def test_solve_univariate_inequality():
assert isolve(x**2 >= 4,
x, relational=False) == Union(Interval(-oo, -2),
Interval(2, oo))
assert isolve(x**2 >= 4, x) == Or(And(Le(2, x), Lt(x, oo)),
And(Le(x, -2), Lt(-oo, x)))
assert isolve((x - 1)*(x - 2)*(x - 3) >= 0, x, relational=False) == \
Union(Interval(1, 2), Interval(3, oo))
assert isolve((x - 1)*(x - 2)*(x - 3) >= 0, x) == \
Or(And(Le(1, x), Le(x, 2)), And(Le(3, x), Lt(x, oo)))
assert isolve((x - 1)*(x - 2)*(x - 4) < 0, x, domain = FiniteSet(0, 3)) == \
Or(Eq(x, 0), Eq(x, 3))
# issue 2785:
assert isolve(x**3 - 2*x - 1 > 0, x, relational=False) == \
Union(Interval(-1, -sqrt(5)/2 + S.Half, True, True),
Interval(S.Half + sqrt(5)/2, oo, True, True))
# issue 2794:
assert isolve(x**3 - x**2 + x - 1 > 0, x, relational=False) == \
Interval(1, oo, True)
#issue 13105
assert isolve((x + I) * (x + 2 * I) < 0, x) == Eq(x, 0)
assert isolve(((x - 1) * (x - 2) + I) * ((x - 1) * (x - 2) + 2 * I) < 0,
x) == Or(Eq(x, 1), Eq(x, 2))
assert isolve(
(((x - 1) * (x - 2) + I) * ((x - 1) * (x - 2) + 2 * I)) / (x - 2) > 0,
x) == Eq(x, 1)
raises(ValueError, lambda: isolve((x**2 - 3 * x * I + 2) / x < 0, x))
# numerical testing in valid() is needed
assert isolve(x**7 - x - 2 > 0, x) == \
And(rootof(x**7 - x - 2, 0) < x, x < oo)
# handle numerator and denominator; although these would be handled as
# rational inequalities, these test confirm that the right thing is done
# when the domain is EX (e.g. when 2 is replaced with sqrt(2))
assert isolve(1 / (x - 2) > 0, x) == And(S(2) < x, x < oo)
den = ((x - 1) * (x - 2)).expand()
assert isolve((x - 1)/den <= 0, x) == \
(x > -oo) & (x < 2) & Ne(x, 1)
n = Dummy('n')
raises(NotImplementedError,
lambda: isolve(Abs(x) <= n, x, relational=False))
c1 = Dummy("c1", positive=True)
raises(NotImplementedError, lambda: isolve(n / c1 < 0, c1))
n = Dummy('n', negative=True)
assert isolve(n / c1 > -2, c1) == (-n / 2 < c1)
assert isolve(n / c1 < 0, c1) == True
assert isolve(n / c1 > 0, c1) == False
zero = cos(1)**2 + sin(1)**2 - 1
raises(NotImplementedError, lambda: isolve(x**2 < zero, x))
raises(NotImplementedError, lambda: isolve(x**2 < zero * I, x))
raises(NotImplementedError, lambda: isolve(1 / (x - y) < 2, x))
raises(NotImplementedError, lambda: isolve(1 / (x - y) < 0, x))
raises(TypeError, lambda: isolve(x - I < 0, x))
zero = x**2 + x - x * (x + 1)
assert isolve(zero < 0, x, relational=False) is S.EmptySet
assert isolve(zero <= 0, x, relational=False) is S.Reals
# make sure iter_solutions gets a default value
raises(NotImplementedError,
lambda: isolve(Eq(cos(x)**2 + sin(x)**2, 1), x))
def test_pretty_relational():
expr = Eq(x, y)
ascii_str = \
"""\
x = y\
"""
ucode_str = \
u"""\
x = y\
"""
assert pretty(expr) == ascii_str
assert upretty(expr) == ucode_str
expr = Lt(x, y)
ascii_str = \
"""\
x < y\
"""
ucode_str = \
u"""\
x < y\
"""
assert pretty(expr) == ascii_str
assert upretty(expr) == ucode_str
expr = Gt(x, y)
ascii_str = \
"""\
y < x\
"""
ucode_str = \
u"""\
y < x\
"""
assert pretty(expr) == ascii_str
assert upretty(expr) == ucode_str
expr = Le(x, y)
ascii_str = \
"""\
x <= y\
"""
ucode_str = \
u"""\
x ≤ y\
"""
assert pretty(expr) == ascii_str
assert upretty(expr) == ucode_str
expr = Ge(x, y)
ascii_str = \
"""\
y <= x\
"""
ucode_str = \
u"""\
y ≤ x\
"""
assert pretty(expr) == ascii_str
assert upretty(expr) == ucode_str
expr = Ne(x / (y + 1), y**2)
ascii_str_1 = \
"""\
x 2\n\
----- != y \n\
1 + y \
"""
ascii_str_2 = \
"""\
x 2\n\
----- != y \n\
y + 1 \
"""
ucode_str_1 = \
u"""\
x 2\n\
───── ≠ y \n\
1 + y \
"""
ucode_str_2 = \
u"""\
x 2\n\
───── ≠ y \n\
y + 1 \
"""
assert pretty(expr) in [ascii_str_1, ascii_str_2]
assert upretty(expr) in [ucode_str_1, ucode_str_2]
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(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, ImmutableDenseMatrix)
# 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 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 (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._transient2absorbing() == None
raises(ValueError, lambda: Y3.fundamental_matrix())
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._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_probabilities() == ImmutableMatrix(
[[Rational(3, 4), Rational(1, 4)], [S.Half, S.Half],
[Rational(1, 4), Rational(3, 4)]])
# 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(0.739072, 6)
assert Float(P(Eq(Y[500], Y[240]), Eq(Y[120], 1)),
14) == Float(1 - P(Ne(Y[500], Y[240]), Eq(Y[120], 1)), 14)
assert Float(P(Gt(Y[350], Y[100]), Eq(Y[75], 2)),
14) == Float(1 - P(Le(Y[350], Y[100]), Eq(Y[75], 2)), 14)
assert Float(P(Lt(Y[400], Y[210]), Eq(Y[161], 0)),
14) == Float(1 - P(Ge(Y[400], Y[210]), Eq(Y[161], 0)), 14)
def test_reduce_poly_inequalities_complex_relational():
cond = Eq(im(x), 0)
assert reduce_rational_inequalities([[Eq(x**2, 0)]], x,
relational=True) == And(
Eq(re(x), 0), cond)
assert reduce_rational_inequalities([[Le(x**2, 0)]], x,
relational=True) == And(
Eq(re(x), 0), cond)
assert reduce_rational_inequalities([[Lt(x**2, 0)]], x,
relational=True) is False
assert reduce_rational_inequalities([[Ge(x**2, 0)]], x,
relational=True) == cond
assert reduce_rational_inequalities([[Gt(x**2, 0)]], x,
relational=True) == And(
Or(Lt(re(x), 0), Lt(0, re(x))),
cond)
assert reduce_rational_inequalities([[Ne(x**2, 0)]], x,
relational=True) == And(
Or(Lt(re(x), 0), Lt(0, re(x))),
cond)
assert reduce_rational_inequalities([[Eq(x**2, 1)]], x,
relational=True) == And(
Or(Eq(re(x), -1), Eq(re(x), 1)),
cond)
assert reduce_rational_inequalities([[Le(x**2, 1)]], x,
relational=True) == And(
And(Le(-1, re(x)), Le(re(x), 1)),
cond)
assert reduce_rational_inequalities([[Lt(x**2, 1)]], x,
relational=True) == And(
And(Lt(-1, re(x)), Lt(re(x), 1)),
cond)
assert reduce_rational_inequalities([[Ge(x**2, 1)]], x,
relational=True) == And(
Or(Le(re(x), -1), Le(1, re(x))),
cond)
assert reduce_rational_inequalities([[Gt(x**2, 1)]], x,
relational=True) == And(
Or(Lt(re(x), -1), Lt(1, re(x))),
cond)
assert reduce_rational_inequalities(
[[Ne(x**2, 1)]], x, relational=True) == And(
Or(Lt(re(x), -1), And(Lt(-1, re(x)), Lt(re(x), 1)), Lt(1, re(x))),
cond)
assert reduce_rational_inequalities([[Le(x**2, 1.0)]], x,
relational=True) == And(
And(Le(-1.0, re(x)),
Le(re(x), 1.0)), cond)
assert reduce_rational_inequalities([[Lt(x**2, 1.0)]], x,
relational=True) == And(
And(Lt(-1.0, re(x)),
Lt(re(x), 1.0)), cond)
assert reduce_rational_inequalities([[Ge(x**2, 1.0)]], x,
relational=True) == And(
Or(Le(re(x), -1.0),
Le(1.0, re(x))), cond)
assert reduce_rational_inequalities([[Gt(x**2, 1.0)]], x,
relational=True) == And(
Or(Lt(re(x), -1.0),
Lt(1.0, re(x))), cond)
assert reduce_rational_inequalities([[Ne(x**2, 1.0)]], x,
relational=True) == And(
Or(
Lt(re(x), -1.0),
And(Lt(-1.0, re(x)),
Lt(re(x), 1.0)),
Lt(1.0, re(x))), cond)
def test_reduce_poly_inequalities_real_interval():
assert reduce_rational_inequalities([[Eq(x**2, 0)]], x,
relational=False) == FiniteSet(0)
assert reduce_rational_inequalities([[Le(x**2, 0)]], x,
relational=False) == FiniteSet(0)
assert reduce_rational_inequalities([[Lt(x**2, 0)]], x,
relational=False) == S.EmptySet
assert reduce_rational_inequalities(
[[Ge(x**2, 0)]], x, relational=False) == \
S.Reals if x.is_real else Interval(-oo, oo)
assert reduce_rational_inequalities(
[[Gt(x**2, 0)]], x, relational=False) == \
FiniteSet(0).complement(S.Reals)
assert reduce_rational_inequalities(
[[Ne(x**2, 0)]], x, relational=False) == \
FiniteSet(0).complement(S.Reals)
assert reduce_rational_inequalities([[Eq(x**2, 1)]], x,
relational=False) == FiniteSet(-1, 1)
assert reduce_rational_inequalities([[Le(x**2, 1)]], x,
relational=False) == Interval(-1, 1)
assert reduce_rational_inequalities([[Lt(x**2, 1)]], x,
relational=False) == Interval(
-1, 1, True, True)
assert reduce_rational_inequalities(
[[Ge(x**2, 1)]], x, relational=False) == \
Union(Interval(-oo, -1), Interval(1, oo))
assert reduce_rational_inequalities(
[[Gt(x**2, 1)]], x, relational=False) == \
Interval(-1, 1).complement(S.Reals)
assert reduce_rational_inequalities(
[[Ne(x**2, 1)]], x, relational=False) == \
FiniteSet(-1, 1).complement(S.Reals)
assert reduce_rational_inequalities([[Eq(x**2, 1.0)]], x,
relational=False) == FiniteSet(
-1.0, 1.0).evalf()
assert reduce_rational_inequalities([[Le(x**2, 1.0)]], x,
relational=False) == Interval(
-1.0, 1.0)
assert reduce_rational_inequalities([[Lt(x**2, 1.0)]], x,
relational=False) == Interval(
-1.0, 1.0, True, True)
assert reduce_rational_inequalities(
[[Ge(x**2, 1.0)]], x, relational=False) == \
Union(Interval(-inf, -1.0), Interval(1.0, inf))
assert reduce_rational_inequalities(
[[Gt(x**2, 1.0)]], x, relational=False) == \
Union(Interval(-inf, -1.0, right_open=True),
Interval(1.0, inf, left_open=True))
assert reduce_rational_inequalities([[Ne(
x**2, 1.0)]], x, relational=False) == \
FiniteSet(-1.0, 1.0).complement(S.Reals)
s = sqrt(2)
assert reduce_rational_inequalities(
[[Lt(x**2 - 1, 0), Gt(x**2 - 1, 0)]], x,
relational=False) == S.EmptySet
assert reduce_rational_inequalities(
[[Le(x**2 - 1, 0), Ge(x**2 - 1, 0)]], x,
relational=False) == FiniteSet(-1, 1)
assert reduce_rational_inequalities(
[[Le(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x,
relational=False) == Union(Interval(-s, -1, False, False),
Interval(1, s, False, False))
assert reduce_rational_inequalities(
[[Le(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x,
relational=False) == Union(Interval(-s, -1, False, True),
Interval(1, s, True, False))
assert reduce_rational_inequalities(
[[Lt(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x,
relational=False) == Union(Interval(-s, -1, True, False),
Interval(1, s, False, True))
assert reduce_rational_inequalities(
[[Lt(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x,
relational=False) == Union(Interval(-s, -1, True, True),
Interval(1, s, True, True))
assert reduce_rational_inequalities(
[[Lt(x**2 - 2, 0), Ne(x**2 - 1, 0)]], x,
relational=False) == Union(Interval(-s, -1, True, True),
Interval(-1, 1, True, True),
Interval(1, s, True, True))
def test_issue_11045():
assert integrate(1 / (x * sqrt(x**2 - 1)), (x, 1, 2)) == pi / 3
# handle And with Or arguments
assert Piecewise((1, And(Or(x < 1, x > 3), x < 2)), (0, True)).integrate(
(x, 0, 3)) == 1
# hidden false
assert Piecewise((1, x > 1), (2, x > x + 1), (3, True)).integrate(
(x, 0, 3)) == 5
# targetcond is Eq
assert Piecewise((1, x > 1), (2, Eq(1, x)), (3, True)).integrate(
(x, 0, 4)) == 6
# And has Relational needing to be solved
assert Piecewise((1, And(2 * x > x + 1, x < 2)), (0, True)).integrate(
(x, 0, 3)) == 1
# Or has Relational needing to be solved
assert Piecewise((1, Or(2 * x > x + 2, x < 1)), (0, True)).integrate(
(x, 0, 3)) == 2
# ignore hidden false (handled in canonicalization)
assert Piecewise((1, x > 1), (2, x > x + 1), (3, True)).integrate(
(x, 0, 3)) == 5
# watch for hidden True Piecewise
assert Piecewise((2, Eq(1 - x, x * (1 / x - 1))), (0, True)).integrate(
(x, 0, 3)) == 6
# overlapping conditions of targetcond are recognized and ignored;
# the condition x > 3 will be pre-empted by the first condition
assert Piecewise((1, Or(x < 1, x > 2)), (2, x > 3), (3, True)).integrate(
(x, 0, 4)) == 6
# convert Ne to Or
assert Piecewise((1, Ne(x, 0)), (2, True)).integrate((x, -1, 1)) == 2
# no default but well defined
assert Piecewise((x, (x > 1) & (x < 3)), (1, (x < 4))).integrate(
(x, 1, 4)) == 5
p = Piecewise((x, (x > 1) & (x < 3)), (1, (x < 4)))
nan = Undefined
i = p.integrate((x, 1, y))
assert i == Piecewise(
(y - 1, y < 1),
(Min(3, y)**2 / 2 - Min(3, y) + Min(4, y) - S(1) / 2, y <= Min(4, y)),
(nan, True))
assert p.integrate((x, 1, -1)) == i.subs(y, -1)
assert p.integrate((x, 1, 4)) == 5
assert p.integrate((x, 1, 5)) == nan
# handle Not
p = Piecewise((1, x > 1), (2, Not(And(x > 1, x < 3))), (3, True))
assert p.integrate((x, 0, 3)) == 4
# handle updating of int_expr when there is overlap
p = Piecewise((1, And(5 > x, x > 1)), (2, Or(x < 3, x > 7)), (4, x < 8))
assert p.integrate((x, 0, 10)) == 20
# And with Eq arg handling
assert Piecewise((1, x < 1), (2, And(Eq(x, 3), x > 1))).integrate(
(x, 0, 3)) == S.NaN
assert Piecewise((1, x < 1), (2, And(Eq(x, 3), x > 1)),
(3, True)).integrate((x, 0, 3)) == 7
assert Piecewise((1, x < 0), (2, And(Eq(x, 3), x < 1)),
(3, True)).integrate((x, -1, 1)) == 4
# middle condition doesn't matter: it's a zero width interval
assert Piecewise((1, x < 1), (2, Eq(x, 3) & (y < x)), (3, True)).integrate(
(x, 0, 3)) == 7
def test_issue_12251():
assert manualintegrate(x**y, x) == Piecewise(
(x**(y + 1) / (y + 1), Ne(y, -1)), (log(x), True))
def test_frac():
assert isinstance(frac(x), frac)
assert frac(oo) == AccumBounds(0, 1)
assert frac(-oo) == AccumBounds(0, 1)
assert frac(zoo) is nan
assert frac(n) == 0
assert frac(nan) == nan
assert frac(Rational(4, 3)) == Rational(1, 3)
assert frac(-Rational(4, 3)) == Rational(2, 3)
r = Symbol('r', real=True)
assert frac(I * r) == I * frac(r)
assert frac(1 + I * r) == I * frac(r)
assert frac(0.5 + I * r) == 0.5 + I * frac(r)
assert frac(n + I * r) == I * frac(r)
assert frac(n + I * k) == 0
assert unchanged(frac, x + I * x)
assert frac(x + I * n) == frac(x)
assert frac(x).rewrite(floor) == x - floor(x)
assert frac(x).rewrite(ceiling) == x + ceiling(-x)
assert frac(y).rewrite(floor).subs(y, pi) == frac(pi)
assert frac(y).rewrite(floor).subs(y, -E) == frac(-E)
assert frac(y).rewrite(ceiling).subs(y, -pi) == frac(-pi)
assert frac(y).rewrite(ceiling).subs(y, E) == frac(E)
assert Eq(frac(y), y - floor(y))
assert Eq(frac(y), y + ceiling(-y))
r = Symbol('r', real=True)
p_i = Symbol('p_i', integer=True, positive=True)
n_i = Symbol('p_i', integer=True, negative=True)
np_i = Symbol('np_i', integer=True, nonpositive=True)
nn_i = Symbol('nn_i', integer=True, nonnegative=True)
p_r = Symbol('p_r', real=True, positive=True)
n_r = Symbol('n_r', real=True, negative=True)
np_r = Symbol('np_r', real=True, nonpositive=True)
nn_r = Symbol('nn_r', real=True, nonnegative=True)
# Real frac argument, integer rhs
assert frac(r) <= p_i
assert not frac(r) <= n_i
assert (frac(r) <= np_i).has(Le)
assert (frac(r) <= nn_i).has(Le)
assert frac(r) < p_i
assert not frac(r) < n_i
assert not frac(r) < np_i
assert (frac(r) < nn_i).has(Lt)
assert not frac(r) >= p_i
assert frac(r) >= n_i
assert frac(r) >= np_i
assert (frac(r) >= nn_i).has(Ge)
assert not frac(r) > p_i
assert frac(r) > n_i
assert (frac(r) > np_i).has(Gt)
assert (frac(r) > nn_i).has(Gt)
assert not Eq(frac(r), p_i)
assert not Eq(frac(r), n_i)
assert Eq(frac(r), np_i).has(Eq)
assert Eq(frac(r), nn_i).has(Eq)
assert Ne(frac(r), p_i)
assert Ne(frac(r), n_i)
assert Ne(frac(r), np_i).has(Ne)
assert Ne(frac(r), nn_i).has(Ne)
# Real frac argument, real rhs
assert (frac(r) <= p_r).has(Le)
assert not frac(r) <= n_r
assert (frac(r) <= np_r).has(Le)
assert (frac(r) <= nn_r).has(Le)
assert (frac(r) < p_r).has(Lt)
assert not frac(r) < n_r
assert not frac(r) < np_r
assert (frac(r) < nn_r).has(Lt)
assert (frac(r) >= p_r).has(Ge)
assert frac(r) >= n_r
assert frac(r) >= np_r
assert (frac(r) >= nn_r).has(Ge)
assert (frac(r) > p_r).has(Gt)
assert frac(r) > n_r
assert (frac(r) > np_r).has(Gt)
assert (frac(r) > nn_r).has(Gt)
assert not Eq(frac(r), n_r)
assert Eq(frac(r), p_r).has(Eq)
assert Eq(frac(r), np_r).has(Eq)
assert Eq(frac(r), nn_r).has(Eq)
assert Ne(frac(r), p_r).has(Ne)
assert Ne(frac(r), n_r)
assert Ne(frac(r), np_r).has(Ne)
assert Ne(frac(r), nn_r).has(Ne)
# Real frac argument, +/- oo rhs
assert frac(r) < oo
assert frac(r) <= oo
assert not frac(r) > oo
assert not frac(r) >= oo
assert not frac(r) < -oo
assert not frac(r) <= -oo
assert frac(r) > -oo
assert frac(r) >= -oo
assert frac(r) < 1
assert frac(r) <= 1
assert not frac(r) > 1
assert not frac(r) >= 1
assert not frac(r) < 0
assert (frac(r) <= 0).has(Le)
assert (frac(r) > 0).has(Gt)
assert frac(r) >= 0
# Some test for numbers
assert frac(r) <= sqrt(2)
assert (frac(r) <= sqrt(3) - sqrt(2)).has(Le)
assert not frac(r) <= sqrt(2) - sqrt(3)
assert not frac(r) >= sqrt(2)
assert (frac(r) >= sqrt(3) - sqrt(2)).has(Ge)
assert frac(r) >= sqrt(2) - sqrt(3)
assert not Eq(frac(r), sqrt(2))
assert Eq(frac(r), sqrt(3) - sqrt(2)).has(Eq)
assert not Eq(frac(r), sqrt(2) - sqrt(3))
assert Ne(frac(r), sqrt(2))
assert Ne(frac(r), sqrt(3) - sqrt(2)).has(Ne)
assert Ne(frac(r), sqrt(2) - sqrt(3))
assert frac(p_i, evaluate=False).is_zero
assert frac(p_i, evaluate=False).is_finite
assert frac(p_i, evaluate=False).is_integer
assert frac(p_i, evaluate=False).is_real
assert frac(r).is_finite
assert frac(r).is_real
assert frac(r).is_zero is None
assert frac(r).is_integer is None
assert frac(oo).is_finite
assert frac(oo).is_real
def test_eval_rewrite_as_KroneckerDelta():
x, y, z, n, t, m = symbols('x y z n t m')
K = KroneckerDelta
f = lambda p: expand(p.rewrite(K))
p1 = Piecewise((0, Eq(x, y)), (1, True))
assert f(p1) == 1 - K(x, y)
p2 = Piecewise((x, Eq(y,0)), (z, Eq(t,0)), (n, True))
assert f(p2) == n*K(0, t)*K(0, y) - n*K(0, t) - n*K(0, y) + n + \
x*K(0, y) - z*K(0, t)*K(0, y) + z*K(0, t)
p3 = Piecewise((1, Ne(x, y)), (0, True))
assert f(p3) == 1 - K(x, y)
p4 = Piecewise((1, Eq(x, 3)), (4, True), (5, True))
assert f(p4) == 4 - 3*K(3, x)
p5 = Piecewise((3, Ne(x, 2)), (4, Eq(y, 2)), (5, True))
assert f(p5) == -K(2, x)*K(2, y) + 2*K(2, x) + 3
p6 = Piecewise((0, Ne(x, 1) & Ne(y, 4)), (1, True))
assert f(p6) == -K(1, x)*K(4, y) + K(1, x) + K(4, y)
p7 = Piecewise((2, Eq(y, 3) & Ne(x, 2)), (1, True))
assert f(p7) == -K(2, x)*K(3, y) + K(3, y) + 1
p8 = Piecewise((4, Eq(x, 3) & Ne(y, 2)), (1, True))
assert f(p8) == -3*K(2, y)*K(3, x) + 3*K(3, x) + 1
p9 = Piecewise((6, Eq(x, 4) & Eq(y, 1)), (1, True))
assert f(p9) == 5 * K(1, y) * K(4, x) + 1
p10 = Piecewise((4, Ne(x, -4) | Ne(y, 1)), (1, True))
assert f(p10) == -3 * K(-4, x) * K(1, y) + 4
p11 = Piecewise((1, Eq(y, 2) | Ne(x, -3)), (2, True))
assert f(p11) == -K(-3, x)*K(2, y) + K(-3, x) + 1
p12 = Piecewise((-1, Eq(x, 1) | Ne(y, 3)), (1, True))
assert f(p12) == -2*K(1, x)*K(3, y) + 2*K(3, y) - 1
p13 = Piecewise((3, Eq(x, 2) | Eq(y, 4)), (1, True))
assert f(p13) == -2*K(2, x)*K(4, y) + 2*K(2, x) + 2*K(4, y) + 1
p14 = Piecewise((1, Ne(x, 0) | Ne(y, 1)), (3, True))
assert f(p14) == 2 * K(0, x) * K(1, y) + 1
p15 = Piecewise((2, Eq(x, 3) | Ne(y, 2)), (3, Eq(x, 4) & Eq(y, 5)), (1, True))
assert f(p15) == -2*K(2, y)*K(3, x)*K(4, x)*K(5, y) + K(2, y)*K(3, x) + \
2*K(2, y)*K(4, x)*K(5, y) - K(2, y) + 2
p16 = Piecewise((0, Ne(m, n)), (1, True))*Piecewise((0, Ne(n, t)), (1, True))\
*Piecewise((0, Ne(n, x)), (1, True)) - Piecewise((0, Ne(t, x)), (1, True))
assert f(p16) == K(m, n)*K(n, t)*K(n, x) - K(t, x)
p17 = Piecewise((0, Ne(t, x) & (Ne(m, n) | Ne(n, t) | Ne(n, x))),
(1, Ne(t, x)), (-1, Ne(m, n) | Ne(n, t) | Ne(n, x)), (0, True))
assert f(p17) == K(m, n)*K(n, t)*K(n, x) - K(t, x)
p18 = Piecewise((-4, Eq(y, 1) | (Eq(x, -5) & Eq(x, z))), (4, True))
assert f(p18) == 8*K(-5, x)*K(1, y)*K(x, z) - 8*K(-5, x)*K(x, z) - 8*K(1, y) + 4
p19 = Piecewise((0, x > 2), (1, True))
assert f(p19) == p19
p20 = Piecewise((0, And(x < 2, x > -5)), (1, True))
assert f(p20) == p20
p21 = Piecewise((0, Or(x > 1, x < 0)), (1, True))
assert f(p21) == p21
p22 = Piecewise((0, ~((Eq(y, -1) | Ne(x, 0)) & (Ne(x, 1) | Ne(y, -1)))), (1, True))
assert f(p22) == K(-1, y)*K(0, x) - K(-1, y)*K(1, x) - K(0, x) + 1
def test_manualintegrate_inversetrig():
# atan
assert manualintegrate(exp(x) / (1 + exp(2 * x)), x) == atan(exp(x))
assert manualintegrate(1 / (4 + 9 * x**2), x) == atan(3 * x / 2) / 6
assert manualintegrate(1 / (16 + 16 * x**2), x) == atan(x) / 16
assert manualintegrate(1 / (4 + x**2), x) == atan(x / 2) / 2
assert manualintegrate(1 / (1 + 4 * x**2), x) == atan(2 * x) / 2
ra = Symbol('a', real=True)
rb = Symbol('b', real=True)
assert manualintegrate(1/(ra + rb*x**2), x) == \
Piecewise((atan(x/sqrt(ra/rb))/(rb*sqrt(ra/rb)), ra/rb > 0),
(-acoth(x/sqrt(-ra/rb))/(rb*sqrt(-ra/rb)), And(ra/rb < 0, x**2 > -ra/rb)),
(-atanh(x/sqrt(-ra/rb))/(rb*sqrt(-ra/rb)), And(ra/rb < 0, x**2 < -ra/rb)))
assert manualintegrate(1/(4 + rb*x**2), x) == \
Piecewise((atan(x/(2*sqrt(1/rb)))/(2*rb*sqrt(1/rb)), 4/rb > 0),
(-acoth(x/(2*sqrt(-1/rb)))/(2*rb*sqrt(-1/rb)), And(4/rb < 0, x**2 > -4/rb)),
(-atanh(x/(2*sqrt(-1/rb)))/(2*rb*sqrt(-1/rb)), And(4/rb < 0, x**2 < -4/rb)))
assert manualintegrate(1/(ra + 4*x**2), x) == \
Piecewise((atan(2*x/sqrt(ra))/(2*sqrt(ra)), ra/4 > 0),
(-acoth(2*x/sqrt(-ra))/(2*sqrt(-ra)), And(ra/4 < 0, x**2 > -ra/4)),
(-atanh(2*x/sqrt(-ra))/(2*sqrt(-ra)), And(ra/4 < 0, x**2 < -ra/4)))
assert manualintegrate(1 / (4 + 4 * x**2), x) == atan(x) / 4
assert manualintegrate(1 / (a + b * x**2),
x) == atan(x / sqrt(a / b)) / (b * sqrt(a / b))
# asin
assert manualintegrate(1 / sqrt(1 - x**2), x) == asin(x)
assert manualintegrate(1 / sqrt(4 - 4 * x**2), x) == asin(x) / 2
assert manualintegrate(3 / sqrt(1 - 9 * x**2), x) == asin(3 * x)
assert manualintegrate(1 / sqrt(4 - 9 * x**2),
x) == asin(x * Rational(3, 2)) / 3
# asinh
assert manualintegrate(1/sqrt(x**2 + 1), x) == \
asinh(x)
assert manualintegrate(1/sqrt(x**2 + 4), x) == \
asinh(x/2)
assert manualintegrate(1/sqrt(4*x**2 + 4), x) == \
asinh(x)/2
assert manualintegrate(1/sqrt(4*x**2 + 1), x) == \
asinh(2*x)/2
assert manualintegrate(1/sqrt(a*x**2 + 1), x) == \
Piecewise((sqrt(-1/a)*asin(x*sqrt(-a)), a < 0), (sqrt(1/a)*asinh(sqrt(a)*x), a > 0))
assert manualintegrate(1/sqrt(a + x**2), x) == \
Piecewise((asinh(x*sqrt(1/a)), a > 0), (acosh(x*sqrt(-1/a)), a < 0))
# acosh
assert manualintegrate(1/sqrt(x**2 - 1), x) == \
acosh(x)
assert manualintegrate(1/sqrt(x**2 - 4), x) == \
acosh(x/2)
assert manualintegrate(1/sqrt(4*x**2 - 4), x) == \
acosh(x)/2
assert manualintegrate(1/sqrt(9*x**2 - 1), x) == \
acosh(3*x)/3
assert manualintegrate(1/sqrt(a*x**2 - 4), x) == \
Piecewise((sqrt(1/a)*acosh(sqrt(a)*x/2), a > 0))
assert manualintegrate(1/sqrt(-a + 4*x**2), x) == \
Piecewise((asinh(2*x*sqrt(-1/a))/2, -a > 0), (acosh(2*x*sqrt(1/a))/2, -a < 0))
# From https://www.wikiwand.com/en/List_of_integrals_of_inverse_trigonometric_functions
# asin
assert manualintegrate(asin(x), x) == x * asin(x) + sqrt(1 - x**2)
assert manualintegrate(asin(a * x), x) == Piecewise(
((a * x * asin(a * x) + sqrt(-a**2 * x**2 + 1)) / a, Ne(a, 0)),
(0, True))
assert manualintegrate(x * asin(a * x), x) == -a * Integral(
x**2 / sqrt(-a**2 * x**2 + 1), x) / 2 + x**2 * asin(a * x) / 2
# acos
assert manualintegrate(acos(x), x) == x * acos(x) - sqrt(1 - x**2)
assert manualintegrate(acos(a * x), x) == Piecewise(
((a * x * acos(a * x) - sqrt(-a**2 * x**2 + 1)) / a, Ne(a, 0)),
(pi * x / 2, True))
assert manualintegrate(x * acos(a * x), x) == a * Integral(
x**2 / sqrt(-a**2 * x**2 + 1), x) / 2 + x**2 * acos(a * x) / 2
# atan
assert manualintegrate(atan(x), x) == x * atan(x) - log(x**2 + 1) / 2
assert manualintegrate(atan(a * x), x) == Piecewise(
((a * x * atan(a * x) - log(a**2 * x**2 + 1) / 2) / a, Ne(a, 0)),
(0, True))
assert manualintegrate(
x * atan(a * x),
x) == -a * (x / a**2 - atan(x / sqrt(a**(-2))) /
(a**4 * sqrt(a**(-2)))) / 2 + x**2 * atan(a * x) / 2
# acsc
assert manualintegrate(
acsc(x), x) == x * acsc(x) + Integral(1 / (x * sqrt(1 - 1 / x**2)), x)
assert manualintegrate(
acsc(a * x),
x) == x * acsc(a * x) + Integral(1 / (x * sqrt(1 - 1 /
(a**2 * x**2))), x) / a
assert manualintegrate(x * acsc(a * x),
x) == x**2 * acsc(a * x) / 2 + Integral(
1 / sqrt(1 - 1 / (a**2 * x**2)), x) / (2 * a)
# asec
assert manualintegrate(
asec(x), x) == x * asec(x) - Integral(1 / (x * sqrt(1 - 1 / x**2)), x)
assert manualintegrate(
asec(a * x),
x) == x * asec(a * x) - Integral(1 / (x * sqrt(1 - 1 /
(a**2 * x**2))), x) / a
assert manualintegrate(x * asec(a * x),
x) == x**2 * asec(a * x) / 2 - Integral(
1 / sqrt(1 - 1 / (a**2 * x**2)), x) / (2 * a)
# acot
assert manualintegrate(acot(x), x) == x * acot(x) + log(x**2 + 1) / 2
assert manualintegrate(acot(a * x), x) == Piecewise(
((a * x * acot(a * x) + log(a**2 * x**2 + 1) / 2) / a, Ne(a, 0)),
(pi * x / 2, True))
assert manualintegrate(
x * acot(a * x),
x) == a * (x / a**2 - atan(x / sqrt(a**(-2))) /
(a**4 * sqrt(a**(-2)))) / 2 + x**2 * acot(a * x) / 2
# piecewise
assert manualintegrate(1/sqrt(a-b*x**2), x) == \
Piecewise((sqrt(a/b)*asin(x*sqrt(b/a))/sqrt(a), And(-b < 0, a > 0)),
(sqrt(-a/b)*asinh(x*sqrt(-b/a))/sqrt(a), And(-b > 0, a > 0)),
(sqrt(a/b)*acosh(x*sqrt(b/a))/sqrt(-a), And(-b > 0, a < 0)))
assert manualintegrate(1/sqrt(a + b*x**2), x) == \
Piecewise((sqrt(-a/b)*asin(x*sqrt(-b/a))/sqrt(a), And(a > 0, b < 0)),
(sqrt(a/b)*asinh(x*sqrt(b/a))/sqrt(a), And(a > 0, b > 0)),
(sqrt(-a/b)*acosh(x*sqrt(-b/a))/sqrt(-a), And(a < 0, b > 0)))
# Declare any required symbolic variables
from sympy import symbols, Eq, Ne
from util import constraint, displayBoard
X = symbols("X:2")
Y = symbols("Y:2")
# Define diffRow and diffDiag constraints
diffRow = constraint("diffRow", Ne(X[0], X[1]))
diffDiag = constraint("diffDiag", Ne(abs(X[0] - X[1]), abs(Y[0] - Y[1])))
class NQueensCSP:
"""CSP representation of the N-queens problem
Parameters
----------
N : Integer
The side length of a square chess board to use for the problem, and
the number of queens that must be placed on the board
"""
def __init__(self, N):
_vars = symbols('x:%d' % N)
_domain = set(range(N))
self.size = N
self.variables = _vars
self.domains = {v: _domain for v in _vars}
self._constraints = {x: set() for x in _vars}
print(self.variables)
def test_reduce_poly_inequalities_real_relational():
x = Symbol('x', real=True)
y = Symbol('y', real=True)
assert reduce_rational_inequalities([[Eq(x**2, 0)]], x,
relational=True) == Eq(x, 0)
assert reduce_rational_inequalities([[Le(x**2, 0)]], x,
relational=True) == Eq(x, 0)
assert reduce_rational_inequalities([[Lt(x**2, 0)]], x,
relational=True) == False
assert reduce_rational_inequalities([[Ge(x**2, 0)]], x,
relational=True) == And(
Lt(-oo, x), Lt(x, oo))
assert reduce_rational_inequalities([[Gt(x**2, 0)]], x,
relational=True) == Or(
And(Lt(-oo, x), Lt(x, 0)),
And(Lt(0, x), Lt(x, oo)))
assert reduce_rational_inequalities([[Ne(x**2, 0)]], x,
relational=True) == Or(
And(Lt(-oo, x), Lt(x, 0)),
And(Lt(0, x), Lt(x, oo)))
assert reduce_rational_inequalities([[Eq(x**2, 1)]], x,
relational=True) == Or(
Eq(x, -1), Eq(x, 1))
assert reduce_rational_inequalities([[Le(x**2, 1)]], x,
relational=True) == And(
Le(-1, x), Le(x, 1))
assert reduce_rational_inequalities([[Lt(x**2, 1)]], x,
relational=True) == And(
Lt(-1, x), Lt(x, 1))
assert reduce_rational_inequalities([[Ge(x**2, 1)]], x,
relational=True) == Or(
And(Le(1, x), Lt(x, oo)),
And(Le(x, -1), Lt(-oo, x)))
assert reduce_rational_inequalities([[Gt(x**2, 1)]], x,
relational=True) == Or(
And(Lt(1, x), Lt(x, oo)),
And(Lt(x, -1), Lt(-oo, x)))
assert reduce_rational_inequalities([[Ne(x**2, 1)]], x,
relational=True) == Or(
And(Lt(-oo, x), Lt(x, -1)),
And(Lt(-1, x), Lt(x, 1)),
And(Lt(1, x), Lt(x, oo)))
assert reduce_rational_inequalities([[Le(x**2, 1.0)]], x,
relational=True) == And(
Le(-1.0, x), Le(x, 1.0))
assert reduce_rational_inequalities([[Lt(x**2, 1.0)]], x,
relational=True) == And(
Lt(-1.0, x), Lt(x, 1.0))
assert reduce_rational_inequalities([[Ge(x**2, 1.0)]], x,
relational=True) == Or(
And(Lt(Float('-inf'), x),
Le(x, -1.0)),
And(Le(1.0, x),
Lt(x, Float('+inf'))))
assert reduce_rational_inequalities([[Gt(x**2, 1.0)]], x,
relational=True) == Or(
And(Lt(Float('-inf'), x),
Lt(x, -1.0)),
And(Lt(1.0, x),
Lt(x, Float('+inf'))))
assert reduce_rational_inequalities([[Ne(x**2, 1.0)]], x, relational=True) == \
Or(And(Lt(-1.0, x), Lt(x, 1.0)), And(Lt(Float('-inf'), x), Lt(x, -1.0)),
And(Lt(1.0, x), Lt(x, Float('+inf'))))
def __ne__(self, other):
return Ne(self.symbol, _param_to_symbol(other))
def test_DiscreteMarkovChain():
# pass only the name
X = DiscreteMarkovChain("X")
assert isinstance(X.state_space, Range)
assert isinstance(X.index_of, Range)
assert not X._is_numeric
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(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 = symbols('a', real=True)
state_spaces = [(1, 2, 3), [Str('Hello'), sym, DiscreteMarkovChain],
Tuple(1, exp(sym), Str('World'), sympify=False),
Range(-1, 7, 2)]
chains = [
DiscreteMarkovChain("Y", state_spaces[0]),
DiscreteMarkovChain("Y", state_spaces[1]),
DiscreteMarkovChain("Y", state_spaces[2])
]
for i, Y in enumerate(chains):
assert isinstance(Y.transition_probabilities, MatrixSymbol)
assert Y.state_space == Tuple(*state_spaces[i])
assert Y.number_of_states == 3
assert not Y._is_numeric # because no transition matrix is provided
assert Y.index_of[state_spaces[i][0]] == 0
assert Y.index_of[state_spaces[i][1]] == 1
assert Y.index_of[state_spaces[i][2]] == 2
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)
raises(NotImplementedError, lambda: Y.index_of)
# 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, ImmutableDenseMatrix)
assert isinstance(Z.state_space, Tuple)
assert Z._is_numeric
# 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 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 (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)) 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)
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._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_probabilities() == 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)))
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)))
def gen_constraints(self, input_expr: MutableMapping[int, Expr],
output_expr: MutableMapping[int, Expr]) -> Set[Rel]:
constraints = []
if 1 in input_expr:
constraints.append(Ne(input_expr[1], 0))
return constraints
def expr(self, p):
return Ne(p.expr0, p.expr1)
def test__solve_inequalities():
assert reduce_inequalities(x + y < 1, symbols=[x]) == (x < 1 - y)
assert reduce_inequalities(x + y >= 1, symbols=[x]) == (x < oo) & (x >= -y + 1)
assert reduce_inequalities(Eq(0, x - y), symbols=[x]) == Eq(x, y)
assert reduce_inequalities(Ne(0, x - y), symbols=[x]) == Ne(x, y)
def test_integrate_returns_piecewise():
assert integrate(x**y, x) == Piecewise(
(x**(y + 1)/(y + 1), Ne(y, -1)), (log(x), True))
assert integrate(x**y, y) == Piecewise(
(x**y/log(x), Ne(log(x), 0)), (y, True))
assert integrate(exp(n*x), x) == Piecewise(
(exp(n*x)/n, Ne(n, 0)), (x, True))
assert integrate(x*exp(n*x), x) == Piecewise(
((n**2*x - n)*exp(n*x)/n**3, Ne(n**3, 0)), (x**2/2, True))
assert integrate(x**(n*y), x) == Piecewise(
(x**(n*y + 1)/(n*y + 1), Ne(n*y, -1)), (log(x), True))
assert integrate(x**(n*y), y) == Piecewise(
(x**(n*y)/(n*log(x)), Ne(n*log(x), 0)), (y, True))
assert integrate(cos(n*x), x) == Piecewise(
(sin(n*x)/n, Ne(n, 0)), (x, True))
assert integrate(cos(n*x)**2, x) == Piecewise(
((n*x/2 + sin(n*x)*cos(n*x)/2)/n, Ne(n, 0)), (x, True))
assert integrate(x*cos(n*x), x) == Piecewise(
(x*sin(n*x)/n + cos(n*x)/n**2, Ne(n, 0)), (x**2/2, True))
assert integrate(sin(n*x), x) == Piecewise(
(-cos(n*x)/n, Ne(n, 0)), (0, True))
assert integrate(sin(n*x)**2, x) == Piecewise(
((n*x/2 - sin(n*x)*cos(n*x)/2)/n, Ne(n, 0)), (0, True))
assert integrate(x*sin(n*x), x) == Piecewise(
(-x*cos(n*x)/n + sin(n*x)/n**2, Ne(n, 0)), (0, True))
assert integrate(exp(x*y), (x, 0, z)) == Piecewise(
(exp(y*z)/y - 1/y, (y > -oo) & (y < oo) & Ne(y, 0)), (z, True))
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
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))
# 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))))
# 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 test_reduce_poly_inequalities_real_relational():
with assuming(Q.real(x), Q.real(y)):
assert reduce_rational_inequalities([[Eq(x**2, 0)]],
x,
relational=True) == Eq(x, 0)
assert reduce_rational_inequalities([[Le(x**2, 0)]],
x,
relational=True) == Eq(x, 0)
assert reduce_rational_inequalities(
[[Lt(x**2, 0)]], x, relational=True) is False
assert reduce_rational_inequalities(
[[Ge(x**2, 0)]], x, relational=True) is True
assert reduce_rational_inequalities([[Gt(x**2, 0)]],
x,
relational=True) == Or(
Lt(x, 0), Lt(0, x))
assert reduce_rational_inequalities([[Ne(x**2, 0)]],
x,
relational=True) == Or(
Lt(x, 0), Lt(0, x))
assert reduce_rational_inequalities([[Eq(x**2, 1)]],
x,
relational=True) == Or(
Eq(x, -1), Eq(x, 1))
assert reduce_rational_inequalities([[Le(x**2, 1)]],
x,
relational=True) == And(
Le(-1, x), Le(x, 1))
assert reduce_rational_inequalities([[Lt(x**2, 1)]],
x,
relational=True) == And(
Lt(-1, x), Lt(x, 1))
assert reduce_rational_inequalities([[Ge(x**2, 1)]],
x,
relational=True) == Or(
Le(x, -1), Le(1, x))
assert reduce_rational_inequalities([[Gt(x**2, 1)]],
x,
relational=True) == Or(
Lt(x, -1), Lt(1, x))
assert reduce_rational_inequalities([[Ne(x**2, 1)]],
x,
relational=True) == Or(
Lt(x, -1),
And(Lt(-1, x), Lt(x, 1)),
Lt(1, x))
assert reduce_rational_inequalities([[Le(x**2, 1.0)]],
x,
relational=True) == And(
Le(-1.0, x), Le(x, 1.0))
assert reduce_rational_inequalities([[Lt(x**2, 1.0)]],
x,
relational=True) == And(
Lt(-1.0, x), Lt(x, 1.0))
assert reduce_rational_inequalities([[Ge(x**2, 1.0)]],
x,
relational=True) == Or(
Le(x, -1.0), Le(1.0, x))
assert reduce_rational_inequalities([[Gt(x**2, 1.0)]],
x,
relational=True) == Or(
Lt(x, -1.0), Lt(1.0, x))
assert reduce_rational_inequalities([[Ne(x**2, 1.0)]],
x,
relational=True) == Or(
Lt(x, -1.0),
And(Lt(-1.0, x), Lt(x, 1.0)),
Lt(1.0, x))
#/* 366 */ (a*y*y(x).diff(x)+x)*f(a*y**2+x**2)-x*y(x).diff(x)-y, #/* 367 */ (b*x*y(x).diff(x)-a)*f(x**c*y)-x**a*y**b*(x*y(x).diff(x)+c*y) , ] # add the solution here solution_kamke1=[ 0, #kamke1.1 Eq(y, C1 + Integral(1/sqrt(a0 + a1*x + a2*x**2 + a3*x**3 + a4*x**4), x)), #kamke1.2 Eq(y, (C1 + c*Piecewise((exp(a*x)*exp(b*x)/(a + b), Ne(a, -b)), (x, True)))*exp(-a*x)), #kamke1.3 Eq(y, (C1 + b*Piecewise((0, Eq(a, 0) & Eq(c, 0)), (x*exp(-I*c*x)*sin(c*x)/2 - I*x*exp(-I*c*x)*cos(c*x)/2 - exp(-I*c*x)*cos(c*x)/(2*c), Eq(a, -I*c)), (x*exp(I*c*x)*sin(c*x)/2 + I*x*exp(I*c*x)*cos(c*x)/2 - exp(I*c*x)*cos(c*x)/(2*c), Eq(a, I*c)), (a*exp(a*x)*sin(c*x)/(a**2 + c**2) - c*exp(a*x)*cos(c*x)/(a**2 + c**2), True)))*exp(-a*x)), #kamke1.4 Eq(y, (C1 + x**2/2)*exp(-x**2)), #kamke1.5 Eq(Integral((y*cos(x) - exp(2*x))*exp(sin(x)), x), C1), #kamke1.6 Eq(y, C1*exp(-sin(x)) + sin(x) - 1), #kamke1.7 Eq(y, (C1 + x)*exp(-sin(x))), #kamke1.8 Eq(y, (C1 - 2*cos(x))*cos(x)), #kamke1.9 Eq(y, C1*exp(x*(a + sin(log(x))))), #kamke1.10
def test_hacky_inequalities():
assert reduce_inequalities(x + y < 1, symbols=[x]) == (x < 1 - y)
assert reduce_inequalities(x + y >= 1, symbols=[x]) == (x >= 1 - y)
assert reduce_inequalities(Eq(0, x - y), symbols=[x]) == Eq(x, y)
assert reduce_inequalities(Ne(0, x - y), symbols=[x]) == Ne(x, y)
def test_laplace_transform():
from sympy import fresnels, fresnelc
LT = laplace_transform
a, b, c, = symbols('a b c', positive=True)
t = symbols('t')
w = Symbol("w")
f = Function("f")
# Test unevaluated form
assert laplace_transform(f(t), t, w) == LaplaceTransform(f(t), t, w)
assert inverse_laplace_transform(f(w), w, t,
plane=0) == InverseLaplaceTransform(
f(w), w, t, 0)
# test a bug
spos = symbols('s', positive=True)
assert LT(exp(t), t, spos)[:2] == (1 / (spos - 1), 1)
# basic tests from wikipedia
assert LT((t - a)**b*exp(-c*(t - a))*Heaviside(t - a), t, s) == \
((s + c)**(-b - 1)*exp(-a*s)*gamma(b + 1), -c, True)
assert LT(t**a, t, s) == (s**(-a - 1) * gamma(a + 1), 0, True)
assert LT(Heaviside(t), t, s) == (1 / s, 0, True)
assert LT(Heaviside(t - a), t, s) == (exp(-a * s) / s, 0, True)
assert LT(1 - exp(-a * t), t, s) == (a / (s * (a + s)), 0, True)
assert LT((exp(2*t) - 1)*exp(-b - t)*Heaviside(t)/2, t, s, noconds=True) \
== exp(-b)/(s**2 - 1)
assert LT(exp(t), t, s)[:2] == (1 / (s - 1), 1)
assert LT(exp(2 * t), t, s)[:2] == (1 / (s - 2), 2)
assert LT(exp(a * t), t, s)[:2] == (1 / (s - a), a)
assert LT(log(t / a), t,
s) == ((log(a * s) + EulerGamma) / s / -1, 0, True)
assert LT(erf(t), t, s) == (erfc(s / 2) * exp(s**2 / 4) / s, 0, True)
assert LT(sin(a * t), t, s) == (a / (a**2 + s**2), 0, True)
assert LT(cos(a * t), t, s) == (s / (a**2 + s**2), 0, True)
# TODO would be nice to have these come out better
assert LT(exp(-a * t) * sin(b * t), t,
s) == (b / (b**2 + (a + s)**2), -a, True)
assert LT(exp(-a*t)*cos(b*t), t, s) == \
((a + s)/(b**2 + (a + s)**2), -a, True)
assert LT(besselj(0, t), t, s) == (1 / sqrt(1 + s**2), 0, True)
assert LT(besselj(1, t), t, s) == (1 - 1 / sqrt(1 + 1 / s**2), 0, True)
# TODO general order works, but is a *mess*
# TODO besseli also works, but is an even greater mess
# test a bug in conditions processing
# TODO the auxiliary condition should be recognised/simplified
assert LT(exp(t) * cos(t), t, s)[:-1] in [
((s - 1) / (s**2 - 2 * s + 2), -oo),
((s - 1) / ((s - 1)**2 + 1), -oo),
]
# Fresnel functions
assert laplace_transform(fresnels(t), t, s) == \
((-sin(s**2/(2*pi))*fresnels(s/pi) + sin(s**2/(2*pi))/2 -
cos(s**2/(2*pi))*fresnelc(s/pi) + cos(s**2/(2*pi))/2)/s, 0, True)
assert laplace_transform(
fresnelc(t), t,
s) == (((2 * sin(s**2 / (2 * pi)) * fresnelc(s / pi) -
2 * cos(s**2 / (2 * pi)) * fresnels(s / pi) +
sqrt(2) * cos(s**2 / (2 * pi) + pi / 4)) / (2 * s), 0, True))
cond = Ne(1 / s,
1) & (S(0) < cos(Abs(periodic_argument(s, oo))) * Abs(s) - 1)
assert LT(Matrix([[exp(t), t*exp(-t)], [t*exp(-t), exp(t)]]), t, s) ==\
Matrix([
[(1/(s - 1), 1, True), ((s + 1)**(-2), 0, True)],
[((s + 1)**(-2), 0, True), (1/(s - 1), 1, True)]
])
class TestAllGood(object):
# These latex strings should parse to the corresponding SymPy expression
GOOD_PAIRS = [
("0", 0),
("1", 1),
("-3.14", -3.14),
("5-3", _Add(5, -3)),
("(-7.13)(1.5)", _Mul(Rational('-7.13'), Rational('1.5'))),
("\\left(-7.13\\right)\\left(1.5\\right)", _Mul(Rational('-7.13'), Rational('1.5'))),
("x", x),
("2x", 2 * x),
("x^2", x**2),
("x^{3 + 1}", x**_Add(3, 1)),
("x^{\\left\\{3 + 1\\right\\}}", x**_Add(3, 1)),
("-3y + 2x", _Add(_Mul(2, x), Mul(-1, 3, y, evaluate=False))),
("-c", -c),
("a \\cdot b", a * b),
("a / b", a / b),
("a \\div b", a / b),
("a + b", a + b),
("a + b - a", Add(a, b, _Mul(-1, a), evaluate=False)),
("a^2 + b^2 = c^2", Eq(a**2 + b**2, c**2)),
("a^2 + b^2 != 2c^2", Ne(a**2 + b**2, 2 * c**2)),
("a\\mod b", Mod(a, b)),
("\\sin \\theta", sin(theta)),
("\\sin(\\theta)", sin(theta)),
("\\sin\\left(\\theta\\right)", sin(theta)),
("\\sin^{-1} a", asin(a)),
("\\sin a \\cos b", _Mul(sin(a), cos(b))),
("\\sin \\cos \\theta", sin(cos(theta))),
("\\sin(\\cos \\theta)", sin(cos(theta))),
("\\arcsin(a)", asin(a)),
("\\arccos(a)", acos(a)),
("\\arctan(a)", atan(a)),
("\\sinh(a)", sinh(a)),
("\\cosh(a)", cosh(a)),
("\\tanh(a)", tanh(a)),
("\\sinh^{-1}(a)", asinh(a)),
("\\cosh^{-1}(a)", acosh(a)),
("\\tanh^{-1}(a)", atanh(a)),
("\\arcsinh(a)", asinh(a)),
("\\arccosh(a)", acosh(a)),
("\\arctanh(a)", atanh(a)),
("\\arsinh(a)", asinh(a)),
("\\arcosh(a)", acosh(a)),
("\\artanh(a)", atanh(a)),
("\\operatorname{arcsinh}(a)", asinh(a)),
("\\operatorname{arccosh}(a)", acosh(a)),
("\\operatorname{arctanh}(a)", atanh(a)),
("\\operatorname{arsinh}(a)", asinh(a)),
("\\operatorname{arcosh}(a)", acosh(a)),
("\\operatorname{artanh}(a)", atanh(a)),
("\\cos^2(x)", cos(x)**2),
("\\cos(x)^2", cos(x)**2),
("\\frac{a}{b}", a / b),
("\\frac{a + b}{c}", _Mul(a + b, _Pow(c, -1))),
("\\frac{7}{3}", _Mul(7, _Pow(3, -1))),
("(\\csc x)(\\sec y)", csc(x) * sec(y)),
("\\lim_{x \\to 3} a", Limit(a, x, 3)),
("\\lim_{x \\rightarrow 3} a", Limit(a, x, 3)),
("\\lim_{x \\Rightarrow 3} a", Limit(a, x, 3)),
("\\lim_{x \\longrightarrow 3} a", Limit(a, x, 3)),
("\\lim_{x \\Longrightarrow 3} a", Limit(a, x, 3)),
("\\lim_{x \\to 3^{+}} a", Limit(a, x, 3, dir='+')),
("\\lim_{x \\to 3^{-}} a", Limit(a, x, 3, dir='-')),
("\\infty", oo),
("\\infty\\%", oo),
("\\$\\infty", oo),
("-\\infty", -oo),
("-\\infty\\%", -oo),
("-\\$\\infty", -oo),
("\\lim_{x \\to \\infty} \\frac{1}{x}", Limit(_Mul(1, _Pow(x, -1)), x, oo)),
("\\frac{d}{dx} x", Derivative(x, x)),
("\\frac{d}{dt} x", Derivative(x, t)),
# ("f(x)", f(x)),
# ("f(x, y)", f(x, y)),
# ("f(x, y, z)", f(x, y, z)),
# ("\\frac{d f(x)}{dx}", Derivative(f(x), x)),
# ("\\frac{d\\theta(x)}{dx}", Derivative(theta(x), x)),
("|x|", _Abs(x)),
("\\left|x\\right|", _Abs(x)),
("||x||", _Abs(Abs(x))),
("|x||y|", _Abs(x) * _Abs(y)),
("||x||y||", _Abs(_Abs(x) * _Abs(y))),
("\\pi^{|xy|}", pi**_Abs(x * y)),
("\\frac{\\pi}{3}", _Mul(pi, _Pow(3, -1))),
("\\sin{\\frac{\\pi}{2}}", sin(_Mul(pi, _Pow(2, -1)), evaluate=False)),
("a+bI", a + I * b),
("e^{I\\pi}", -1),
("\\int x dx", Integral(x, x)),
("\\int x d\\theta", Integral(x, theta)),
("\\int (x^2 - y)dx", Integral(x**2 - y, x)),
("\\int x + a dx", Integral(_Add(x, a), x)),
("\\int da", Integral(1, a)),
("\\int_0^7 dx", Integral(1, (x, 0, 7))),
("\\int_a^b x dx", Integral(x, (x, a, b))),
("\\int^b_a x dx", Integral(x, (x, a, b))),
("\\int_{a}^b x dx", Integral(x, (x, a, b))),
("\\int^{b}_a x dx", Integral(x, (x, a, b))),
("\\int_{a}^{b} x dx", Integral(x, (x, a, b))),
("\\int_{ }^{}x dx", Integral(x, x)),
("\\int^{ }_{ }x dx", Integral(x, x)),
("\\int^{b}_{a} x dx", Integral(x, (x, a, b))),
# ("\\int_{f(a)}^{f(b)} f(z) dz", Integral(f(z), (z, f(a), f(b)))),
("\\int (x+a)", Integral(_Add(x, a), x)),
("\\int a + b + c dx", Integral(Add(a, b, c, evaluate=False), x)),
("\\int \\frac{dz}{z}", Integral(Pow(z, -1), z)),
("\\int \\frac{3 dz}{z}", Integral(3 * Pow(z, -1), z)),
("\\int \\frac{1}{x} dx", Integral(Pow(x, -1), x)),
("\\int \\frac{1}{a} + \\frac{1}{b} dx", Integral(_Add(_Pow(a, -1), Pow(b, -1)), x)),
("\\int \\frac{3 \\cdot d\\theta}{\\theta}", Integral(3 * _Pow(theta, -1), theta)),
("\\int \\frac{1}{x} + 1 dx", Integral(_Add(_Pow(x, -1), 1), x)),
("x_0", Symbol('x_{0}', real=True)),
("x_{1}", Symbol('x_{1}', real=True)),
("x_a", Symbol('x_{a}', real=True)),
("x_{b}", Symbol('x_{b}', real=True)),
("h_\\theta", Symbol('h_{theta}', real=True)),
("h_{\\theta}", Symbol('h_{theta}', real=True)),
# ("h_{\\theta}(x_0, x_1)", Symbol('h_{theta}', real=True)(Symbol('x_{0}', real=True), Symbol('x_{1}', real=True))),
("x!", _factorial(x)),
("100!", _factorial(100)),
("\\theta!", _factorial(theta)),
("(x + 1)!", _factorial(_Add(x, 1))),
("\\left(x + 1\\right)!", _factorial(_Add(x, 1))),
("(x!)!", _factorial(_factorial(x))),
("x!!!", _factorial(_factorial(_factorial(x)))),
("5!7!", _Mul(_factorial(5), _factorial(7))),
("\\sqrt{x}", sqrt(x)),
("\\sqrt{x + b}", sqrt(_Add(x, b))),
("\\sqrt[3]{\\sin x}", root(sin(x), 3)),
("\\sqrt[y]{\\sin x}", root(sin(x), y)),
("\\sqrt[\\theta]{\\sin x}", root(sin(x), theta)),
("x < y", StrictLessThan(x, y)),
("x \\leq y", LessThan(x, y)),
("x > y", StrictGreaterThan(x, y)),
("x \\geq y", GreaterThan(x, y)),
("\\sum_{k = 1}^{3} c", Sum(c, (k, 1, 3))),
("\\sum_{k = 1}^3 c", Sum(c, (k, 1, 3))),
("\\sum^{3}_{k = 1} c", Sum(c, (k, 1, 3))),
("\\sum^3_{k = 1} c", Sum(c, (k, 1, 3))),
("\\sum_{k = 1}^{10} k^2", Sum(k**2, (k, 1, 10))),
("\\sum_{n = 0}^{\\infty} \\frac{1}{n!}", Sum(_Pow(_factorial(n), -1), (n, 0, oo))),
("\\prod_{a = b}^{c} x", Product(x, (a, b, c))),
("\\prod_{a = b}^c x", Product(x, (a, b, c))),
("\\prod^{c}_{a = b} x", Product(x, (a, b, c))),
("\\prod^c_{a = b} x", Product(x, (a, b, c))),
("\\ln x", _log(x, E)),
("\\ln xy", _log(x * y, E)),
("\\log x", _log(x, 10)),
("\\log xy", _log(x * y, 10)),
# ("\\log_2 x", _log(x, 2)),
("\\log_{2} x", _log(x, 2)),
# ("\\log_a x", _log(x, a)),
("\\log_{a} x", _log(x, a)),
("\\log_{11} x", _log(x, 11)),
("\\log_{a^2} x", _log(x, _Pow(a, 2))),
("[x]", x),
("[a + b]", _Add(a, b)),
("\\frac{d}{dx} [ \\tan x ]", Derivative(tan(x), x)),
("2\\overline{x}", 2 * Symbol('xbar', real=True)),
("2\\overline{x}_n", 2 * Symbol('xbar_{n}', real=True)),
("\\frac{x}{\\overline{x}_n}", x / Symbol('xbar_{n}', real=True)),
("\\frac{\\sin(x)}{\\overline{x}_n}", sin(Symbol('x', real=True)) / Symbol('xbar_{n}', real=True)),
("2\\bar{x}", 2 * Symbol('xbar', real=True)),
("2\\bar{x}_n", 2 * Symbol('xbar_{n}', real=True)),
("\\sin\\left(\\theta\\right) \\cdot4", sin(theta) * 4),
("\\ln\\left(\\theta\\right)", _log(theta, E)),
("\\ln\\left(x-\\theta\\right)", _log(x - theta, E)),
("\\ln\\left(\\left(x-\\theta\\right)\\right)", _log(x - theta, E)),
("\\ln\\left(\\left[x-\\theta\\right]\\right)", _log(x - theta, E)),
("\\ln\\left(\\left\\{x-\\theta\\right\\}\\right)", _log(x - theta, E)),
("\\ln\\left(\\left|x-\\theta\\right|\\right)", _log(_Abs(x - theta), E)),
("\\frac{1}{2}xy(x+y)", Mul(_Pow(2, -1), x, y, (x + y), evaluate=False)),
("\\frac{1}{2}\\theta(x+y)", Mul(_Pow(2, -1), theta, (x + y), evaluate=False)),
("1-f(x)", 1 - f * x),
("\\begin{matrix}1&2\\\\3&4\\end{matrix}", Matrix([[1, 2], [3, 4]])),
("\\begin{matrix}x&x^2\\\\\\sqrt{x}&x\\end{matrix}", Matrix([[x, x**2], [_Pow(x, S.Half), x]])),
("\\begin{matrix}\\sqrt{x}\\\\\\sin(\\theta)\\end{matrix}", Matrix([_Pow(x, S.Half), sin(theta)])),
("\\begin{pmatrix}1&2\\\\3&4\\end{pmatrix}", Matrix([[1, 2], [3, 4]])),
("\\begin{bmatrix}1&2\\\\3&4\\end{bmatrix}", Matrix([[1, 2], [3, 4]])),
# scientific notation
("2.5\\times 10^2", 250),
("1,500\\times 10^{-1}", 150),
# e notation
("2.5E2", 250),
("1,500E-1", 150),
# multiplication without cmd
("2x2y", Mul(2, x, 2, y, evaluate=False)),
("2x2", Mul(2, x, 2, evaluate=False)),
("x2", x * 2),
# lin alg processing
("\\theta\\begin{matrix}1&2\\\\3&4\\end{matrix}", MatMul(theta, Matrix([[1, 2], [3, 4]]), evaluate=False)),
("\\theta\\begin{matrix}1\\\\3\\end{matrix} - \\begin{matrix}-1\\\\2\\end{matrix}", MatAdd(MatMul(theta, Matrix([[1], [3]]), evaluate=False), MatMul(-1, Matrix([[-1], [2]]), evaluate=False), evaluate=False)),
("\\theta\\begin{matrix}1&0\\\\0&1\\end{matrix}*\\begin{matrix}3\\\\-2\\end{matrix}", MatMul(theta, Matrix([[1, 0], [0, 1]]), Matrix([3, -2]), evaluate=False)),
("\\frac{1}{9}\\theta\\begin{matrix}1&2\\\\3&4\\end{matrix}", MatMul(Pow(9, -1, evaluate=False), theta, Matrix([[1, 2], [3, 4]]), evaluate=False)),
("\\begin{pmatrix}1\\\\2\\\\3\\end{pmatrix},\\begin{pmatrix}4\\\\3\\\\1\\end{pmatrix}", [Matrix([1, 2, 3]), Matrix([4, 3, 1])]),
("\\begin{pmatrix}1\\\\2\\\\3\\end{pmatrix};\\begin{pmatrix}4\\\\3\\\\1\\end{pmatrix}", [Matrix([1, 2, 3]), Matrix([4, 3, 1])]),
("\\left\\{\\begin{pmatrix}1\\\\2\\\\3\\end{pmatrix},\\begin{pmatrix}4\\\\3\\\\1\\end{pmatrix}\\right\\}", [Matrix([1, 2, 3]), Matrix([4, 3, 1])]),
("\\left\\{\\begin{pmatrix}1\\\\2\\\\3\\end{pmatrix},\\begin{pmatrix}4\\\\3\\\\1\\end{pmatrix},\\begin{pmatrix}1\\\\1\\\\1\\end{pmatrix}\\right\\}", [Matrix([1, 2, 3]), Matrix([4, 3, 1]), Matrix([1, 1, 1])]),
("\\left\\{\\begin{pmatrix}1\\\\2\\\\3\\end{pmatrix}\\right\\}", Matrix([1, 2, 3])),
("\\left{\\begin{pmatrix}1\\\\2\\\\3\\end{pmatrix}\\right}", Matrix([1, 2, 3])),
("{\\begin{pmatrix}1\\\\2\\\\3\\end{pmatrix}}", Matrix([1, 2, 3])),
# us dollars
("\\$1,000.00", 1000),
("\\$543.21", 543.21),
("\\$0.009", 0.009),
# percentages
("100\\%", 1),
("1.5\\%", 0.015),
("0.05\\%", 0.0005),
# empty set
("\\emptyset", S.EmptySet)
]
def test_good_pair(self, s, eq):
assert_equal(s, eq)
def test_laplace_transform():
from sympy import fresnels, fresnelc, DiracDelta
LT = laplace_transform
a, b, c, = symbols('a b c', positive=True)
t = symbols('t')
w = Symbol("w")
f = Function("f")
# Test unevaluated form
assert laplace_transform(f(t), t, w) == LaplaceTransform(f(t), t, w)
assert inverse_laplace_transform(f(w), w, t,
plane=0) == InverseLaplaceTransform(
f(w), w, t, 0)
# test a bug
spos = symbols('s', positive=True)
assert LT(exp(t), t, spos)[:2] == (1 / (spos - 1), 1)
# basic tests from wikipedia
assert LT((t - a)**b*exp(-c*(t - a))*Heaviside(t - a), t, s) == \
((s + c)**(-b - 1)*exp(-a*s)*gamma(b + 1), -c, True)
assert LT(t**a, t, s) == (s**(-a - 1) * gamma(a + 1), 0, True)
assert LT(Heaviside(t), t, s) == (1 / s, 0, True)
assert LT(Heaviside(t - a), t, s) == (exp(-a * s) / s, 0, True)
assert LT(1 - exp(-a * t), t, s) == (a / (s * (a + s)), 0, True)
assert LT((exp(2*t) - 1)*exp(-b - t)*Heaviside(t)/2, t, s, noconds=True) \
== exp(-b)/(s**2 - 1)
assert LT(exp(t), t, s)[:2] == (1 / (s - 1), 1)
assert LT(exp(2 * t), t, s)[:2] == (1 / (s - 2), 2)
assert LT(exp(a * t), t, s)[:2] == (1 / (s - a), a)
assert LT(log(t / a), t,
s) == ((log(a * s) + EulerGamma) / s / -1, 0, True)
assert LT(erf(t), t, s) == (erfc(s / 2) * exp(s**2 / 4) / s, 0, True)
assert LT(sin(a * t), t, s) == (a / (a**2 + s**2), 0, True)
assert LT(cos(a * t), t, s) == (s / (a**2 + s**2), 0, True)
# TODO would be nice to have these come out better
assert LT(exp(-a * t) * sin(b * t), t,
s) == (b / (b**2 + (a + s)**2), -a, True)
assert LT(exp(-a*t)*cos(b*t), t, s) == \
((a + s)/(b**2 + (a + s)**2), -a, True)
assert LT(besselj(0, t), t, s) == (1 / sqrt(1 + s**2), 0, True)
assert LT(besselj(1, t), t, s) == (1 - 1 / sqrt(1 + 1 / s**2), 0, True)
# TODO general order works, but is a *mess*
# TODO besseli also works, but is an even greater mess
# test a bug in conditions processing
# TODO the auxiliary condition should be recognised/simplified
assert LT(exp(t) * cos(t), t, s)[:-1] in [
((s - 1) / (s**2 - 2 * s + 2), -oo),
((s - 1) / ((s - 1)**2 + 1), -oo),
]
# DiracDelta function: standard cases
assert LT(DiracDelta(t), t, s) == (1, -oo, True)
assert LT(DiracDelta(a * t), t, s) == (1 / a, -oo, True)
assert LT(DiracDelta(t / 42), t, s) == (42, -oo, True)
assert LT(DiracDelta(t + 42), t, s) == (0, -oo, True)
assert LT(DiracDelta(t)+DiracDelta(t-42), t, s) == \
(1 + exp(-42*s), -oo, True)
assert LT(DiracDelta(t) - a * exp(-a * t), t,
s) == (-a / (a + s) + 1, 0, True)
assert LT(exp(-t)*(DiracDelta(t)+DiracDelta(t-42)), t, s) == \
(exp(-42*s - 42) + 1, -oo, True)
# Collection of cases that cannot be fully evaluated and/or would catch
# some common implementation errors
assert LT(DiracDelta(t**2), t,
s) == LaplaceTransform(DiracDelta(t**2), t, s)
assert LT(DiracDelta(t**2 - 1), t, s) == (exp(-s) / 2, -oo, True)
assert LT(DiracDelta(t*(1 - t)), t, s) == \
LaplaceTransform(DiracDelta(-t**2 + t), t, s)
assert LT((DiracDelta(t) + 1)*(DiracDelta(t - 1) + 1), t, s) == \
(LaplaceTransform(DiracDelta(t)*DiracDelta(t - 1), t, s) + \
1 + exp(-s) + 1/s, 0, True)
assert LT(DiracDelta(2*t - 2*exp(a)), t, s) == \
(exp(-s*exp(a))/2, -oo, True)
# Fresnel functions
assert laplace_transform(fresnels(t), t, s) == \
((-sin(s**2/(2*pi))*fresnels(s/pi) + sin(s**2/(2*pi))/2 -
cos(s**2/(2*pi))*fresnelc(s/pi) + cos(s**2/(2*pi))/2)/s, 0, True)
assert laplace_transform(
fresnelc(t), t,
s) == (((2 * sin(s**2 / (2 * pi)) * fresnelc(s / pi) -
2 * cos(s**2 / (2 * pi)) * fresnels(s / pi) +
sqrt(2) * cos(s**2 / (2 * pi) + pi / 4)) / (2 * s), 0, True))
# What is this testing:
Ne(1 / s, 1) & (0 < cos(Abs(periodic_argument(s, oo))) * Abs(s) - 1)
Mt = Matrix([[exp(t), t * exp(-t)], [t * exp(-t), exp(t)]])
Ms = Matrix([[1 / (s - 1), (s + 1)**(-2)], [(s + 1)**(-2), 1 / (s - 1)]])
# The default behaviour for Laplace tranform of a Matrix returns a Matrix
# of Tuples and is deprecated:
with warns_deprecated_sympy():
Ms_conds = Matrix([[(1 / (s - 1), 1, s > 1), ((s + 1)**(-2), 0, True)],
[((s + 1)**(-2), 0, True),
(1 / (s - 1), 1, s > 1)]])
with warns_deprecated_sympy():
assert LT(Mt, t, s) == Ms_conds
# The new behavior is to return a tuple of a Matrix and the convergence
# conditions for the matrix as a whole:
assert LT(Mt, t, s, legacy_matrix=False) == (Ms, 1, s > 1)
# With noconds=True the transformed matrix is returned without conditions
# either way:
assert LT(Mt, t, s, noconds=True) == Ms
assert LT(Mt, t, s, legacy_matrix=False, noconds=True) == Ms
def test_Relational():
assert str(Rel(x, y, "<")) == "x < y"
assert str(Rel(x + y, y, "==")) == "Eq(x + y, y)"
assert str(Rel(x, y, "!=")) == "Ne(x, y)"
assert str(Eq(x, 1) | Eq(x, 2)) == "Eq(x, 1) | Eq(x, 2)"
assert str(Ne(x, 1) & Ne(x, 2)) == "Ne(x, 1) & Ne(x, 2)"
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))
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(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, ImmutableDenseMatrix)
# 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 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 (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._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_probabilities() == ImmutableMatrix(
[[Rational(3, 4), Rational(1, 4)], [S.Half, S.Half],
[Rational(1, 4), Rational(3, 4)]])
# 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 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)
# 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)))
def test_PoissonProcess():
X = PoissonProcess("X", 3)
assert X.state_space == S.Naturals0
assert X.index_set == Interval(0, oo)
assert X.lamda == 3
t, d, x, y = symbols('t d x y', positive=True)
assert isinstance(X(t), RandomIndexedSymbol)
assert X.distribution(X(t)) == PoissonDistribution(3 * t)
raises(ValueError, lambda: PoissonProcess("X", -1))
raises(NotImplementedError, lambda: X[t])
raises(IndexError, lambda: X(-5))
assert X.joint_distribution(X(2), X(3)) == JointDistributionHandmade(
Lambda((X(2), X(3)), 6**X(2) * 9**X(3) * exp(-15) /
(factorial(X(2)) * factorial(X(3)))))
assert X.joint_distribution(4, 6) == JointDistributionHandmade(
Lambda((X(4), X(6)), 12**X(4) * 18**X(6) * exp(-30) /
(factorial(X(4)) * factorial(X(6)))))
assert P(X(t) < 1) == exp(-3 * t)
assert P(Eq(X(t), 0),
Contains(t, Interval.Lopen(3, 5))) == exp(-6) # exp(-2*lamda)
res = P(Eq(X(t), 1), Contains(t, Interval.Lopen(3, 4)))
assert res == 3 * exp(-3)
# Equivalent to P(Eq(X(t), 1))**4 because of non-overlapping intervals
assert P(
Eq(X(t), 1) & Eq(X(d), 1) & Eq(X(x), 1) & Eq(X(y), 1),
Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Lopen(1, 2)) & Contains(x, Interval.Lopen(2, 3))
& Contains(y, Interval.Lopen(3, 4))) == res**4
# Return Probability because of overlapping intervals
assert P(Eq(X(t), 2) & Eq(X(d), 3), Contains(t, Interval.Lopen(0, 2))
& Contains(d, Interval.Ropen(2, 4))) == \
Probability(Eq(X(d), 3) & Eq(X(t), 2), Contains(t, Interval.Lopen(0, 2))
& Contains(d, Interval.Ropen(2, 4)))
raises(ValueError, lambda: P(
Eq(X(t), 2) & Eq(X(d), 3),
Contains(t, Interval.Lopen(0, 4)) & Contains(d, Interval.Lopen(3, oo)))
) # no bound on d
assert P(Eq(X(3), 2)) == 81 * exp(-9) / 2
assert P(Eq(X(t), 2), Contains(t, Interval.Lopen(0,
5))) == 225 * exp(-15) / 2
# Check that probability works correctly by adding it to 1
res1 = P(X(t) <= 3, Contains(t, Interval.Lopen(0, 5)))
res2 = P(X(t) > 3, Contains(t, Interval.Lopen(0, 5)))
assert res1 == 691 * exp(-15)
assert (res1 + res2).simplify() == 1
# Check Not and Or
assert P(Not(Eq(X(t), 2) & (X(d) > 3)), Contains(t, Interval.Ropen(2, 4)) & \
Contains(d, Interval.Lopen(7, 8))).simplify() == -18*exp(-6) + 234*exp(-9) + 1
assert P(Eq(X(t), 2) | Ne(X(t), 4),
Contains(t, Interval.Ropen(2, 4))) == 1 - 36 * exp(-6)
raises(ValueError, lambda: P(X(t) > 2, X(t) + X(d)))
assert E(
X(t)) == 3 * t # property of the distribution at a given timestamp
assert E(
X(t)**2 + X(d) * 2 + X(y)**3,
Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Lopen(1, 2))
& Contains(y, Interval.Ropen(3, 4))) == 75
assert E(X(t)**2, Contains(t, Interval.Lopen(0, 1))) == 12
assert E(x*(X(t) + X(d))*(X(t)**2+X(d)**2), Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Ropen(1, 2))) == \
Expectation(x*(X(d) + X(t))*(X(d)**2 + X(t)**2), Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Ropen(1, 2)))
# Value Error because of infinite time bound
raises(ValueError, lambda: E(X(t)**3, Contains(t, Interval.Lopen(1, oo))))
# Equivalent to E(X(t)**2) - E(X(d)**2) == E(X(1)**2) - E(X(1)**2) == 0
assert E((X(t) + X(d)) * (X(t) - X(d)),
Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Lopen(1, 2))) == 0
assert E(X(2) + x * E(X(5))) == 15 * x + 6
assert E(x * X(1) + y) == 3 * x + y
assert P(Eq(X(1), 2) & Eq(X(t), 3),
Contains(t, Interval.Lopen(1, 2))) == 81 * exp(-6) / 4
Y = PoissonProcess("Y", 6)
Z = X + Y
assert Z.lamda == X.lamda + Y.lamda == 9
raises(ValueError,
lambda: X + 5) # should be added be only PoissonProcess instance
N, M = Z.split(4, 5)
assert N.lamda == 4
assert M.lamda == 5
raises(ValueError, lambda: Z.split(3, 2)) # 2+3 != 9
raises(
ValueError, lambda: P(Eq(X(t), 0),
Contains(t, Interval.Lopen(1, 3)) & Eq(X(1), 0)))
# check if it handles queries with two random variables in one args
res1 = P(Eq(N(3), N(5)))
assert res1 == P(Eq(N(t), 0), Contains(t, Interval(3, 5)))
res2 = P(N(3) > N(1))
assert res2 == P((N(t) > 0), Contains(t, Interval(1, 3)))
assert P(N(3) < N(1)) == 0 # condition is not possible
res3 = P(N(3) <= N(1)) # holds only for Eq(N(3), N(1))
assert res3 == P(Eq(N(t), 0), Contains(t, Interval(1, 3)))
# tests from https://www.probabilitycourse.com/chapter11/11_1_2_basic_concepts_of_the_poisson_process.php
X = PoissonProcess('X', 10) # 11.1
assert P(Eq(X(S(1) / 3), 3)
& Eq(X(1), 10)) == exp(-10) * Rational(8000000000, 11160261)
assert P(Eq(X(1), 1), Eq(X(S(1) / 3), 3)) == 0
assert P(Eq(X(1), 10), Eq(X(S(1) / 3), 3)) == P(Eq(X(S(2) / 3), 7))
X = PoissonProcess('X', 2) # 11.2
assert P(X(S(1) / 2) < 1) == exp(-1)
assert P(X(3) < 1, Eq(X(1), 0)) == exp(-4)
assert P(Eq(X(4), 3), Eq(X(2), 3)) == exp(-4)
X = PoissonProcess('X', 3)
assert P(Eq(X(2), 5) & Eq(X(1), 2)) == Rational(81, 4) * exp(-6)
# check few properties
assert P(
X(2) <= 3,
X(1) >= 1) == 3 * P(Eq(X(1), 0)) + 2 * P(Eq(X(1), 1)) + P(Eq(X(1), 2))
assert P(X(2) <= 3, X(1) > 1) == 2 * P(Eq(X(1), 0)) + 1 * P(Eq(X(1), 1))
assert P(Eq(X(2), 5) & Eq(X(1), 2)) == P(Eq(X(1), 3)) * P(Eq(X(1), 2))
assert P(Eq(X(3), 4), Eq(X(1), 3)) == P(Eq(X(2), 1))
def findBit(self, index):
for bitRange in self.content:
if isinstance(bitRange, Bit):
if Solver.isUnsat(Ne(index, bitRange.start)):
return bitRange
return None
