Python numerictypes.integer方法代码示例

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import integer [as 别名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
    arr = asanyarray(a)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    ret = umr_sum(arr, axis, dtype, out, keepdims)
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import integer [as 别名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
    arr = asanyarray(a)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning)


    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    ret = um.add.reduce(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    else:
        ret = ret.dtype.type(ret / rcount)

    return ret 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import integer [as 别名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
    arr = asanyarray(a)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning)


    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    ret = umr_sum(arr, axis, dtype, out, keepdims)
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import integer [as 别名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
    arr = asanyarray(a)

    is_float16_result = False
    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None:
        if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
            dtype = mu.dtype('f8')
        elif issubclass(arr.dtype.type, nt.float16):
            dtype = mu.dtype('f4')
            is_float16_result = True

    ret = umr_sum(arr, axis, dtype, out, keepdims)
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
        if is_float16_result and out is None:
            ret = arr.dtype.type(ret)
    elif hasattr(ret, 'dtype'):
        if is_float16_result:
            ret = arr.dtype.type(ret / rcount)
        else:
            ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import integer [as 别名]
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    arr = asanyarray(a)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up on top.
    if ddof >= rcount:
        warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
                      stacklevel=2)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    # Compute the mean.
    # Note that if dtype is not of inexact type then arraymean will
    # not be either.
    arrmean = umr_sum(arr, axis, dtype, keepdims=True)
    if isinstance(arrmean, mu.ndarray):
        arrmean = um.true_divide(
                arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
    else:
        arrmean = arrmean.dtype.type(arrmean / rcount)

    # Compute sum of squared deviations from mean
    # Note that x may not be inexact and that we need it to be an array,
    # not a scalar.
    x = asanyarray(arr - arrmean)
    if issubclass(arr.dtype.type, nt.complexfloating):
        x = um.multiply(x, um.conjugate(x), out=x).real
    else:
        x = um.multiply(x, x, out=x)
    ret = umr_sum(x, axis, dtype, out, keepdims)

    # Compute degrees of freedom and make sure it is not negative.
    rcount = max([rcount - ddof, 0])

    # divide by degrees of freedom
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import integer [as 别名]
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    arr = asanyarray(a)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up on top.
    if ddof >= rcount:
        warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    # Compute the mean.
    # Note that if dtype is not of inexact type then arraymean will
    # not be either.
    arrmean = umr_sum(arr, axis, dtype, keepdims=True)
    if isinstance(arrmean, mu.ndarray):
        arrmean = um.true_divide(
                arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
    else:
        arrmean = arrmean.dtype.type(arrmean / rcount)

    # Compute sum of squared deviations from mean
    # Note that x may not be inexact and that we need it to be an array,
    # not a scalar.
    x = asanyarray(arr - arrmean)
    if issubclass(arr.dtype.type, nt.complexfloating):
        x = um.multiply(x, um.conjugate(x), out=x).real
    else:
        x = um.multiply(x, x, out=x)
    ret = umr_sum(x, axis, dtype, out, keepdims)

    # Compute degrees of freedom and make sure it is not negative.
    rcount = max([rcount - ddof, 0])

    # divide by degrees of freedom
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import integer [as 别名]
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    arr = asanyarray(a)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up on top.
    if ddof >= rcount:
        warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    # Compute the mean.
    # Note that if dtype is not of inexact type then arraymean will
    # not be either.
    arrmean = um.add.reduce(arr, axis=axis, dtype=dtype, keepdims=True)
    if isinstance(arrmean, mu.ndarray):
        arrmean = um.true_divide(
                arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
    else:
        arrmean = arrmean.dtype.type(arrmean / rcount)

    # Compute sum of squared deviations from mean
    # Note that x may not be inexact and that we need it to be an array,
    # not a scalar.
    x = asanyarray(arr - arrmean)
    if issubclass(arr.dtype.type, nt.complexfloating):
        x = um.multiply(x, um.conjugate(x), out=x).real
    else:
        x = um.multiply(x, x, out=x)
    ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out, keepdims=keepdims)

    # Compute degrees of freedom and make sure it is not negative.
    rcount = max([rcount - ddof, 0])

    # divide by degrees of freedom
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    else:
        ret = ret.dtype.type(ret / rcount)

    return ret