本文整理汇总了Python中numpy.matrixlib.defmatrix.asmatrix方法的典型用法代码示例。如果您正苦于以下问题：Python defmatrix.asmatrix方法的具体用法？Python defmatrix.asmatrix怎么用？Python defmatrix.asmatrix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.matrixlib.defmatrix的用法示例。
在下文中一共展示了defmatrix.asmatrix方法的5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: eye
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import asmatrix [as 别名]
def eye(n,M=None, k=0, dtype=float):
"""
Return a matrix with ones on the diagonal and zeros elsewhere.
Parameters
----------
n : int
Number of rows in the output.
M : int, optional
Number of columns in the output, defaults to `n`.
k : int, optional
Index of the diagonal: 0 refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
dtype : dtype, optional
Data-type of the returned matrix.
Returns
-------
I : matrix
A `n` x `M` matrix where all elements are equal to zero,
except for the `k`-th diagonal, whose values are equal to one.
See Also
--------
numpy.eye : Equivalent array function.
identity : Square identity matrix.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.eye(3, k=1, dtype=float)
matrix([[ 0., 1., 0.],
[ 0., 0., 1.],
[ 0., 0., 0.]])
"""
return asmatrix(np.eye(n, M, k, dtype)) 示例2: eye
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import asmatrix [as 别名]
def eye(n,M=None, k=0, dtype=float, order='C'):
"""
Return a matrix with ones on the diagonal and zeros elsewhere.
Parameters
----------
n : int
Number of rows in the output.
M : int, optional
Number of columns in the output, defaults to `n`.
k : int, optional
Index of the diagonal: 0 refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
dtype : dtype, optional
Data-type of the returned matrix.
order : {'C', 'F'}, optional
Whether the output should be stored in row-major (C-style) or
column-major (Fortran-style) order in memory.
.. versionadded:: 1.14.0
Returns
-------
I : matrix
A `n` x `M` matrix where all elements are equal to zero,
except for the `k`-th diagonal, whose values are equal to one.
See Also
--------
numpy.eye : Equivalent array function.
identity : Square identity matrix.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.eye(3, k=1, dtype=float)
matrix([[ 0., 1., 0.],
[ 0., 0., 1.],
[ 0., 0., 0.]])
"""
return asmatrix(np.eye(n, M=M, k=k, dtype=dtype, order=order)) 示例3: rand
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import asmatrix [as 别名]
def rand(*args):
"""
Return a matrix of random values with given shape.
Create a matrix of the given shape and propagate it with
random samples from a uniform distribution over ``[0, 1)``.
Parameters
----------
\\*args : Arguments
Shape of the output.
If given as N integers, each integer specifies the size of one
dimension.
If given as a tuple, this tuple gives the complete shape.
Returns
-------
out : ndarray
The matrix of random values with shape given by `\\*args`.
See Also
--------
randn, numpy.random.rand
Examples
--------
>>> import numpy.matlib
>>> np.matlib.rand(2, 3)
matrix([[ 0.68340382, 0.67926887, 0.83271405],
[ 0.00793551, 0.20468222, 0.95253525]]) #random
>>> np.matlib.rand((2, 3))
matrix([[ 0.84682055, 0.73626594, 0.11308016],
[ 0.85429008, 0.3294825 , 0.89139555]]) #random
If the first argument is a tuple, other arguments are ignored:
>>> np.matlib.rand((2, 3), 4)
matrix([[ 0.46898646, 0.15163588, 0.95188261],
[ 0.59208621, 0.09561818, 0.00583606]]) #random
"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.rand(*args)) 示例4: randn
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import asmatrix [as 别名]
def randn(*args):
"""
Return a random matrix with data from the "standard normal" distribution.
`randn` generates a matrix filled with random floats sampled from a
univariate "normal" (Gaussian) distribution of mean 0 and variance 1.
Parameters
----------
\\*args : Arguments
Shape of the output.
If given as N integers, each integer specifies the size of one
dimension. If given as a tuple, this tuple gives the complete shape.
Returns
-------
Z : matrix of floats
A matrix of floating-point samples drawn from the standard normal
distribution.
See Also
--------
rand, random.randn
Notes
-----
For random samples from :math:`N(\\mu, \\sigma^2)`, use:
``sigma * np.matlib.randn(...) + mu``
Examples
--------
>>> import numpy.matlib
>>> np.matlib.randn(1)
matrix([[-0.09542833]]) #random
>>> np.matlib.randn(1, 2, 3)
matrix([[ 0.16198284, 0.0194571 , 0.18312985],
[-0.7509172 , 1.61055 , 0.45298599]]) #random
Two-by-four matrix of samples from :math:`N(3, 6.25)`:
>>> 2.5 * np.matlib.randn((2, 4)) + 3
matrix([[ 4.74085004, 8.89381862, 4.09042411, 4.83721922],
[ 7.52373709, 5.07933944, -2.64043543, 0.45610557]]) #random
"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.randn(*args)) 示例5: repmat
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import asmatrix [as 别名]
def repmat(a, m, n):
"""
Repeat a 0-D to 2-D array or matrix MxN times.
Parameters
----------
a : array_like
The array or matrix to be repeated.
m, n : int
The number of times `a` is repeated along the first and second axes.
Returns
-------
out : ndarray
The result of repeating `a`.
Examples
--------
>>> import numpy.matlib
>>> a0 = np.array(1)
>>> np.matlib.repmat(a0, 2, 3)
array([[1, 1, 1],
[1, 1, 1]])
>>> a1 = np.arange(4)
>>> np.matlib.repmat(a1, 2, 2)
array([[0, 1, 2, 3, 0, 1, 2, 3],
[0, 1, 2, 3, 0, 1, 2, 3]])
>>> a2 = np.asmatrix(np.arange(6).reshape(2, 3))
>>> np.matlib.repmat(a2, 2, 3)
matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5],
[0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5]])
"""
a = asanyarray(a)
ndim = a.ndim
if ndim == 0:
origrows, origcols = (1, 1)
elif ndim == 1:
origrows, origcols = (1, a.shape[0])
else:
origrows, origcols = a.shape
rows = origrows * m
cols = origcols * n
c = a.reshape(1, a.size).repeat(m, 0).reshape(rows, origcols).repeat(n, 0)
return c.reshape(rows, cols)