Python twodim_base.diag方法代碼示例

# 需要導入模塊: from numpy.lib import twodim_base [as 別名]
# 或者: from numpy.lib.twodim_base import diag [as 別名]
def select(condlist, choicelist, default=0):
    """
    Return an array drawn from elements in choicelist, depending on conditions.

    Parameters
    ----------
    condlist : list of bool ndarrays
        The list of conditions which determine from which array in `choicelist`
        the output elements are taken. When multiple conditions are satisfied,
        the first one encountered in `condlist` is used.
    choicelist : list of ndarrays
        The list of arrays from which the output elements are taken. It has
        to be of the same length as `condlist`.
    default : scalar, optional
        The element inserted in `output` when all conditions evaluate to False.

    Returns
    -------
    output : ndarray
        The output at position m is the m-th element of the array in
        `choicelist` where the m-th element of the corresponding array in
        `condlist` is True.

    See Also
    --------
    where : Return elements from one of two arrays depending on condition.
    take, choose, compress, diag, diagonal

    Examples
    --------
    >>> x = np.arange(10)
    >>> condlist = [x<3, x>5]
    >>> choicelist = [x, x**2]
    >>> np.select(condlist, choicelist)
    array([ 0,  1,  2,  0,  0,  0, 36, 49, 64, 81])

    """
    n = len(condlist)
    n2 = len(choicelist)
    if n2 != n:
        raise ValueError(
                "list of cases must be same length as list of conditions")
    choicelist = [default] + choicelist
    S = 0
    pfac = 1
    for k in range(1, n+1):
        S += k * pfac * asarray(condlist[k-1])
        if k < n:
            pfac *= (1-asarray(condlist[k-1]))
    # handle special case of a 1-element condition but
    #  a multi-element choice
    if type(S) in ScalarType or max(asarray(S).shape)==1:
        pfac = asarray(1)
        for k in range(n2+1):
            pfac = pfac + asarray(choicelist[k])
        if type(S) in ScalarType:
            S = S*ones(asarray(pfac).shape, type(S))
        else:
            S = S*ones(asarray(pfac).shape, S.dtype)
    return choose(S, tuple(choicelist)) 

# 需要導入模塊: from numpy.lib import twodim_base [as 別名]
# 或者: from numpy.lib.twodim_base import diag [as 別名]
def corrcoef(x, y=None, rowvar=1, bias=0, ddof=None):
    """
    Return correlation coefficients.

    Please refer to the documentation for `cov` for more detail.  The
    relationship between the correlation coefficient matrix, `P`, and the
    covariance matrix, `C`, is

    .. math:: P_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} * C_{jj} } }

    The values of `P` are between -1 and 1, inclusive.

    Parameters
    ----------
    x : array_like
        A 1-D or 2-D array containing multiple variables and observations.
        Each row of `m` represents a variable, and each column a single
        observation of all those variables. Also see `rowvar` below.
    y : array_like, optional
        An additional set of variables and observations. `y` has the same
        shape as `m`.
    rowvar : int, optional
        If `rowvar` is non-zero (default), then each row represents a
        variable, with observations in the columns. Otherwise, the relationship
        is transposed: each column represents a variable, while the rows
        contain observations.
    bias : int, optional
        Default normalization is by ``(N - 1)``, where ``N`` is the number of
        observations (unbiased estimate). If `bias` is 1, then
        normalization is by ``N``. These values can be overridden by using
        the keyword ``ddof`` in numpy versions >= 1.5.
    ddof : {None, int}, optional
        .. versionadded:: 1.5
        If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
        the number of observations; this overrides the value implied by
        ``bias``. The default value is ``None``.

    Returns
    -------
    out : ndarray
        The correlation coefficient matrix of the variables.

    See Also
    --------
    cov : Covariance matrix

    """
    c = cov(x, y, rowvar, bias, ddof)
    if c.size == 0:
        # handle empty arrays
        return c
    try:
        d = diag(c)
    except ValueError: # scalar covariance
        return 1
    return c/sqrt(multiply.outer(d, d)) 

# 需要導入模塊: from numpy.lib import twodim_base [as 別名]
# 或者: from numpy.lib.twodim_base import diag [as 別名]
def corrcoef(x, y=None, rowvar=1, bias=0, ddof=None):
    """
    Return correlation coefficients.

    Please refer to the documentation for `cov` for more detail.  The
    relationship between the correlation coefficient matrix, `P`, and the
    covariance matrix, `C`, is

    .. math:: P_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} * C_{jj} } }

    The values of `P` are between -1 and 1, inclusive.

    Parameters
    ----------
    x : array_like
        A 1-D or 2-D array containing multiple variables and observations.
        Each row of `m` represents a variable, and each column a single
        observation of all those variables. Also see `rowvar` below.
    y : array_like, optional
        An additional set of variables and observations. `y` has the same
        shape as `m`.
    rowvar : int, optional
        If `rowvar` is non-zero (default), then each row represents a
        variable, with observations in the columns. Otherwise, the relationship
        is transposed: each column represents a variable, while the rows
        contain observations.
    bias : int, optional
        Default normalization is by ``(N - 1)``, where ``N`` is the number of
        observations (unbiased estimate). If `bias` is 1, then
        normalization is by ``N``. These values can be overridden by using
        the keyword ``ddof`` in numpy versions >= 1.5.
    ddof : {None, int}, optional
        .. versionadded:: 1.5
        If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
        the number of observations; this overrides the value implied by
        ``bias``. The default value is ``None``.

    Returns
    -------
    out : ndarray
        The correlation coefficient matrix of the variables.

    See Also
    --------
    cov : Covariance matrix

    """
    c = cov(x, y, rowvar, bias, ddof)
    try:
        d = diag(c)
    except ValueError:  # scalar covariance
        # nan if incorrect value (nan, inf, 0), 1 otherwise
        return c / c
    return c / sqrt(multiply.outer(d, d))