1 = (A + B)*(C + D)
expr2 = A*C + B*C + A*D + B*D
assert expr1 != expr2
assert expand(expr1) == expr2
assert factor(expr2) == expr1
expr = B**(-1)*(A**(-1)*B**(-1) - A**(-1)*C*B**(-1))**(-1)*A**(-1)
I = Identity(n)
# Ideally we get the first, but we at least don't want a wrong answer
assert factor(expr) in [I - C, B**-1*(A**-1*(I - C)*B**-1)**-1*A**-1]
def test_multiplication():
A = MatrixSymbol('A', n, m)
B = MatrixSymbol('B', m, l)
C = MatrixSymbol('C', n, n)
assert (2 * A * B).shape == (n, l)
assert (A * 0 * B) == ZeroMatrix(n, l)
raises(ShapeError, lambda: B * A)
assert (2 * A).shape == A.shape
assert A * ZeroMatrix(m, m) * B == ZeroMatrix(n, l)
assert C * Identity(n) * C.I == Identity(n)
assert B / 2 == S.Half * B
raises(NotImplementedError, lambda: 2 / B)
A = MatrixSymbol('A', n, n)
B = MatrixSymbol('B', n, n)
assert Identity(n) * (A + B) == A + B
def test_NumPyPrinter():
from sympy import (
Lambda,
ZeroMatrix,
OneMatrix,
FunctionMatrix,
HadamardProduct,
KroneckerProduct,
Adjoint,
DiagonalOf,
DiagMatrix,
DiagonalMatrix,
)
from sympy.abc import a, b
p = NumPyPrinter()
assert p.doprint(sign(x)) == "numpy.sign(x)"
A = MatrixSymbol("A", 2, 2)
B = MatrixSymbol("B", 2, 2)
C = MatrixSymbol("C", 1, 5)
D = MatrixSymbol("D", 3, 4)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
assert p.doprint(Identity(3)) == "numpy.eye(3)"
u = MatrixSymbol("x", 2, 1)
v = MatrixSymbol("y", 2, 1)
assert p.doprint(MatrixSolve(A, u)) == "numpy.linalg.solve(A, x)"
assert p.doprint(MatrixSolve(A, u) + v) == "numpy.linalg.solve(A, x) + y"
assert p.doprint(ZeroMatrix(2, 3)) == "numpy.zeros((2, 3))"
assert p.doprint(OneMatrix(2, 3)) == "numpy.ones((2, 3))"
assert (p.doprint(FunctionMatrix(4, 5, Lambda(
(a, b), a + b))) == "numpy.fromfunction(lambda a, b: a + b, (4, 5))")
assert p.doprint(HadamardProduct(A, B)) == "numpy.multiply(A, B)"
assert p.doprint(KroneckerProduct(A, B)) == "numpy.kron(A, B)"
assert p.doprint(Adjoint(A)) == "numpy.conjugate(numpy.transpose(A))"
assert p.doprint(DiagonalOf(A)) == "numpy.reshape(numpy.diag(A), (-1, 1))"
assert p.doprint(DiagMatrix(C)) == "numpy.diagflat(C)"
assert p.doprint(DiagonalMatrix(D)) == "numpy.multiply(D, numpy.eye(3, 4))"
# Workaround for numpy negative integer power errors
assert p.doprint(x**-1) == "x**(-1.0)"
assert p.doprint(x**-2) == "x**(-2.0)"
assert p.doprint(S.Exp1) == "numpy.e"
assert p.doprint(S.Pi) == "numpy.pi"
assert p.doprint(S.EulerGamma) == "numpy.euler_gamma"
assert p.doprint(S.NaN) == "numpy.nan"
assert p.doprint(S.Infinity) == "numpy.PINF"
assert p.doprint(S.NegativeInfinity) == "numpy.NINF"
def test_linear_factors():
from sympy.matrices import MatrixSymbol, linear_factors
n, m, l = symbols('n m l')
A = MatrixSymbol('A', n, m)
B = MatrixSymbol('B', m, l)
C = MatrixSymbol('C', n, l)
assert linear_factors(2 * A * B + C, B, C) == {C: Identity(n), B: 2 * A}
assert linear_factors(2 * A * B + C, B) == {B: 2 * A}
assert linear_factors(2 * A * B, B) == {B: 2 * A}
assert linear_factors(2 * A * B, C) == {C: ZeroMatrix(n, n)}
A = MatrixSymbol('A', n, n)
B = MatrixSymbol('B', n, n)
C = MatrixSymbol('C', n, n)
D = MatrixSymbol('C', m, m)
raises(ValueError, lambda: linear_factors(2 * A * A + B, A))
raises(ValueError, lambda: linear_factors(2 * A * A, A))
raises(ValueError, lambda: linear_factors(2 * A * B, A, B))
raises(ShapeError, lambda: linear_factors(2 * A * B, D))
raises(ShapeError, lambda: linear_factors(2 * A * B + C, D))
assert linear_factors(A, A) == {A: Identity(n)}
def test_MatPow():
A = MatrixSymbol('A', n, n)
AA = MatPow(A, 2)
assert AA.exp == 2
assert AA.base == A
assert (A**n).exp == n
assert A**0 == Identity(n)
assert A**1 == A
assert A**2 == AA
assert A**-1 == Inverse(A)
assert A**S.Half == sqrt(A)
raises(ShapeError, lambda: MatrixSymbol('B', 3, 2)**2)
def test_NumPyPrinter():
from sympy.core.function import Lambda
from sympy.matrices.expressions.adjoint import Adjoint
from sympy.matrices.expressions.diagonal import (DiagMatrix,
DiagonalMatrix,
DiagonalOf)
from sympy.matrices.expressions.funcmatrix import FunctionMatrix
from sympy.matrices.expressions.hadamard import HadamardProduct
from sympy.matrices.expressions.kronecker import KroneckerProduct
from sympy.matrices.expressions.special import (OneMatrix, ZeroMatrix)
from sympy.abc import a, b
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
A = MatrixSymbol("A", 2, 2)
B = MatrixSymbol("B", 2, 2)
C = MatrixSymbol("C", 1, 5)
D = MatrixSymbol("D", 3, 4)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
assert p.doprint(Identity(3)) == "numpy.eye(3)"
u = MatrixSymbol('x', 2, 1)
v = MatrixSymbol('y', 2, 1)
assert p.doprint(MatrixSolve(A, u)) == 'numpy.linalg.solve(A, x)'
assert p.doprint(MatrixSolve(A, u) + v) == 'numpy.linalg.solve(A, x) + y'
assert p.doprint(ZeroMatrix(2, 3)) == "numpy.zeros((2, 3))"
assert p.doprint(OneMatrix(2, 3)) == "numpy.ones((2, 3))"
assert p.doprint(FunctionMatrix(4, 5, Lambda((a, b), a + b))) == \
"numpy.fromfunction(lambda a, b: a + b, (4, 5))"
assert p.doprint(HadamardProduct(A, B)) == "numpy.multiply(A, B)"
assert p.doprint(KroneckerProduct(A, B)) == "numpy.kron(A, B)"
assert p.doprint(Adjoint(A)) == "numpy.conjugate(numpy.transpose(A))"
assert p.doprint(DiagonalOf(A)) == "numpy.reshape(numpy.diag(A), (-1, 1))"
assert p.doprint(DiagMatrix(C)) == "numpy.diagflat(C)"
assert p.doprint(DiagonalMatrix(D)) == "numpy.multiply(D, numpy.eye(3, 4))"
# Workaround for numpy negative integer power errors
assert p.doprint(x**-1) == 'x**(-1.0)'
assert p.doprint(x**-2) == 'x**(-2.0)'
expr = Pow(2, -1, evaluate=False)
assert p.doprint(expr) == "2**(-1.0)"
assert p.doprint(S.Exp1) == 'numpy.e'
assert p.doprint(S.Pi) == 'numpy.pi'
assert p.doprint(S.EulerGamma) == 'numpy.euler_gamma'
assert p.doprint(S.NaN) == 'numpy.nan'
assert p.doprint(S.Infinity) == 'numpy.PINF'
assert p.doprint(S.NegativeInfinity) == 'numpy.NINF'
def test_MatMul():
n, m, l = symbols('n m l', integer=True)
A = MatrixSymbol('A', n, m)
B = MatrixSymbol('B', m, l)
C = MatrixSymbol('C', n, n)
assert (2 * A * B).shape == (n, l)
assert (A * 0 * B) == ZeroMatrix(n, l)
raises(ShapeError, lambda: B * A)
assert (2 * A).shape == A.shape
assert MatMul(A, ZeroMatrix(m, m), B) == ZeroMatrix(n, l)
assert MatMul(C * Identity(n) * C.I) == Identity(n)
assert B / 2 == S.Half * B
raises(NotImplementedError, lambda: 2 / B)
A = MatrixSymbol('A', n, n)
B = MatrixSymbol('B', n, n)
assert MatMul(Identity(n), (A + B)).is_Add
def test_MatPow():
n = Symbol('n', integer=True)
A = MatrixSymbol('A', n, n)
assert Inverse(A).is_Pow
assert (A * A).is_Pow
assert (A * A).exp == 2
assert (A * A).base == A
assert (A**n).exp == n
assert A**0 == Identity(n)
assert A**1 == A
assert A**-1 == Inverse(A)
raises(ShapeError, "MatrixSymbol('B', 3,2)**2")
def test_inverse():
raises(ShapeError, lambda: Inverse(A))
raises(ShapeError, lambda: Inverse(A * B))
assert Inverse(C).shape == (n, n)
assert Inverse(A * E).shape == (n, n)
assert Inverse(E * A).shape == (m, m)
assert Inverse(C).inverse() == C
assert isinstance(Inverse(Inverse(C)), Inverse)
assert C.inverse().inverse() == C
assert C.inverse() * C == Identity(C.rows)
assert Identity(n).inverse() == Identity(n)
assert (3 * Identity(n)).inverse() == Identity(n) / 3
# Simplifies Muls if possible (i.e. submatrices are square)
assert (C * D).inverse() == D.I * C.I
# But still works when not possible
assert isinstance((A * E).inverse(), Inverse)
assert Inverse(eye(3)).doit() == eye(3)
assert Inverse(eye(3)).doit(deep=False) == eye(3)
def test_NumPyPrinter():
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
A = MatrixSymbol("A", 2, 2)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
assert p.doprint(Identity(3)) == "numpy.eye(3)"
u = MatrixSymbol('x', 2, 1)
v = MatrixSymbol('y', 2, 1)
assert p.doprint(MatrixSolve(A, u)) == 'numpy.linalg.solve(A, x)'
assert p.doprint(MatrixSolve(A, u) + v) == 'numpy.linalg.solve(A, x) + y'
# Workaround for numpy negative integer power errors
assert p.doprint(x**-1) == 'x**(-1.0)'
assert p.doprint(x**-2) == 'x**(-2.0)'
def test_MatrixSymbol():
n = Symbol('n', integer=True)
A = MatrixSymbol('A', n, n)
B = MatrixSymbol('B', n, n)
assert maple_code(A * B) == "A.B"
assert maple_code(B * A) == "B.A"
assert maple_code(2 * A * B) == "2*A.B"
assert maple_code(B * 2 * A) == "2*B.A"
assert maple_code(
A * (B + 3 * Identity(n))) == "A.(3*Matrix(n, shape = identity) + B)"
assert maple_code(A**(x**2)) == "MatrixPower(A, x^2)"
assert maple_code(A**3) == "MatrixPower(A, 3)"
assert maple_code(A**(S.Half)) == "MatrixPower(A, 1/2)"
def test_ZeroMatrix():
A = MatrixSymbol('A', n, m)
Z = ZeroMatrix(n, m)
assert A + Z == A
assert A * Z.T == ZeroMatrix(n, n)
assert Z * A.T == ZeroMatrix(n, n)
assert A - A == ZeroMatrix(*A.shape)
assert transpose(Z) == ZeroMatrix(m, n)
assert Z.conjugate() == Z
assert ZeroMatrix(n, n)**0 == Identity(n)
with raises(ShapeError):
Z**0
with raises(ShapeError):
Z**2
def test_one_matrix_creation():
assert OneMatrix(2, 2)
assert OneMatrix(0, 0)
assert Eq(OneMatrix(1, 1), Identity(1))
raises(ValueError, lambda: OneMatrix(-1, 2))
raises(ValueError, lambda: OneMatrix(2.0, 2))
raises(ValueError, lambda: OneMatrix(2j, 2))
raises(ValueError, lambda: OneMatrix(2, -1))
raises(ValueError, lambda: OneMatrix(2, 2.0))
raises(ValueError, lambda: OneMatrix(2, 2j))
n = symbols('n')
assert OneMatrix(n, n)
n = symbols('n', integer=False)
raises(ValueError, lambda: OneMatrix(n, n))
n = symbols('n', negative=True)
raises(ValueError, lambda: OneMatrix(n, n))
def test_MatPow():
A = MatrixSymbol('A', n, n)
AA = MatPow(A, 2)
assert AA.exp == 2
assert AA.base == A
assert (A**n).exp == n
assert A**0 == Identity(n)
assert A**1 == A
assert A**2 == AA
assert A**-1 == Inverse(A)
assert (A**-1)**-1 == A
assert (A**2)**3 == A**6
assert A**S.Half == sqrt(A)
assert A**Rational(1, 3) == cbrt(A)
raises(NonSquareMatrixError, lambda: MatrixSymbol('B', 3, 2)**2)
def test_invariants():
A = MatrixSymbol('A', n, m)
B = MatrixSymbol('B', m, l)
X = MatrixSymbol('X', n, n)
objs = [
Identity(n),
ZeroMatrix(m, n), A,
MatMul(A, B),
MatAdd(A, A),
Transpose(A),
Adjoint(A),
Inverse(X),
MatPow(X, 2),
MatPow(X, -1),
MatPow(X, 0)
]
for obj in objs:
assert obj == obj.__class__(*obj.args)
def test_OneMatrix():
A = MatrixSymbol('A', n, m)
a = MatrixSymbol('a', n, 1)
U = OneMatrix(n, m)
assert U.shape == (n, m)
assert isinstance(A + U, Add)
assert transpose(U) == OneMatrix(m, n)
assert U.conjugate() == U
assert OneMatrix(n, n)**0 == Identity(n)
with raises(ShapeError):
U**0
with raises(ShapeError):
U**2
U = OneMatrix(n, n)
assert U[1, 2] == 1
U = OneMatrix(2, 3)
assert U.as_explicit() == ImmutableMatrix.ones(2, 3)
def test_NumPyPrinter():
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
A = MatrixSymbol("A", 2, 2)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
assert p.doprint(Identity(3)) == "numpy.eye(3)"
u = MatrixSymbol('x', 2, 1)
v = MatrixSymbol('y', 2, 1)
assert p.doprint(MatrixSolve(A, u)) == 'numpy.linalg.solve(A, x)'
assert p.doprint(MatrixSolve(A, u) + v) == 'numpy.linalg.solve(A, x) + y'
# Workaround for numpy negative integer power errors
assert p.doprint(x**-1) == 'x**(-1.0)'
assert p.doprint(x**-2) == 'x**(-2.0)'
assert p.doprint(S.Exp1) == 'numpy.e'
assert p.doprint(S.Pi) == 'numpy.pi'
assert p.doprint(S.EulerGamma) == 'numpy.euler_gamma'
assert p.doprint(S.NaN) == 'numpy.nan'
assert p.doprint(S.Infinity) == 'numpy.PINF'
assert p.doprint(S.NegativeInfinity) == 'numpy.NINF'
def test_ZeroMatrix():
A = MatrixSymbol('A', n, m)
Z = ZeroMatrix(n, m)
assert A + Z == A
assert A*Z.T == ZeroMatrix(n, n)
assert Z*A.T == ZeroMatrix(n, n)
assert A - A == ZeroMatrix(*A.shape)
assert Z
assert transpose(Z) == ZeroMatrix(m, n)
assert Z.conjugate() == Z
assert ZeroMatrix(n, n)**0 == Identity(n)
with raises(NonSquareMatrixError):
Z**0
with raises(NonSquareMatrixError):
Z**1
with raises(NonSquareMatrixError):
Z**2
assert ZeroMatrix(3, 3).as_explicit() == ImmutableMatrix.zeros(3, 3)
def test_trace():
A = MatrixSymbol('A', n, n)
B = MatrixSymbol('B', n, n)
assert isinstance(Trace(A), Trace)
assert not isinstance(Trace(A), MatrixExpr)
raises(ShapeError, lambda: Trace(MatrixSymbol('B', 3, 4)))
assert Trace(eye(3)) == 3
assert Trace(Matrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 9])) == 15
A / Trace(A) # Make sure this is possible
# Some easy simplifications
assert Trace(Identity(5)) == 5
assert Trace(ZeroMatrix(5, 5)) == 0
assert Trace(2 * A * B) == 2 * Trace(A * B)
assert Trace(A.T) == Trace(A)
i, j = symbols('i,j')
F = FunctionMatrix(3, 3, Lambda((i, j), i + j))
assert Trace(F).doit() == (0 + 0) + (1 + 1) + (2 + 2)
raises(TypeError, lambda: Trace(S.One))
assert Trace(A).arg is A
def test_Identity():
A = MatrixSymbol('A', n, m)
i, j = symbols('i j')
In = Identity(n)
Im = Identity(m)
assert A*Im == A
assert In*A == A
assert transpose(In) == In
assert In.inverse() == In
assert In.conjugate() == In
assert In[i, j] != 0
assert Sum(In[i, j], (i, 0, n-1), (j, 0, n-1)).subs(n,3).doit() == 3
assert Sum(Sum(In[i, j], (i, 0, n-1)), (j, 0, n-1)).subs(n,3).doit() == 3
# If range exceeds the limit `(0, n-1)`, do not remove `Piecewise`:
expr = Sum(In[i, j], (i, 0, n-1))
assert expr.doit() == 1
expr = Sum(In[i, j], (i, 0, n-2))
assert expr.doit().dummy_eq(
Piecewise(
(1, (j >= 0) & (j <= n-2)),
(0, True)
)
)
expr = Sum(In[i, j], (i, 1, n-1))
assert expr.doit().dummy_eq(
Piecewise(
(1, (j >= 1) & (j <= n-1)),
(0, True)
)
)
assert Identity(3).as_explicit() == ImmutableMatrix.eye(3)
def test_Identity_doit():
Inn = Identity(Add(n, n, evaluate=False))
assert isinstance(Inn.rows, Add)
assert Inn.doit() == Identity(2*n)
assert isinstance(Inn.doit().rows, Mul)
def test_xxinv():
assert xxinv(MatMul(D, Inverse(D), D, evaluate=False)) == \
MatMul(Identity(n), D, evaluate=False)
def test_refine():
assert refine(C * C.T * D, Q.orthogonal(C)).doit() == D
kC = k * C
assert refine(kC * C.T, Q.orthogonal(C)).doit() == k * Identity(n)
assert refine(kC * kC.T, Q.orthogonal(C)).doit() == (k**2) * Identity(n)
def test_remove_ids():
assert remove_ids(MatMul(A, Identity(m), B, evaluate=False)) == \
MatMul(A, B, evaluate=False)
assert null_safe(remove_ids)(MatMul(Identity(n), evaluate=False)) == \
MatMul(Identity(n), evaluate=False)
def test_Identity_doit():
Inn = Identity(Add(n, n, evaluate=False))
assert isinstance(Inn.rows, Add)
assert Inn.doit() == Identity(2 * n)
assert isinstance(Inn.doit().rows, Mul)
def test_special_matrices():
assert mcode(6 * Identity(3)) == "6*eye(3)"
"""
from sympy.core.symbol import symbols
from sympy.matrices import (Matrix, MatrixSymbol, eye, Identity,
ImmutableMatrix)
from sympy.matrices.expressions import MatrixExpr, MatAdd
from sympy.matrices.common import classof
from sympy.testing.pytest import raises
SM = MatrixSymbol('X', 3, 3)
SV = MatrixSymbol('v', 3, 1)
MM = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
IM = ImmutableMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
meye = eye(3)
imeye = ImmutableMatrix(eye(3))
ideye = Identity(3)
a, b, c = symbols('a,b,c')
def test_IM_MM():
assert isinstance(MM + IM, ImmutableMatrix)
assert isinstance(IM + MM, ImmutableMatrix)
assert isinstance(2 * IM + MM, ImmutableMatrix)
assert MM.equals(IM)
def test_ME_MM():
assert isinstance(Identity(3) + MM, MatrixExpr)
assert isinstance(SM + MM, MatAdd)
assert isinstance(MM + SM, MatAdd)
assert (Identity(3) + MM)[1, 1] == 6
def test_ME_MM():
assert isinstance(Identity(3) + MM, MatrixExpr)
assert isinstance(SM + MM, MatAdd)
assert isinstance(MM + SM, MatAdd)
assert (Identity(3) + MM)[1, 1] == 6
def test_combine_powers():
assert combine_powers(MatMul(D, Inverse(D), D, evaluate=False)) == \
MatMul(Identity(n), D, evaluate=False)
def test_equality():
a, b, c = Identity(3), eye(3), ImmutableMatrix(eye(3))
for x in [a, b, c]:
for y in [a, b, c]:
assert x.equals(y)
def test_KroneckerProduct_identity():
assert KroneckerProduct(Identity(m), Identity(n)) == Identity(m * n)
assert KroneckerProduct(eye(2), eye(3)) == eye(6)
