def test_fuzzy_and():
assert fuzzy_and([T, T]) == T
assert fuzzy_and([T, F]) == F
assert fuzzy_and([T, U]) == U
assert fuzzy_and([F, F]) == F
assert fuzzy_and([F, U]) == F
assert fuzzy_and([U, U]) == U
assert [fuzzy_and([w]) for w in [U, T, F]] == [U, T, F]
assert fuzzy_and([T, F, U]) == F
assert fuzzy_and([]) == T
pytest.raises(TypeError, lambda: fuzzy_and())
def __new__(cls, *args):
n = args[BinomialDistribution._argnames.index('n')]
p = args[BinomialDistribution._argnames.index('p')]
n_sym = sympify(n)
p_sym = sympify(p)
if fuzzy_not(fuzzy_and((n_sym.is_integer, n_sym.is_nonnegative))):
raise ValueError("'n' must be positive integer. n = %s." % str(n))
elif fuzzy_not(
fuzzy_and((p_sym.is_nonnegative, (p_sym - 1).is_nonpositive))):
raise ValueError("'p' must be: 0 <= p <= 1 . p = %s" % str(p))
else:
return super(BinomialDistribution, cls).__new__(cls, *args)
def cond():
d = self._smat
yield self.is_square
if len(d) <= self.rows:
yield fuzzy_and(d[i, i].is_extended_real for i, j in d
if i == j)
else:
yield fuzzy_and(d[i, i].is_extended_real
for i in range(self.rows) if (i, i) in d)
yield fuzzy_and(
((self[i, j] -
self[j, i].conjugate()).is_zero if (j, i) in d else False)
for (i, j) in d)
def is_hermitian(self):
"""Checks if the matrix is Hermitian.
In a Hermitian matrix element i,j is the complex conjugate of
element j,i.
Examples
========
>>> from diofant.matrices import SparseMatrix
>>> from diofant import I
>>> from diofant.abc import x
>>> a = SparseMatrix([[1, I], [-I, 1]])
>>> a
Matrix([
[ 1, I],
[-I, 1]])
>>> a.is_hermitian
True
>>> a[0, 0] = 2*I
>>> a.is_hermitian
False
>>> a[0, 0] = x
>>> a.is_hermitian
>>> a[0, 1] = a[1, 0]*I
>>> a.is_hermitian
False
"""
def cond():
d = self._smat
yield self.is_square
if len(d) <= self.rows:
yield fuzzy_and(d[i, i].is_extended_real for i, j in d
if i == j)
else:
yield fuzzy_and(d[i, i].is_extended_real
for i in range(self.rows) if (i, i) in d)
yield fuzzy_and(
((self[i, j] -
self[j, i].conjugate()).is_zero if (j, i) in d else False)
for (i, j) in d)
return fuzzy_and(i for i in cond())
def __new__(cls, sides):
sides_sym = sympify(sides)
if fuzzy_not(fuzzy_and((sides_sym.is_integer, sides_sym.is_positive))):
raise ValueError("'sides' must be a positive integer.")
else:
return super(DieDistribution, cls).__new__(cls, sides)