def test__intervals():
assert Piecewise((x + 2, Eq(x, 3)))._intervals(x) == []
assert Piecewise(
(1, x > x + 1),
(Piecewise((1, x < x + 1)), 2*x < 2*x + 1),
(1, True))._intervals(x) == [(-oo, oo, 1, 1)]
assert Piecewise((1, Ne(x, I)), (0, True))._intervals(x) == [
(-oo, oo, 1, 0)]
assert Piecewise((-cos(x), sin(x) >= 0), (cos(x), True)
)._intervals(x) == [(0, pi, -cos(x), 0), (-oo, oo, cos(x), 1)]
# the following tests that duplicates are removed and that non-Eq
# generated zero-width intervals are removed
assert Piecewise((1, Abs(x**(-2)) > 1), (0, True)
)._intervals(x) == [(-1, 0, 1, 0), (0, 1, 1, 0), (-oo, oo, 0, 1)]
def test_reduce_poly_inequalities_complex_relational():
cond = Eq(im(x), 0)
assert reduce_poly_inequalities([[Eq(x**2, 0)]], x, relational=True) == And(Eq(re(x), 0), cond)
assert reduce_poly_inequalities([[Le(x**2, 0)]], x, relational=True) == And(Eq(re(x), 0), cond)
assert reduce_poly_inequalities([[Lt(x**2, 0)]], x, relational=True) == False
assert reduce_poly_inequalities([[Ge(x**2, 0)]], x, relational=True) == cond
assert reduce_poly_inequalities([[Gt(x**2, 0)]], x, relational=True) == And(Or(Lt(re(x), 0), Lt(0, re(x))), cond)
assert reduce_poly_inequalities([[Ne(x**2, 0)]], x, relational=True) == And(Or(Lt(re(x), 0), Lt(0, re(x))), cond)
assert reduce_poly_inequalities([[Eq(x**2, 1)]], x, relational=True) == And(Or(Eq(re(x), -1), Eq(re(x), 1)), cond)
assert reduce_poly_inequalities([[Le(x**2, 1)]], x, relational=True) == And(And(Le(-1, re(x)), Le(re(x), 1)), cond)
assert reduce_poly_inequalities([[Lt(x**2, 1)]], x, relational=True) == And(And(Lt(-1, re(x)), Lt(re(x), 1)), cond)
assert reduce_poly_inequalities([[Ge(x**2, 1)]], x, relational=True) == And(Or(Le(re(x), -1), Le(1, re(x))), cond)
assert reduce_poly_inequalities([[Gt(x**2, 1)]], x, relational=True) == And(Or(Lt(re(x), -1), Lt(1, re(x))), cond)
assert reduce_poly_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_poly_inequalities([[Eq(x**2, 1.0)]], x, relational=True).evalf() == And(Or(Eq(re(x), -1.0), Eq(re(x), 1.0)), cond)
assert reduce_poly_inequalities([[Le(x**2, 1.0)]], x, relational=True) == And(And(Le(-1.0, re(x)), Le(re(x), 1.0)), cond)
assert reduce_poly_inequalities([[Lt(x**2, 1.0)]], x, relational=True) == And(And(Lt(-1.0, re(x)), Lt(re(x), 1.0)), cond)
assert reduce_poly_inequalities([[Ge(x**2, 1.0)]], x, relational=True) == And(Or(Le(re(x), -1.0), Le(1.0, re(x))), cond)
assert reduce_poly_inequalities([[Gt(x**2, 1.0)]], x, relational=True) == And(Or(Lt(re(x), -1.0), Lt(1.0, re(x))), cond)
assert reduce_poly_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_logistic():
mu = Symbol("mu", real=True)
s = Symbol("s", positive=True)
p = Symbol("p", positive=True)
X = Logistic('x', mu, s)
#Tests characteristics_function
assert characteristic_function(X)(x) == \
(Piecewise((pi*s*x*exp(I*mu*x)/sinh(pi*s*x), Ne(x, 0)), (1, True)))
assert density(X)(x) == exp((-x + mu)/s)/(s*(exp((-x + mu)/s) + 1)**2)
assert cdf(X)(x) == 1/(exp((mu - x)/s) + 1)
assert quantile(X)(p) == mu - s*log(-S.One + 1/p)
def test_reduce_poly_inequalities_real_interval():
global_assumptions.add(x_assume)
global_assumptions.add(y_assume)
assert reduce_poly_inequalities([[Eq(x**2, 0)]], x, relational=False) == [Interval(0, 0)]
assert reduce_poly_inequalities([[Le(x**2, 0)]], x, relational=False) == [Interval(0, 0)]
assert reduce_poly_inequalities([[Lt(x**2, 0)]], x, relational=False) == []
assert reduce_poly_inequalities([[Ge(x**2, 0)]], x, relational=False) == [Interval(-oo, oo)]
assert reduce_poly_inequalities([[Gt(x**2, 0)]], x, relational=False) == [Interval(-oo, 0, right_open=True), Interval(0, oo, left_open=True)]
assert reduce_poly_inequalities([[Ne(x**2, 0)]], x, relational=False) == [Interval(-oo, 0, right_open=True), Interval(0, oo, left_open=True)]
assert reduce_poly_inequalities([[Eq(x**2, 1)]], x, relational=False) == [Interval(-1,-1), Interval(1, 1)]
assert reduce_poly_inequalities([[Le(x**2, 1)]], x, relational=False) == [Interval(-1, 1)]
assert reduce_poly_inequalities([[Lt(x**2, 1)]], x, relational=False) == [Interval(-1, 1, True, True)]
assert reduce_poly_inequalities([[Ge(x**2, 1)]], x, relational=False) == [Interval(-oo, -1), Interval(1, oo)]
assert reduce_poly_inequalities([[Gt(x**2, 1)]], x, relational=False) == [Interval(-oo, -1, right_open=True), Interval(1, oo, left_open=True)]
assert reduce_poly_inequalities([[Ne(x**2, 1)]], x, relational=False) == [Interval(-oo, -1, right_open=True), Interval(-1, 1, True, True), Interval(1, oo, left_open=True)]
assert reduce_poly_inequalities([[Eq(x**2, 1.0)]], x, relational=False) == [Interval(-1.0,-1.0), Interval(1.0, 1.0)]
assert reduce_poly_inequalities([[Le(x**2, 1.0)]], x, relational=False) == [Interval(-1.0, 1.0)]
assert reduce_poly_inequalities([[Lt(x**2, 1.0)]], x, relational=False) == [Interval(-1.0, 1.0, True, True)]
assert reduce_poly_inequalities([[Ge(x**2, 1.0)]], x, relational=False) == [Interval(-inf, -1.0), Interval(1.0, inf)]
assert reduce_poly_inequalities([[Gt(x**2, 1.0)]], x, relational=False) == [Interval(-inf, -1.0, right_open=True), Interval(1.0, inf, left_open=True)]
assert reduce_poly_inequalities([[Ne(x**2, 1.0)]], x, relational=False) == [Interval(-inf, -1.0, right_open=True), Interval(-1.0, 1.0, True, True), Interval(1.0, inf, left_open=True)]
s = sqrt(2)
assert reduce_poly_inequalities([[Lt(x**2 - 1, 0), Gt(x**2 - 1, 0)]], x, relational=False) == []
assert reduce_poly_inequalities([[Le(x**2 - 1, 0), Ge(x**2 - 1, 0)]], x, relational=False) == [Interval(-1,-1), Interval(1, 1)]
assert reduce_poly_inequalities([[Le(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x, relational=False) == [Interval(-s, -1, False, False), Interval(1, s, False, False)]
assert reduce_poly_inequalities([[Le(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x, relational=False) == [Interval(-s, -1, False, True), Interval(1, s, True, False)]
assert reduce_poly_inequalities([[Lt(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x, relational=False) == [Interval(-s, -1, True, False), Interval(1, s, False, True)]
assert reduce_poly_inequalities([[Lt(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x, relational=False) == [Interval(-s, -1, True, True), Interval(1, s, True, True)]
assert reduce_poly_inequalities([[Lt(x**2 - 2, 0), Ne(x**2 - 1, 0)]], x, relational=False) == [Interval(-s, -1, True, True), Interval(-1, 1, True, True), Interval(1, s, True, True)]
global_assumptions.remove(x_assume)
global_assumptions.remove(y_assume)
def test_issue_11865():
matplotlib = import_module('matplotlib',
min_module_version='1.1.0',
catch=(RuntimeError, ))
if not matplotlib:
skip("Matplotlib not the default backend")
k = Symbol('k', integer=True)
f = Piecewise(
(-I * exp(I * pi * k) / k + I * exp(-I * pi * k) / k, Ne(k, 0)),
(2 * pi, True))
p = plot(f, show=False)
# Random number of segments, probably more than 100, but we want to see
# that there are segments generated, as opposed to when the bug was present
# and that there are no exceptions.
assert len(p[0].get_segments()) >= 30
def test_reduce_poly_inequalities_real_interval():
global_assumptions.add(Q.real(x))
global_assumptions.add(Q.real(y))
assert reduce_poly_inequalities([[Eq(x**2, 0)]], x, relational=False) == FiniteSet(0)
assert reduce_poly_inequalities([[Le(x**2, 0)]], x, relational=False) == FiniteSet(0)
assert reduce_poly_inequalities([[Lt(x**2, 0)]], x, relational=False) == S.EmptySet
assert reduce_poly_inequalities([[Ge(x**2, 0)]], x, relational=False) == Interval(-oo, oo)
assert reduce_poly_inequalities([[Gt(x**2, 0)]], x, relational=False) == FiniteSet(0).complement
assert reduce_poly_inequalities([[Ne(x**2, 0)]], x, relational=False) == FiniteSet(0).complement
assert reduce_poly_inequalities([[Eq(x**2, 1)]], x, relational=False) == FiniteSet(-1, 1)
assert reduce_poly_inequalities([[Le(x**2, 1)]], x, relational=False) == Interval(-1, 1)
assert reduce_poly_inequalities([[Lt(x**2, 1)]], x, relational=False) == Interval(-1, 1, True, True)
assert reduce_poly_inequalities([[Ge(x**2, 1)]], x, relational=False) == Union(Interval(-oo, -1), Interval(1, oo))
assert reduce_poly_inequalities([[Gt(x**2, 1)]], x, relational=False) == Interval(-1,1).complement
assert reduce_poly_inequalities([[Ne(x**2, 1)]], x, relational=False) == FiniteSet(-1,1).complement
assert reduce_poly_inequalities([[Eq(x**2, 1.0)]], x, relational=False) == FiniteSet(-1.0,1.0).evalf()
assert reduce_poly_inequalities([[Le(x**2, 1.0)]], x, relational=False) == Interval(-1.0, 1.0)
assert reduce_poly_inequalities([[Lt(x**2, 1.0)]], x, relational=False) == Interval(-1.0, 1.0, True, True)
assert reduce_poly_inequalities([[Ge(x**2, 1.0)]], x, relational=False) == Union(Interval(-inf, -1.0), Interval(1.0, inf))
assert reduce_poly_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_poly_inequalities([[Ne(x**2, 1.0)]], x, relational=False) == FiniteSet(-1.0, 1.0).complement
s = sqrt(2)
assert reduce_poly_inequalities([[Lt(x**2 - 1, 0), Gt(x**2 - 1, 0)]], x, relational=False) == S.EmptySet
assert reduce_poly_inequalities([[Le(x**2 - 1, 0), Ge(x**2 - 1, 0)]], x, relational=False) == FiniteSet(-1,1)
assert reduce_poly_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_poly_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_poly_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_poly_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_poly_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))
global_assumptions.remove(Q.real(x))
global_assumptions.remove(Q.real(y))
def test__solve_inequality():
for op in (Gt, Lt, Le, Ge, Eq, Ne):
assert _solve_inequality(op(x, 1), x).lhs == x
assert _solve_inequality(op(S.One, x), x).lhs == x
# don't get tricked by symbol on right: solve it
assert _solve_inequality(Eq(2 * x - 1, x), x) == Eq(x, 1)
ie = Eq(S.One, y)
assert _solve_inequality(ie, x) == ie
for fx in (x**2, exp(x), sin(x) + cos(x), x * (1 + x)):
for c in (0, 1):
e = 2 * fx - c > 0
assert _solve_inequality(e, x, linear=True) == (fx > c / 2)
assert _solve_inequality(2 * x**2 + 2 * x - 1 < 0, x,
linear=True) == (x * (x + 1) < S.Half)
assert _solve_inequality(Eq(x * y, 1), x) == Eq(x * y, 1)
nz = Symbol('nz', nonzero=True)
assert _solve_inequality(Eq(x * nz, 1), x) == Eq(x, 1 / nz)
assert _solve_inequality(x * nz < 1, x) == (x * nz < 1)
a = Symbol('a', positive=True)
assert _solve_inequality(a / x > 1, x, linear=True) == (1 / x > 1 / a)
# make sure to include conditions under which solution is valid
e = Eq(1 - x, x * (1 / x - 1))
assert _solve_inequality(e, x) == Ne(x, 0)
assert _solve_inequality(x < x * (1 / x - 1), x) == (x < S.Half) & Ne(x, 0)
def convergence_statement(self):
""" Return a condition on z under which the series converges. """
from sympy import And, Or, re, Ne, oo
R = self.radius_of_convergence
if R == 0:
return False
if R == oo:
return True
# The special functions and their approximations, page 44
e = self.eta
z = self.argument
c1 = And(re(e) < 0, abs(z) <= 1)
c2 = And(0 <= re(e), re(e) < 1, abs(z) <= 1, Ne(z, 1))
c3 = And(re(e) >= 1, abs(z) < 1)
return Or(c1, c2, c3)
def test_heurisch_radicals():
assert heurisch(1/sqrt(x), x) == 2*sqrt(x)
assert heurisch(1/sqrt(x)**3, x) == -2/sqrt(x)
assert heurisch(sqrt(x)**3, x) == 2*sqrt(x)**5/5
assert heurisch(sin(x)*sqrt(cos(x)), x) == -2*sqrt(cos(x))**3/3
y = Symbol('y')
assert heurisch(sin(y*sqrt(x)), x) == 2/y**2*sin(y*sqrt(x)) - \
2*sqrt(x)*cos(y*sqrt(x))/y
assert heurisch_wrapper(sin(y*sqrt(x)), x) == Piecewise(
(-2*sqrt(x)*cos(sqrt(x)*y)/y + 2*sin(sqrt(x)*y)/y**2, Ne(y, 0)),
(0, True))
y = Symbol('y', positive=True)
assert heurisch_wrapper(sin(y*sqrt(x)), x) == 2/y**2*sin(y*sqrt(x)) - \
2*sqrt(x)*cos(y*sqrt(x))/y
def test_issue_6746():
y = Symbol('y')
n = Symbol('n')
assert manualintegrate(y**x, x) == Piecewise(
(y**x / log(y), Ne(log(y), 0)), (x, True))
assert manualintegrate(y**(n * x), x) == Piecewise((Piecewise(
(y**(n * x) / log(y), Ne(log(y), 0)), (n * x, True)) / n, Ne(n, 0)),
(x, True))
assert manualintegrate(exp(n * x), x) == Piecewise(
(exp(n * x) / n, Ne(n, 0)), (x, True))
y = Symbol('y', positive=True)
assert manualintegrate((y + 1)**x, x) == (y + 1)**x / log(y + 1)
y = Symbol('y', zero=True)
assert manualintegrate((y + 1)**x, x) == x
y = Symbol('y')
n = Symbol('n', nonzero=True)
assert manualintegrate(y**(n * x), x) == Piecewise(
(y**(n * x) / log(y), Ne(log(y), 0)), (n * x, True)) / n
y = Symbol('y', positive=True)
assert manualintegrate((y + 1)**(n*x), x) == \
(y + 1)**(n*x)/(n*log(y + 1))
a = Symbol('a', negative=True)
b = Symbol('b')
assert manualintegrate(1 / (a + b * x**2),
x) == atan(x / sqrt(a / b)) / (b * sqrt(a / b))
b = Symbol('b', negative=True)
assert manualintegrate(1/(a + b*x**2), x) == \
atan(x/(sqrt(-a)*sqrt(-1/b)))/(b*sqrt(-a)*sqrt(-1/b))
assert manualintegrate(1/((x**a + y**b + 4)*sqrt(a*x**2 + 1)), x) == \
y**(-b)*Integral(x**(-a)/(y**(-b)*sqrt(a*x**2 + 1) +
x**(-a)*sqrt(a*x**2 + 1) + 4*x**(-a)*y**(-b)*sqrt(a*x**2 + 1)), x)
assert manualintegrate(1/((x**2 + 4)*sqrt(4*x**2 + 1)), x) == \
Integral(1/((x**2 + 4)*sqrt(4*x**2 + 1)), x)
assert manualintegrate(1/(x - a**x + x*b**2), x) == \
Integral(1/(-a**x + b**2*x + x), x)
def test_heurisch_wrapper():
f = 1 / (y + x)
assert heurisch_wrapper(f, x) == log(x + y)
f = 1 / (y - x)
assert heurisch_wrapper(f, x) == -log(x - y)
f = 1 / ((y - x) * (y + x))
assert heurisch_wrapper(f, x) == Piecewise(
(-log(x - y) / (2 * y) + log(x + y) / (2 * y), Ne(y, 0)), (1 / x, True)
)
# issue 6926
f = sqrt(x ** 2 / ((y - x) * (y + x)))
assert (
heurisch_wrapper(f, x)
== x * sqrt(x ** 2) * sqrt(1 / (-(x ** 2) + y ** 2))
- y ** 2 * sqrt(x ** 2) * sqrt(1 / (-(x ** 2) + y ** 2)) / x
)
def test_kronecker_delta():
i, j = symbols('i j')
k = Symbol('k', nonzero=True)
assert KroneckerDelta(1, 1) == 1
assert KroneckerDelta(1, 2) == 0
assert KroneckerDelta(k, 0) == 0
assert KroneckerDelta(x, x) == 1
assert KroneckerDelta(x**2 - y**2, x**2 - y**2) == 1
assert KroneckerDelta(i, i) == 1
assert KroneckerDelta(i, i + 1) == 0
assert KroneckerDelta(0, 0) == 1
assert KroneckerDelta(0, 1) == 0
assert KroneckerDelta(i + k, i) == 0
assert KroneckerDelta(i + k, i + k) == 1
assert KroneckerDelta(i + k, i + 1 + k) == 0
assert KroneckerDelta(i, j).subs(dict(i=1, j=0)) == 0
assert KroneckerDelta(i, j).subs(dict(i=3, j=3)) == 1
assert KroneckerDelta(i, j)**0 == 1
for n in range(1, 10):
assert KroneckerDelta(i, j)**n == KroneckerDelta(i, j)
assert KroneckerDelta(i, j)**-n == 1 / KroneckerDelta(i, j)
assert KroneckerDelta(i, j).is_integer is True
assert adjoint(KroneckerDelta(i, j)) == KroneckerDelta(i, j)
assert conjugate(KroneckerDelta(i, j)) == KroneckerDelta(i, j)
assert transpose(KroneckerDelta(i, j)) == KroneckerDelta(i, j)
# to test if canonical
assert (KroneckerDelta(i, j) == KroneckerDelta(j, i)) == True
assert KroneckerDelta(i, j).rewrite(Piecewise) == Piecewise((0, Ne(i, j)),
(1, True))
# Tests with range:
assert KroneckerDelta(i, j, (0, i)).args == (i, j, (0, i))
assert KroneckerDelta(i, j, (-j, i)).delta_range == (-j, i)
# If index is out of range, return zero:
assert KroneckerDelta(i, j, (0, i - 1)) == 0
assert KroneckerDelta(-1, j, (0, i - 1)) == 0
assert KroneckerDelta(j, -1, (0, i - 1)) == 0
assert KroneckerDelta(j, i, (0, i - 1)) == 0
def test_presentation_mathml_relational():
mml_1 = mpp._print(Eq(x, 1))
assert len(mml_1.childNodes) == 3
assert mml_1.childNodes[0].nodeName == 'mi'
assert mml_1.childNodes[0].childNodes[0].nodeValue == 'x'
assert mml_1.childNodes[1].nodeName == 'mo'
assert mml_1.childNodes[1].childNodes[0].nodeValue == '='
assert mml_1.childNodes[2].nodeName == 'mn'
assert mml_1.childNodes[2].childNodes[0].nodeValue == '1'
mml_2 = mpp._print(Ne(1, x))
assert len(mml_2.childNodes) == 3
assert mml_2.childNodes[0].nodeName == 'mn'
assert mml_2.childNodes[0].childNodes[0].nodeValue == '1'
assert mml_2.childNodes[1].nodeName == 'mo'
assert mml_2.childNodes[1].childNodes[0].nodeValue == '≠'
assert mml_2.childNodes[2].nodeName == 'mi'
assert mml_2.childNodes[2].childNodes[0].nodeValue == 'x'
mml_3 = mpp._print(Ge(1, x))
assert len(mml_3.childNodes) == 3
assert mml_3.childNodes[0].nodeName == 'mn'
assert mml_3.childNodes[0].childNodes[0].nodeValue == '1'
assert mml_3.childNodes[1].nodeName == 'mo'
assert mml_3.childNodes[1].childNodes[0].nodeValue == '≥'
assert mml_3.childNodes[2].nodeName == 'mi'
assert mml_3.childNodes[2].childNodes[0].nodeValue == 'x'
mml_4 = mpp._print(Lt(1, x))
assert len(mml_4.childNodes) == 3
assert mml_4.childNodes[0].nodeName == 'mn'
assert mml_4.childNodes[0].childNodes[0].nodeValue == '1'
assert mml_4.childNodes[1].nodeName == 'mo'
assert mml_4.childNodes[1].childNodes[0].nodeValue == '<'
assert mml_4.childNodes[2].nodeName == 'mi'
assert mml_4.childNodes[2].childNodes[0].nodeValue == 'x'
def test_issue_12557():
'''
# 3200 seconds to compute the fourier part of issue
import sympy as sym
x,y,z,t = sym.symbols('x y z t')
k = sym.symbols("k", integer=True)
fourier = sym.fourier_series(sym.cos(k*x)*sym.sqrt(x**2),
(x, -sym.pi, sym.pi))
assert fourier == FourierSeries(
sqrt(x**2)*cos(k*x), (x, -pi, pi), (Piecewise((pi**2,
Eq(k, 0)), (2*(-1)**k/k**2 - 2/k**2, True))/(2*pi),
SeqFormula(Piecewise((pi**2, (Eq(_n, 0) & Eq(k, 0)) | (Eq(_n, 0) &
Eq(_n, k) & Eq(k, 0)) | (Eq(_n, 0) & Eq(k, 0) & Eq(_n, -k)) | (Eq(_n,
0) & Eq(_n, k) & Eq(k, 0) & Eq(_n, -k))), (pi**2/2, Eq(_n, k) | Eq(_n,
-k) | (Eq(_n, 0) & Eq(_n, k)) | (Eq(_n, k) & Eq(k, 0)) | (Eq(_n, 0) &
Eq(_n, -k)) | (Eq(_n, k) & Eq(_n, -k)) | (Eq(k, 0) & Eq(_n, -k)) |
(Eq(_n, 0) & Eq(_n, k) & Eq(_n, -k)) | (Eq(_n, k) & Eq(k, 0) & Eq(_n,
-k))), ((-1)**k*pi**2*_n**3*sin(pi*_n)/(pi*_n**4 - 2*pi*_n**2*k**2 +
pi*k**4) - (-1)**k*pi**2*_n**3*sin(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2
- pi*k**4) + (-1)**k*pi*_n**2*cos(pi*_n)/(pi*_n**4 - 2*pi*_n**2*k**2 +
pi*k**4) - (-1)**k*pi*_n**2*cos(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2 -
pi*k**4) - (-1)**k*pi**2*_n*k**2*sin(pi*_n)/(pi*_n**4 -
2*pi*_n**2*k**2 + pi*k**4) +
(-1)**k*pi**2*_n*k**2*sin(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2 -
pi*k**4) + (-1)**k*pi*k**2*cos(pi*_n)/(pi*_n**4 - 2*pi*_n**2*k**2 +
pi*k**4) - (-1)**k*pi*k**2*cos(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2 -
pi*k**4) - (2*_n**2 + 2*k**2)/(_n**4 - 2*_n**2*k**2 + k**4),
True))*cos(_n*x)/pi, (_n, 1, oo)), SeqFormula(0, (_k, 1, oo))))
'''
x = symbols("x", real=True)
k = symbols('k', integer=True, finite=True)
abs2 = lambda x: Piecewise((-x, x <= 0), (x, x > 0))
assert integrate(abs2(x), (x, -pi, pi)) == pi**2
func = cos(k * x) * sqrt(x**2)
assert integrate(func, (x, -pi, pi)) == Piecewise(
(2 * (-1)**k / k**2 - 2 / k**2, Ne(k, 0)), (pi**2, True))
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))
assert reduce_rational_inequalities([[Lt(x**2, -1.)]], x) is S.false
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_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_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 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
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([[]]))
# 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 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_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 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_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_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_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) is nan
assert frac(Rational(4, 3)) == Rational(1, 3)
assert frac(-Rational(4, 3)) == Rational(2, 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_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) - 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_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)))
# with y unknown, this fails because there might be a hole
# in intervals [Min(1, Max(4, y)), 1] and [Min(4, y), y]. The
# first one should simplify (i.e. since 1 is less than the
# minumum value of Max(4, y) that interval should be [1, 1]
raises(ValueError, lambda: p.integrate((x, 1, y)))
assert p.integrate((x, 1, 4)) == 5
# 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
class TestAllGood(object):
# These latex strings should parse to the corresponding SymPy expression
GOOD_PAIRS = [
("0", Rational(0)),
("1", Rational(1)),
("-3.14", Rational(-314, 100)),
("5-3", _Add(5, _Mul(-1, 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)),
("\\operatorname{gcd}(a, b)", UnevaluatedExpr(gcd(a, b))),
("\\operatorname{lcm}(a, b)", UnevaluatedExpr(lcm(a, b))),
("\\operatorname{gcd}(a,b)", UnevaluatedExpr(gcd(a, b))),
("\\operatorname{lcm}(a,b)", UnevaluatedExpr(lcm(a, b))),
("\\operatorname{floor}(a)", floor(a)),
("\\operatorname{ceil}(b)", ceiling(b)),
("\\cos^2(x)", cos(x)**2),
("\\cos(x)^2", cos(x)**2),
("\\gcd(a, b)", UnevaluatedExpr(gcd(a, b))),
("\\lcm(a, b)", UnevaluatedExpr(lcm(a, b))),
("\\gcd(a,b)", UnevaluatedExpr(gcd(a, b))),
("\\lcm(a,b)", UnevaluatedExpr(lcm(a, b))),
("\\floor(a)", floor(a)),
("\\ceil(b)", ceiling(b)),
("\\max(a, b)", Max(a, b)),
("\\min(a, b)", Min(a, b)),
("\\frac{a}{b}", a / b),
("\\frac{a + b}{c}", _Mul(a + b, _Pow(c, -1))),
("\\frac{7}{3}", Rational(7, 3)),
("(\\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))),
("\\lfloor x\\rfloor", floor(x)),
("\\lceil y\\rceil", ceiling(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}", Integer(-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, positive=True)),
("x_{1}", Symbol('x_1', real=True, positive=True)),
("x_a", Symbol('x_a', real=True, positive=True)),
("x_{b}", Symbol('x_b', real=True, positive=True)),
("h_\\theta", Symbol('h_{\\theta}', real=True, positive=True)),
("h_\\theta ", Symbol('h_{\\theta}', real=True, positive=True)),
("h_{\\theta}", Symbol('h_{\\theta}', real=True, positive=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, positive=True)),
("2\\overline{x}_n", 2 * Symbol('xbar_n', real=True, positive=True)),
("\\frac{x}{\\overline{x}_n}", x / Symbol('xbar_n', real=True, positive=True)),
("\\frac{\\sin(x)}{\\overline{x}_n}", sin(x) / Symbol('xbar_n', real=True, positive=True)),
("2\\bar{x}", 2 * Symbol('xbar', real=True, positive=True)),
("2\\bar{x}_n", 2 * Symbol('xbar_n', real=True, positive=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(Rational(1, 2), x, y, (x + y), evaluate=False)),
("\\frac{1}{2}\\theta(x+y)", Mul(Rational(1, 2), 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", Rational(250)),
("1,500\\times 10^{-1}", Rational(150)),
# e notation
("2.5E2", Rational(250)),
("1,500E-1", Rational(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(Rational(1, 9), 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", Rational(1000)),
("\\$543.21", Rational(54321, 100)),
("\\$0.009", Rational(9, 1000)),
# percentages
("100\\%", Rational(1)),
("1.5\\%", Rational(15, 1000)),
("0.05\\%", Rational(5, 10000)),
# empty set
("\\emptyset", S.EmptySet),
# divide by zero
("\\frac{1}{0}", _Pow(0, -1)),
("1+\\frac{5}{0}", _Add(1, _Mul(5, _Pow(0, -1)))),
# adjacent single char sub sup
("4^26^2", _Mul(_Pow(4, 2), _Pow(6, 2))),
("x_22^2", _Mul(Symbol('x_2', real=True, positive=True), _Pow(2, 2)))
]
def test_good_pair(self, s, eq):
assert_equal(s, eq)
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_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]
