Python logic.fuzzy_or函数代码示例

def test_fuzzy_or():
    assert fuzzy_or([T, T]) == T
    assert fuzzy_or([T, F]) == T
    assert fuzzy_or([T, U]) == T
    assert fuzzy_or([F, F]) == F
    assert fuzzy_or([F, U]) == U
    assert fuzzy_or([U, U]) == U
    assert [fuzzy_or([w]) for w in [U, T, F]] == [U, T, F]
    assert fuzzy_or([T, F, U]) == T
    assert fuzzy_or([]) == F
    raises(TypeError, lambda: fuzzy_or())

def is_monotonic(f, interval=S.Reals, symbol=None):
    """
    Returns if a function is monotonic or not, in the given
    ``Interval``.

    Examples
    ========

    >>> from sympy import is_monotonic
    >>> from sympy.abc import x, y
    >>> from sympy import S, Interval, oo
    >>> is_monotonic(1/(x**2 - 3*x), Interval.open(1.5, 3))
    True
    >>> is_monotonic(1/(x**2 - 3*x), Interval.Lopen(3, oo))
    True
    >>> is_monotonic(x**3 - 3*x**2 + 4*x, S.Reals)
    True
    >>> is_monotonic(-x**2, S.Reals)
    False
    >>> is_monotonic(x**2 + y + 1, Interval(1, 2), x)
    True

    """
    from sympy.core.logic import fuzzy_or
    f = sympify(f)
    free_sym = f.free_symbols

    if symbol is None and len(free_sym) > 1:
        raise NotImplementedError('is_monotonic has not yet been '
                                'for all multivariate expressions')

    inc = is_increasing(f, interval, symbol)
    dec = is_decreasing(f, interval, symbol)

    return fuzzy_or([inc, dec])

def is_monotonic(f, interval=S.Reals):
    """
    Returns if a function is monotonic or not, in the given
    ``Interval``.

    Examples
    ========

    >>> from sympy import is_monotonic
    >>> from sympy.abc import x
    >>> from sympy import S, Interval, oo
    >>> is_monotonic(1/(x**2 - 3*x), Interval.open(1.5, 3))
    True
    >>> is_monotonic(1/(x**2 - 3*x), Interval.Lopen(3, oo))
    True
    >>> is_monotonic(x**3 - 3*x**2 + 4*x, S.Reals)
    True
    >>> is_monotonic(-x**2, S.Reals)
    False

    """
    from sympy.core.logic import fuzzy_or
    if len(f.free_symbols) > 1:
        raise NotImplementedError('is_monotonic has not yet been '
                                  'implemented for multivariate expressions')
    inc = is_increasing(f, interval)
    dec = is_decreasing(f, interval)

    return fuzzy_or([inc, dec])

 def Basic(expr, assumptions):
     if expr.is_number:
         # if there are no symbols just evalf
         i = expr.evalf(2)
         def nonz(i):
             if i._prec != 1:
                 return i != 0
         return fuzzy_or(nonz(i) for i in i.as_real_imag())

def _old_assump_replacer(obj):
    # Things to be careful of:
    # - real means real or infinite in the old assumptions.
    # - nonzero does not imply real in the old assumptions.
    # - finite means finite and not zero in the old assumptions.
    if not isinstance(obj, AppliedPredicate):
        return obj

    e = obj.args[0]
    ret = None

    if obj.func == Q.positive:
        ret = fuzzy_and([e.is_finite, e.is_positive])
    if obj.func == Q.zero:
        ret = e.is_zero
    if obj.func == Q.negative:
        ret = fuzzy_and([e.is_finite, e.is_negative])
    if obj.func == Q.nonpositive:
        ret = fuzzy_and([e.is_finite, e.is_nonpositive])
    if obj.func == Q.nonzero:
        ret = fuzzy_and([e.is_nonzero, e.is_finite])
    if obj.func == Q.nonnegative:
        ret = fuzzy_and([fuzzy_or([e.is_zero, e.is_finite]),
        e.is_nonnegative])

    if obj.func == Q.rational:
        ret = e.is_rational
    if obj.func == Q.irrational:
        ret = e.is_irrational

    if obj.func == Q.even:
        ret = e.is_even
    if obj.func == Q.odd:
        ret = e.is_odd
    if obj.func == Q.integer:
        ret = e.is_integer
    if obj.func == Q.imaginary:
        ret = e.is_imaginary
    if obj.func == Q.commutative:
        ret = e.is_commutative

    if ret is None:
        return obj
    return ret

 def _eval_is_zero(self):
     # is_imaginary implies nonzero
     return fuzzy_or([self.args[0].is_imaginary, self.args[0].is_zero])

 def Mul(expr, assumptions):
     # TODO: This should be deducible from the nonzero handler
     return fuzzy_or(ask(Q.zero(arg), assumptions) for arg in expr.args)

 def _eval_is_negative(self):
     return fuzzy_or(a.is_negative for a in self.args)