/usr/lib/python2.7/dist-packages/numpy/matlib.py is in python-numpy 1:1.12.1-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 | from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.matrixlib.defmatrix import matrix, asmatrix
# need * as we're copying the numpy namespace
from numpy import *
__version__ = np.__version__
__all__ = np.__all__[:] # copy numpy namespace
__all__ += ['rand', 'randn', 'repmat']
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)
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
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
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
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))
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))
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))
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)
|