本文整理汇总了Python中numpy.lib.twodim_base.diag方法的典型用法代码示例。如果您正苦于以下问题:Python twodim_base.diag方法的具体用法?Python twodim_base.diag怎么用?Python twodim_base.diag使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类numpy.lib.twodim_base
的用法示例。
在下文中一共展示了twodim_base.diag方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: select
# 需要导入模块: from numpy.lib import twodim_base [as 别名]
# 或者: from numpy.lib.twodim_base import diag [as 别名]
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))
示例2: corrcoef
# 需要导入模块: from numpy.lib import twodim_base [as 别名]
# 或者: from numpy.lib.twodim_base import diag [as 别名]
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))
示例3: corrcoef
# 需要导入模块: from numpy.lib import twodim_base [as 别名]
# 或者: from numpy.lib.twodim_base import diag [as 别名]
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))