Python numeric.ndarray方法代碼示例

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def __getitem__(self, index):
        self._getitem = True

        try:
            out = N.ndarray.__getitem__(self, index)
        finally:
            self._getitem = False

        if not isinstance(out, N.ndarray):
            return out

        if out.ndim == 0:
            return out[()]
        if out.ndim == 1:
            sh = out.shape[0]
            # Determine when we should have a column array
            try:
                n = len(index)
            except Exception:
                n = 0
            if n > 1 and isscalar(index[1]):
                out.shape = (sh, 1)
            else:
                out.shape = (1, sh)
        return out 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def tolist(self):
        """
        Return the matrix as a (possibly nested) list.

        See `ndarray.tolist` for full documentation.

        See Also
        --------
        ndarray.tolist

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.tolist()
        [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]

        """
        return self.__array__().tolist()

    # To preserve orientation of result... 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def any(self, axis=None, out=None):
        """
        Test whether any array element along a given axis evaluates to True.

        Refer to `numpy.any` for full documentation.

        Parameters
        ----------
        axis : int, optional
            Axis along which logical OR is performed
        out : ndarray, optional
            Output to existing array instead of creating new one, must have
            same shape as expected output

        Returns
        -------
            any : bool, ndarray
                Returns a single bool if `axis` is ``None``; otherwise,
                returns `ndarray`

        """
        return N.ndarray.any(self, axis, out, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def __getitem__(self, index):
        self._getitem = True

        try:
            out = N.ndarray.__getitem__(self, index)
        finally:
            self._getitem = False

        if not isinstance(out, N.ndarray):
            return out

        if out.ndim == 0:
            return out[()]
        if out.ndim == 1:
            sh = out.shape[0]
            # Determine when we should have a column array
            try:
                n = len(index)
            except:
                n = 0
            if n > 1 and isscalar(index[1]):
                out.shape = (sh, 1)
            else:
                out.shape = (1, sh)
        return out 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def asmatrix(data, dtype=None):
    """
    Interpret the input as a matrix.

    Unlike `matrix`, `asmatrix` does not make a copy if the input is already
    a matrix or an ndarray.  Equivalent to ``matrix(data, copy=False)``.

    Parameters
    ----------
    data : array_like
        Input data.
    dtype : data-type
       Data-type of the output matrix.

    Returns
    -------
    mat : matrix
        `data` interpreted as a matrix.

    Examples
    --------
    >>> x = np.array([[1, 2], [3, 4]])

    >>> m = np.asmatrix(x)

    >>> x[0,0] = 5

    >>> m
    matrix([[5, 2],
            [3, 4]])

    """
    return matrix(data, dtype=dtype, copy=False) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def __mul__(self, other):
        if isinstance(other, (N.ndarray, list, tuple)) :
            # This promotes 1-D vectors to row vectors
            return N.dot(self, asmatrix(other))
        if isscalar(other) or not hasattr(other, '__rmul__') :
            return N.dot(self, other)
        return NotImplemented 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def flatten(self, order='C'):
        """
        Return a flattened copy of the matrix.

        All `N` elements of the matrix are placed into a single row.

        Parameters
        ----------
        order : {'C', 'F', 'A', 'K'}, optional
            'C' means to flatten in row-major (C-style) order. 'F' means to
            flatten in column-major (Fortran-style) order. 'A' means to
            flatten in column-major order if `m` is Fortran *contiguous* in
            memory, row-major order otherwise. 'K' means to flatten `m` in
            the order the elements occur in memory. The default is 'C'.

        Returns
        -------
        y : matrix
            A copy of the matrix, flattened to a `(1, N)` matrix where `N`
            is the number of elements in the original matrix.

        See Also
        --------
        ravel : Return a flattened array.
        flat : A 1-D flat iterator over the matrix.

        Examples
        --------
        >>> m = np.matrix([[1,2], [3,4]])
        >>> m.flatten()
        matrix([[1, 2, 3, 4]])
        >>> m.flatten('F')
        matrix([[1, 3, 2, 4]])

        """
        return N.ndarray.flatten(self, order=order) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def mean(self, axis=None, dtype=None, out=None):
        """
        Returns the average of the matrix elements along the given axis.

        Refer to `numpy.mean` for full documentation.

        See Also
        --------
        numpy.mean

        Notes
        -----
        Same as `ndarray.mean` except that, where that returns an `ndarray`,
        this returns a `matrix` object.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
        >>> x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.mean()
        5.5
        >>> x.mean(0)
        matrix([[ 4.,  5.,  6.,  7.]])
        >>> x.mean(1)
        matrix([[ 1.5],
                [ 5.5],
                [ 9.5]])

        """
        return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def std(self, axis=None, dtype=None, out=None, ddof=0):
        """
        Return the standard deviation of the array elements along the given axis.

        Refer to `numpy.std` for full documentation.

        See Also
        --------
        numpy.std

        Notes
        -----
        This is the same as `ndarray.std`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
        >>> x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.std()
        3.4520525295346629
        >>> x.std(0)
        matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]])
        >>> x.std(1)
        matrix([[ 1.11803399],
                [ 1.11803399],
                [ 1.11803399]])

        """
        return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def var(self, axis=None, dtype=None, out=None, ddof=0):
        """
        Returns the variance of the matrix elements, along the given axis.

        Refer to `numpy.var` for full documentation.

        See Also
        --------
        numpy.var

        Notes
        -----
        This is the same as `ndarray.var`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
        >>> x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.var()
        11.916666666666666
        >>> x.var(0)
        matrix([[ 10.66666667,  10.66666667,  10.66666667,  10.66666667]])
        >>> x.var(1)
        matrix([[ 1.25],
                [ 1.25],
                [ 1.25]])

        """
        return N.ndarray.var(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def all(self, axis=None, out=None):
        """
        Test whether all matrix elements along a given axis evaluate to True.

        Parameters
        ----------
        See `numpy.all` for complete descriptions

        See Also
        --------
        numpy.all

        Notes
        -----
        This is the same as `ndarray.all`, but it returns a `matrix` object.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> y = x[0]; y
        matrix([[0, 1, 2, 3]])
        >>> (x == y)
        matrix([[ True,  True,  True,  True],
                [False, False, False, False],
                [False, False, False, False]])
        >>> (x == y).all()
        False
        >>> (x == y).all(0)
        matrix([[False, False, False, False]])
        >>> (x == y).all(1)
        matrix([[ True],
                [False],
                [False]])

        """
        return N.ndarray.all(self, axis, out, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def max(self, axis=None, out=None):
        """
        Return the maximum value along an axis.

        Parameters
        ----------
        See `amax` for complete descriptions

        See Also
        --------
        amax, ndarray.max

        Notes
        -----
        This is the same as `ndarray.max`, but returns a `matrix` object
        where `ndarray.max` would return an ndarray.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.max()
        11
        >>> x.max(0)
        matrix([[ 8,  9, 10, 11]])
        >>> x.max(1)
        matrix([[ 3],
                [ 7],
                [11]])

        """
        return N.ndarray.max(self, axis, out, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def argmax(self, axis=None, out=None):
        """
        Indexes of the maximum values along an axis.

        Return the indexes of the first occurrences of the maximum values
        along the specified axis.  If axis is None, the index is for the
        flattened matrix.

        Parameters
        ----------
        See `numpy.argmax` for complete descriptions

        See Also
        --------
        numpy.argmax

        Notes
        -----
        This is the same as `ndarray.argmax`, but returns a `matrix` object
        where `ndarray.argmax` would return an `ndarray`.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.argmax()
        11
        >>> x.argmax(0)
        matrix([[2, 2, 2, 2]])
        >>> x.argmax(1)
        matrix([[3],
                [3],
                [3]])

        """
        return N.ndarray.argmax(self, axis, out)._align(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def min(self, axis=None, out=None):
        """
        Return the minimum value along an axis.

        Parameters
        ----------
        See `amin` for complete descriptions.

        See Also
        --------
        amin, ndarray.min

        Notes
        -----
        This is the same as `ndarray.min`, but returns a `matrix` object
        where `ndarray.min` would return an ndarray.

        Examples
        --------
        >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[  0,  -1,  -2,  -3],
                [ -4,  -5,  -6,  -7],
                [ -8,  -9, -10, -11]])
        >>> x.min()
        -11
        >>> x.min(0)
        matrix([[ -8,  -9, -10, -11]])
        >>> x.min(1)
        matrix([[ -3],
                [ -7],
                [-11]])

        """
        return N.ndarray.min(self, axis, out, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndarray [as 別名]
def ptp(self, axis=None, out=None):
        """
        Peak-to-peak (maximum - minimum) value along the given axis.

        Refer to `numpy.ptp` for full documentation.

        See Also
        --------
        numpy.ptp

        Notes
        -----
        Same as `ndarray.ptp`, except, where that would return an `ndarray` object,
        this returns a `matrix` object.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.ptp()
        11
        >>> x.ptp(0)
        matrix([[8, 8, 8, 8]])
        >>> x.ptp(1)
        matrix([[3],
                [3],
                [3]])

        """
        return N.ndarray.ptp(self, axis, out)._align(axis)