def test_matrices(): from sympy.matrices import MutableDenseMatrix, MutableSparseMatrix, \ ImmutableDenseMatrix, ImmutableSparseMatrix A = MutableDenseMatrix( [[1, -1, 0, 0], [0, 1, -1, 0], [0, 0, 1, -1], [0, 0, 0, 1]] ) B = MutableSparseMatrix(A) C = ImmutableDenseMatrix(A) D = ImmutableSparseMatrix(A) assert mcode(C) == mcode(A) == \ "{{1, -1, 0, 0}, " \ "{0, 1, -1, 0}, " \ "{0, 0, 1, -1}, " \ "{0, 0, 0, 1}}" assert mcode(D) == mcode(B) == \ "SparseArray[{" \ "{1, 1} -> 1, {1, 2} -> -1, {2, 2} -> 1, {2, 3} -> -1, " \ "{3, 3} -> 1, {3, 4} -> -1, {4, 4} -> 1" \ "}, {4, 4}]" # Trivial cases of matrices assert mcode(MutableDenseMatrix(0, 0, [])) == '{}' assert mcode(MutableSparseMatrix(0, 0, [])) == 'SparseArray[{}, {0, 0}]' assert mcode(MutableDenseMatrix(0, 3, [])) == '{}' assert mcode(MutableSparseMatrix(0, 3, [])) == 'SparseArray[{}, {0, 3}]' assert mcode(MutableDenseMatrix(3, 0, [])) == '{{}, {}, {}}' assert mcode(MutableSparseMatrix(3, 0, [])) == 'SparseArray[{}, {3, 0}]'
def test_matrices(): from sympy.matrices import MutableDenseMatrix, MutableSparseMatrix A = MutableDenseMatrix([[1, -1, 0, 0], [0, 1, -1, 0], [0, 0, 1, -1], [0, 0, 0, 1]]) B = MutableSparseMatrix([[1, -1, 0, 0], [0, 1, -1, 0], [0, 0, 1, -1], [0, 0, 0, 1]]) assert mcode(A) == """\ {{1, -1, 0, 0}, \ {0, 1, -1, 0}, \ {0, 0, 1, -1}, \ {0, 0, 0, 1}}\ """ assert mcode(B) == """\ SparseArray[\ {{1, 1} -> 1, {1, 2} -> -1, {2, 2} -> 1, {2, 3} -> -1, \ {3, 3} -> 1, {3, 4} -> -1, {4, 4} -> 1}, {4, 4}]\ """ # Trivial cases of matrices assert mcode(MutableDenseMatrix(0, 0, [])) == '{}' assert mcode(MutableSparseMatrix(0, 0, [])) == 'SparseArray[{}, {0, 0}]' assert mcode(MutableDenseMatrix(0, 3, [])) == '{}' assert mcode(MutableSparseMatrix(0, 3, [])) == 'SparseArray[{}, {0, 3}]' assert mcode(MutableDenseMatrix(3, 0, [])) == '{{}, {}, {}}' assert mcode(MutableSparseMatrix(3, 0, [])) == 'SparseArray[{}, {3, 0}]'
def test_todok(): a, b, c, d = symbols('a:d') m1 = MutableDenseMatrix([[a, b], [c, d]]) m2 = ImmutableDenseMatrix([[a, b], [c, d]]) m3 = MutableSparseMatrix([[a, b], [c, d]]) m4 = ImmutableSparseMatrix([[a, b], [c, d]]) assert m1.todok() == m2.todok() == m3.todok() == m4.todok() == \ {(0, 0): a, (0, 1): b, (1, 0): c, (1, 1): d}
def test_diagonal_solve(): a, d = symbols('a d') u, v, w, x = symbols('u:x') A = SparseMatrix([[a, 0], [0, d]]) B = MutableSparseMatrix([[u, v], [w, x]]) C = ImmutableSparseMatrix([[u, v], [w, x]]) sol = Matrix([[u / a, v / a], [w / d, x / d]]) assert A.diagonal_solve(B) == sol assert A.diagonal_solve(C) == sol
def test_upper_triangular_solve(): a, b, c, d = symbols('a:d') u, v, w, x = symbols('u:x') A = SparseMatrix([[a, b], [0, d]]) B = MutableSparseMatrix([[u, v], [w, x]]) C = ImmutableSparseMatrix([[u, v], [w, x]]) sol = Matrix([[(u - b * w / d) / a, (v - b * x / d) / a], [w / d, x / d]]) assert A.upper_triangular_solve(B) == sol assert A.upper_triangular_solve(C) == sol
def test_lower_triangular_solve(): a, b, c, d = symbols('a:d') u, v, w, x = symbols('u:x') A = SparseMatrix([[a, 0], [c, d]]) B = MutableSparseMatrix([[u, v], [w, x]]) C = ImmutableSparseMatrix([[u, v], [w, x]]) sol = Matrix([[u / a, v / a], [(w - c * u / a) / d, (x - c * v / a) / d]]) assert A.lower_triangular_solve(B) == sol assert A.lower_triangular_solve(C) == sol
def test_issue_15791(): class CrashingCodePrinter(CodePrinter): def emptyPrinter(self, obj): raise NotImplementedError from sympy.matrices import ( MutableSparseMatrix, ImmutableSparseMatrix, ) c = CrashingCodePrinter() # these should not silently succeed with raises(NotImplementedError): c.doprint(ImmutableSparseMatrix(2, 2, {})) with raises(NotImplementedError): c.doprint(MutableSparseMatrix(2, 2, {}))
def test_upper_triangular_solve(): raises( NonSquareMatrixError, lambda: SparseMatrix([[1, 2]]). upper_triangular_solve(Matrix([[1, 2]]))) raises( ShapeError, lambda: SparseMatrix([[1, 2], [0, 4]]).upper_triangular_solve( Matrix([1]))) raises( TypeError, lambda: SparseMatrix([[1, 2], [3, 4]]).upper_triangular_solve( Matrix([[1, 2], [3, 4]]))) a, b, c, d = symbols('a:d') u, v, w, x = symbols('u:x') A = SparseMatrix([[a, b], [0, d]]) B = MutableSparseMatrix([[u, v], [w, x]]) C = ImmutableSparseMatrix([[u, v], [w, x]]) sol = Matrix([[(u - b * w / d) / a, (v - b * x / d) / a], [w / d, x / d]]) assert A.upper_triangular_solve(B) == sol assert A.upper_triangular_solve(C) == sol
def test_lower_triangular_solve(): raises( NonSquareMatrixError, lambda: SparseMatrix([[1, 2]]). lower_triangular_solve(Matrix([[1, 2]]))) raises( ShapeError, lambda: SparseMatrix([[1, 2], [0, 4]]).lower_triangular_solve( Matrix([1]))) raises( ValueError, lambda: SparseMatrix([[1, 2], [3, 4]]).lower_triangular_solve( Matrix([[1, 2], [3, 4]]))) a, b, c, d = symbols('a:d') u, v, w, x = symbols('u:x') A = SparseMatrix([[a, 0], [c, d]]) B = MutableSparseMatrix([[u, v], [w, x]]) C = ImmutableSparseMatrix([[u, v], [w, x]]) sol = Matrix([[u / a, v / a], [(w - c * u / a) / d, (x - c * v / a) / d]]) assert A.lower_triangular_solve(B) == sol assert A.lower_triangular_solve(C) == sol
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