def test_matrix_expression_from_index_summation():
from sympy.abc import a,b,c,d
A = MatrixSymbol("A", k, k)
B = MatrixSymbol("B", k, k)
C = MatrixSymbol("C", k, k)
expr = Sum(W[a,b]*X[b,c]*Z[c,d], (b, 0, l-1), (c, 0, m-1))
assert MatrixExpr.from_index_summation(expr, a) == W*X*Z
expr = Sum(W.T[b,a]*X[b,c]*Z[c,d], (b, 0, l-1), (c, 0, m-1))
assert MatrixExpr.from_index_summation(expr, a) == W*X*Z
expr = Sum(A[b, a]*B[b, c]*C[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixSymbol.from_index_summation(expr, a) == A.T*B*C
expr = Sum(A[b, a]*B[c, b]*C[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixSymbol.from_index_summation(expr, a) == A.T*B.T*C
expr = Sum(C[c, d]*A[b, a]*B[c, b], (b, 0, k-1), (c, 0, k-1))
assert MatrixSymbol.from_index_summation(expr, a) == A.T*B.T*C
expr = Sum(A[a, b] + B[a, b], (a, 0, k-1), (b, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == A + B
expr = Sum((A[a, b] + B[a, b])*C[b, c], (b, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == (A+B)*C
expr = Sum((A[a, b] + B[b, a])*C[b, c], (b, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == (A+B.T)*C
expr = Sum(A[a, b]*A[b, c]*A[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == MatMul(A, A, A)
expr = Sum(A[a, b]*A[b, c]*B[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == MatMul(A, A, B)
# Parse the trace of a matrix:
expr = Sum(A[a, a], (a, 0, k-1))
assert MatrixExpr.from_index_summation(expr, None) == trace(A)
expr = Sum(A[a, a]*B[b, c]*C[c, d], (a, 0, k-1), (c, 0, k-1))
assert MatrixExpr.from_index_summation(expr, b) == trace(A)*B*C
# Check wrong sum ranges (should raise an exception):
## Case 1: 0 to m instead of 0 to m-1
expr = Sum(W[a,b]*X[b,c]*Z[c,d], (b, 0, l-1), (c, 0, m))
raises(ValueError, lambda: MatrixExpr.from_index_summation(expr, a))
## Case 2: 1 to m-1 instead of 0 to m-1
expr = Sum(W[a,b]*X[b,c]*Z[c,d], (b, 0, l-1), (c, 1, m-1))
raises(ValueError, lambda: MatrixExpr.from_index_summation(expr, a))
# Parse nested sums:
expr = Sum(A[a, b]*Sum(B[b, c]*C[c, d], (c, 0, k-1)), (b, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == A*B*C
# Test Kronecker delta:
expr = Sum(A[a, b]*KroneckerDelta(b, c)*B[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == A*B
def test_MatrixSlice():
from sympy.matrices.expressions import MatrixSymbol
assert str(MatrixSymbol('X', 10, 10)[:5, 1:9:2]) == 'X[:5, 1:9:2]'
assert str(MatrixSymbol('X', 10, 10)[5, :5:2]) == 'X[5, :5:2]'
def test_exp_MatrixSymbol():
A = MatrixSymbol("A", 2, 2)
assert exp(A).has(exp)
B = sp.Matrix([
[-9,-5],
[1,-1]
])
C = sp.Matrix([
[1,3,3],
[-4,-5,3]
])
B*"1/2"
x1,x2 = sp.symbols("x1 x2")
x,params=C.gauss_jordan_solve(B)
sp.pprint(x)
from sympy import MatrixSymbol, BlockMatrix, symbols, Identity, Matrix, ZeroMatrix, block_collapse
n,m,l = symbols('n m l')
X = MatrixSymbol('X', n, n)
Y = MatrixSymbol('Y', m ,m)
Z = MatrixSymbol('Z', n, m)
# B = BlockMatrix([[X, Z], [ZeroMatrix(m, n), Y]])
# print(B)
# C = BlockMatrix([[Identity(n), Z]])
# print(C)
# print(C*B)
A = sp.Matrix(sp.BlockMatrix([[C,B]]))
sp.pprint(X.inverse())
def test_MatrixSymbol_printing():
A = MatrixSymbol("A", 3, 3)
B = MatrixSymbol("B", 3, 3)
assert str(A - A * B - B) == "-B - A*B + A"
assert str(A * B - (A + B)) == "-(A + B) + A*B"
def test_re():
x, y = symbols('x,y')
a, b = symbols('a,b', real=True)
r = Symbol('r', real=True)
i = Symbol('i', imaginary=True)
assert re(nan) == nan
assert re(oo) == oo
assert re(-oo) == -oo
assert re(0) == 0
assert re(1) == 1
assert re(-1) == -1
assert re(E) == E
assert re(-E) == -E
assert re(x) == re(x)
assert re(x * I) == -im(x)
assert re(r * I) == 0
assert re(r) == r
assert re(i * I) == I * i
assert re(i) == 0
assert re(x + y) == re(x + y)
assert re(x + r) == re(x) + r
assert re(re(x)) == re(x)
assert re(2 + I) == 2
assert re(x + I) == re(x)
assert re(x + y * I) == re(x) - im(y)
assert re(x + r * I) == re(x)
assert re(log(2 * I)) == log(2)
assert re((2 + I)**2).expand(complex=True) == 3
assert re(conjugate(x)) == re(x)
assert conjugate(re(x)) == re(x)
assert re(x).as_real_imag() == (re(x), 0)
assert re(i * r * x).diff(r) == re(i * x)
assert re(i * r * x).diff(i) == I * r * im(x)
assert re(
sqrt(a +
b * I)) == (a**2 + b**2)**Rational(1, 4) * cos(atan2(b, a) / 2)
assert re(a * (2 + b * I)) == 2 * a
assert re((1 + sqrt(a + b*I))/2) == \
(a**2 + b**2)**Rational(1, 4)*cos(atan2(b, a)/2)/2 + Rational(1, 2)
assert re(x).rewrite(im) == x - S.ImaginaryUnit * im(x)
assert (x + re(y)).rewrite(re, im) == x + y - S.ImaginaryUnit * im(y)
a = Symbol('a', algebraic=True)
t = Symbol('t', transcendental=True)
x = Symbol('x')
assert re(a).is_algebraic
assert re(x).is_algebraic is None
assert re(t).is_algebraic is False
assert re(S.ComplexInfinity) == S.NaN
n, m, l = symbols('n m l')
A = MatrixSymbol('A', n, m)
assert re(A) == (S(1) / 2) * (A + conjugate(A))
A = Matrix([[1 + 4 * I, 2], [0, -3 * I]])
assert re(A) == Matrix([[1, 2], [0, 0]])
A = ImmutableMatrix([[1 + 3 * I, 3 - 2 * I], [0, 2 * I]])
assert re(A) == ImmutableMatrix([[1, 3], [0, 0]])
X = SparseMatrix([[2 * j + i * I for i in range(5)] for j in range(5)])
assert re(X) - Matrix([[0, 0, 0, 0, 0], [2, 2, 2, 2, 2], [4, 4, 4, 4, 4],
[6, 6, 6, 6, 6], [8, 8, 8, 8, 8]
]) == Matrix.zeros(5)
assert im(X) - Matrix([[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4],
[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]
]) == Matrix.zeros(5)
X = FunctionMatrix(3, 3, Lambda((n, m), n + m * I))
assert re(X) == Matrix([[0, 0, 0], [1, 1, 1], [2, 2, 2]])
from sympy import Matrix, symbols, MatrixSymbol, NDimArray
from sympy.matrices.common import MatrixCommon
from sympy.tensor.array.array_derivatives import ArrayDerivative
x, y, z, t = symbols("x y z t")
m = Matrix([[x, y], [z, t]])
M = MatrixSymbol("M", 3, 2)
N = MatrixSymbol("N", 4, 3)
def test_array_derivative_construction():
d = ArrayDerivative(x, m, evaluate=False)
assert d.shape == (2, 2)
expr = d.doit()
assert isinstance(expr, MatrixCommon)
assert expr.shape == (2, 2)
d = ArrayDerivative(m, m, evaluate=False)
assert d.shape == (2, 2, 2, 2)
expr = d.doit()
assert isinstance(expr, NDimArray)
assert expr.shape == (2, 2, 2, 2)
d = ArrayDerivative(m, x, evaluate=False)
assert d.shape == (2, 2)
expr = d.doit()
assert isinstance(expr, MatrixCommon)
assert expr.shape == (2, 2)
def init_symbols(self, init_type='symbol'):
"""
Create symbol singletons in the first place for parameters and variables
Dictionaries:
- _gcall_int
- _fcall_int
- _var_int_computed
- _var_int_custom
Lists:
- _param_int
Returns
-------
None
"""
assert init_type in ('symbol', 'array')
meta_lists = [
'_param_int', '_algeb_int', '_state_int', '_algeb_ext',
'_param_ext'
]
meta_dicts = [
'_gcall_int', '_fcall_int', '_param_int_computed',
'_var_int_computed'
]
with_types = ['_var_int_custom']
for item in meta_lists:
for symbol_str in self.__dict__[item]:
if symbol_str in self._symbol_singleton:
continue
if init_type == 'symbol':
self._symbol_singleton[symbol_str] = symbols(symbol_str)
elif init_type == 'array':
self._symbol_singleton[symbol_str] = MatrixSymbol(
symbol_str, self.n, 1)
for item in meta_dicts:
for symbol_str in self.__dict__[item].keys():
if symbol_str in self._symbol_singleton:
continue
if init_type == 'symbol':
self._symbol_singleton[symbol_str] = symbols(symbol_str)
elif init_type == 'array':
self._symbol_singleton[symbol_str] = MatrixSymbol(
symbol_str, self.n, 1)
for item in with_types:
for symbol_str in self.__dict__[item].keys():
if symbol_str in self._symbol_singleton:
continue
if init_type == 'symbol':
self._symbol_singleton[symbol_str] = symbols(
symbol_str, commutative=False)
elif init_type == 'array':
# TODO: this is to be implemented. The matrix size of the custom variable need to be
# specifiable by the device definition
# raise NotImplementedError
pass
# self._symbol_singleton[symbol_str] = MatrixSymbol(symbol_str,)
logger.debug(f'\n--> {self.__class__.__name__}: Initialized symbols: '
f'{pprint.pformat(self._symbol_singleton)}')
def test_applyfunc_matrix():
double = Lambda(x, x**2)
expr = ElementwiseApplyFunction(double, Xd)
assert isinstance(expr, ElementwiseApplyFunction)
assert expr.doit() == Xd.applyfunc(lambda x: x**2)
assert expr.shape == (3, 3)
assert expr.func(*expr.args) == expr
assert simplify(expr) == expr
assert expr[0, 0] == double(Xd[0, 0])
expr = ElementwiseApplyFunction(double, X)
assert isinstance(expr, ElementwiseApplyFunction)
assert isinstance(expr.doit(), ElementwiseApplyFunction)
assert expr == X.applyfunc(double)
assert expr.func(*expr.args) == expr
expr = ElementwiseApplyFunction(exp, X*Y)
assert expr.expr == X*Y
assert expr.function == exp
assert expr == (X*Y).applyfunc(exp)
assert expr.func(*expr.args) == expr
assert isinstance(X*expr, MatMul)
assert (X*expr).shape == (3, 3)
Z = MatrixSymbol("Z", 2, 3)
assert (Z*expr).shape == (2, 3)
expr = ElementwiseApplyFunction(exp, Z.T)*ElementwiseApplyFunction(exp, Z)
assert expr.shape == (3, 3)
expr = ElementwiseApplyFunction(exp, Z)*ElementwiseApplyFunction(exp, Z.T)
assert expr.shape == (2, 2)
raises(ShapeError, lambda: ElementwiseApplyFunction(exp, Z)*ElementwiseApplyFunction(exp, Z))
M = Matrix([[x, y], [z, t]])
expr = ElementwiseApplyFunction(sin, M)
assert isinstance(expr, ElementwiseApplyFunction)
assert expr.function == sin
assert expr.expr == M
assert expr.doit() == M.applyfunc(sin)
assert expr.doit() == Matrix([[sin(x), sin(y)], [sin(z), sin(t)]])
assert expr.func(*expr.args) == expr
expr = ElementwiseApplyFunction(double, Xk)
assert expr.doit() == expr
assert expr.subs(k, 2).shape == (2, 2)
assert (expr*expr).shape == (k, k)
M = MatrixSymbol("M", k, t)
expr2 = M.T*expr*M
assert isinstance(expr2, MatMul)
assert expr2.args[1] == expr
assert expr2.shape == (t, t)
expr3 = expr*M
assert expr3.shape == (k, t)
raises(ShapeError, lambda: M*expr)
expr1 = ElementwiseApplyFunction(lambda x: x+1, Xk)
expr2 = ElementwiseApplyFunction(lambda x: x, Xk)
assert expr1 != expr2
from sympy.matrices.expressions.applyfunc import ElementwiseApplyFunction
from sympy import (Matrix, Lambda, MatrixSymbol, exp, symbols, MatMul, sin, simplify)
from sympy.utilities.pytest import raises
from sympy.matrices.common import ShapeError
X = MatrixSymbol("X", 3, 3)
Y = MatrixSymbol("Y", 3, 3)
k = symbols("k")
Xk = MatrixSymbol("X", k, k)
Xd = X.as_explicit()
x, y, z, t = symbols("x y z t")
def test_applyfunc_matrix():
double = Lambda(x, x**2)
expr = ElementwiseApplyFunction(double, Xd)
assert isinstance(expr, ElementwiseApplyFunction)
assert expr.doit() == Xd.applyfunc(lambda x: x**2)
assert expr.shape == (3, 3)
assert expr.func(*expr.args) == expr
assert simplify(expr) == expr
assert expr[0, 0] == double(Xd[0, 0])
expr = ElementwiseApplyFunction(double, X)
assert isinstance(expr, ElementwiseApplyFunction)
assert isinstance(expr.doit(), ElementwiseApplyFunction)
Matrix([[1, 2], [3, 4]])*Matrix([x, y])
# %% {"slideshow": {"slide_type": "fragment"}}
x, y, z = sym.symbols('x y z')
Matrix([sin(x) + y, cos(y) + x, z]).jacobian([x, y, z])
# %% [markdown] {"slideshow": {"slide_type": "slide"}}
# ## Matrix symbols
#
# SymPy can also operate on matrices of symbolic dimension ($n \times m$). `MatrixSymbol("M", n, m)` creates a matrix $M$ of shape $n \times m$.
# %% {"slideshow": {"slide_type": "fragment"}}
from sympy import MatrixSymbol, Transpose
n, m = sym.symbols('n m', integer=True)
M = MatrixSymbol("M", n, m)
b = MatrixSymbol("b", m, 1)
Transpose(M*b)
# %% {"slideshow": {"slide_type": "fragment"}}
Transpose(M*b).doit()
# %% [markdown] {"slideshow": {"slide_type": "slide"}}
# ## Solving systems of equations
#
# `solve` solves equations symbolically (not numerically). The return value is a list of solutions. It automatically assumes that it is equal to 0.
# %% {"slideshow": {"slide_type": "fragment"}}
from sympy import Eq, solve
solve(Eq(x**2, 4), x)
#import pythonista
from sympy.matrices.expressions.factorizations import lu, LofCholesky, qr, svd
from sympy import Symbol, MatrixSymbol, ask, Q
n = Symbol('n')
X = MatrixSymbol('X', n, n)
def test_LU():
L, U = lu(X)
assert L.shape == U.shape == X.shape
assert ask(Q.lower_triangular(L))
assert ask(Q.upper_triangular(U))
def test_Cholesky():
L = LofCholesky(X)
def test_QR():
Q_, R = qr(X)
assert Q_.shape == R.shape == X.shape
assert ask(Q.orthogonal(Q_))
assert ask(Q.upper_triangular(R))
def test_svd():
U, S, V = svd(X)
assert U.shape == S.shape == V.shape == X.shape
assert ask(Q.orthogonal(U))
assert ask(Q.orthogonal(V))
assert ask(Q.diagonal(S))
from sympy import MatrixSymbol, Matrix, symbols, exp, pprint, log, hessian
from sympy.tensor.array import derive_by_array
import numpy as np
x = symbols('x')
y = symbols('y')
THETA = MatrixSymbol('THETA', 2, 1)
f = x**4 + 3 * y**4 + x**2 + x**2 * y**2 + x + 2 + y**2 + y**3 + y
gradP = Matrix(derive_by_array(f, [x, y]))
pprint(gradP)
hessenP = Matrix(hessian(f, [x, y]))
pprint(hessenP)
thetaResult = Matrix([[1], [1]])
xyMap = {x: 1, y: 1}
while True:
xOld = xyMap[x]
yOld = xyMap[y]
gradPTemp = gradP.subs(xyMap).evalf()
hessenPTemp = hessenP.subs(xyMap).evalf()
diff = hessenPTemp.inv() * gradPTemp
xyMap[x] = xyMap[x] - Matrix(diff).tolist()[0][0]
xyMap[y] = xyMap[y] - Matrix(diff).tolist()[1][0]
xDelta = xOld - xyMap[x]
yDelta = yOld - xyMap[y]
temp = xDelta * xDelta + yDelta * yDelta
print(temp)
if (temp < 0.00000000001):
break
def test_latex_MatrixSlice():
from sympy.matrices.expressions import MatrixSymbol
assert latex(MatrixSymbol('X', 10, 10)[:5, 1:9:2]) == \
r'X\left[:5, 1:9:2\right]'
assert latex(MatrixSymbol('X', 10, 10)[5, :5:2]) == \
r'X\left[5, :5:2\right]'
_RecognizeMatMulLines, _unfold_recognized_expr,
parse_indexed_expression, recognize_matrix_expression,
_parse_matrix_expression)
from sympy import (MatrixSymbol, Sum)
from sympy.combinatorics import Permutation
from sympy.functions.special.tensor_functions import KroneckerDelta
from sympy.matrices.expressions.diagonal import DiagonalizeVector
from sympy.matrices.expressions.matexpr import MatrixElement
from sympy.matrices import (Trace, MatAdd, MatMul, Transpose)
from sympy.utilities.pytest import raises, XFAIL
A, B = symbols("A B", cls=IndexedBase)
i, j, k, l, m, n = symbols("i j k l m n")
M = MatrixSymbol("M", k, k)
N = MatrixSymbol("N", k, k)
P = MatrixSymbol("P", k, k)
Q = MatrixSymbol("Q", k, k)
def test_codegen_array_contraction_construction():
cg = CodegenArrayContraction(A)
assert cg == A
s = Sum(A[i]*B[i], (i, 0, 3))
cg = parse_indexed_expression(s)
assert cg == CodegenArrayContraction(CodegenArrayTensorProduct(A, B), (0, 1))
cg = CodegenArrayContraction(CodegenArrayTensorProduct(A, B), (1, 0))
assert cg == CodegenArrayContraction(CodegenArrayTensorProduct(A, B), (0, 1))
from sympy.matrices.expressions.applyfunc import ElementwiseApplyFunction
from sympy import (Matrix, Lambda, MatrixBase, MatrixSymbol, exp, symbols, MatMul, sin)
from sympy.utilities.pytest import raises
from sympy.matrices.common import ShapeError
X = MatrixSymbol("X", 3, 3)
Y = MatrixSymbol("Y", 3, 3)
k = symbols("k")
Xk = MatrixSymbol("X", k, k)
Xd = X.as_explicit()
x, y, z, t = symbols("x y z t")
def test_applyfunc_matrix():
double = Lambda(x, x**2)
expr = ElementwiseApplyFunction(double, Xd)
assert isinstance(expr, ElementwiseApplyFunction)
assert expr.doit() == Xd.applyfunc(lambda x: x**2)
assert expr.shape == (3, 3)
expr = ElementwiseApplyFunction(double, X)
assert isinstance(expr, ElementwiseApplyFunction)
assert isinstance(expr.doit(), ElementwiseApplyFunction)
assert expr == X.applyfunc(double)
expr = ElementwiseApplyFunction(exp, X*Y)
def test_matrix_expression_from_index_summation():
from sympy.abc import a, b, c, d
A = MatrixSymbol("A", k, k)
B = MatrixSymbol("B", k, k)
C = MatrixSymbol("C", k, k)
w1 = MatrixSymbol("w1", k, 1)
i0, i1, i2, i3, i4 = symbols("i0:5", cls=Dummy)
expr = Sum(W[a, b] * X[b, c] * Z[c, d], (b, 0, l - 1), (c, 0, m - 1))
assert MatrixExpr.from_index_summation(expr, a) == W * X * Z
expr = Sum(W.T[b, a] * X[b, c] * Z[c, d], (b, 0, l - 1), (c, 0, m - 1))
assert MatrixExpr.from_index_summation(expr, a) == W * X * Z
expr = Sum(A[b, a] * B[b, c] * C[c, d], (b, 0, k - 1), (c, 0, k - 1))
assert MatrixSymbol.from_index_summation(expr, a) == A.T * B * C
expr = Sum(A[b, a] * B[c, b] * C[c, d], (b, 0, k - 1), (c, 0, k - 1))
assert MatrixSymbol.from_index_summation(expr, a) == A.T * B.T * C
expr = Sum(C[c, d] * A[b, a] * B[c, b], (b, 0, k - 1), (c, 0, k - 1))
assert MatrixSymbol.from_index_summation(expr, a) == A.T * B.T * C
expr = Sum(A[a, b] + B[a, b], (a, 0, k - 1), (b, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, a) == A + B
expr = Sum((A[a, b] + B[a, b]) * C[b, c], (b, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, a) == (A + B) * C
expr = Sum((A[a, b] + B[b, a]) * C[b, c], (b, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, a) == (A + B.T) * C
expr = Sum(A[a, b] * A[b, c] * A[c, d], (b, 0, k - 1), (c, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, a) == MatMul(A, A, A)
expr = Sum(A[a, b] * A[b, c] * B[c, d], (b, 0, k - 1), (c, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, a) == MatMul(A, A, B)
# Parse the trace of a matrix:
expr = Sum(A[a, a], (a, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, None) == trace(A)
expr = Sum(A[a, a] * B[b, c] * C[c, d], (a, 0, k - 1), (c, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, b) == trace(A) * B * C
# Check wrong sum ranges (should raise an exception):
## Case 1: 0 to m instead of 0 to m-1
expr = Sum(W[a, b] * X[b, c] * Z[c, d], (b, 0, l - 1), (c, 0, m))
raises(ValueError, lambda: MatrixExpr.from_index_summation(expr, a))
## Case 2: 1 to m-1 instead of 0 to m-1
expr = Sum(W[a, b] * X[b, c] * Z[c, d], (b, 0, l - 1), (c, 1, m - 1))
raises(ValueError, lambda: MatrixExpr.from_index_summation(expr, a))
# Parse nested sums:
expr = Sum(A[a, b] * Sum(B[b, c] * C[c, d], (c, 0, k - 1)), (b, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, a) == A * B * C
# Test Kronecker delta:
expr = Sum(A[a, b] * KroneckerDelta(b, c) * B[c, d], (b, 0, k - 1),
(c, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, a) == A * B
expr = Sum(
KroneckerDelta(i1, m) * KroneckerDelta(i2, n) * A[i, i1] * A[j, i2],
(i1, 0, k - 1), (i2, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, m) == A.T * A[j, n]
# Test numbered indices:
expr = Sum(A[i1, i2] * w1[i2, 0], (i2, 0, k - 1))
assert MatrixExpr.from_index_summation(expr, i1) == A * w1
expr = Sum(A[i1, i2] * B[i2, 0], (i2, 0, k - 1))
assert MatrixExpr.from_index_summation(expr,
i1) == MatrixElement(A * B, i1, 0)
def test_issue_14216():
from sympy.functions.elementary.complexes import unpolarify
A = MatrixSymbol("A", 2, 2)
assert unpolarify(A[0, 0]) == A[0, 0]
assert unpolarify(A[0, 0] * A[1, 0]) == A[0, 0] * A[1, 0]
from sympy import MatrixSymbol, Add, MatAdd, Mul, MatMul, Symbol, Matrix
from sympy import \
MutableDenseMatrix, ImmutableDenseMatrix, \
MutableSparseMatrix, ImmutableSparseMatrix
from sympy import S
n = Symbol('n')
A = MatrixSymbol("A", n, n)
B = MatrixSymbol("B", n, n)
args = (A, B, A * B, B * A, A**2, B**2, A**-1, B**-1, A * B**-1, B * A**-1,
A**-1 * B, B * A**-1, A**-2, B**-2, A**-1 * B**-1, B**-1 * A**-1, A, B,
A * B, B * A, A**2, B**2, A**-1, B**-1, A * B**-1, B * A**-1,
A**-1 * B, B * A**-1, A**-2, B**-2, A**-1 * B**-1, B**-1 * A**-1)
class TimeMatrixExpression:
def time_Add(self):
Add(*args)
def time_MatAdd(self):
MatAdd(*args)
def time_MatAdd_doit(self):
MatAdd(*args).doit()
def time_Mul(self):
Mul(*args)
def time_MatMul(self):
MatMul(*args)
def test_im():
x, y = symbols('x,y')
a, b = symbols('a,b', real=True)
r = Symbol('r', real=True)
i = Symbol('i', imaginary=True)
assert im(nan) == nan
assert im(oo * I) == oo
assert im(-oo * I) == -oo
assert im(0) == 0
assert im(1) == 0
assert im(-1) == 0
assert im(E * I) == E
assert im(-E * I) == -E
assert im(x) == im(x)
assert im(x * I) == re(x)
assert im(r * I) == r
assert im(r) == 0
assert im(i * I) == 0
assert im(i) == -I * i
assert im(x + y) == im(x + y)
assert im(x + r) == im(x)
assert im(x + r * I) == im(x) + r
assert im(im(x) * I) == im(x)
assert im(2 + I) == 1
assert im(x + I) == im(x) + 1
assert im(x + y * I) == im(x) + re(y)
assert im(x + r * I) == im(x) + r
assert im(log(2 * I)) == pi / 2
assert im((2 + I)**2).expand(complex=True) == 4
assert im(conjugate(x)) == -im(x)
assert conjugate(im(x)) == im(x)
assert im(x).as_real_imag() == (im(x), 0)
assert im(i * r * x).diff(r) == im(i * x)
assert im(i * r * x).diff(i) == -I * re(r * x)
assert im(
sqrt(a +
b * I)) == (a**2 + b**2)**Rational(1, 4) * sin(atan2(b, a) / 2)
assert im(a * (2 + b * I)) == a * b
assert im((1 + sqrt(a + b*I))/2) == \
(a**2 + b**2)**Rational(1, 4)*sin(atan2(b, a)/2)/2
assert im(x).rewrite(re) == -S.ImaginaryUnit * (x - re(x))
assert (x + im(y)).rewrite(im, re) == x - S.ImaginaryUnit * (y - re(y))
a = Symbol('a', algebraic=True)
t = Symbol('t', transcendental=True)
x = Symbol('x')
assert re(a).is_algebraic
assert re(x).is_algebraic is None
assert re(t).is_algebraic is False
assert im(S.ComplexInfinity) == S.NaN
n, m, l = symbols('n m l')
A = MatrixSymbol('A', n, m)
assert im(A) == (S(1) / (2 * I)) * (A - conjugate(A))
A = Matrix([[1 + 4 * I, 2], [0, -3 * I]])
assert im(A) == Matrix([[4, 0], [0, -3]])
A = ImmutableMatrix([[1 + 3 * I, 3 - 2 * I], [0, 2 * I]])
assert im(A) == ImmutableMatrix([[3, -2], [0, 2]])
X = ImmutableSparseMatrix([[i * I + i for i in range(5)]
for i in range(5)])
Y = SparseMatrix([[i for i in range(5)] for i in range(5)])
assert im(X).as_immutable() == Y
X = FunctionMatrix(3, 3, Lambda((n, m), n + m * I))
assert im(X) == Matrix([[0, 1, 2], [0, 1, 2], [0, 1, 2]])
from sympy import MatrixSymbol, Q, ask, Identity, ZeroMatrix, Trace
X = MatrixSymbol('X', 2, 2)
Y = MatrixSymbol('Y', 2, 3)
Z = MatrixSymbol('Z', 2, 2)
def test_invertible():
assert ask(Q.invertible(X), Q.invertible(X))
assert ask(Q.invertible(Y)) is False
assert ask(Q.invertible(X * Y), Q.invertible(X)) is False
assert ask(Q.invertible(X * Z), Q.invertible(X)) is None
assert ask(Q.invertible(X * Z), Q.invertible(X) & Q.invertible(Z)) is True
assert ask(Q.invertible(X.T)) is None
assert ask(Q.invertible(X.T), Q.invertible(X)) is True
assert ask(Q.invertible(X.I)) is True
assert ask(Q.invertible(Identity(3))) is True
assert ask(Q.invertible(ZeroMatrix(3, 3))) is False
def test_symmetric():
assert ask(Q.symmetric(X), Q.symmetric(X))
assert ask(Q.symmetric(X * Z), Q.symmetric(X)) is None
assert ask(Q.symmetric(X * Z), Q.symmetric(X) & Q.symmetric(Z)) is True
assert ask(Q.symmetric(X + Z), Q.symmetric(X) & Q.symmetric(Z)) is True
assert ask(Q.symmetric(Y)) is False
assert ask(Q.symmetric(Y * Y.T)) is True
assert ask(Q.symmetric(Y.T * X * Y)) is None
assert ask(Q.symmetric(Y.T * X * Y), Q.symmetric(X)) is True
assert ask(Q.symmetric(X * X * X * X * X * X * X * X * X * X),
Q.symmetric(X)) is True
def test_recognize_diagonalized_vectors():
a = MatrixSymbol("a", k, 1)
b = MatrixSymbol("b", k, 1)
A = MatrixSymbol("A", k, k)
B = MatrixSymbol("B", k, k)
C = MatrixSymbol("C", k, k)
X = MatrixSymbol("X", k, k)
x = MatrixSymbol("x", k, 1)
I1 = Identity(1)
I = Identity(k)
# Check matrix recognition over trivial dimensions:
cg = CodegenArrayTensorProduct(a, b)
assert recognize_matrix_expression(cg) == a * b.T
cg = CodegenArrayTensorProduct(I1, a, b)
assert recognize_matrix_expression(cg) == a * I1 * b.T
# Recognize trace inside a tensor product:
cg = CodegenArrayContraction(CodegenArrayTensorProduct(A, B, C), (0, 3),
(1, 2))
assert recognize_matrix_expression(cg) == Trace(A * B) * C
# Transform diagonal operator to contraction:
cg = CodegenArrayDiagonal(CodegenArrayTensorProduct(A, a), (1, 2))
assert cg.transform_to_product() == CodegenArrayContraction(
CodegenArrayTensorProduct(A, DiagMatrix(a)), (1, 2))
assert recognize_matrix_expression(cg) == A * DiagMatrix(a)
cg = CodegenArrayDiagonal(CodegenArrayTensorProduct(a, b), (0, 2))
assert cg.transform_to_product() == CodegenArrayContraction(
CodegenArrayTensorProduct(DiagMatrix(a), b), (0, 2))
assert recognize_matrix_expression(cg).doit() == DiagMatrix(a) * b
cg = CodegenArrayDiagonal(CodegenArrayTensorProduct(A, a), (0, 2))
assert cg.transform_to_product() == CodegenArrayContraction(
CodegenArrayTensorProduct(A, DiagMatrix(a)), (0, 2))
assert recognize_matrix_expression(cg) == A.T * DiagMatrix(a)
cg = CodegenArrayDiagonal(CodegenArrayTensorProduct(I, x, I1), (0, 2),
(3, 5))
assert cg.transform_to_product() == CodegenArrayContraction(
CodegenArrayTensorProduct(I, DiagMatrix(x), I1), (0, 2))
cg = CodegenArrayDiagonal(CodegenArrayTensorProduct(I, x, A, B), (1, 2),
(5, 6))
assert cg.transform_to_product() == CodegenArrayDiagonal(
CodegenArrayContraction(
CodegenArrayTensorProduct(I, DiagMatrix(x), A, B), (1, 2)), (3, 4))
cg = CodegenArrayDiagonal(CodegenArrayTensorProduct(x, I1), (1, 2))
assert isinstance(cg, CodegenArrayDiagonal)
assert cg.diagonal_indices == ((1, 2), )
assert recognize_matrix_expression(cg) == x
cg = CodegenArrayDiagonal(CodegenArrayTensorProduct(x, I), (0, 2))
assert cg.transform_to_product() == CodegenArrayContraction(
CodegenArrayTensorProduct(DiagMatrix(x), I), (0, 2))
assert recognize_matrix_expression(cg).doit() == DiagMatrix(x)
cg = CodegenArrayDiagonal(x, (1, ))
assert cg == x
# Ignore identity matrices with contractions:
cg = CodegenArrayContraction(CodegenArrayTensorProduct(I, A, I, I), (0, 2),
(1, 3), (5, 7))
assert cg.split_multiple_contractions() == cg
assert recognize_matrix_expression(cg) == Trace(A) * I
cg = CodegenArrayContraction(CodegenArrayTensorProduct(Trace(A) * I, I, I),
(1, 5), (3, 4))
assert cg.split_multiple_contractions() == cg
assert recognize_matrix_expression(cg).doit() == Trace(A) * I
# Add DiagMatrix when required:
cg = CodegenArrayContraction(CodegenArrayTensorProduct(A, a), (1, 2))
assert cg.split_multiple_contractions() == cg
assert recognize_matrix_expression(cg) == A * a
cg = CodegenArrayContraction(CodegenArrayTensorProduct(A, a, B), (1, 2, 4))
assert cg.split_multiple_contractions() == CodegenArrayContraction(
CodegenArrayTensorProduct(A, DiagMatrix(a), B), (1, 2), (3, 4))
assert recognize_matrix_expression(cg) == A * DiagMatrix(a) * B
cg = CodegenArrayContraction(CodegenArrayTensorProduct(A, a, B), (0, 2, 4))
assert cg.split_multiple_contractions() == CodegenArrayContraction(
CodegenArrayTensorProduct(A, DiagMatrix(a), B), (0, 2), (3, 4))
assert recognize_matrix_expression(cg) == A.T * DiagMatrix(a) * B
cg = CodegenArrayContraction(CodegenArrayTensorProduct(A, a, b, a.T, B),
(0, 2, 4, 7, 9))
assert cg.split_multiple_contractions() == CodegenArrayContraction(
CodegenArrayTensorProduct(A, DiagMatrix(a), DiagMatrix(b),
DiagMatrix(a), B), (0, 2), (3, 4), (5, 7),
(6, 9))
assert recognize_matrix_expression(
cg).doit() == A.T * DiagMatrix(a) * DiagMatrix(b) * DiagMatrix(a) * B.T
cg = CodegenArrayContraction(CodegenArrayTensorProduct(I1, I1, I1),
(1, 2, 4))
assert cg.split_multiple_contractions() == CodegenArrayContraction(
CodegenArrayTensorProduct(I1, I1, I1), (1, 2), (3, 4))
assert recognize_matrix_expression(cg).doit() == Identity(1)
cg = CodegenArrayContraction(CodegenArrayTensorProduct(I, I, I, I, A),
(1, 2, 8), (5, 6, 9))
assert recognize_matrix_expression(
cg.split_multiple_contractions()).doit() == A
cg = CodegenArrayContraction(CodegenArrayTensorProduct(A, a, C, a, B),
(1, 2, 4), (5, 6, 8))
assert cg.split_multiple_contractions() == CodegenArrayContraction(
CodegenArrayTensorProduct(A, DiagMatrix(a), C, DiagMatrix(a), B),
(1, 2), (3, 4), (5, 6), (7, 8))
assert recognize_matrix_expression(
cg) == A * DiagMatrix(a) * C * DiagMatrix(a) * B
cg = CodegenArrayContraction(
CodegenArrayTensorProduct(a, I1, b, I1, (a.T * b).applyfunc(cos)),
(1, 2, 8), (5, 6, 9))
assert cg.split_multiple_contractions() == CodegenArrayContraction(
CodegenArrayTensorProduct(a, I1, b, I1, (a.T * b).applyfunc(cos)),
(1, 2), (3, 8), (5, 6), (7, 9))
assert recognize_matrix_expression(cg) == MatMul(a, I1,
(a.T * b).applyfunc(cos),
Transpose(I1), b.T)
cg = CodegenArrayContraction(
CodegenArrayTensorProduct(A.T, a, b, b.T, (A * X * b).applyfunc(cos)),
(1, 2, 8), (5, 6, 9))
assert cg.split_multiple_contractions() == CodegenArrayContraction(
CodegenArrayTensorProduct(A.T, DiagMatrix(a), b, b.T,
(A * X * b).applyfunc(cos)), (1, 2), (3, 8),
(5, 6, 9))
# assert recognize_matrix_expression(cg)
# Check no overlap of lines:
cg = CodegenArrayContraction(CodegenArrayTensorProduct(A, a, C, a, B),
(1, 2, 4), (5, 6, 8), (3, 7))
assert cg.split_multiple_contractions() == cg
cg = CodegenArrayContraction(CodegenArrayTensorProduct(a, b, A), (0, 2, 4),
(1, 3))
assert cg.split_multiple_contractions() == cg
def test_Matrix_and_non_basics():
from sympy import MatrixSymbol
n = Symbol('n')
assert dotprint(MatrixSymbol('X', n, n)) == \
"""digraph{
def test_DiscreteMarkovChain():
# pass only the name
X = DiscreteMarkovChain("X")
assert X.state_space == S.Reals
assert X.index_set == S.Naturals0
assert X.transition_probabilities == None
t = symbols('t', positive=True, integer=True)
assert isinstance(X[t], RandomIndexedSymbol)
assert E(X[0]) == Expectation(X[0])
raises(TypeError, lambda: DiscreteMarkovChain(1))
raises(NotImplementedError, lambda: X(t))
raises(ValueError, lambda: sample_stochastic_process(t))
raises(ValueError, lambda: next(sample_stochastic_process(X)))
# pass name and state_space
Y = DiscreteMarkovChain("Y", [1, 2, 3])
assert Y.transition_probabilities == None
assert Y.state_space == FiniteSet(1, 2, 3)
assert P(Eq(Y[2], 1), Eq(Y[0], 2)) == Probability(Eq(Y[2], 1), Eq(Y[0], 2))
assert E(X[0]) == Expectation(X[0])
raises(TypeError, lambda: DiscreteMarkovChain("Y", dict((1, 1))))
raises(ValueError, lambda: next(sample_stochastic_process(Y)))
# pass name, state_space and transition_probabilities
T = Matrix([[0.5, 0.2, 0.3], [0.2, 0.5, 0.3], [0.2, 0.3, 0.5]])
TS = MatrixSymbol('T', 3, 3)
Y = DiscreteMarkovChain("Y", [0, 1, 2], T)
YS = DiscreteMarkovChain("Y", [0, 1, 2], TS)
assert YS._transient2transient() == None
assert YS._transient2absorbing() == None
assert Y.joint_distribution(1, Y[2],
3) == JointDistribution(Y[1], Y[2], Y[3])
raises(ValueError, lambda: Y.joint_distribution(Y[1].symbol, Y[2].symbol))
assert P(Eq(Y[3], 2), Eq(Y[1], 1)).round(2) == Float(0.36, 2)
assert str(P(Eq(YS[3], 2), Eq(YS[1], 1))) == \
"T[0, 2]*T[1, 0] + T[1, 1]*T[1, 2] + T[1, 2]*T[2, 2]"
assert P(Eq(YS[1], 1), Eq(YS[2], 2)) == Probability(Eq(YS[1], 1))
assert P(Eq(YS[3], 3), Eq(YS[1], 1)) is S.Zero
TO = Matrix([[0.25, 0.75, 0], [0, 0.25, 0.75], [0.75, 0, 0.25]])
assert P(Eq(Y[3], 2),
Eq(Y[1], 1) & TransitionMatrixOf(Y, TO)).round(3) == Float(
0.375, 3)
assert E(Y[3], evaluate=False) == Expectation(Y[3])
assert E(Y[3], Eq(Y[2], 1)).round(2) == Float(1.1, 3)
TSO = MatrixSymbol('T', 4, 4)
raises(
ValueError,
lambda: str(P(Eq(YS[3], 2),
Eq(YS[1], 1) & TransitionMatrixOf(YS, TSO))))
raises(TypeError,
lambda: DiscreteMarkovChain("Z", [0, 1, 2], symbols('M')))
raises(
ValueError,
lambda: DiscreteMarkovChain("Z", [0, 1, 2], MatrixSymbol('T', 3, 4)))
raises(ValueError, lambda: E(Y[3], Eq(Y[2], 6)))
raises(ValueError, lambda: E(Y[2], Eq(Y[3], 1)))
# extended tests for probability queries
TO1 = Matrix([[Rational(1, 4), Rational(3, 4), 0],
[Rational(1, 3),
Rational(1, 3),
Rational(1, 3)], [0, Rational(1, 4),
Rational(3, 4)]])
assert P(
And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)),
Eq(Probability(Eq(Y[0], 0)), Rational(1, 4))
& TransitionMatrixOf(Y, TO1)) == Rational(1, 16)
assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), TransitionMatrixOf(Y, TO1)) == \
Probability(Eq(Y[0], 0))/4
assert P(
Lt(X[1], 2) & Gt(X[1], 0),
Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2])
& TransitionMatrixOf(X, TO1)) == Rational(1, 4)
assert P(
Ne(X[1], 2) & Ne(X[1], 1),
Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2])
& TransitionMatrixOf(X, TO1)) is S.Zero
assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)),
Eq(Y[1], 1)) == 0.1 * Probability(Eq(Y[0], 0))
# testing properties of Markov chain
TO2 = Matrix([[S.One, 0, 0],
[Rational(1, 3),
Rational(1, 3),
Rational(1, 3)], [0, Rational(1, 4),
Rational(3, 4)]])
TO3 = Matrix([[Rational(1, 4), Rational(3, 4), 0],
[Rational(1, 3),
Rational(1, 3),
Rational(1, 3)], [0, Rational(1, 4),
Rational(3, 4)]])
Y2 = DiscreteMarkovChain('Y', trans_probs=TO2)
Y3 = DiscreteMarkovChain('Y', trans_probs=TO3)
assert Y3._transient2absorbing() == None
raises(ValueError, lambda: Y3.fundamental_matrix())
assert Y2.is_absorbing_chain() == True
assert Y3.is_absorbing_chain() == False
TO4 = Matrix([[Rational(1, 5),
Rational(2, 5),
Rational(2, 5)], [Rational(1, 10), S.Half,
Rational(2, 5)],
[Rational(3, 5),
Rational(3, 10),
Rational(1, 10)]])
Y4 = DiscreteMarkovChain('Y', trans_probs=TO4)
w = ImmutableMatrix([[Rational(11, 39),
Rational(16, 39),
Rational(4, 13)]])
assert Y4.limiting_distribution == w
assert Y4.is_regular() == True
TS1 = MatrixSymbol('T', 3, 3)
Y5 = DiscreteMarkovChain('Y', trans_probs=TS1)
assert Y5.limiting_distribution(w, TO4).doit() == True
TO6 = Matrix([[S.One, 0, 0, 0, 0], [S.Half, 0, S.Half, 0, 0],
[0, S.Half, 0, S.Half, 0], [0, 0, S.Half, 0, S.Half],
[0, 0, 0, 0, 1]])
Y6 = DiscreteMarkovChain('Y', trans_probs=TO6)
assert Y6._transient2absorbing() == ImmutableMatrix([[S.Half, 0], [0, 0],
[0, S.Half]])
assert Y6._transient2transient() == ImmutableMatrix([[0, S.Half, 0],
[S.Half, 0, S.Half],
[0, S.Half, 0]])
assert Y6.fundamental_matrix() == ImmutableMatrix(
[[Rational(3, 2), S.One, S.Half], [S.One, S(2), S.One],
[S.Half, S.One, Rational(3, 2)]])
assert Y6.absorbing_probabilites() == ImmutableMatrix(
[[Rational(3, 4), Rational(1, 4)], [S.Half, S.Half],
[Rational(1, 4), Rational(3, 4)]])
# testing miscellaneous queries
T = Matrix([[S.Half, Rational(1, 4),
Rational(1, 4)], [Rational(1, 3), 0,
Rational(2, 3)], [S.Half, S.Half, 0]])
X = DiscreteMarkovChain('X', [0, 1, 2], T)
assert P(
Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0),
Eq(P(Eq(X[1], 0)), Rational(1, 4))
& Eq(P(Eq(X[1], 1)), Rational(1, 4))) == Rational(1, 12)
assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == Rational(2, 3)
assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) is S.Zero
assert P(Ne(X[2], 2), Eq(X[1], 1)) == Rational(1, 3)
assert E(X[1]**2, Eq(X[0], 1)) == Rational(8, 3)
assert variance(X[1], Eq(X[0], 1)) == Rational(8, 9)
raises(ValueError, lambda: E(X[1], Eq(X[2], 1)))
def normalization_constant(self):
n = self.dimension
_H = MatrixSymbol('_H', n, n)
return Integral(exp(-S(n) * Trace(_H**2)))
http://www.math.uwaterloo.ca/~hwolkowi//matrixcookbook.pdf
"""
from sympy import (MatrixSymbol, Inverse, symbols, Determinant, Trace,
Derivative, sin, exp, cos, tan, log, S, sqrt,
hadamard_product, DiagonalizeVector, OneMatrix,
HadamardProduct, HadamardPower, KroneckerDelta, Sum,
Rational)
from sympy import MatAdd, Identity, MatMul, ZeroMatrix
from sympy.matrices.expressions import hadamard_power
k = symbols("k")
i, j = symbols("i j")
m, n = symbols("m n")
X = MatrixSymbol("X", k, k)
x = MatrixSymbol("x", k, 1)
y = MatrixSymbol("y", k, 1)
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)
c = MatrixSymbol("c", k, 1)
d = MatrixSymbol("d", k, 1)
KDelta = lambda i, j: KroneckerDelta(i, j, (0, k - 1))
from sympy import (symbols, MatrixSymbol, Symbol, MatPow, BlockMatrix,
Identity, ZeroMatrix, ImmutableMatrix, eye)
from sympy.utilities.pytest import raises, XFAIL
k, l, m, n = symbols('k l m n', integer=True)
i, j = symbols('i j', integer=True)
W = MatrixSymbol('W', k, l)
X = MatrixSymbol('X', l, m)
Y = MatrixSymbol('Y', l, m)
Z = MatrixSymbol('Z', m, n)
A = MatrixSymbol('A', 2, 2)
B = MatrixSymbol('B', 2, 2)
x = MatrixSymbol('x', 1, 2)
y = MatrixSymbol('x', 2, 1)
def test_symbolic_indexing():
x12 = X[1, 2]
assert x12 == Symbol(X.name + "_12")
assert X[i, j] == Symbol(X.name + "_ij")
def test_add_index():
assert (X + Y)[i, j] == X[i, j] + Y[i, j]
def test_mul_index():
assert (A * y)[0, 0] == A[0, 0] * y[0, 0] + A[0, 1] * y[1, 0]
assert (A * B).as_mutable() == (A.as_mutable() * B.as_mutable())
def test_MatMul_MatAdd():
from sympy import MatrixSymbol
assert str(2*(MatrixSymbol("X", 2, 2) + MatrixSymbol("Y", 2, 2))) == \
"2*(X + Y)"
def test_issue_9324_powsimp_on_matrix_symbol():
M = MatrixSymbol('M', 10, 10)
expr = powsimp(M, deep=True)
assert expr == M
assert expr.args[0] == Symbol('M')
def test_issue_9324_simplify():
M = MatrixSymbol('M', 10, 10)
e = M[0, 0] + M[5, 4] + 1304
assert simplify(e) == e
def test_Adjoint():
from sympy.matrices import MatrixSymbol, Adjoint
X = MatrixSymbol('X', 2, 2)
Y = MatrixSymbol('Y', 2, 2)
# Either of these would be fine
assert latex(Adjoint(X + Y)) == r'X^\dag + Y^\dag'
def test_matrix_expression_from_index_summation():
from sympy.abc import a,b,c,d
A = MatrixSymbol("A", k, k)
B = MatrixSymbol("B", k, k)
C = MatrixSymbol("C", k, k)
w1 = MatrixSymbol("w1", k, 1)
i0, i1, i2, i3, i4 = symbols("i0:5", cls=Dummy)
expr = Sum(W[a,b]*X[b,c]*Z[c,d], (b, 0, l-1), (c, 0, m-1))
assert MatrixExpr.from_index_summation(expr, a) == W*X*Z
expr = Sum(W.T[b,a]*X[b,c]*Z[c,d], (b, 0, l-1), (c, 0, m-1))
assert MatrixExpr.from_index_summation(expr, a) == W*X*Z
expr = Sum(A[b, a]*B[b, c]*C[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixSymbol.from_index_summation(expr, a) == A.T*B*C
expr = Sum(A[b, a]*B[c, b]*C[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixSymbol.from_index_summation(expr, a) == A.T*B.T*C
expr = Sum(C[c, d]*A[b, a]*B[c, b], (b, 0, k-1), (c, 0, k-1))
assert MatrixSymbol.from_index_summation(expr, a) == A.T*B.T*C
expr = Sum(A[a, b] + B[a, b], (a, 0, k-1), (b, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == A + B
expr = Sum((A[a, b] + B[a, b])*C[b, c], (b, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == (A+B)*C
expr = Sum((A[a, b] + B[b, a])*C[b, c], (b, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == (A+B.T)*C
expr = Sum(A[a, b]*A[b, c]*A[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == MatMul(A, A, A)
expr = Sum(A[a, b]*A[b, c]*B[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == MatMul(A, A, B)
# Parse the trace of a matrix:
expr = Sum(A[a, a], (a, 0, k-1))
assert MatrixExpr.from_index_summation(expr, None) == trace(A)
expr = Sum(A[a, a]*B[b, c]*C[c, d], (a, 0, k-1), (c, 0, k-1))
assert MatrixExpr.from_index_summation(expr, b) == trace(A)*B*C
# Check wrong sum ranges (should raise an exception):
## Case 1: 0 to m instead of 0 to m-1
expr = Sum(W[a,b]*X[b,c]*Z[c,d], (b, 0, l-1), (c, 0, m))
raises(ValueError, lambda: MatrixExpr.from_index_summation(expr, a))
## Case 2: 1 to m-1 instead of 0 to m-1
expr = Sum(W[a,b]*X[b,c]*Z[c,d], (b, 0, l-1), (c, 1, m-1))
raises(ValueError, lambda: MatrixExpr.from_index_summation(expr, a))
# Parse nested sums:
expr = Sum(A[a, b]*Sum(B[b, c]*C[c, d], (c, 0, k-1)), (b, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == A*B*C
# Test Kronecker delta:
expr = Sum(A[a, b]*KroneckerDelta(b, c)*B[c, d], (b, 0, k-1), (c, 0, k-1))
assert MatrixExpr.from_index_summation(expr, a) == A*B
expr = Sum(KroneckerDelta(i1, m)*KroneckerDelta(i2, n)*A[i, i1]*A[j, i2], (i1, 0, k-1), (i2, 0, k-1))
assert MatrixExpr.from_index_summation(expr, m) == A.T*A[j, n]
# Test numbered indices:
expr = Sum(A[i1, i2]*w1[i2, 0], (i2, 0, k-1))
assert MatrixExpr.from_index_summation(expr, i1) == A*w1
expr = Sum(A[i1, i2]*B[i2, 0], (i2, 0, k-1))
assert MatrixExpr.from_index_summation(expr, i1) == MatrixElement(A*B, i1, 0)
def test_Hadamard():
from sympy.matrices import MatrixSymbol, HadamardProduct
X = MatrixSymbol('X', 2, 2)
Y = MatrixSymbol('Y', 2, 2)
assert latex(HadamardProduct(X, Y * Y)) == r'X \circ \left(Y Y\right)'
assert latex(HadamardProduct(X, Y) * Y) == r'\left(X \circ Y\right) Y'
