Python numpy.core.numeric.empty() Examples

The following are 25 code examples of numpy.core.numeric.empty(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy.core.numeric , or try the search function .
Example #1
Source File: shape_base.py    From Fluid-Designer with GNU General Public License v3.0 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if len(_nx.shape(sub_arys[i])) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #2
Source File: shape_base.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if len(_nx.shape(sub_arys[i])) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #3
Source File: shape_base.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #4
Source File: shape_base.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #5
Source File: shape_base.py    From Carnets with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #6
Source File: shape_base.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #7
Source File: shape_base.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #8
Source File: shape_base.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #9
Source File: shape_base.py    From ImageFusion with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if len(_nx.shape(sub_arys[i])) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #10
Source File: shape_base.py    From mxnet-lambda with Apache License 2.0 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #11
Source File: shape_base.py    From pySINDy with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #12
Source File: shape_base.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #13
Source File: shape_base.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #14
Source File: shape_base.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #15
Source File: shape_base.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #16
Source File: shape_base.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #17
Source File: shape_base.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if len(_nx.shape(sub_arys[i])) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #18
Source File: shape_base.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if len(_nx.shape(sub_arys[i])) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #19
Source File: shape_base.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _replace_zero_by_x_arrays(sub_arys):
    for i in range(len(sub_arys)):
        if _nx.ndim(sub_arys[i]) == 0:
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
    return sub_arys
Example #20
Source File: ufunclike.py    From Fluid-Designer with GNU General Public License v3.0 4 votes vote down vote up 
def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y
Example #21
Source File: ufunclike.py    From ImageFusion with MIT License 4 votes vote down vote up 
def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y
Example #22
Source File: ufunclike.py    From Computable with MIT License 4 votes vote down vote up 
def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y
Example #23
Source File: ufunclike.py    From auto-alt-text-lambda-api with MIT License 4 votes vote down vote up 
def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y
Example #24
Source File: ufunclike.py    From lambda-packs with MIT License 4 votes vote down vote up 
def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y
Example #25
Source File: ufunclike.py    From keras-lambda with MIT License 4 votes vote down vote up 
def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y