def test_permanent():
M = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert M.per() == 450
for i in range(1, 12):
assert ones(i, i).per() == ones(i, i).T.per() == factorial(i)
assert (ones(i, i)-eye(i)).per() == (ones(i, i)-eye(i)).T.per() == subfactorial(i)
a1, a2, a3, a4, a5 = symbols('a_1 a_2 a_3 a_4 a_5')
M = Matrix([a1, a2, a3, a4, a5])
assert M.per() == M.T.per() == a1 + a2 + a3 + a4 + a5
def demo_corr_bound(n):
def corr(i, j):
if i == j:
return 1
else:
return rho
Rho = Matrix(n, n, corr)
print "what's the bound of rho to make below a correlation matrix?\n"
pprint(Rho)
print "\ncan be viewed as the sum of 2 matrices Rho = A + B:\n"
A = (1-rho)*eye(n)
B = rho*ones(n,n)
pprint(A)
pprint(B)
print "\nthe eigen value and its dimention of first matrix A is:"
pprint(A.eigenvects())
print "\nas for the seconde matrix B\n"\
"it's product of any vector v:"
v = IndexedBase('v')
vec = Matrix(n, 1, lambda i, j: v[i+1])
pprint(vec)
pprint(B*vec)
print "\nin order for it's to equal to a linear transform of v\n"\
"we can see its eigen values, vectors and dimentions are:"
pprint(B.eigenvects())
print "\nfor any eigen vector of B v, we have Rho*v :\n"
pprint(Rho*vec)
print "\nwhich means v is also an eigen vector of Rho,\n"\
"the eigen values, vectors and dimentions of Rho are:\n"
pprint(Rho.eigenvects())
print "\nsince have no negative eigen values <=> positive semidefinite:\n"\
"the boundaries for rho are: [1/(%d-1),1]" %n
def test_issue_17247_expression_blowup_32():
M = Matrix([[x + 1, 1 - x, 0, 0], [1 - x, x + 1, 0, x + 1],
[0, 1 - x, x + 1, 0], [0, 0, 0, x + 1]])
assert M.LUsolve(ones(4, 1)) == Matrix([[(x + 1) / (4 * x)],
[(x - 1) / (4 * x)],
[(x + 1) / (4 * x)],
[1 / (x + 1)]])
def test_permanent():
assert isinstance(Permanent(A), Permanent)
assert not isinstance(Permanent(A), MatrixExpr)
assert isinstance(Permanent(C), Permanent)
assert Permanent(ones(3, 3)).doit() == 6
_ = C / per(C)
assert per(Matrix(3, 3, [1, 3, 2, 4, 1, 3, 2, 5, 2])) == 103
raises(TypeError, lambda: Permanent(S.One))
assert Permanent(A).arg is A
def test_issue_17247_expression_blowup_30():
M = Matrix(
S('''[
[ -3/4, 45/32 - 37*I/16, 0, 0],
[-149/64 + 49*I/32, -177/128 - 1369*I/128, 0, -2063/256 + 541*I/128],
[ 0, 9/4 + 55*I/16, 2473/256 + 137*I/64, 0],
[ 0, 0, 0, -177/128 - 1369*I/128]]'''
))
assert M.cholesky_solve(ones(4, 1)) == Matrix(
S('''[
[ -32549314808672/3306971225785 - 17397006745216*I/3306971225785],
[ 67439348256/3306971225785 - 9167503335872*I/3306971225785],
[-15091965363354518272/21217636514687010905 + 16890163109293858304*I/21217636514687010905],
[ -11328/952745 + 87616*I/952745]]'''
))
def test_diagonal():
m = Matrix(3, 3, range(9))
d = m.diagonal()
assert d == m.diagonal(0)
assert tuple(d) == (0, 4, 8)
assert tuple(m.diagonal(1)) == (1, 5)
assert tuple(m.diagonal(-1)) == (3, 7)
assert tuple(m.diagonal(2)) == (2, )
assert type(m.diagonal()) == type(m)
s = SparseMatrix(3, 3, {(1, 1): 1})
assert type(s.diagonal()) == type(s)
assert type(m) != type(s)
raises(ValueError, lambda: m.diagonal(3))
raises(ValueError, lambda: m.diagonal(-3))
raises(ValueError, lambda: m.diagonal(pi))
M = ones(2, 3)
assert banded({i: list(M.diagonal(i))
for i in range(1 - M.rows, M.cols)}) == M
def test_util():
R = Rational
v1 = Matrix(1, 3, [1, 2, 3])
v2 = Matrix(1, 3, [3, 4, 5])
assert v1.norm() == sqrt(14)
assert v1.project(v2) == Matrix(1, 3, [R(39) / 25, R(52) / 25, R(13) / 5])
assert Matrix.zeros(1, 2) == Matrix(1, 2, [0, 0])
assert ones(1, 2) == Matrix(1, 2, [1, 1])
assert v1.copy() == v1
# cofactor
assert eye(3) == eye(3).cofactor_matrix()
test = Matrix([[1, 3, 2], [2, 6, 3], [2, 3, 6]])
assert test.cofactor_matrix() == \
Matrix([[27, -6, -6], [-12, 2, 3], [-3, 1, 0]])
test = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert test.cofactor_matrix() == \
Matrix([[-3, 6, -3], [6, -12, 6], [-3, 6, -3]])
def test_applyfunc():
m0 = OperationsOnlyMatrix(eye(3))
assert m0.applyfunc(lambda x: 2 * x) == eye(3) * 2
assert m0.applyfunc(lambda x: 0) == zeros(3)
assert m0.applyfunc(lambda x: 1) == ones(3)
def test_is_Identity():
assert eye_Properties(3).is_Identity
assert not PropertiesOnlyMatrix(zeros(3)).is_Identity
assert not PropertiesOnlyMatrix(ones(3)).is_Identity
# issue 6242
assert not PropertiesOnlyMatrix([[1, 0, 0]]).is_Identity
def test_diag_make():
diag = SpecialOnlyMatrix.diag
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
assert diag(a, b, b) == Matrix([
[1, 2, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0],
[0, 0, y, 3, 0, 0],
[0, 0, 0, 0, 3, x],
[0, 0, 0, 0, y, 3],
])
assert diag(a, b, c) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0, 0],
[0, 0, y, 3, 0, 0, 0],
[0, 0, 0, 0, 3, x, 3],
[0, 0, 0, 0, y, 3, z],
[0, 0, 0, 0, x, y, z],
])
assert diag(a, c, b) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 3, 0, 0],
[0, 0, y, 3, z, 0, 0],
[0, 0, x, y, z, 0, 0],
[0, 0, 0, 0, 0, 3, x],
[0, 0, 0, 0, 0, y, 3],
])
a = Matrix([x, y, z])
b = Matrix([[1, 2], [3, 4]])
c = Matrix([[5, 6]])
# this "wandering diagonal" is what makes this
# a block diagonal where each block is independent
# of the others
assert diag(a, 7, b, c) == Matrix([[x, 0, 0, 0, 0, 0], [y, 0, 0, 0, 0, 0],
[z, 0, 0, 0, 0, 0], [0, 7, 0, 0, 0, 0],
[0, 0, 1, 2, 0, 0], [0, 0, 3, 4, 0, 0],
[0, 0, 0, 0, 5, 6]])
raises(ValueError, lambda: diag(a, 7, b, c, rows=5))
assert diag(1) == Matrix([[1]])
assert diag(1, rows=2) == Matrix([[1, 0], [0, 0]])
assert diag(1, cols=2) == Matrix([[1, 0], [0, 0]])
assert diag(1, rows=3, cols=2) == Matrix([[1, 0], [0, 0], [0, 0]])
assert diag(*[2, 3]) == Matrix([[2, 0], [0, 3]])
assert diag(Matrix([2, 3])) == Matrix([[2], [3]])
assert diag([1, [2, 3], 4], unpack=False) == \
diag([[1], [2, 3], [4]], unpack=False) == Matrix([
[1, 0],
[2, 3],
[4, 0]])
assert type(diag(1)) == SpecialOnlyMatrix
assert type(diag(1, cls=Matrix)) == Matrix
assert Matrix.diag([1, 2, 3]) == Matrix.diag(1, 2, 3)
assert Matrix.diag([1, 2, 3], unpack=False).shape == (3, 1)
assert Matrix.diag([[1, 2, 3]]).shape == (3, 1)
assert Matrix.diag([[1, 2, 3]], unpack=False).shape == (1, 3)
assert Matrix.diag([[[1, 2, 3]]]).shape == (1, 3)
# kerning can be used to move the starting point
assert Matrix.diag(ones(0, 2), 1, 2) == Matrix([[0, 0, 1, 0], [0, 0, 0,
2]])
assert Matrix.diag(ones(2, 0), 1, 2) == Matrix([[0, 0], [0, 0], [1, 0],
[0, 2]])
def test_sparse_matrix():
def sparse_eye(n):
return SparseMatrix.eye(n)
def sparse_zeros(n):
return SparseMatrix.zeros(n)
# creation args
raises(TypeError, lambda: SparseMatrix(1, 2))
a = SparseMatrix((
(1, 0),
(0, 1)
))
assert SparseMatrix(a) == a
from sympy.matrices import MutableSparseMatrix, MutableDenseMatrix
a = MutableSparseMatrix([])
b = MutableDenseMatrix([1, 2])
assert a.row_join(b) == b
assert a.col_join(b) == b
assert type(a.row_join(b)) == type(a)
assert type(a.col_join(b)) == type(a)
# make sure 0 x n matrices get stacked correctly
sparse_matrices = [SparseMatrix.zeros(0, n) for n in range(4)]
assert SparseMatrix.hstack(*sparse_matrices) == Matrix(0, 6, [])
sparse_matrices = [SparseMatrix.zeros(n, 0) for n in range(4)]
assert SparseMatrix.vstack(*sparse_matrices) == Matrix(6, 0, [])
# test element assignment
a = SparseMatrix((
(1, 0),
(0, 1)
))
a[3] = 4
assert a[1, 1] == 4
a[3] = 1
a[0, 0] = 2
assert a == SparseMatrix((
(2, 0),
(0, 1)
))
a[1, 0] = 5
assert a == SparseMatrix((
(2, 0),
(5, 1)
))
a[1, 1] = 0
assert a == SparseMatrix((
(2, 0),
(5, 0)
))
assert a._smat == {(0, 0): 2, (1, 0): 5}
# test_multiplication
a = SparseMatrix((
(1, 2),
(3, 1),
(0, 6),
))
b = SparseMatrix((
(1, 2),
(3, 0),
))
c = a*b
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
try:
eval('c = a @ b')
except SyntaxError:
pass
else:
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
x = Symbol("x")
c = b * Symbol("x")
assert isinstance(c, SparseMatrix)
assert c[0, 0] == x
assert c[0, 1] == 2*x
assert c[1, 0] == 3*x
assert c[1, 1] == 0
c = 5 * b
assert isinstance(c, SparseMatrix)
assert c[0, 0] == 5
assert c[0, 1] == 2*5
assert c[1, 0] == 3*5
assert c[1, 1] == 0
#test_power
A = SparseMatrix([[2, 3], [4, 5]])
assert (A**5)[:] == [6140, 8097, 10796, 14237]
A = SparseMatrix([[2, 1, 3], [4, 2, 4], [6, 12, 1]])
assert (A**3)[:] == [290, 262, 251, 448, 440, 368, 702, 954, 433]
# test_creation
x = Symbol("x")
a = SparseMatrix([[x, 0], [0, 0]])
m = a
assert m.cols == m.rows
assert m.cols == 2
assert m[:] == [x, 0, 0, 0]
b = SparseMatrix(2, 2, [x, 0, 0, 0])
m = b
assert m.cols == m.rows
assert m.cols == 2
assert m[:] == [x, 0, 0, 0]
assert a == b
S = sparse_eye(3)
S.row_del(1)
assert S == SparseMatrix([
[1, 0, 0],
[0, 0, 1]])
S = sparse_eye(3)
S.col_del(1)
assert S == SparseMatrix([
[1, 0],
[0, 0],
[0, 1]])
S = SparseMatrix.eye(3)
S[2, 1] = 2
S.col_swap(1, 0)
assert S == SparseMatrix([
[0, 1, 0],
[1, 0, 0],
[2, 0, 1]])
a = SparseMatrix(1, 2, [1, 2])
b = a.copy()
c = a.copy()
assert a[0] == 1
a.row_del(0)
assert a == SparseMatrix(0, 2, [])
b.col_del(1)
assert b == SparseMatrix(1, 1, [1])
assert SparseMatrix([[1, 2, 3], [1, 2], [1]]) == Matrix([
[1, 2, 3],
[1, 2, 0],
[1, 0, 0]])
assert SparseMatrix(4, 4, {(1, 1): sparse_eye(2)}) == Matrix([
[0, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 0]])
raises(ValueError, lambda: SparseMatrix(1, 1, {(1, 1): 1}))
assert SparseMatrix(1, 2, [1, 2]).tolist() == [[1, 2]]
assert SparseMatrix(2, 2, [1, [2, 3]]).tolist() == [[1, 0], [2, 3]]
raises(ValueError, lambda: SparseMatrix(2, 2, [1]))
raises(ValueError, lambda: SparseMatrix(1, 1, [[1, 2]]))
assert SparseMatrix([.1]).has(Float)
# autosizing
assert SparseMatrix(None, {(0, 1): 0}).shape == (0, 0)
assert SparseMatrix(None, {(0, 1): 1}).shape == (1, 2)
assert SparseMatrix(None, None, {(0, 1): 1}).shape == (1, 2)
raises(ValueError, lambda: SparseMatrix(None, 1, [[1, 2]]))
raises(ValueError, lambda: SparseMatrix(1, None, [[1, 2]]))
raises(ValueError, lambda: SparseMatrix(3, 3, {(0, 0): ones(2), (1, 1): 2}))
# test_determinant
x, y = Symbol('x'), Symbol('y')
assert SparseMatrix(1, 1, [0]).det() == 0
assert SparseMatrix([[1]]).det() == 1
assert SparseMatrix(((-3, 2), (8, -5))).det() == -1
assert SparseMatrix(((x, 1), (y, 2*y))).det() == 2*x*y - y
assert SparseMatrix(( (1, 1, 1),
(1, 2, 3),
(1, 3, 6) )).det() == 1
assert SparseMatrix(( ( 3, -2, 0, 5),
(-2, 1, -2, 2),
( 0, -2, 5, 0),
( 5, 0, 3, 4) )).det() == -289
assert SparseMatrix(( ( 1, 2, 3, 4),
( 5, 6, 7, 8),
( 9, 10, 11, 12),
(13, 14, 15, 16) )).det() == 0
assert SparseMatrix(( (3, 2, 0, 0, 0),
(0, 3, 2, 0, 0),
(0, 0, 3, 2, 0),
(0, 0, 0, 3, 2),
(2, 0, 0, 0, 3) )).det() == 275
assert SparseMatrix(( (1, 0, 1, 2, 12),
(2, 0, 1, 1, 4),
(2, 1, 1, -1, 3),
(3, 2, -1, 1, 8),
(1, 1, 1, 0, 6) )).det() == -55
assert SparseMatrix(( (-5, 2, 3, 4, 5),
( 1, -4, 3, 4, 5),
( 1, 2, -3, 4, 5),
( 1, 2, 3, -2, 5),
( 1, 2, 3, 4, -1) )).det() == 11664
assert SparseMatrix(( ( 2, 7, -1, 3, 2),
( 0, 0, 1, 0, 1),
(-2, 0, 7, 0, 2),
(-3, -2, 4, 5, 3),
( 1, 0, 0, 0, 1) )).det() == 123
# test_slicing
m0 = sparse_eye(4)
assert m0[:3, :3] == sparse_eye(3)
assert m0[2:4, 0:2] == sparse_zeros(2)
m1 = SparseMatrix(3, 3, lambda i, j: i + j)
assert m1[0, :] == SparseMatrix(1, 3, (0, 1, 2))
assert m1[1:3, 1] == SparseMatrix(2, 1, (2, 3))
m2 = SparseMatrix(
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]])
assert m2[:, -1] == SparseMatrix(4, 1, [3, 7, 11, 15])
assert m2[-2:, :] == SparseMatrix([[8, 9, 10, 11], [12, 13, 14, 15]])
assert SparseMatrix([[1, 2], [3, 4]])[[1], [1]] == Matrix([[4]])
# test_submatrix_assignment
m = sparse_zeros(4)
m[2:4, 2:4] = sparse_eye(2)
assert m == SparseMatrix([(0, 0, 0, 0),
(0, 0, 0, 0),
(0, 0, 1, 0),
(0, 0, 0, 1)])
assert len(m._smat) == 2
m[:2, :2] = sparse_eye(2)
assert m == sparse_eye(4)
m[:, 0] = SparseMatrix(4, 1, (1, 2, 3, 4))
assert m == SparseMatrix([(1, 0, 0, 0),
(2, 1, 0, 0),
(3, 0, 1, 0),
(4, 0, 0, 1)])
m[:, :] = sparse_zeros(4)
assert m == sparse_zeros(4)
m[:, :] = ((1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12), (13, 14, 15, 16))
assert m == SparseMatrix((( 1, 2, 3, 4),
( 5, 6, 7, 8),
( 9, 10, 11, 12),
(13, 14, 15, 16)))
m[:2, 0] = [0, 0]
assert m == SparseMatrix((( 0, 2, 3, 4),
( 0, 6, 7, 8),
( 9, 10, 11, 12),
(13, 14, 15, 16)))
# test_reshape
m0 = sparse_eye(3)
assert m0.reshape(1, 9) == SparseMatrix(1, 9, (1, 0, 0, 0, 1, 0, 0, 0, 1))
m1 = SparseMatrix(3, 4, lambda i, j: i + j)
assert m1.reshape(4, 3) == \
SparseMatrix([(0, 1, 2), (3, 1, 2), (3, 4, 2), (3, 4, 5)])
assert m1.reshape(2, 6) == \
SparseMatrix([(0, 1, 2, 3, 1, 2), (3, 4, 2, 3, 4, 5)])
# test_applyfunc
m0 = sparse_eye(3)
assert m0.applyfunc(lambda x: 2*x) == sparse_eye(3)*2
assert m0.applyfunc(lambda x: 0 ) == sparse_zeros(3)
# test__eval_Abs
assert abs(SparseMatrix(((x, 1), (y, 2*y)))) == SparseMatrix(((Abs(x), 1), (Abs(y), 2*Abs(y))))
# test_LUdecomp
testmat = SparseMatrix([[ 0, 2, 5, 3],
[ 3, 3, 7, 4],
[ 8, 4, 0, 2],
[-2, 6, 3, 4]])
L, U, p = testmat.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - testmat == sparse_zeros(4)
testmat = SparseMatrix([[ 6, -2, 7, 4],
[ 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]
])
# mul
assert a*Matrix(2, 2, [1, 0, 0, 1]) == a
assert a + Matrix(2, 2, [1, 1, 1, 1]) == SparseMatrix([
[2, 3 + I],
[4, 5]
])
# col join
assert a.col_join(sparse_eye(2)) == SparseMatrix([
[1, 2 + I],
[3, 4],
[1, 0],
[0, 1]
])
# symmetric
assert not a.is_symmetric(simplify=False)
# test_cofactor
assert sparse_eye(3) == sparse_eye(3).cofactor_matrix()
test = SparseMatrix([[1, 3, 2], [2, 6, 3], [2, 3, 6]])
assert test.cofactor_matrix() == \
SparseMatrix([[27, -6, -6], [-12, 2, 3], [-3, 1, 0]])
test = SparseMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert test.cofactor_matrix() == \
SparseMatrix([[-3, 6, -3], [6, -12, 6], [-3, 6, -3]])
# test_jacobian
x = Symbol('x')
y = Symbol('y')
L = SparseMatrix(1, 2, [x**2*y, 2*y**2 + x*y])
syms = [x, y]
assert L.jacobian(syms) == Matrix([[2*x*y, x**2], [y, 4*y + x]])
L = SparseMatrix(1, 2, [x, x**2*y**3])
assert L.jacobian(syms) == SparseMatrix([[1, 0], [2*x*y**3, x**2*3*y**2]])
# test_QR
A = Matrix([[1, 2], [2, 3]])
Q, S = A.QRdecomposition()
R = Rational
assert Q == Matrix([
[ 5**R(-1, 2), (R(2)/5)*(R(1)/5)**R(-1, 2)],
[2*5**R(-1, 2), (-R(1)/5)*(R(1)/5)**R(-1, 2)]])
assert S == Matrix([
[5**R(1, 2), 8*5**R(-1, 2)],
[ 0, (R(1)/5)**R(1, 2)]])
assert Q*S == A
assert Q.T * Q == sparse_eye(2)
R = Rational
# test nullspace
# first test reduced row-ech form
M = SparseMatrix([[5, 7, 2, 1],
[1, 6, 2, -1]])
out, tmp = M.rref()
assert out == Matrix([[1, 0, -R(2)/23, R(13)/23],
[0, 1, R(8)/23, R(-6)/23]])
M = SparseMatrix([[ 1, 3, 0, 2, 6, 3, 1],
[-2, -6, 0, -2, -8, 3, 1],
[ 3, 9, 0, 0, 6, 6, 2],
[-1, -3, 0, 1, 0, 9, 3]])
out, tmp = M.rref()
assert out == Matrix([[1, 3, 0, 0, 2, 0, 0],
[0, 0, 0, 1, 2, 0, 0],
[0, 0, 0, 0, 0, 1, R(1)/3],
[0, 0, 0, 0, 0, 0, 0]])
# now check the vectors
basis = M.nullspace()
assert basis[0] == Matrix([-3, 1, 0, 0, 0, 0, 0])
assert basis[1] == Matrix([0, 0, 1, 0, 0, 0, 0])
assert basis[2] == Matrix([-2, 0, 0, -2, 1, 0, 0])
assert basis[3] == Matrix([0, 0, 0, 0, 0, R(-1)/3, 1])
# test eigen
x = Symbol('x')
y = Symbol('y')
sparse_eye3 = sparse_eye(3)
assert sparse_eye3.charpoly(x) == PurePoly(((x - 1)**3))
assert sparse_eye3.charpoly(y) == PurePoly(((y - 1)**3))
# test values
M = Matrix([( 0, 1, -1),
( 1, 1, 0),
(-1, 0, 1)])
vals = M.eigenvals()
assert sorted(vals.keys()) == [-1, 1, 2]
R = Rational
M = Matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
assert M.eigenvects() == [(1, 3, [
Matrix([1, 0, 0]),
Matrix([0, 1, 0]),
Matrix([0, 0, 1])])]
M = Matrix([[5, 0, 2],
[3, 2, 0],
[0, 0, 1]])
assert M.eigenvects() == [(1, 1, [Matrix([R(-1)/2, R(3)/2, 1])]),
(2, 1, [Matrix([0, 1, 0])]),
(5, 1, [Matrix([1, 1, 0])])]
assert M.zeros(3, 5) == SparseMatrix(3, 5, {})
A = SparseMatrix(10, 10, {(0, 0): 18, (0, 9): 12, (1, 4): 18, (2, 7): 16, (3, 9): 12, (4, 2): 19, (5, 7): 16, (6, 2): 12, (9, 7): 18})
assert A.row_list() == [(0, 0, 18), (0, 9, 12), (1, 4, 18), (2, 7, 16), (3, 9, 12), (4, 2, 19), (5, 7, 16), (6, 2, 12), (9, 7, 18)]
assert A.col_list() == [(0, 0, 18), (4, 2, 19), (6, 2, 12), (1, 4, 18), (2, 7, 16), (5, 7, 16), (9, 7, 18), (0, 9, 12), (3, 9, 12)]
assert SparseMatrix.eye(2).nnz() == 2
def test_applyfunc():
m0 = OperationsOnlyMatrix(eye(3))
assert m0.applyfunc(lambda x: 2*x) == eye(3)*2
assert m0.applyfunc(lambda x: 0) == zeros(3)
assert m0.applyfunc(lambda x: 1) == ones(3)
N = 24
k = sympy.symbols("k")
xinit = [
4.1437478, 7.0870599, 7.5626616, 11.746180, 28.888109, 28.979446,
33.737079, 33.829258, 35.048137, 35.139869, 39.898342, 39.989845,
52.630050, 57.872253, 61.893707, 62.010196, 67.135734, 67.253265,
73.687667, 73.806480, 74.882408, 81.997367, 89.673179, 96.831947
]
x = Matrix(N, 1, xinit)
print x
A0 = ones(N, 1)
A1 = x
A2 = x.multiply_elementwise(x)
#print(A0)
#print(A1)
#print(A2)
A = BlockMatrix([A0, A1, A2])
A = A.as_explicit()
yErr = [0.0005205, 0.0006515, 0.0004069, 0.0047973, 0.0055708, 0.0055551, \
0.0054941, 0.0043147, 0.3925145, 0.0041837, 0.4294512, 0.0042747, \
0.4657444, 0.0038275, 0.0038912, 0.0037950, 0.2330908, 0.0038282, \
0.2650930, 0.0029776, 0.2709266, 0.0030374, 0.3159595, 0.0023857]
def test_multiplication():
a = ArithmeticOnlyMatrix((
(1, 2),
(3, 1),
(0, 6),
))
b = ArithmeticOnlyMatrix((
(1, 2),
(3, 0),
))
raises(ShapeError, lambda: b * a)
raises(TypeError, lambda: a * {})
c = a * b
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
try:
eval('c = a @ b')
except SyntaxError:
pass
else:
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
h = a.multiply_elementwise(c)
assert h == matrix_multiply_elementwise(a, c)
assert h[0, 0] == 7
assert h[0, 1] == 4
assert h[1, 0] == 18
assert h[1, 1] == 6
assert h[2, 0] == 0
assert h[2, 1] == 0
raises(ShapeError, lambda: a.multiply_elementwise(b))
c = b * Symbol("x")
assert isinstance(c, ArithmeticOnlyMatrix)
assert c[0, 0] == x
assert c[0, 1] == 2 * x
assert c[1, 0] == 3 * x
assert c[1, 1] == 0
c2 = x * b
assert c == c2
c = 5 * b
assert isinstance(c, ArithmeticOnlyMatrix)
assert c[0, 0] == 5
assert c[0, 1] == 2 * 5
assert c[1, 0] == 3 * 5
assert c[1, 1] == 0
try:
eval('c = 5 @ b')
except SyntaxError:
pass
else:
assert isinstance(c, ArithmeticOnlyMatrix)
assert c[0, 0] == 5
assert c[0, 1] == 2 * 5
assert c[1, 0] == 3 * 5
assert c[1, 1] == 0
# https://github.com/sympy/sympy/issues/22353
A = Matrix(ones(3, 1))
_h = -Rational(1, 2)
B = Matrix([_h, _h, _h])
assert A.multiply_elementwise(B) == Matrix([[_h], [_h], [_h]])
def __init__(self,re,rx,se,sx,b,H,vd=None):
"""
Initialize by passing parameters (re,rx,ve,vx,b,H)
"""
self.re = re
self.rx = rx
self.se = se
self.sx = sx
self.b = b
self.H = H
self.theta = Matrix([-1,self.re,0])
if vd is None:
self.sd = Symbol('sd')
else:
self.sd = sd
self.m = 'm'
self.V = 'V'
self.mu0 = zeros(3,3)
self.Sigma0 = Matrix([[self.se**2,0,0],[0,self.sd**2,0],[0,0,self.sx**2]])
self.I = eye(3)
self.Ii = Matrix([[0,0,0],[0,0,0],[0,0,1]])
self.Ic = self.I - self.Ii
self.psi1 = Matrix([1,self.rx-self.re,1])
self.Psi1 = self.bayes_coef(self.psi1,self.Sigma0)
self.beliefs0 = {self.m : self.mu0, self.V : self.Sigma0}
self.beliefs1 = self.bayes_update(self.Psi1,self.beliefs0)
self.phi = (self.b*self.re/(1 - self.b*self.re)*self.theta.transpose()*(self.I - self.beliefs1[self.m])).transpose() + self.Ii*ones(3,1)
self.phic = self.Ic*self.phi
self.phii = self.Ii*self.phi
#self.bstar = normdist.ppf(1-self.H,(loc=0,scale=self.rx**2/(1-self.rx**2) + self.phi[2]**2))
self.psi2 = self.phii + Matrix([0,self.rx,0])
self.psi2 = self.phic + Matrix([0,-self.rx])
self.psi2alt = self.phic - 1/(1-self.b*self.re)*self.theta
self.Psi2 = self.bayes_coef(self.psi2, self.beliefs1[self.V])
self.beliefs2 = self.bayes_update(self.Psi2, self.beliefs1)
self.beliefs2alt= self.bayes_update(self.bayes_coef(self.psi2alt,self.beliefs1[self.V]),self.beliefs1)
self.delxta = (self.theta.transpose()*(self.I - self.beliefs2[self.m])).transpose()
self.deltac = self.Ic*self.delta
self.deltai = self.Ii*self.delta
self.sdvalue = (self.deltac.transpose()*self.beliefs0[self.V]*self.deltac)[0]**0.5
self.stvalue = (self.deltai.transpose()*self.beliefs0[self.V]*self.deltai)[0]**0.5
self.sol = self.solve_root()
self.sd_sol = self.sol.x[0]
self.Sigma0_sol = self.Sigma0.subs(self.sd,self.sd_sol)
self.phi_sol = self.phi.subs(self.sd,self.sd_sol)
self.delta_sol = self.delta.subs(self.sd,self.sd_sol)
def test_is_Identity():
assert eye_Properties(3).is_Identity
assert not PropertiesOnlyMatrix(zeros(3)).is_Identity
assert not PropertiesOnlyMatrix(ones(3)).is_Identity
# issue 6242
assert not PropertiesOnlyMatrix([[1, 0, 0]]).is_Identity
def test_sparse_matrix():
def sparse_eye(n):
return SparseMatrix.eye(n)
def sparse_zeros(n):
return SparseMatrix.zeros(n)
# creation args
raises(TypeError, lambda: SparseMatrix(1, 2))
a = SparseMatrix(((1, 0), (0, 1)))
assert SparseMatrix(a) == a
from sympy.matrices import MutableSparseMatrix, MutableDenseMatrix
a = MutableSparseMatrix([])
b = MutableDenseMatrix([1, 2])
assert a.row_join(b) == b
assert a.col_join(b) == b
assert type(a.row_join(b)) == type(a)
assert type(a.col_join(b)) == type(a)
# make sure 0 x n matrices get stacked correctly
sparse_matrices = [SparseMatrix.zeros(0, n) for n in range(4)]
assert SparseMatrix.hstack(*sparse_matrices) == Matrix(0, 6, [])
sparse_matrices = [SparseMatrix.zeros(n, 0) for n in range(4)]
assert SparseMatrix.vstack(*sparse_matrices) == Matrix(6, 0, [])
# test element assignment
a = SparseMatrix(((1, 0), (0, 1)))
a[3] = 4
assert a[1, 1] == 4
a[3] = 1
a[0, 0] = 2
assert a == SparseMatrix(((2, 0), (0, 1)))
a[1, 0] = 5
assert a == SparseMatrix(((2, 0), (5, 1)))
a[1, 1] = 0
assert a == SparseMatrix(((2, 0), (5, 0)))
assert a._smat == {(0, 0): 2, (1, 0): 5}
# test_multiplication
a = SparseMatrix((
(1, 2),
(3, 1),
(0, 6),
))
b = SparseMatrix((
(1, 2),
(3, 0),
))
c = a * b
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
try:
eval('c = a @ b')
except SyntaxError:
pass
else:
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
x = Symbol("x")
c = b * Symbol("x")
assert isinstance(c, SparseMatrix)
assert c[0, 0] == x
assert c[0, 1] == 2 * x
assert c[1, 0] == 3 * x
assert c[1, 1] == 0
c = 5 * b
assert isinstance(c, SparseMatrix)
assert c[0, 0] == 5
assert c[0, 1] == 2 * 5
assert c[1, 0] == 3 * 5
assert c[1, 1] == 0
#test_power
A = SparseMatrix([[2, 3], [4, 5]])
assert (A**5)[:] == [6140, 8097, 10796, 14237]
A = SparseMatrix([[2, 1, 3], [4, 2, 4], [6, 12, 1]])
assert (A**3)[:] == [290, 262, 251, 448, 440, 368, 702, 954, 433]
# test_creation
x = Symbol("x")
a = SparseMatrix([[x, 0], [0, 0]])
m = a
assert m.cols == m.rows
assert m.cols == 2
assert m[:] == [x, 0, 0, 0]
b = SparseMatrix(2, 2, [x, 0, 0, 0])
m = b
assert m.cols == m.rows
assert m.cols == 2
assert m[:] == [x, 0, 0, 0]
assert a == b
S = sparse_eye(3)
S.row_del(1)
assert S == SparseMatrix([[1, 0, 0], [0, 0, 1]])
S = sparse_eye(3)
S.col_del(1)
assert S == SparseMatrix([[1, 0], [0, 0], [0, 1]])
S = SparseMatrix.eye(3)
S[2, 1] = 2
S.col_swap(1, 0)
assert S == SparseMatrix([[0, 1, 0], [1, 0, 0], [2, 0, 1]])
a = SparseMatrix(1, 2, [1, 2])
b = a.copy()
c = a.copy()
assert a[0] == 1
a.row_del(0)
assert a == SparseMatrix(0, 2, [])
b.col_del(1)
assert b == SparseMatrix(1, 1, [1])
assert SparseMatrix([[1, 2, 3], [1, 2], [1]]) == Matrix([[1, 2, 3],
[1, 2, 0],
[1, 0, 0]])
assert SparseMatrix(4, 4, {(1, 1): sparse_eye(2)}) == Matrix([[0, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0,
0]])
raises(ValueError, lambda: SparseMatrix(1, 1, {(1, 1): 1}))
assert SparseMatrix(1, 2, [1, 2]).tolist() == [[1, 2]]
assert SparseMatrix(2, 2, [1, [2, 3]]).tolist() == [[1, 0], [2, 3]]
raises(ValueError, lambda: SparseMatrix(2, 2, [1]))
raises(ValueError, lambda: SparseMatrix(1, 1, [[1, 2]]))
assert SparseMatrix([.1]).has(Float)
# autosizing
assert SparseMatrix(None, {(0, 1): 0}).shape == (0, 0)
assert SparseMatrix(None, {(0, 1): 1}).shape == (1, 2)
assert SparseMatrix(None, None, {(0, 1): 1}).shape == (1, 2)
raises(ValueError, lambda: SparseMatrix(None, 1, [[1, 2]]))
raises(ValueError, lambda: SparseMatrix(1, None, [[1, 2]]))
raises(ValueError, lambda: SparseMatrix(3, 3, {
(0, 0): ones(2),
(1, 1): 2
}))
# test_determinant
x, y = Symbol('x'), Symbol('y')
assert SparseMatrix(1, 1, [0]).det() == 0
assert SparseMatrix([[1]]).det() == 1
assert SparseMatrix(((-3, 2), (8, -5))).det() == -1
assert SparseMatrix(((x, 1), (y, 2 * y))).det() == 2 * x * y - y
assert SparseMatrix(((1, 1, 1), (1, 2, 3), (1, 3, 6))).det() == 1
assert SparseMatrix(((3, -2, 0, 5), (-2, 1, -2, 2), (0, -2, 5, 0),
(5, 0, 3, 4))).det() == -289
assert SparseMatrix(((1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12),
(13, 14, 15, 16))).det() == 0
assert SparseMatrix(((3, 2, 0, 0, 0), (0, 3, 2, 0, 0), (0, 0, 3, 2, 0),
(0, 0, 0, 3, 2), (2, 0, 0, 0, 3))).det() == 275
assert SparseMatrix(((1, 0, 1, 2, 12), (2, 0, 1, 1, 4), (2, 1, 1, -1, 3),
(3, 2, -1, 1, 8), (1, 1, 1, 0, 6))).det() == -55
assert SparseMatrix(((-5, 2, 3, 4, 5), (1, -4, 3, 4, 5), (1, 2, -3, 4, 5),
(1, 2, 3, -2, 5), (1, 2, 3, 4, -1))).det() == 11664
assert SparseMatrix(((2, 7, -1, 3, 2), (0, 0, 1, 0, 1), (-2, 0, 7, 0, 2),
(-3, -2, 4, 5, 3), (1, 0, 0, 0, 1))).det() == 123
# test_slicing
m0 = sparse_eye(4)
assert m0[:3, :3] == sparse_eye(3)
assert m0[2:4, 0:2] == sparse_zeros(2)
m1 = SparseMatrix(3, 3, lambda i, j: i + j)
assert m1[0, :] == SparseMatrix(1, 3, (0, 1, 2))
assert m1[1:3, 1] == SparseMatrix(2, 1, (2, 3))
m2 = SparseMatrix([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11],
[12, 13, 14, 15]])
assert m2[:, -1] == SparseMatrix(4, 1, [3, 7, 11, 15])
assert m2[-2:, :] == SparseMatrix([[8, 9, 10, 11], [12, 13, 14, 15]])
assert SparseMatrix([[1, 2], [3, 4]])[[1], [1]] == Matrix([[4]])
# test_submatrix_assignment
m = sparse_zeros(4)
m[2:4, 2:4] = sparse_eye(2)
assert m == SparseMatrix([(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0),
(0, 0, 0, 1)])
assert len(m._smat) == 2
m[:2, :2] = sparse_eye(2)
assert m == sparse_eye(4)
m[:, 0] = SparseMatrix(4, 1, (1, 2, 3, 4))
assert m == SparseMatrix([(1, 0, 0, 0), (2, 1, 0, 0), (3, 0, 1, 0),
(4, 0, 0, 1)])
m[:, :] = sparse_zeros(4)
assert m == sparse_zeros(4)
m[:, :] = ((1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12), (13, 14, 15, 16))
assert m == SparseMatrix(
((1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12), (13, 14, 15, 16)))
m[:2, 0] = [0, 0]
assert m == SparseMatrix(
((0, 2, 3, 4), (0, 6, 7, 8), (9, 10, 11, 12), (13, 14, 15, 16)))
# test_reshape
m0 = sparse_eye(3)
assert m0.reshape(1, 9) == SparseMatrix(1, 9, (1, 0, 0, 0, 1, 0, 0, 0, 1))
m1 = SparseMatrix(3, 4, lambda i, j: i + j)
assert m1.reshape(4, 3) == \
SparseMatrix([(0, 1, 2), (3, 1, 2), (3, 4, 2), (3, 4, 5)])
assert m1.reshape(2, 6) == \
SparseMatrix([(0, 1, 2, 3, 1, 2), (3, 4, 2, 3, 4, 5)])
# test_applyfunc
m0 = sparse_eye(3)
assert m0.applyfunc(lambda x: 2 * x) == sparse_eye(3) * 2
assert m0.applyfunc(lambda x: 0) == sparse_zeros(3)
# test__eval_Abs
assert abs(SparseMatrix(((x, 1), (y, 2 * y)))) == SparseMatrix(
((Abs(x), 1), (Abs(y), 2 * Abs(y))))
# test_LUdecomp
testmat = SparseMatrix([[0, 2, 5, 3], [3, 3, 7, 4], [8, 4, 0, 2],
[-2, 6, 3, 4]])
L, U, p = testmat.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L * U).permute_rows(p, 'backward') - testmat == sparse_zeros(4)
testmat = SparseMatrix([[6, -2, 7, 4], [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]])
# mul
assert a * Matrix(2, 2, [1, 0, 0, 1]) == a
assert a + Matrix(2, 2, [1, 1, 1, 1]) == SparseMatrix([[2, 3 + I], [4, 5]])
# col join
assert a.col_join(sparse_eye(2)) == SparseMatrix([[1, 2 + I], [3, 4],
[1, 0], [0, 1]])
# symmetric
assert not a.is_symmetric(simplify=False)
# test_cofactor
assert sparse_eye(3) == sparse_eye(3).cofactor_matrix()
test = SparseMatrix([[1, 3, 2], [2, 6, 3], [2, 3, 6]])
assert test.cofactor_matrix() == \
SparseMatrix([[27, -6, -6], [-12, 2, 3], [-3, 1, 0]])
test = SparseMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert test.cofactor_matrix() == \
SparseMatrix([[-3, 6, -3], [6, -12, 6], [-3, 6, -3]])
# test_jacobian
x = Symbol('x')
y = Symbol('y')
L = SparseMatrix(1, 2, [x**2 * y, 2 * y**2 + x * y])
syms = [x, y]
assert L.jacobian(syms) == Matrix([[2 * x * y, x**2], [y, 4 * y + x]])
L = SparseMatrix(1, 2, [x, x**2 * y**3])
assert L.jacobian(syms) == SparseMatrix([[1, 0],
[2 * x * y**3, x**2 * 3 * y**2]])
# test_QR
A = Matrix([[1, 2], [2, 3]])
Q, S = A.QRdecomposition()
R = Rational
assert Q == Matrix([[5**R(-1, 2), (R(2) / 5) * (R(1) / 5)**R(-1, 2)],
[2 * 5**R(-1, 2), (-R(1) / 5) * (R(1) / 5)**R(-1, 2)]])
assert S == Matrix([[5**R(1, 2), 8 * 5**R(-1, 2)],
[0, (R(1) / 5)**R(1, 2)]])
assert Q * S == A
assert Q.T * Q == sparse_eye(2)
R = Rational
# test nullspace
# first test reduced row-ech form
M = SparseMatrix([[5, 7, 2, 1], [1, 6, 2, -1]])
out, tmp = M.rref()
assert out == Matrix([[1, 0, -R(2) / 23, R(13) / 23],
[0, 1, R(8) / 23, R(-6) / 23]])
M = SparseMatrix([[1, 3, 0, 2, 6, 3, 1], [-2, -6, 0, -2, -8, 3, 1],
[3, 9, 0, 0, 6, 6, 2], [-1, -3, 0, 1, 0, 9, 3]])
out, tmp = M.rref()
assert out == Matrix([[1, 3, 0, 0, 2, 0, 0], [0, 0, 0, 1, 2, 0, 0],
[0, 0, 0, 0, 0, 1, R(1) / 3], [0, 0, 0, 0, 0, 0, 0]])
# now check the vectors
basis = M.nullspace()
assert basis[0] == Matrix([-3, 1, 0, 0, 0, 0, 0])
assert basis[1] == Matrix([0, 0, 1, 0, 0, 0, 0])
assert basis[2] == Matrix([-2, 0, 0, -2, 1, 0, 0])
assert basis[3] == Matrix([0, 0, 0, 0, 0, R(-1) / 3, 1])
# test eigen
x = Symbol('x')
y = Symbol('y')
sparse_eye3 = sparse_eye(3)
assert sparse_eye3.charpoly(x) == PurePoly(((x - 1)**3))
assert sparse_eye3.charpoly(y) == PurePoly(((y - 1)**3))
# test values
M = Matrix([(0, 1, -1), (1, 1, 0), (-1, 0, 1)])
vals = M.eigenvals()
assert sorted(vals.keys()) == [-1, 1, 2]
R = Rational
M = Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
assert M.eigenvects() == [
(1, 3, [Matrix([1, 0, 0]),
Matrix([0, 1, 0]),
Matrix([0, 0, 1])])
]
M = Matrix([[5, 0, 2], [3, 2, 0], [0, 0, 1]])
assert M.eigenvects() == [(1, 1, [Matrix([R(-1) / 2,
R(3) / 2, 1])]),
(2, 1, [Matrix([0, 1, 0])]),
(5, 1, [Matrix([1, 1, 0])])]
assert M.zeros(3, 5) == SparseMatrix(3, 5, {})
A = SparseMatrix(
10, 10, {
(0, 0): 18,
(0, 9): 12,
(1, 4): 18,
(2, 7): 16,
(3, 9): 12,
(4, 2): 19,
(5, 7): 16,
(6, 2): 12,
(9, 7): 18
})
assert A.row_list() == [(0, 0, 18), (0, 9, 12), (1, 4, 18), (2, 7, 16),
(3, 9, 12), (4, 2, 19), (5, 7, 16), (6, 2, 12),
(9, 7, 18)]
assert A.col_list() == [(0, 0, 18), (4, 2, 19), (6, 2, 12), (1, 4, 18),
(2, 7, 16), (5, 7, 16), (9, 7, 18), (0, 9, 12),
(3, 9, 12)]
assert SparseMatrix.eye(2).nnz() == 2
def generate_b_full_rank(alpha):
"""
Generate a row vector with all entries 1 and length alpha
"""
b = ones(1, alpha)
return b
