本文整理汇总了Python中numpy.matrixlib.defmatrix.matrix方法的典型用法代码示例。如果您正苦于以下问题：Python defmatrix.matrix方法的具体用法？Python defmatrix.matrix怎么用？Python defmatrix.matrix使用的例子？那么恭喜您, 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.matrix方法的8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: empty
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import matrix [as 别名]
def empty(shape, dtype=None, order='C'):
"""Return a new matrix of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
Shape of the empty matrix.
dtype : data-type, optional
Desired output data-type.
order : {'C', 'F'}, optional
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
See Also
--------
empty_like, zeros
Notes
-----
`empty`, unlike `zeros`, does not set the matrix values to zero,
and may therefore be marginally faster. On the other hand, it requires
the user to manually set all the values in the array, and should be
used with caution.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.empty((2, 2)) # filled with random data
matrix([[ 6.76425276e-320, 9.79033856e-307],
[ 7.39337286e-309, 3.22135945e-309]]) #random
>>> np.matlib.empty((2, 2), dtype=int)
matrix([[ 6600475, 0],
[ 6586976, 22740995]]) #random
"""
return ndarray.__new__(matrix, shape, dtype, order=order) 示例2: identity
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import matrix [as 别名]
def identity(n,dtype=None):
"""
Returns the square identity matrix of given size.
Parameters
----------
n : int
Size of the returned identity matrix.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : matrix
`n` x `n` matrix with its main diagonal set to one,
and all other elements zero.
See Also
--------
numpy.identity : Equivalent array function.
matlib.eye : More general matrix identity function.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.identity(3, dtype=int)
matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
"""
a = array([1]+n*[0], dtype=dtype)
b = empty((n, n), dtype=dtype)
b.flat = a
return b 示例3: eye
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import matrix [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4: empty
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import matrix [as 别名]
def empty(shape, dtype=None, order='C'):
"""
Return a new matrix of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
Shape of the empty matrix.
dtype : data-type, optional
Desired output data-type.
order : {'C', 'F'}, optional
Whether to store multi-dimensional data in C (row-major) or
Fortran (column-major) order in memory.
See Also
--------
empty_like, zeros
Notes
-----
`empty`, unlike `zeros`, does not set the matrix values to zero,
and may therefore be marginally faster. On the other hand, it requires
the user to manually set all the values in the array, and should be
used with caution.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.empty((2, 2)) # filled with random data
matrix([[ 6.76425276e-320, 9.79033856e-307],
[ 7.39337286e-309, 3.22135945e-309]]) #random
>>> np.matlib.empty((2, 2), dtype=int)
matrix([[ 6600475, 0],
[ 6586976, 22740995]]) #random
"""
return ndarray.__new__(matrix, shape, dtype, order=order) 示例5: ones
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import matrix [as 别名]
def ones(shape, dtype=None, order='C'):
"""
Matrix of ones.
Return a matrix of given shape and type, filled with ones.
Parameters
----------
shape : {sequence of ints, int}
Shape of the matrix
dtype : data-type, optional
The desired data-type for the matrix, default is np.float64.
order : {'C', 'F'}, optional
Whether to store matrix in C- or Fortran-contiguous order,
default is 'C'.
Returns
-------
out : matrix
Matrix of ones of given shape, dtype, and order.
See Also
--------
ones : Array of ones.
matlib.zeros : Zero matrix.
Notes
-----
If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
`out` becomes a single row matrix of shape ``(1,N)``.
Examples
--------
>>> np.matlib.ones((2,3))
matrix([[ 1., 1., 1.],
[ 1., 1., 1.]])
>>> np.matlib.ones(2)
matrix([[ 1., 1.]])
"""
a = ndarray.__new__(matrix, shape, dtype, order=order)
a.fill(1)
return a 示例6: zeros
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import matrix [as 别名]
def zeros(shape, dtype=None, order='C'):
"""
Return a matrix of given shape and type, filled with zeros.
Parameters
----------
shape : int or sequence of ints
Shape of the matrix
dtype : data-type, optional
The desired data-type for the matrix, default is float.
order : {'C', 'F'}, optional
Whether to store the result in C- or Fortran-contiguous order,
default is 'C'.
Returns
-------
out : matrix
Zero matrix of given shape, dtype, and order.
See Also
--------
numpy.zeros : Equivalent array function.
matlib.ones : Return a matrix of ones.
Notes
-----
If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
`out` becomes a single row matrix of shape ``(1,N)``.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.zeros((2, 3))
matrix([[ 0., 0., 0.],
[ 0., 0., 0.]])
>>> np.matlib.zeros(2)
matrix([[ 0., 0.]])
"""
a = ndarray.__new__(matrix, shape, dtype, order=order)
a.fill(0)
return a 示例7: eye
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import matrix [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8: randn
# 需要导入模块: from numpy.matrixlib import defmatrix [as 别名]
# 或者: from numpy.matrixlib.defmatrix import matrix [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))