def test_function_return_types():
# Lets ensure that decompositions of immutable matrices remain immutable
# I.e. do MatrixBase methods return the correct class?
X = ImmutableMatrix([[1, 2], [3, 4]])
Y = ImmutableMatrix([[1], [0]])
q, r = X.QRdecomposition()
assert (type(q), type(r)) == (ImmutableMatrix, ImmutableMatrix)
assert type(X.LUsolve(Y)) == ImmutableMatrix
assert type(X.QRsolve(Y)) == ImmutableMatrix
X = ImmutableMatrix([[5, 2], [2, 7]])
assert X.T == X
assert X.is_symmetric
assert type(X.cholesky()) == ImmutableMatrix
L, D = X.LDLdecomposition()
assert (type(L), type(D)) == (ImmutableMatrix, ImmutableMatrix)
X = ImmutableMatrix([[1, 2], [2, 1]])
assert X.is_diagonalizable()
assert X.det() == -3
assert X.norm(2) == 3
assert type(X.eigenvects()[0][2][0]) == ImmutableMatrix
assert type(zeros(3, 3).as_immutable().nullspace()[0]) == ImmutableMatrix
X = ImmutableMatrix([[1, 0], [2, 1]])
assert type(X.lower_triangular_solve(Y)) == ImmutableMatrix
assert type(X.T.upper_triangular_solve(Y)) == ImmutableMatrix
assert type(X.minor_submatrix(0, 0)) == ImmutableMatrix
def test_immutable_evaluation():
X = ImmutableMatrix(eye(3))
A = ImmutableMatrix(3, 3, range(9))
assert isinstance(X + A, ImmutableMatrix)
assert isinstance(X * A, ImmutableMatrix)
assert isinstance(X * 2, ImmutableMatrix)
assert isinstance(2 * X, ImmutableMatrix)
assert isinstance(A**2, ImmutableMatrix)
def test_subs():
A = ImmutableMatrix([[1, 2], [3, 4]])
B = ImmutableMatrix([[1, 2], [x, 4]])
C = ImmutableMatrix([[-x, x * y], [-(x + y), y**2]])
assert B.subs(x, 3) == A
assert (x * B).subs(x, 3) == 3 * A
assert (x * eye(2) + B).subs(x, 3) == 3 * eye(2) + A
assert C.subs([[x, -1], [y, -2]]) == A
assert C.subs([(x, -1), (y, -2)]) == A
assert C.subs({x: -1, y: -2}) == A
assert C.subs({x: y - 1, y: x - 1}, simultaneous=True) == \
ImmutableMatrix([[1 - y, (x - 1)*(y - 1)], [2 - x - y, (x - 1)**2]])
def __new__(cls, *mats):
from sympy.matrices.immutable import ImmutableMatrix
data = [[
mats[i] if i == j else ZeroMatrix(mats[i].rows, mats[j].cols)
for j in range(len(mats))
] for i in range(len(mats))]
return Basic.__new__(BlockDiagMatrix, ImmutableMatrix(data))
def blocks(self):
from sympy.matrices.immutable import ImmutableMatrix
mats = self.args
data = [[mats[i] if i == j else ZeroMatrix(mats[i].rows, mats[j].cols)
for j in range(len(mats))]
for i in range(len(mats))]
return ImmutableMatrix(data)
def __new__(cls, *args):
from sympy.matrices.immutable import ImmutableMatrix
args = map(sympify, args)
mat = ImmutableMatrix(*args)
obj = Basic.__new__(cls, mat)
return obj
def as_explicit(self):
"""
Returns a dense Matrix with elements represented explicitly
Returns an object of type ImmutableMatrix.
Examples
========
>>> from sympy import Identity
>>> I = Identity(3)
>>> I
I
>>> I.as_explicit()
[1, 0, 0]
[0, 1, 0]
[0, 0, 1]
See Also
========
as_mutable: returns mutable Matrix type
"""
from sympy.matrices.immutable import ImmutableMatrix
return ImmutableMatrix([[ self[i, j]
for j in range(self.cols)]
for i in range(self.rows)])
def test_as_immutable():
data = [[1, 2], [3, 4]]
X = Matrix(data)
assert sympify(X) == X.as_immutable() == ImmutableMatrix(data)
data = {(0, 0): 1, (0, 1): 2, (1, 0): 3, (1, 1): 4}
X = SparseMatrix(2, 2, data)
assert sympify(X) == X.as_immutable() == ImmutableSparseMatrix(2, 2, data)
def wigner_d(J, alpha, beta, gamma):
"""Return the Wigner D matrix for angular momentum J.
Explanation
===========
J :
An integer, half-integer, or SymPy symbol for the total angular
momentum of the angular momentum space being rotated.
alpha, beta, gamma - Real numbers representing the Euler.
Angles of rotation about the so-called vertical, line of nodes, and
figure axes. See [Edmonds74]_.
Returns
=======
A matrix representing the corresponding Euler angle rotation( in the basis
of eigenvectors of `J_z`).
.. math ::
\\mathcal{D}_{\\alpha \\beta \\gamma} =
\\exp\\big( \\frac{i\\alpha}{\\hbar} J_z\\big)
\\exp\\big( \\frac{i\\beta}{\\hbar} J_y\\big)
\\exp\\big( \\frac{i\\gamma}{\\hbar} J_z\\big)
The components are calculated using the general form [Edmonds74]_,
equation 4.1.12.
Examples
========
The simplest possible example:
>>> from sympy.physics.wigner import wigner_d
>>> from sympy import Integer, symbols, pprint
>>> half = 1/Integer(2)
>>> alpha, beta, gamma = symbols("alpha, beta, gamma", real=True)
>>> pprint(wigner_d(half, alpha, beta, gamma), use_unicode=True)
⎡ ⅈ⋅α ⅈ⋅γ ⅈ⋅α -ⅈ⋅γ ⎤
⎢ ─── ─── ─── ───── ⎥
⎢ 2 2 ⎛β⎞ 2 2 ⎛β⎞ ⎥
⎢ ℯ ⋅ℯ ⋅cos⎜─⎟ ℯ ⋅ℯ ⋅sin⎜─⎟ ⎥
⎢ ⎝2⎠ ⎝2⎠ ⎥
⎢ ⎥
⎢ -ⅈ⋅α ⅈ⋅γ -ⅈ⋅α -ⅈ⋅γ ⎥
⎢ ───── ─── ───── ───── ⎥
⎢ 2 2 ⎛β⎞ 2 2 ⎛β⎞⎥
⎢-ℯ ⋅ℯ ⋅sin⎜─⎟ ℯ ⋅ℯ ⋅cos⎜─⎟⎥
⎣ ⎝2⎠ ⎝2⎠⎦
"""
d = wigner_d_small(J, beta)
M = [J - i for i in range(2 * J + 1)]
D = [[
exp(I * Mi * alpha) * d[i, j] * exp(I * Mj * gamma)
for j, Mj in enumerate(M)
] for i, Mi in enumerate(M)]
return ImmutableMatrix(D)
def doit(self, **ignored):
expr = self.func(*[i.doit(**ignored) for i in self.args])
# Check for explicit matrices:
from sympy import MatrixBase
from sympy.matrices.immutable import ImmutableMatrix
explicit = [i for i in expr.args if isinstance(i, MatrixBase)]
if explicit:
remainder = [i for i in expr.args if i not in explicit]
expl_mat = ImmutableMatrix(
[Mul.fromiter(i) for i in zip(*explicit)]).reshape(*self.shape)
expr = HadamardProduct(*([expl_mat] + remainder))
return canonicalize(expr)
def test_Equality():
assert Equality(IM, IM) is S.true
assert Unequality(IM, IM) is S.false
assert Equality(IM, IM.subs(1, 2)) is S.false
assert Unequality(IM, IM.subs(1, 2)) is S.true
assert Equality(IM, 2) is S.false
assert Unequality(IM, 2) is S.true
M = ImmutableMatrix([x, y])
assert Equality(M, IM) is S.false
assert Unequality(M, IM) is S.true
assert Equality(M, M.subs(x, 2)).subs(x, 2) is S.true
assert Unequality(M, M.subs(x, 2)).subs(x, 2) is S.false
assert Equality(M, M.subs(x, 2)).subs(x, 3) is S.false
assert Unequality(M, M.subs(x, 2)).subs(x, 3) is S.true
def test_block_index():
I = Identity(3)
Z = ZeroMatrix(3, 3)
B = BlockMatrix([[I, I], [I, I]])
e3 = ImmutableMatrix(eye(3))
BB = BlockMatrix([[e3, e3], [e3, e3]])
assert B[0, 0] == B[3, 0] == B[0, 3] == B[3, 3] == 1
assert B[4, 3] == B[5, 1] == 0
BB = BlockMatrix([[e3, e3], [e3, e3]])
assert B.as_explicit() == BB.as_explicit()
BI = BlockMatrix([[I, Z], [Z, I]])
assert BI.as_explicit().equals(eye(6))
def test_DiscreteMarkovChain():
# pass only the name
X = DiscreteMarkovChain("X")
assert isinstance(X.state_space, Range)
assert X.index_set == S.Naturals0
assert isinstance(X.transition_probabilities, MatrixSymbol)
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(NotImplementedError, lambda: X.communication_classes())
raises(NotImplementedError, lambda: X.canonical_form())
raises(NotImplementedError, lambda: X.decompose())
nz = Symbol('n', integer=True)
TZ = MatrixSymbol('M', nz, nz)
SZ = Range(nz)
YZ = DiscreteMarkovChain('Y', SZ, TZ)
assert P(Eq(YZ[2], 1), Eq(YZ[1], 0)) == TZ[0, 1]
raises(ValueError, lambda: sample_stochastic_process(t))
raises(ValueError, lambda: next(sample_stochastic_process(X)))
# pass name and state_space
# any hashable object should be a valid state
# states should be valid as a tuple/set/list/Tuple/Range
sym, rainy, cloudy, sunny = symbols('a Rainy Cloudy Sunny', real=True)
state_spaces = [(1, 2, 3), [Str('Hello'), sym, DiscreteMarkovChain],
Tuple(S(1), exp(sym), Str('World'), sympify=False),
Range(-1, 5, 2), [rainy, cloudy, sunny]]
chains = [
DiscreteMarkovChain("Y", state_space) for state_space in state_spaces
]
for i, Y in enumerate(chains):
assert isinstance(Y.transition_probabilities, MatrixSymbol)
assert Y.state_space == state_spaces[i] or Y.state_space == FiniteSet(
*state_spaces[i])
assert Y.number_of_states == 3
with ignore_warnings(
UserWarning): # TODO: Restore tests once warnings are removed
assert P(Eq(Y[2], 1), Eq(Y[0], 2),
evaluate=False) == Probability(Eq(Y[2], 1), Eq(Y[0], 2))
assert E(Y[0]) == Expectation(Y[0])
raises(ValueError, lambda: next(sample_stochastic_process(Y)))
raises(TypeError, lambda: DiscreteMarkovChain("Y", dict((1, 1))))
Y = DiscreteMarkovChain("Y", Range(1, t, 2))
assert Y.number_of_states == ceiling((t - 1) / 2)
# pass name and transition_probabilities
chains = [
DiscreteMarkovChain("Y", trans_probs=Matrix([[]])),
DiscreteMarkovChain("Y", trans_probs=Matrix([[0, 1], [1, 0]])),
DiscreteMarkovChain("Y",
trans_probs=Matrix([[pi, 1 - pi], [sym, 1 - sym]]))
]
for Z in chains:
assert Z.number_of_states == Z.transition_probabilities.shape[0]
assert isinstance(Z.transition_probabilities, ImmutableMatrix)
# 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", ['One', 'Two', 3], TS)
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 (P(Eq(YS[3], 2), Eq(YS[1], 1)) -
(TS[0, 2] * TS[1, 0] + TS[1, 1] * TS[1, 2] +
TS[1, 2] * TS[2, 2])).simplify() == 0
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)) == TS[0, 2] * TS[1, 0] + TS[1, 1] * TS[1, 2] + TS[1, 2] * TS[2, 2]
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)
with ignore_warnings(
UserWarning): ### TODO: Restore tests once warnings are removed
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(
Lt(X[1], 2) & Gt(X[1], 0),
Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1])
& 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(
Ne(X[1], 2) & Ne(X[1], 1),
Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1])
& 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.fundamental_matrix() == ImmutableMatrix(
[[176, 81, -132], [36, 141, -52], [-44, -39, 208]]) / 125
assert Y2.is_absorbing_chain() == True
assert Y3.is_absorbing_chain() == False
assert Y2.canonical_form() == ([0, 1, 2], TO2)
assert Y3.canonical_form() == ([0, 1, 2], TO3)
assert Y2.decompose() == ([0, 1,
2], TO2[0:1, 0:1], TO2[1:3, 0:1], TO2[1:3, 1:3])
assert Y3.decompose() == ([0, 1, 2], TO3, Matrix(0, 3,
[]), Matrix(0, 0, []))
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
assert Y4.is_ergodic() == True
TS1 = MatrixSymbol('T', 3, 3)
Y5 = DiscreteMarkovChain('Y', trans_probs=TS1)
assert Y5.limiting_distribution(w, TO4).doit() == True
assert Y5.stationary_distribution(condition_set=True).subs(
TS1, TO4).contains(w).doit() == S.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.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_probabilities() == ImmutableMatrix(
[[Rational(3, 4), Rational(1, 4)], [S.Half, S.Half],
[Rational(1, 4), Rational(3, 4)]])
TO7 = Matrix([[Rational(1, 2),
Rational(1, 4),
Rational(1, 4)], [Rational(1, 2), 0,
Rational(1, 2)],
[Rational(1, 4),
Rational(1, 4),
Rational(1, 2)]])
Y7 = DiscreteMarkovChain('Y', trans_probs=TO7)
assert Y7.is_absorbing_chain() == False
assert Y7.fundamental_matrix() == ImmutableMatrix(
[[Rational(86, 75),
Rational(1, 25),
Rational(-14, 75)],
[Rational(2, 25), Rational(21, 25),
Rational(2, 25)],
[Rational(-14, 75),
Rational(1, 25),
Rational(86, 75)]])
# test for zero-sized matrix functionality
X = DiscreteMarkovChain('X', trans_probs=Matrix([[]]))
assert X.number_of_states == 0
assert X.stationary_distribution() == Matrix([[]])
assert X.communication_classes() == []
assert X.canonical_form() == ([], Matrix([[]]))
assert X.decompose() == ([], Matrix([[]]), Matrix([[]]), Matrix([[]]))
assert X.is_regular() == False
assert X.is_ergodic() == False
# test communication_class
# see https://drive.google.com/drive/folders/1HbxLlwwn2b3U8Lj7eb_ASIUb5vYaNIjg?usp=sharing
# tutorial 2.pdf
TO7 = Matrix([[0, 5, 5, 0, 0], [0, 0, 0, 10, 0], [5, 0, 5, 0, 0],
[0, 10, 0, 0, 0], [0, 3, 0, 3, 4]]) / 10
Y7 = DiscreteMarkovChain('Y', trans_probs=TO7)
tuples = Y7.communication_classes()
classes, recurrence, periods = list(zip(*tuples))
assert classes == ([1, 3], [0, 2], [4])
assert recurrence == (True, False, False)
assert periods == (2, 1, 1)
TO8 = Matrix([[0, 0, 0, 10, 0, 0], [5, 0, 5, 0, 0, 0], [0, 4, 0, 0, 0, 6],
[10, 0, 0, 0, 0, 0], [0, 10, 0, 0, 0, 0], [0, 0, 0, 5, 5, 0]
]) / 10
Y8 = DiscreteMarkovChain('Y', trans_probs=TO8)
tuples = Y8.communication_classes()
classes, recurrence, periods = list(zip(*tuples))
assert classes == ([0, 3], [1, 2, 5, 4])
assert recurrence == (True, False)
assert periods == (2, 2)
TO9 = Matrix(
[[2, 0, 0, 3, 0, 0, 3, 2, 0, 0], [0, 10, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 2, 2, 0, 0, 0, 0, 0, 3, 3], [0, 0, 0, 3, 0, 0, 6, 1, 0, 0],
[0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 10, 0, 0, 0, 0],
[4, 0, 0, 5, 0, 0, 1, 0, 0, 0], [2, 0, 0, 4, 0, 0, 2, 2, 0, 0],
[3, 0, 1, 0, 0, 0, 0, 0, 4, 2], [0, 0, 4, 0, 0, 0, 0, 0, 3, 3]]) / 10
Y9 = DiscreteMarkovChain('Y', trans_probs=TO9)
tuples = Y9.communication_classes()
classes, recurrence, periods = list(zip(*tuples))
assert classes == ([0, 3, 6, 7], [1], [2, 8, 9], [5], [4])
assert recurrence == (True, True, False, True, False)
assert periods == (1, 1, 1, 1, 1)
# test canonical form
# see https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf
# example 11.13
T = Matrix([[1, 0, 0, 0, 0], [S(1) / 2, 0, S(1) / 2, 0, 0],
[0, S(1) / 2, 0, S(1) / 2, 0], [0, 0,
S(1) / 2, 0,
S(1) / 2], [0, 0, 0, 0,
S(1)]])
DW = DiscreteMarkovChain('DW', [0, 1, 2, 3, 4], T)
states, A, B, C = DW.decompose()
assert states == [0, 4, 1, 2, 3]
assert A == Matrix([[1, 0], [0, 1]])
assert B == Matrix([[S(1) / 2, 0], [0, 0], [0, S(1) / 2]])
assert C == Matrix([[0, S(1) / 2, 0], [S(1) / 2, 0, S(1) / 2],
[0, S(1) / 2, 0]])
states, new_matrix = DW.canonical_form()
assert states == [0, 4, 1, 2, 3]
assert new_matrix == Matrix([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0],
[S(1) / 2, 0, 0, S(1) / 2, 0],
[0, 0, S(1) / 2, 0,
S(1) / 2], [0, S(1) / 2, 0,
S(1) / 2, 0]])
# test regular and ergodic
# https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf
T = Matrix([[0, 4, 0, 0, 0], [1, 0, 3, 0, 0], [0, 2, 0, 2, 0],
[0, 0, 3, 0, 1], [0, 0, 0, 4, 0]]) / 4
X = DiscreteMarkovChain('X', trans_probs=T)
assert not X.is_regular()
assert X.is_ergodic()
T = Matrix([[0, 1], [1, 0]])
X = DiscreteMarkovChain('X', trans_probs=T)
assert not X.is_regular()
assert X.is_ergodic()
# http://www.math.wisc.edu/~valko/courses/331/MC2.pdf
T = Matrix([[2, 1, 1], [2, 0, 2], [1, 1, 2]]) / 4
X = DiscreteMarkovChain('X', trans_probs=T)
assert X.is_regular()
assert X.is_ergodic()
# https://docs.ufpr.br/~lucambio/CE222/1S2014/Kemeny-Snell1976.pdf
T = Matrix([[1, 1], [1, 1]]) / 2
X = DiscreteMarkovChain('X', trans_probs=T)
assert X.is_regular()
assert X.is_ergodic()
# test is_absorbing_chain
T = Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
X = DiscreteMarkovChain('X', trans_probs=T)
assert not X.is_absorbing_chain()
# https://en.wikipedia.org/wiki/Absorbing_Markov_chain
T = Matrix([[1, 1, 0, 0], [0, 1, 1, 0], [1, 0, 0, 1], [0, 0, 0, 2]]) / 2
X = DiscreteMarkovChain('X', trans_probs=T)
assert X.is_absorbing_chain()
T = Matrix([[2, 0, 0, 0, 0], [1, 0, 1, 0, 0], [0, 1, 0, 1, 0],
[0, 0, 1, 0, 1], [0, 0, 0, 0, 2]]) / 2
X = DiscreteMarkovChain('X', trans_probs=T)
assert X.is_absorbing_chain()
# test custom state space
Y10 = DiscreteMarkovChain('Y', [1, 2, 3], TO2)
tuples = Y10.communication_classes()
classes, recurrence, periods = list(zip(*tuples))
assert classes == ([1], [2, 3])
assert recurrence == (True, False)
assert periods == (1, 1)
assert Y10.canonical_form() == ([1, 2, 3], TO2)
assert Y10.decompose() == ([1, 2, 3], TO2[0:1, 0:1], TO2[1:3,
0:1], TO2[1:3,
1:3])
# 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)))
raises(ValueError, lambda: DiscreteMarkovChain('X', [0, 1], T))
# testing miscellaneous queries with different state space
X = DiscreteMarkovChain('X', ['A', 'B', 'C'], 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)
a = X.state_space.args[0]
c = X.state_space.args[2]
assert (E(X[1]**2, Eq(X[0], 1)) -
(a**2 / 3 + 2 * c**2 / 3)).simplify() == 0
assert (variance(X[1], Eq(X[0], 1)) -
(2 * (-a / 3 + c / 3)**2 / 3 +
(2 * a / 3 - 2 * c / 3)**2 / 3)).simplify() == 0
raises(ValueError, lambda: E(X[1], Eq(X[2], 1)))
#testing queries with multiple RandomIndexedSymbols
T = Matrix([[Rational(5, 10),
Rational(3, 10),
Rational(2, 10)],
[Rational(2, 10),
Rational(7, 10),
Rational(1, 10)],
[Rational(3, 10),
Rational(3, 10),
Rational(4, 10)]])
Y = DiscreteMarkovChain("Y", [0, 1, 2], T)
assert P(Eq(Y[7], Y[5]), Eq(Y[2], 0)).round(5) == Float(0.44428, 5)
assert P(Gt(Y[3], Y[1]), Eq(Y[0], 0)).round(2) == Float(0.36, 2)
assert P(Le(Y[5], Y[10]), Eq(Y[4], 2)).round(6) == Float(0.583120, 6)
assert Float(P(Eq(Y[10], Y[5]), Eq(Y[4], 1)),
14) == Float(1 - P(Ne(Y[10], Y[5]), Eq(Y[4], 1)), 14)
assert Float(P(Gt(Y[8], Y[9]), Eq(Y[3], 2)),
14) == Float(1 - P(Le(Y[8], Y[9]), Eq(Y[3], 2)), 14)
assert Float(P(Lt(Y[1], Y[4]), Eq(Y[0], 0)),
14) == Float(1 - P(Ge(Y[1], Y[4]), Eq(Y[0], 0)), 14)
assert P(Eq(Y[5], Y[10]), Eq(Y[2], 1)) == P(Eq(Y[10], Y[5]), Eq(Y[2], 1))
assert P(Gt(Y[1], Y[2]), Eq(Y[0], 1)) == P(Lt(Y[2], Y[1]), Eq(Y[0], 1))
assert P(Ge(Y[7], Y[6]), Eq(Y[4], 1)) == P(Le(Y[6], Y[7]), Eq(Y[4], 1))
#test symbolic queries
a, b, c, d = symbols('a b c d')
T = Matrix([[Rational(1, 10),
Rational(4, 10),
Rational(5, 10)],
[Rational(3, 10),
Rational(4, 10),
Rational(3, 10)],
[Rational(7, 10),
Rational(2, 10),
Rational(1, 10)]])
Y = DiscreteMarkovChain("Y", [0, 1, 2], T)
query = P(Eq(Y[a], b), Eq(Y[c], d))
assert query.subs({
a: 10,
b: 2,
c: 5,
d: 1
}).evalf().round(4) == P(Eq(Y[10], 2), Eq(Y[5], 1)).round(4)
assert query.subs({
a: 15,
b: 0,
c: 10,
d: 1
}).evalf().round(4) == P(Eq(Y[15], 0), Eq(Y[10], 1)).round(4)
query_gt = P(Gt(Y[a], b), Eq(Y[c], d))
query_le = P(Le(Y[a], b), Eq(Y[c], d))
assert query_gt.subs({
a: 5,
b: 2,
c: 1,
d: 0
}).evalf() + query_le.subs({
a: 5,
b: 2,
c: 1,
d: 0
}).evalf() == 1
query_ge = P(Ge(Y[a], b), Eq(Y[c], d))
query_lt = P(Lt(Y[a], b), Eq(Y[c], d))
assert query_ge.subs({
a: 4,
b: 1,
c: 0,
d: 2
}).evalf() + query_lt.subs({
a: 4,
b: 1,
c: 0,
d: 2
}).evalf() == 1
#test issue 20078
assert (2 * Y[1] + 3 * Y[1]).simplify() == 5 * Y[1]
assert (2 * Y[1] - 3 * Y[1]).simplify() == -Y[1]
assert (2 * (0.25 * Y[1])).simplify() == 0.5 * Y[1]
assert ((2 * Y[1]) * (0.25 * Y[1])).simplify() == 0.5 * Y[1]**2
assert (Y[1]**2 + Y[1]**3).simplify() == (Y[1] + 1) * Y[1]**2
def wigner_d_small(J, beta):
"""Return the small Wigner d matrix for angular momentum J.
Explanation
===========
J : An integer, half-integer, or SymPy symbol for the total angular
momentum of the angular momentum space being rotated.
beta : A real number representing the Euler angle of rotation about
the so-called line of nodes. See [Edmonds74]_.
Returns
=======
A matrix representing the corresponding Euler angle rotation( in the basis
of eigenvectors of `J_z`).
.. math ::
\\mathcal{d}_{\\beta} = \\exp\\big( \\frac{i\\beta}{\\hbar} J_y\\big)
The components are calculated using the general form [Edmonds74]_,
equation 4.1.15.
Examples
========
>>> from sympy import Integer, symbols, pi, pprint
>>> from sympy.physics.wigner import wigner_d_small
>>> half = 1/Integer(2)
>>> beta = symbols("beta", real=True)
>>> pprint(wigner_d_small(half, beta), use_unicode=True)
⎡ ⎛β⎞ ⎛β⎞⎤
⎢cos⎜─⎟ sin⎜─⎟⎥
⎢ ⎝2⎠ ⎝2⎠⎥
⎢ ⎥
⎢ ⎛β⎞ ⎛β⎞⎥
⎢-sin⎜─⎟ cos⎜─⎟⎥
⎣ ⎝2⎠ ⎝2⎠⎦
>>> pprint(wigner_d_small(2*half, beta), use_unicode=True)
⎡ 2⎛β⎞ ⎛β⎞ ⎛β⎞ 2⎛β⎞ ⎤
⎢ cos ⎜─⎟ √2⋅sin⎜─⎟⋅cos⎜─⎟ sin ⎜─⎟ ⎥
⎢ ⎝2⎠ ⎝2⎠ ⎝2⎠ ⎝2⎠ ⎥
⎢ ⎥
⎢ ⎛β⎞ ⎛β⎞ 2⎛β⎞ 2⎛β⎞ ⎛β⎞ ⎛β⎞⎥
⎢-√2⋅sin⎜─⎟⋅cos⎜─⎟ - sin ⎜─⎟ + cos ⎜─⎟ √2⋅sin⎜─⎟⋅cos⎜─⎟⎥
⎢ ⎝2⎠ ⎝2⎠ ⎝2⎠ ⎝2⎠ ⎝2⎠ ⎝2⎠⎥
⎢ ⎥
⎢ 2⎛β⎞ ⎛β⎞ ⎛β⎞ 2⎛β⎞ ⎥
⎢ sin ⎜─⎟ -√2⋅sin⎜─⎟⋅cos⎜─⎟ cos ⎜─⎟ ⎥
⎣ ⎝2⎠ ⎝2⎠ ⎝2⎠ ⎝2⎠ ⎦
From table 4 in [Edmonds74]_
>>> pprint(wigner_d_small(half, beta).subs({beta:pi/2}), use_unicode=True)
⎡ √2 √2⎤
⎢ ── ──⎥
⎢ 2 2 ⎥
⎢ ⎥
⎢-√2 √2⎥
⎢──── ──⎥
⎣ 2 2 ⎦
>>> pprint(wigner_d_small(2*half, beta).subs({beta:pi/2}),
... use_unicode=True)
⎡ √2 ⎤
⎢1/2 ── 1/2⎥
⎢ 2 ⎥
⎢ ⎥
⎢-√2 √2 ⎥
⎢──── 0 ── ⎥
⎢ 2 2 ⎥
⎢ ⎥
⎢ -√2 ⎥
⎢1/2 ──── 1/2⎥
⎣ 2 ⎦
>>> pprint(wigner_d_small(3*half, beta).subs({beta:pi/2}),
... use_unicode=True)
⎡ √2 √6 √6 √2⎤
⎢ ── ── ── ──⎥
⎢ 4 4 4 4 ⎥
⎢ ⎥
⎢-√6 -√2 √2 √6⎥
⎢──── ──── ── ──⎥
⎢ 4 4 4 4 ⎥
⎢ ⎥
⎢ √6 -√2 -√2 √6⎥
⎢ ── ──── ──── ──⎥
⎢ 4 4 4 4 ⎥
⎢ ⎥
⎢-√2 √6 -√6 √2⎥
⎢──── ── ──── ──⎥
⎣ 4 4 4 4 ⎦
>>> pprint(wigner_d_small(4*half, beta).subs({beta:pi/2}),
... use_unicode=True)
⎡ √6 ⎤
⎢1/4 1/2 ── 1/2 1/4⎥
⎢ 4 ⎥
⎢ ⎥
⎢-1/2 -1/2 0 1/2 1/2⎥
⎢ ⎥
⎢ √6 √6 ⎥
⎢ ── 0 -1/2 0 ── ⎥
⎢ 4 4 ⎥
⎢ ⎥
⎢-1/2 1/2 0 -1/2 1/2⎥
⎢ ⎥
⎢ √6 ⎥
⎢1/4 -1/2 ── -1/2 1/4⎥
⎣ 4 ⎦
"""
M = [J - i for i in range(2 * J + 1)]
d = zeros(2 * J + 1)
for i, Mi in enumerate(M):
for j, Mj in enumerate(M):
# We get the maximum and minimum value of sigma.
sigmamax = max([-Mi - Mj, J - Mj])
sigmamin = min([0, J - Mi])
dij = sqrt(
factorial(J + Mi) * factorial(J - Mi) / factorial(J + Mj) /
factorial(J - Mj))
terms = [(-1)**(J - Mi - s) * binomial(J + Mj, J - Mi - s) *
binomial(J - Mj, s) * cos(beta / 2)**(2 * s + Mi + Mj) *
sin(beta / 2)**(2 * J - 2 * s - Mj - Mi)
for s in range(sigmamin, sigmamax + 1)]
d[i, j] = dij * Add(*terms)
return ImmutableMatrix(d)
def test_deterimant():
assert ImmutableMatrix(4, 4, lambda i, j: i + j).det() == 0
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) is nan
assert re(oo) is oo
assert re(-oo) is -oo
assert re(0) == 0
assert re(1) == 1
assert re(-1) == -1
assert re(E) == E
assert re(-E) == -E
assert unchanged(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) + re(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 + S.Half
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) is S.NaN
n, m, l = symbols('n m l')
A = MatrixSymbol('A', n, m)
assert re(A) == (S.Half) * (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]])
def test_ContinuousMarkovChain():
T1 = Matrix([[S(-2), S(2), S.Zero], [S.Zero, S.NegativeOne, S.One],
[Rational(3, 2), Rational(3, 2),
S(-3)]])
C1 = ContinuousMarkovChain('C', [0, 1, 2], T1)
assert C1.limiting_distribution() == ImmutableMatrix(
[[Rational(3, 19), Rational(12, 19),
Rational(4, 19)]])
T2 = Matrix([[-S.One, S.One, S.Zero], [S.One, -S.One, S.Zero],
[S.Zero, S.One, -S.One]])
C2 = ContinuousMarkovChain('C', [0, 1, 2], T2)
A, t = C2.generator_matrix, symbols('t', positive=True)
assert C2.transition_probabilities(A)(t) == Matrix(
[[S.Half + exp(-2 * t) / 2, S.Half - exp(-2 * t) / 2, 0],
[S.Half - exp(-2 * t) / 2, S.Half + exp(-2 * t) / 2, 0],
[
S.Half - exp(-t) + exp(-2 * t) / 2, S.Half - exp(-2 * t) / 2,
exp(-t)
]])
with ignore_warnings(
UserWarning): ### TODO: Restore tests once warnings are removed
assert P(Eq(C2(1), 1), Eq(C2(0), 1),
evaluate=False) == Probability(Eq(C2(1), 1), Eq(C2(0), 1))
assert P(Eq(C2(1), 1), Eq(C2(0), 1)) == exp(-2) / 2 + S.Half
assert P(
Eq(C2(1), 0) & Eq(C2(2), 1) & Eq(C2(3), 1),
Eq(P(Eq(C2(1), 0)),
S.Half)) == (Rational(1, 4) - exp(-2) / 4) * (exp(-2) / 2 + S.Half)
assert P(
Not(Eq(C2(1), 0) & Eq(C2(2), 1) & Eq(C2(3), 2)) |
(Eq(C2(1), 0) & Eq(C2(2), 1) & Eq(C2(3), 2)),
Eq(P(Eq(C2(1), 0)), Rational(1, 4))
& Eq(P(Eq(C2(1), 1)), Rational(1, 4))) is S.One
assert E(C2(Rational(3, 2)),
Eq(C2(0), 2)) == -exp(-3) / 2 + 2 * exp(Rational(-3, 2)) + S.Half
assert variance(C2(Rational(3, 2)), Eq(
C2(0),
1)) == ((S.Half - exp(-3) / 2)**2 * (exp(-3) / 2 + S.Half) +
(Rational(-1, 2) - exp(-3) / 2)**2 * (S.Half - exp(-3) / 2))
raises(KeyError, lambda: P(Eq(C2(1), 0), Eq(P(Eq(C2(1), 1)), S.Half)))
assert P(Eq(C2(1), 0), Eq(P(Eq(C2(5), 1)),
S.Half)) == Probability(Eq(C2(1), 0))
TS1 = MatrixSymbol('G', 3, 3)
CS1 = ContinuousMarkovChain('C', [0, 1, 2], TS1)
A = CS1.generator_matrix
assert CS1.transition_probabilities(A)(t) == exp(t * A)
C3 = ContinuousMarkovChain(
'C', [Symbol('0'), Symbol('1'), Symbol('2')], T2)
assert P(Eq(C3(1), 1), Eq(C3(0), 1)) == exp(-2) / 2 + S.Half
assert P(Eq(C3(1), Symbol('1')), Eq(C3(0),
Symbol('1'))) == exp(-2) / 2 + S.Half
#test probability queries
G = Matrix([[-S(1), Rational(1, 10),
Rational(9, 10)], [Rational(2, 5), -S(1),
Rational(3, 5)],
[Rational(1, 2), Rational(1, 2), -S(1)]])
C = ContinuousMarkovChain('C', state_space=[0, 1, 2], gen_mat=G)
assert P(Eq(C(7.385), C(3.19)), Eq(C(0.862),
0)).round(5) == Float(0.35469, 5)
assert P(Gt(C(98.715), C(19.807)), Eq(C(11.314),
2)).round(5) == Float(0.32452, 5)
assert P(Le(C(5.9), C(10.112)), Eq(C(4), 1)).round(6) == Float(0.675214, 6)
assert Float(P(Eq(C(7.32), C(2.91)), Eq(C(2.63), 1)),
14) == Float(1 - P(Ne(C(7.32), C(2.91)), Eq(C(2.63), 1)), 14)
assert Float(P(Gt(C(3.36), C(1.101)), Eq(C(0.8), 2)),
14) == Float(1 - P(Le(C(3.36), C(1.101)), Eq(C(0.8), 2)), 14)
assert Float(P(Lt(C(4.9), C(2.79)), Eq(C(1.61), 0)),
14) == Float(1 - P(Ge(C(4.9), C(2.79)), Eq(C(1.61), 0)), 14)
assert P(Eq(C(5.243), C(10.912)), Eq(C(2.174),
1)) == P(Eq(C(10.912), C(5.243)),
Eq(C(2.174), 1))
assert P(Gt(C(2.344), C(9.9)), Eq(C(1.102),
1)) == P(Lt(C(9.9), C(2.344)),
Eq(C(1.102), 1))
assert P(Ge(C(7.87), C(1.008)), Eq(C(0.153),
1)) == P(Le(C(1.008), C(7.87)),
Eq(C(0.153), 1))
#test symbolic queries
a, b, c, d = symbols('a b c d')
query = P(Eq(C(a), b), Eq(C(c), d))
assert query.subs({
a: 3.65,
b: 2,
c: 1.78,
d: 1
}).evalf().round(10) == P(Eq(C(3.65), 2), Eq(C(1.78), 1)).round(10)
query_gt = P(Gt(C(a), b), Eq(C(c), d))
query_le = P(Le(C(a), b), Eq(C(c), d))
assert query_gt.subs({
a: 13.2,
b: 0,
c: 3.29,
d: 2
}).evalf() + query_le.subs({
a: 13.2,
b: 0,
c: 3.29,
d: 2
}).evalf() == 1
query_ge = P(Ge(C(a), b), Eq(C(c), d))
query_lt = P(Lt(C(a), b), Eq(C(c), d))
assert query_ge.subs({
a: 7.43,
b: 1,
c: 1.45,
d: 0
}).evalf() + query_lt.subs({
a: 7.43,
b: 1,
c: 1.45,
d: 0
}).evalf() == 1
#test issue 20078
assert (2 * C(1) + 3 * C(1)).simplify() == 5 * C(1)
assert (2 * C(1) - 3 * C(1)).simplify() == -C(1)
assert (2 * (0.25 * C(1))).simplify() == 0.5 * C(1)
assert (2 * C(1) * 0.25 * C(1)).simplify() == 0.5 * C(1)**2
assert (C(1)**2 + C(1)**3).simplify() == (C(1) + 1) * C(1)**2
def test_bidiagonalize():
M = Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
assert M.bidiagonalize() == M
assert M.bidiagonalize(upper=False) == M
assert M.bidiagonalize() == M
assert M.bidiagonal_decomposition() == (M, M, M)
assert M.bidiagonal_decomposition(upper=False) == (M, M, M)
assert M.bidiagonalize() == M
import random
#Real Tests
for real_test in range(2):
test_values = []
row = 2
col = 2
for _ in range(row * col):
value = random.randint(-1000000000, 1000000000)
test_values = test_values + [value]
# L -> Lower Bidiagonalization
# M -> Mutable Matrix
# N -> Immutable Matrix
# 0 -> Bidiagonalized form
# 1,2,3 -> Bidiagonal_decomposition matrices
# 4 -> Product of 1 2 3
M = Matrix(row, col, test_values)
N = ImmutableMatrix(M)
N1, N2, N3 = N.bidiagonal_decomposition()
M1, M2, M3 = M.bidiagonal_decomposition()
M0 = M.bidiagonalize()
N0 = N.bidiagonalize()
N4 = N1 * N2 * N3
M4 = M1 * M2 * M3
N2.simplify()
N4.simplify()
N0.simplify()
M0.simplify()
M2.simplify()
M4.simplify()
LM0 = M.bidiagonalize(upper=False)
LM1, LM2, LM3 = M.bidiagonal_decomposition(upper=False)
LN0 = N.bidiagonalize(upper=False)
LN1, LN2, LN3 = N.bidiagonal_decomposition(upper=False)
LN4 = LN1 * LN2 * LN3
LM4 = LM1 * LM2 * LM3
LN2.simplify()
LN4.simplify()
LN0.simplify()
LM0.simplify()
LM2.simplify()
LM4.simplify()
assert M == M4
assert M2 == M0
assert N == N4
assert N2 == N0
assert M == LM4
assert LM2 == LM0
assert N == LN4
assert LN2 == LN0
#Complex Tests
for complex_test in range(2):
test_values = []
size = 2
for _ in range(size * size):
real = random.randint(-1000000000, 1000000000)
comp = random.randint(-1000000000, 1000000000)
value = real + comp * I
test_values = test_values + [value]
M = Matrix(size, size, test_values)
N = ImmutableMatrix(M)
# L -> Lower Bidiagonalization
# M -> Mutable Matrix
# N -> Immutable Matrix
# 0 -> Bidiagonalized form
# 1,2,3 -> Bidiagonal_decomposition matrices
# 4 -> Product of 1 2 3
N1, N2, N3 = N.bidiagonal_decomposition()
M1, M2, M3 = M.bidiagonal_decomposition()
M0 = M.bidiagonalize()
N0 = N.bidiagonalize()
N4 = N1 * N2 * N3
M4 = M1 * M2 * M3
N2.simplify()
N4.simplify()
N0.simplify()
M0.simplify()
M2.simplify()
M4.simplify()
LM0 = M.bidiagonalize(upper=False)
LM1, LM2, LM3 = M.bidiagonal_decomposition(upper=False)
LN0 = N.bidiagonalize(upper=False)
LN1, LN2, LN3 = N.bidiagonal_decomposition(upper=False)
LN4 = LN1 * LN2 * LN3
LM4 = LM1 * LM2 * LM3
LN2.simplify()
LN4.simplify()
LN0.simplify()
LM0.simplify()
LM2.simplify()
LM4.simplify()
assert M == M4
assert M2 == M0
assert N == N4
assert N2 == N0
assert M == LM4
assert LM2 == LM0
assert N == LN4
assert LN2 == LN0
M = Matrix(18, 8, range(1, 145))
M = M.applyfunc(lambda i: Float(i))
assert M.bidiagonal_decomposition()[1] == M.bidiagonalize()
assert M.bidiagonal_decomposition(upper=False)[1] == M.bidiagonalize(
upper=False)
a, b, c = M.bidiagonal_decomposition()
diff = a * b * c - M
assert abs(max(diff)) < 10**-12
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) is nan
assert im(oo * I) is oo
assert im(-oo * I) is -oo
assert im(0) == 0
assert im(1) == 0
assert im(-1) == 0
assert im(E * I) == E
assert im(-E * I) == -E
assert unchanged(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) + im(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) is S.NaN
n, m, l = symbols('n m l')
A = MatrixSymbol('A', n, m)
assert im(A) == (S.One / (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]])