def _get_newpspace(self, evaluate=False):
x = Dummy('x')
parent_dist = self.distribution.args[0]
func = Lambda(x, self.distribution.pdf(x, evaluate))
new_pspace = self._transform_pspace(self.symbol, parent_dist, func)
if new_pspace is not None:
return new_pspace
message = ("Compound Distribution for %s is not implemeted yet" %
str(parent_dist))
raise NotImplementedError(message)
def _invert_complex(f, g_ys, symbol):
""" Helper function for invert_complex """
if not f.has(symbol):
raise ValueError("Inverse of constant function doesn't exist")
if f is symbol:
return (f, g_ys)
n = Dummy('n')
if f.is_Add:
# f = g + h
g, h = f.as_independent(symbol)
if g != S.Zero:
return _invert_complex(h, imageset(Lambda(n, n - g), g_ys), symbol)
if f.is_Mul:
# f = g*h
g, h = f.as_independent(symbol)
if g != S.One:
return _invert_complex(h, imageset(Lambda(n, n / g), g_ys), symbol)
if hasattr(f, 'inverse') and \
not isinstance(f, TrigonometricFunction) and \
not isinstance(f, exp):
if len(f.args) > 1:
raise ValueError("Only functions with one argument are supported.")
return _invert_complex(f.args[0],
imageset(Lambda(n,
f.inverse()(n)), g_ys), symbol)
if isinstance(f, exp):
if isinstance(g_ys, FiniteSet):
exp_invs = Union(*[
imageset(
Lambda(n,
I * (2 * n * pi + arg(g_y)) +
log(Abs(g_y))), S.Integers) for g_y in g_ys
if g_y != 0
])
return _invert_complex(f.args[0], exp_invs, symbol)
return (f, g_ys)
def test_as_dummy():
u, v, x, y, z, _0, _1 = symbols('u v x y z _0 _1')
assert Lambda(x, x + 1).as_dummy() == Lambda(_0, _0 + 1)
assert Lambda(x, x + _0).as_dummy() == Lambda(_1, _0 + _1)
eq = (1 + Sum(x, (x, 1, x)))
ans = 1 + Sum(_0, (_0, 1, x))
once = eq.as_dummy()
assert once == ans
twice = once.as_dummy()
assert twice == ans
assert Integral(x + _0, (x, x + 1),
(_0, 1, 2)).as_dummy() == Integral(_0 + _1, (_0, x + 1),
(_1, 1, 2))
for T in (Symbol, Dummy):
d = T('x', real=True)
D = d.as_dummy()
assert D != d and D.func == Dummy and D.is_real is None
assert Dummy().as_dummy().is_commutative
assert Dummy(commutative=False).as_dummy().is_commutative is False
def _print_FunctionMatrix(self, expr):
from sympy.core.function import Lambda
from sympy.abc import i, j
lamda = expr.lamda
if not isinstance(lamda, Lambda):
lamda = Lambda((i, j), lamda(i, j))
return '{}(lambda {}: {}, {})'.format(
self._module_format('numpy.fromfunction'),
', '.join(self._print(arg) for arg in lamda.args[0]),
self._print(lamda.args[1]), self._print(expr.shape))
def fdiff(self, argindex=1):
"""
Returns the first derivative of the function.
"""
if argindex == 1:
return 1 / self.args[0]
s = C.Dummy('x')
return Lambda(s**(-1), s)
else:
raise ArgumentIndexError(self, argindex)
def test_functions_subs():
f, g = symbols('f g', cls=Function)
l = Lambda((x, y), sin(x) + y)
assert (g(y, x) + cos(x)).subs(g, l) == sin(y) + x + cos(x)
assert (f(x)**2).subs(f, sin) == sin(x)**2
assert (f(x, y)).subs(f, log) == log(x, y)
assert (f(x, y)).subs(f, sin) == f(x, y)
assert (sin(x) + atan2(x, y)).subs([[atan2, f], [sin, g]]) == \
f(x, y) + g(x)
assert (g(f(x + y, x))).subs([[f, l], [g, exp]]) == exp(x + sin(x + y))
def _intersect(self, other):
from sympy.solvers.diophantine import diophantine
if self.base_set is S.Integers:
if isinstance(other, ImageSet) and other.base_set is S.Integers:
f, g = self.lamda.expr, other.lamda.expr
n, m = self.lamda.variables[0], other.lamda.variables[0]
# Diophantine sorts the solutions according to the alphabetic
# order of the variable names, since the result should not depend
# on the variable name, they are replaced by the dummy variables
# below
a, b = Dummy('a'), Dummy('b')
f, g = f.subs(n, a), g.subs(m, b)
solns_set = diophantine(f - g)
if solns_set == set():
return EmptySet()
solns = list(diophantine(f - g))
if len(solns) == 1:
t = list(solns[0][0].free_symbols)[0]
else:
return None
# since 'a' < 'b'
return imageset(Lambda(t, f.subs(a, solns[0][0])), S.Integers)
if other == S.Reals:
from sympy.solvers.solveset import solveset_real
from sympy.core.function import expand_complex
if len(self.lamda.variables) > 1:
return None
f = self.lamda.expr
n = self.lamda.variables[0]
n_ = Dummy(n.name, real=True)
f_ = f.subs(n, n_)
re, im = f_.as_real_imag()
im = expand_complex(im)
return imageset(Lambda(n_, re),
self.base_set.intersect(solveset_real(im, n_)))
def test_applyfunc_shape_11_matrices():
M = MatrixSymbol("M", 1, 1)
double = Lambda(x, x * 2)
expr = M.applyfunc(sin)
assert isinstance(expr, ElementwiseApplyFunction)
expr = M.applyfunc(double)
assert isinstance(expr, MatMul)
assert expr == 2 * M
def test_DifferentialExtension_misc():
# Odd ends
assert DifferentialExtension(sin(y)*exp(x), x)._important_attrs == \
(Poly(sin(y)*t0, t0, domain='ZZ[sin(y)]'), Poly(1, t0, domain='ZZ'),
[Poly(1, x, domain='ZZ'), Poly(t0, t0, domain='ZZ')], [x, t0],
[Lambda(i, exp(i))], [], [None, 'exp'], [None, x])
raises(NotImplementedError, lambda: DifferentialExtension(sin(x), x))
assert DifferentialExtension(10**x, x)._important_attrs == \
(Poly(t0, t0), Poly(1, t0), [Poly(1, x), Poly(log(10)*t0, t0)], [x, t0],
[Lambda(i, exp(i*log(10)))], [(exp(x*log(10)), 10**x)], [None, 'exp'],
[None, x*log(10)])
assert DifferentialExtension(log(x) + log(x**2), x)._important_attrs in [
(Poly(3*t0, t0), Poly(2, t0), [Poly(1, x), Poly(2/x, t0)], [x, t0],
[Lambda(i, log(i**2))], [], [None, ], [], [1], [x**2]),
(Poly(3*t0, t0), Poly(1, t0), [Poly(1, x), Poly(1/x, t0)], [x, t0],
[Lambda(i, log(i))], [], [None, 'log'], [None, x])]
assert DifferentialExtension(S.Zero, x)._important_attrs == \
(Poly(0, x), Poly(1, x), [Poly(1, x)], [x], [], [], [None], [None])
assert DifferentialExtension(tan(atan(x).rewrite(log)), x)._important_attrs == \
(Poly(x, x), Poly(1, x), [Poly(1, x)], [x], [], [], [None], [None])
def compute_quantile(self, expr, **kwargs):
if not self.domain.set.is_Interval:
raise ValueError(
"Quantile not well defined on multivariate expressions")
d = self.compute_cdf(expr, **kwargs)
x = Dummy('x', real=True)
p = Dummy('p', positive=True)
quantile = solveset(d(x) - p, x, self.set)
return Lambda(p, quantile)
def compute_quantile(self, **kwargs):
""" Compute the Quantile from the PDF.
Returns a Lambda.
"""
x, p = symbols('x, p', real=True, cls=Dummy)
left_bound = self.set.start
pdf = self.pdf(x)
cdf = integrate(pdf, (x, left_bound, x), **kwargs)
quantile = solveset(cdf - p, x, self.set)
return Lambda(p, Piecewise((quantile, (p >= 0) & (p <= 1) ), (nan, True)))
def test_Subs_Derivative():
a = Derivative(f(g(x), h(x)), g(x), h(x),x)
b = Derivative(Derivative(f(g(x), h(x)), g(x), h(x)),x)
c = f(g(x), h(x)).diff(g(x), h(x), x)
d = f(g(x), h(x)).diff(g(x), h(x)).diff(x)
e = Derivative(f(g(x), h(x)), x)
eqs = (a, b, c, d, e)
subs = lambda arg: arg.subs(f, Lambda((x, y), exp(x + y))
).subs(g(x), 1/x).subs(h(x), x**3)
ans = 3*x**2*exp(1/x)*exp(x**3) - exp(1/x)*exp(x**3)/x**2
assert all(subs(i).doit().expand() == ans for i in eqs)
assert all(subs(i.doit()).doit().expand() == ans for i in eqs)
def _solve_real_trig(f, symbol):
""" Helper to solve trigonometric equations """
f = trigsimp(f)
f = f.rewrite(exp)
f = together(f)
g, h = fraction(f)
y = Dummy('y')
g, h = g.expand(), h.expand()
g, h = g.subs(exp(I*symbol), y), h.subs(exp(I*symbol), y)
if g.has(symbol) or h.has(symbol):
return ConditionSet(Lambda(symbol, Eq(f, 0)), S.Reals)
solns = solveset_complex(g, y) - solveset_complex(h, y)
if isinstance(solns, FiniteSet):
return Union(*[invert_complex(exp(I*symbol), s, symbol)[1]
for s in solns])
elif solns is S.EmptySet:
return S.EmptySet
else:
return ConditionSet(Lambda(symbol, Eq(f, 0)), S.Reals)
def _marginal_distribution(self, indices, sym):
sym = ImmutableMatrix([Indexed(sym, i) for i in indices])
_mu, _sigma = self.mu, self.sigma
k = self.mu.shape[0]
for i in range(k):
if i not in indices:
_mu = _mu.row_del(i)
_sigma = _sigma.col_del(i)
_sigma = _sigma.row_del(i)
return Lambda(tuple(sym), S.One/sqrt((2*pi)**(len(_mu))*det(_sigma))*exp(
Rational(-1, 2)*(_mu - sym).transpose()*(_sigma.inv()*\
(_mu - sym)))[0])
def pdf(self, x, evaluate=False):
dist = self.args[0]
randoms = [rv for rv in dist.args if is_random(rv)]
if isinstance(dist, SingleFiniteDistribution):
y = Dummy('y', integer=True, negative=False)
expr = dist.pmf(y)
else:
y = Dummy('y')
expr = dist.pdf(y)
for rv in randoms:
expr = self._marginalise(expr, rv, evaluate)
return Lambda(y, expr)(x)
def compute_quantile(self, **kwargs):
""" Compute the Quantile from the PDF.
Returns a Lambda.
"""
x = Dummy('x', integer=True)
p = Dummy('p', real=True)
left_bound = self.set.inf
pdf = self.pdf(x)
cdf = summation(pdf, (x, left_bound, x), **kwargs)
set = ((x, p <= cdf), )
return Lambda(p, Piecewise(*set))
def test_integrate_primitive():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)],
'Tfuncs': [log]})
assert integrate_primitive(Poly(t, t), Poly(1, t), DE) == (x*log(x), -1, True)
assert integrate_primitive(Poly(x, t), Poly(t, t), DE) == (0, NonElementaryIntegral(x/log(x), x), False)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t1), Poly(1/(x + 1), t2)],
'Tfuncs': [log, Lambda(i, log(i + 1))]})
assert integrate_primitive(Poly(t1, t2), Poly(t2, t2), DE) == \
(0, NonElementaryIntegral(log(x)/log(1 + x), x), False)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t1), Poly(1/(x*t1), t2)],
'Tfuncs': [log, Lambda(i, log(log(i)))]})
assert integrate_primitive(Poly(t2, t2), Poly(t1, t2), DE) == \
(0, NonElementaryIntegral(log(log(x))/log(x), x), False)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t0)],
'Tfuncs': [log]})
assert integrate_primitive(Poly(x**2*t0**3 + (3*x**2 + x)*t0**2 + (3*x**2
+ 2*x)*t0 + x**2 + x, t0), Poly(x**2*t0**4 + 4*x**2*t0**3 + 6*x**2*t0**2 +
4*x**2*t0 + x**2, t0), DE) == \
(-1/(log(x) + 1), NonElementaryIntegral(1/(log(x) + 1), x), False)
def test_Tuple_Eq():
assert Eq(Tuple(), Tuple()) is S.true
assert Eq(Tuple(1), 1) is S.false
assert Eq(Tuple(1, 2), Tuple(1)) is S.false
assert Eq(Tuple(1), Tuple(1)) is S.true
assert Eq(Tuple(1, 2), Tuple(1, 3)) is S.false
assert Eq(Tuple(1, 2), Tuple(1, 2)) is S.true
assert unchanged(Eq, Tuple(1, x), Tuple(1, 2))
assert Eq(Tuple(1, x), Tuple(1, 2)).subs(x, 2) is S.true
assert unchanged(Eq, Tuple(1, 2), x)
f = Function("f")
assert unchanged(Eq, Tuple(1), f(x))
assert Eq(Tuple(1), f(x)).subs(x, 1).subs(f, Lambda(y, (y, ))) is S.true
def test_subs_CondSet():
s = FiniteSet(z, y)
c = ConditionSet(x, x < 2, s)
assert c.subs(x, y) == c
assert c.subs(z, y) == ConditionSet(x, x < 2, FiniteSet(y))
assert c.xreplace({x: y}) == ConditionSet(y, y < 2, s)
assert ConditionSet(x, x < y, s
).subs(y, w) == ConditionSet(x, x < w, s.subs(y, w))
# if the user uses assumptions that cause the condition
# to evaluate, that can't be helped from SymPy's end
n = Symbol('n', negative=True)
assert ConditionSet(n, 0 < n, S.Integers) is S.EmptySet
p = Symbol('p', positive=True)
assert ConditionSet(n, n < y, S.Integers
).subs(n, x) == ConditionSet(n, n < y, S.Integers)
raises(ValueError, lambda: ConditionSet(
x + 1, x < 1, S.Integers))
assert ConditionSet(
p, n < x, Interval(-5, 5)).subs(x, p) == Interval(-5, 5), ConditionSet(
p, n < x, Interval(-5, 5)).subs(x, p)
assert ConditionSet(
n, n < x, Interval(-oo, 0)).subs(x, p
) == Interval(-oo, 0)
assert ConditionSet(f(x), f(x) < 1, {w, z}
).subs(f(x), y) == ConditionSet(f(x), f(x) < 1, {w, z})
# issue 17341
k = Symbol('k')
img1 = ImageSet(Lambda(k, 2*k*pi + asin(y)), S.Integers)
img2 = ImageSet(Lambda(k, 2*k*pi + asin(S.One/3)), S.Integers)
assert ConditionSet(x, Contains(
y, Interval(-1,1)), img1).subs(y, S.One/3).dummy_eq(img2)
assert (0, 1) in ConditionSet((x, y), x + y < 3, S.Integers**2)
raises(TypeError, lambda: ConditionSet(n, n < -10, Interval(0, 10)))
def compute_moment_generating_function(self, expr):
if self._is_symbolic:
d = self.compute_density(expr)
t = Dummy('t', real=True)
ki = Dummy('ki')
return Lambda(
t,
Sum(
d(ki) * exp(ki * t),
(ki, self.args[1].low, self.args[1].high)))
expr = rv_subs(expr, self.values)
return FinitePSpace(
self.domain,
self.distribution).compute_moment_generating_function(expr)
def compute_cdf(self, **kwargs):
""" Compute the CDF from the PDF.
Returns a Lambda.
"""
x, z = symbols('x, z', real=True, cls=Dummy)
left_bound = self.set.start
# CDF is integral of PDF from left bound to z
pdf = self.pdf(x)
cdf = integrate(pdf.doit(), (x, left_bound, z), **kwargs)
# CDF Ensure that CDF left of left_bound is zero
cdf = Piecewise((cdf, z >= left_bound), (0, True))
return Lambda(z, cdf)
def compute_cdf(self, expr, **kwargs):
if not self.domain.set.is_Interval:
raise ValueError(
"CDF not well defined on multivariate expressions")
d = self.compute_density(expr, **kwargs)
x, z = symbols('x, z', real=True, cls=Dummy)
left_bound = self.domain.set.start
# CDF is integral of PDF from left bound to z
cdf = integrate(d(x), (x, left_bound, z), **kwargs)
# CDF Ensure that CDF left of left_bound is zero
cdf = Piecewise((cdf, z >= left_bound), (0, True))
return Lambda(z, cdf)
def JointRV(symbol, pdf, _set=None):
"""
Create a Joint Random Variable where each of its component is continuous,
given the following:
Parameters
==========
symbol : Symbol
Represents name of the random variable.
pdf : A PDF in terms of indexed symbols of the symbol given
as the first argument
NOTE
====
As of now, the set for each component for a ``JointRV`` is
equal to the set of all integers, which cannot be changed.
Examples
========
>>> from sympy import exp, pi, Indexed, S
>>> from sympy.stats import density, JointRV
>>> x1, x2 = (Indexed('x', i) for i in (1, 2))
>>> pdf = exp(-x1**2/2 + x1 - x2**2/2 - S(1)/2)/(2*pi)
>>> N1 = JointRV('x', pdf) #Multivariate Normal distribution
>>> density(N1)(1, 2)
exp(-2)/(2*pi)
Returns
=======
RandomSymbol
"""
#TODO: Add support for sets provided by the user
symbol = sympify(symbol)
syms = list(i for i in pdf.free_symbols if isinstance(i, Indexed)
and i.base == IndexedBase(symbol))
syms = tuple(sorted(syms, key = lambda index: index.args[1]))
_set = S.Reals**len(syms)
pdf = Lambda(syms, pdf)
dist = JointDistributionHandmade(pdf, _set)
jrv = JointPSpace(symbol, dist).value
rvs = random_symbols(pdf)
if len(rvs) != 0:
dist = MarginalDistribution(dist, (jrv,))
return JointPSpace(symbol, dist).value
return jrv
def test_MatrixExpressions():
n = Symbol('n', integer=True)
X = MatrixSymbol('X', n, n)
assert str(X) == "X"
# Apply function elementwise (`ElementwiseApplyFunc`):
expr = (X.T*X).applyfunc(sin)
assert str(expr) == 'Lambda(_d, sin(_d)).(X.T*X)'
lamda = Lambda(x, 1/x)
expr = (n*X).applyfunc(lamda)
assert str(expr) == 'Lambda(x, 1/x).(n*X)'
def test_NumPyPrinter():
from sympy.core.function import Lambda
from sympy.matrices.expressions.adjoint import Adjoint
from sympy.matrices.expressions.diagonal import (DiagMatrix,
DiagonalMatrix,
DiagonalOf)
from sympy.matrices.expressions.funcmatrix import FunctionMatrix
from sympy.matrices.expressions.hadamard import HadamardProduct
from sympy.matrices.expressions.kronecker import KroneckerProduct
from sympy.matrices.expressions.special import (OneMatrix, ZeroMatrix)
from sympy.abc import a, b
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
A = MatrixSymbol("A", 2, 2)
B = MatrixSymbol("B", 2, 2)
C = MatrixSymbol("C", 1, 5)
D = MatrixSymbol("D", 3, 4)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
assert p.doprint(Identity(3)) == "numpy.eye(3)"
u = MatrixSymbol('x', 2, 1)
v = MatrixSymbol('y', 2, 1)
assert p.doprint(MatrixSolve(A, u)) == 'numpy.linalg.solve(A, x)'
assert p.doprint(MatrixSolve(A, u) + v) == 'numpy.linalg.solve(A, x) + y'
assert p.doprint(ZeroMatrix(2, 3)) == "numpy.zeros((2, 3))"
assert p.doprint(OneMatrix(2, 3)) == "numpy.ones((2, 3))"
assert p.doprint(FunctionMatrix(4, 5, Lambda((a, b), a + b))) == \
"numpy.fromfunction(lambda a, b: a + b, (4, 5))"
assert p.doprint(HadamardProduct(A, B)) == "numpy.multiply(A, B)"
assert p.doprint(KroneckerProduct(A, B)) == "numpy.kron(A, B)"
assert p.doprint(Adjoint(A)) == "numpy.conjugate(numpy.transpose(A))"
assert p.doprint(DiagonalOf(A)) == "numpy.reshape(numpy.diag(A), (-1, 1))"
assert p.doprint(DiagMatrix(C)) == "numpy.diagflat(C)"
assert p.doprint(DiagonalMatrix(D)) == "numpy.multiply(D, numpy.eye(3, 4))"
# Workaround for numpy negative integer power errors
assert p.doprint(x**-1) == 'x**(-1.0)'
assert p.doprint(x**-2) == 'x**(-2.0)'
expr = Pow(2, -1, evaluate=False)
assert p.doprint(expr) == "2**(-1.0)"
assert p.doprint(S.Exp1) == 'numpy.e'
assert p.doprint(S.Pi) == 'numpy.pi'
assert p.doprint(S.EulerGamma) == 'numpy.euler_gamma'
assert p.doprint(S.NaN) == 'numpy.nan'
assert p.doprint(S.Infinity) == 'numpy.PINF'
assert p.doprint(S.NegativeInfinity) == 'numpy.NINF'
def _set_function(f, self): # noqa:F811
expr = f.expr
if not isinstance(expr, Expr):
return
n = f.variables[0]
if expr == abs(n):
return S.Naturals0
# f(x) + c and f(-x) + c cover the same integers
# so choose the form that has the fewest negatives
c = f(0)
fx = f(n) - c
f_x = f(-n) - c
neg_count = lambda e: sum(_.could_extract_minus_sign()
for _ in Add.make_args(e))
if neg_count(f_x) < neg_count(fx):
expr = f_x + c
a = Wild('a', exclude=[n])
b = Wild('b', exclude=[n])
match = expr.match(a * n + b)
if match and match[a] and (not match[a].atoms(Float)
and not match[b].atoms(Float)):
# canonical shift
a, b = match[a], match[b]
if a in [1, -1]:
# drop integer addends in b
nonint = []
for bi in Add.make_args(b):
if not bi.is_integer:
nonint.append(bi)
b = Add(*nonint)
if b.is_number and a.is_real:
# avoid Mod for complex numbers, #11391
br, bi = match_real_imag(b)
if br and br.is_comparable and a.is_comparable:
br %= a
b = br + S.ImaginaryUnit * bi
elif b.is_number and a.is_imaginary:
br, bi = match_real_imag(b)
ai = a / S.ImaginaryUnit
if bi and bi.is_comparable and ai.is_comparable:
bi %= ai
b = br + S.ImaginaryUnit * bi
expr = a * n + b
if expr != f.expr:
return ImageSet(Lambda(n, expr), S.Integers)
def apply(*given):
forall_a, forall_b, equality_a, equality_b = given
A, B, a, b, fa, gb = analyze(forall_a, forall_b, equality_a)
eqs = Equality(b, Lambda(a, fa)(gb))
if equality_b.is_ForAll:
assert equality_b.variable == b
assert equality_b.limits == forall_b.limits
equality_b = equality_b.function
assert equality_b.is_Equal
assert equality_b == eqs or equality_b.reversed == eqs
return Equality(Abs(A), Abs(B), given=given)
def test_apart_full():
f = 1 / (x**2 + 1)
assert apart(f, full=False) == f
assert apart(f, full=True).dummy_eq(
-RootSum(x**2 + 1, Lambda(a, a / (x - a)), auto=False) / 2)
f = 1 / (x**3 + x + 1)
assert apart(f, full=False) == f
assert apart(f, full=True).dummy_eq(
RootSum(x**3 + x + 1,
Lambda(a, (a**2 * Rational(6, 31) - a * Rational(9, 31) +
Rational(4, 31)) / (x - a)),
auto=False))
f = 1 / (x**5 + 1)
assert apart(f, full=False) == \
(Rational(-1, 5))*((x**3 - 2*x**2 + 3*x - 4)/(x**4 - x**3 + x**2 -
x + 1)) + (Rational(1, 5))/(x + 1)
assert apart(f, full=True).dummy_eq(-RootSum(
x**4 - x**3 + x**2 - x + 1, Lambda(a, a / (x - a)), auto=False) / 5 +
(Rational(1, 5)) / (x + 1))
def eval_prob(self, _domain):
sym = list(self.symbols)[0]
if isinstance(_domain, Range):
n = symbols('n', integer=True)
inf, sup, step = (r for r in _domain.args)
summand = ((self.pdf).replace(sym, n * step))
rv = summation(summand, (n, inf / step, (sup) / step - 1)).doit()
return rv
elif isinstance(_domain, FiniteSet):
pdf = Lambda(sym, self.pdf)
rv = sum(pdf(x) for x in _domain)
return rv
elif isinstance(_domain, Union):
rv = sum(self.eval_prob(x) for x in _domain.args)
return rv
def compute_cdf(self, **kwargs):
""" Compute the CDF from the PDF.
Returns a Lambda.
"""
x = symbols('x', integer=True, cls=Dummy)
z = symbols('z', real=True, cls=Dummy)
left_bound = self.set.inf
# CDF is integral of PDF from left bound to z
pdf = self.pdf(x)
cdf = summation(pdf, (x, left_bound, floor(z)), **kwargs)
# CDF Ensure that CDF left of left_bound is zero
cdf = Piecewise((cdf, z >= left_bound), (0, True))
return Lambda(z, cdf)
