本文整理汇总了Python中sympy.matrices.SparseMatrix.inv方法的典型用法代码示例。如果您正苦于以下问题：Python SparseMatrix.inv方法的具体用法？Python SparseMatrix.inv怎么用？Python SparseMatrix.inv使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sympy.matrices.SparseMatrix的用法示例。
在下文中一共展示了SparseMatrix.inv方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_sparse_solve
# 需要导入模块: from sympy.matrices import SparseMatrix [as 别名]
# 或者: from sympy.matrices.SparseMatrix import inv [as 别名]
def test_sparse_solve():
from sympy.matrices import SparseMatrix
A = SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))
assert A.cholesky() == Matrix([
[ 5, 0, 0],
[ 3, 3, 0],
[-1, 1, 3]])
assert A.cholesky() * A.cholesky().T == Matrix([
[25, 15, -5],
[15, 18, 0],
[-5, 0, 11]])
A = SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))
L, D = A.LDLdecomposition()
assert 15*L == Matrix([
[15, 0, 0],
[ 9, 15, 0],
[-3, 5, 15]])
assert D == Matrix([
[25, 0, 0],
[ 0, 9, 0],
[ 0, 0, 9]])
assert L * D * L.T == A
A = SparseMatrix(((3, 0, 2), (0, 0, 1), (1, 2, 0)))
assert A.inv() * A == SparseMatrix(eye(3))
A = SparseMatrix([
[ 2, -1, 0],
[-1, 2, -1],
[ 0, 0, 2]])
ans = SparseMatrix([
[S(2)/3, S(1)/3, S(1)/6],
[S(1)/3, S(2)/3, S(1)/3],
[ 0, 0, S(1)/2]])
assert A.inv(method='CH') == ans
assert A.inv(method='LDL') == ans
assert A * ans == SparseMatrix(eye(3))
s = A.solve(A[:, 0], 'LDL')
assert A*s == A[:, 0]
s = A.solve(A[:, 0], 'CH')
assert A*s == A[:, 0]
A = A.col_join(A)
s = A.solve_least_squares(A[:, 0], 'CH')
assert A*s == A[:, 0]
s = A.solve_least_squares(A[:, 0], 'LDL')
assert A*s == A[:, 0]示例2: test_sparse_matrix
# 需要导入模块: from sympy.matrices import SparseMatrix [as 别名]
# 或者: from sympy.matrices.SparseMatrix import inv [as 别名]
#.........这里部分代码省略.........
[ 0, 3, 6, 7],
[ 1, -2, 7, 4],
[-9, 2, 6, 3]])
L, U, p = testmat.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - testmat == sparse_zeros(4)
x, y, z = Symbol('x'), Symbol('y'), Symbol('z')
M = Matrix(((1, x, 1), (2, y, 0), (y, 0, z)))
L, U, p = M.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - M == sparse_zeros(3)
# test_LUsolve
A = SparseMatrix([[2, 3, 5],
[3, 6, 2],
[8, 3, 6]])
x = SparseMatrix(3, 1, [3, 7, 5])
b = A*x
soln = A.LUsolve(b)
assert soln == x
A = SparseMatrix([[0, -1, 2],
[5, 10, 7],
[8, 3, 4]])
x = SparseMatrix(3, 1, [-1, 2, 5])
b = A*x
soln = A.LUsolve(b)
assert soln == x
# test_inverse
A = sparse_eye(4)
assert A.inv() == sparse_eye(4)
assert A.inv(method="CH") == sparse_eye(4)
assert A.inv(method="LDL") == sparse_eye(4)
A = SparseMatrix([[2, 3, 5],
[3, 6, 2],
[7, 2, 6]])
Ainv = SparseMatrix(Matrix(A).inv())
assert A*Ainv == sparse_eye(3)
assert A.inv(method="CH") == Ainv
assert A.inv(method="LDL") == Ainv
A = SparseMatrix([[2, 3, 5],
[3, 6, 2],
[5, 2, 6]])
Ainv = SparseMatrix(Matrix(A).inv())
assert A*Ainv == sparse_eye(3)
assert A.inv(method="CH") == Ainv
assert A.inv(method="LDL") == Ainv
# test_cross
v1 = Matrix(1, 3, [1, 2, 3])
v2 = Matrix(1, 3, [3, 4, 5])
assert v1.cross(v2) == Matrix(1, 3, [-2, 4, -2])
assert v1.norm(2)**2 == 14
# conjugate
a = SparseMatrix(((1, 2 + I), (3, 4)))
assert a.C == SparseMatrix([
[1, 2 - I],
[3, 4]
])