Python numpy.nanprod方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nanprod [as 別名]
def __matmul__(self, other):
        if isinstance(other, Option):
            return self @ Domain(other)

        if self._is_array_option():
            that = self._to_scalar_product()
        else:
            that = self

        if other._is_array_option():
            other = other._to_scalar_product()

        if that._is_scalar_product() and other._is_scalar_product():
            if len(that.cubes) == len(other.cubes):
                cubes = [cube_1 + cube_2 for cube_1, cube_2 in zip(that.cubes, other.cubes)]
                weights = np.nanprod(np.stack([that.weights, other.weights]), axis=0)
                nan_mask = np.logical_and(np.isnan(that.weights), np.isnan(other.weights))
                weights[nan_mask] = np.nan
                return Domain(domain=cubes, weights=weights)
        raise ValueError("The numbers of domain cubes must conincide.") 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nanprod [as 別名]
def test_nanfunctions_matrices_general():
    # Check that it works and that type and
    # shape are preserved
    # 2018-04-29: moved here from core.tests.test_nanfunctions
    mat = np.matrix(np.eye(3))
    for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod,
              np.nanmean, np.nanvar, np.nanstd):
        res = f(mat, axis=0)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (1, 3))
        res = f(mat, axis=1)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (3, 1))
        res = f(mat)
        assert_(np.isscalar(res))

    for f in np.nancumsum, np.nancumprod:
        res = f(mat, axis=0)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (3, 3))
        res = f(mat, axis=1)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (3, 3))
        res = f(mat)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (1, 3*3)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nanprod [as 別名]
def test_nanprod(self):
        tgt = np.prod(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nanprod(mat), tgt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nanprod [as 別名]
def test_empty(self):
        for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]):
            mat = np.zeros((0, 3))
            tgt = [tgt_value]*3
            res = f(mat, axis=0)
            assert_equal(res, tgt)
            tgt = []
            res = f(mat, axis=1)
            assert_equal(res, tgt)
            tgt = tgt_value
            res = f(mat, axis=None)
            assert_equal(res, tgt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nanprod [as 別名]
def test_prod(self):
        self._check_stat_op('prod', np.prod, skipna_alternative=np.nanprod) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nanprod [as 別名]
def test_nanprod(self):
        self.check_funs(nanops.nanprod, np.prod, allow_str=False,
                        allow_date=False, allow_tdelta=False,
                        empty_targfunc=np.nanprod) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nanprod [as 別名]
def __mul__(self, other):
        if isinstance(other, float) and np.isnan(other):
            return self
        if self.cubes is None:
            result = other
        elif isinstance(other, (int, float)):
            result = self
            weights = self.weights
            weights[np.isnan(weights)] = 1
            result.weights = weights * other
        elif isinstance(other, Domain):
            if other.cubes is None:
                result = self
            else:
                res = list(product(self.cubes, other.cubes))
                res = [item[0] + item[1] for item in res]
                pairs = np.array(list(product(self.weights, other.weights)))
                weights = np.array([np.nanprod(item) for item in pairs])
                nan_mask = np.array([np.isnan(item).all() for item in pairs])
                weights[nan_mask] = np.nan
            result = Domain(res, weights=weights)
        elif isinstance(other, Option):
            result = self * Domain(other)
        else:
            raise TypeError('Arguments must be numeric, Domains or Options')
        return result 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nanprod [as 別名]
def nanprod(a, axis=None, dtype=None, out=None, keepdims=False):
    """Returns the product of an array along given axes treating Not a Numbers
    (NaNs) as zero.

    Args:
        a (cupy.ndarray): Array to take product.
        axis (int or sequence of ints): Axes along which the product is taken.
        dtype: Data type specifier.
        out (cupy.ndarray): Output array.
        keepdims (bool): If ``True``, the specified axes are remained as axes
            of length one.

    Returns:
        cupy.ndarray: The result array.

    .. seealso:: :func:`numpy.nanprod`

    """
    if _fusion_thread_local.is_fusing():
        if keepdims:
            raise NotImplementedError(
                'cupy.nanprod does not support `keepdims` in fusion yet.')
        if dtype is None:
            func = _math._nanprod_auto_dtype
        else:
            func = _math._nanprod_keep_dtype
        return _fusion_thread_local.call_reduction(
            func, a, axis=axis, dtype=dtype, out=out)

    # TODO(okuta): check type
    return _math._nanprod(a, axis, dtype, out, keepdims)