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annotate CSP2/CSP2_env/env-d9b9114564458d9d-741b3de822f2aaca6c6caa4325c4afce/lib/python3.8/site-packages/numpy/matlib.py @ 69:33d812a61356

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planemo upload commit 2e9511a184a1ca667c7be0c6321a36dc4e3d116d
author jpayne
date Tue, 18 Mar 2025 17:55:14 -0400
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jpayne@69 1 import warnings
jpayne@69 2
jpayne@69 3 # 2018-05-29, PendingDeprecationWarning added to matrix.__new__
jpayne@69 4 # 2020-01-23, numpy 1.19.0 PendingDeprecatonWarning
jpayne@69 5 warnings.warn("Importing from numpy.matlib is deprecated since 1.19.0. "
jpayne@69 6 "The matrix subclass is not the recommended way to represent "
jpayne@69 7 "matrices or deal with linear algebra (see "
jpayne@69 8 "https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). "
jpayne@69 9 "Please adjust your code to use regular ndarray. ",
jpayne@69 10 PendingDeprecationWarning, stacklevel=2)
jpayne@69 11
jpayne@69 12 import numpy as np
jpayne@69 13 from numpy.matrixlib.defmatrix import matrix, asmatrix
jpayne@69 14 # Matlib.py contains all functions in the numpy namespace with a few
jpayne@69 15 # replacements. See doc/source/reference/routines.matlib.rst for details.
jpayne@69 16 # Need * as we're copying the numpy namespace.
jpayne@69 17 from numpy import * # noqa: F403
jpayne@69 18
jpayne@69 19 __version__ = np.__version__
jpayne@69 20
jpayne@69 21 __all__ = np.__all__[:] # copy numpy namespace
jpayne@69 22 __all__ += ['rand', 'randn', 'repmat']
jpayne@69 23
jpayne@69 24 def empty(shape, dtype=None, order='C'):
jpayne@69 25 """Return a new matrix of given shape and type, without initializing entries.
jpayne@69 26
jpayne@69 27 Parameters
jpayne@69 28 ----------
jpayne@69 29 shape : int or tuple of int
jpayne@69 30 Shape of the empty matrix.
jpayne@69 31 dtype : data-type, optional
jpayne@69 32 Desired output data-type.
jpayne@69 33 order : {'C', 'F'}, optional
jpayne@69 34 Whether to store multi-dimensional data in row-major
jpayne@69 35 (C-style) or column-major (Fortran-style) order in
jpayne@69 36 memory.
jpayne@69 37
jpayne@69 38 See Also
jpayne@69 39 --------
jpayne@69 40 empty_like, zeros
jpayne@69 41
jpayne@69 42 Notes
jpayne@69 43 -----
jpayne@69 44 `empty`, unlike `zeros`, does not set the matrix values to zero,
jpayne@69 45 and may therefore be marginally faster. On the other hand, it requires
jpayne@69 46 the user to manually set all the values in the array, and should be
jpayne@69 47 used with caution.
jpayne@69 48
jpayne@69 49 Examples
jpayne@69 50 --------
jpayne@69 51 >>> import numpy.matlib
jpayne@69 52 >>> np.matlib.empty((2, 2)) # filled with random data
jpayne@69 53 matrix([[ 6.76425276e-320, 9.79033856e-307], # random
jpayne@69 54 [ 7.39337286e-309, 3.22135945e-309]])
jpayne@69 55 >>> np.matlib.empty((2, 2), dtype=int)
jpayne@69 56 matrix([[ 6600475, 0], # random
jpayne@69 57 [ 6586976, 22740995]])
jpayne@69 58
jpayne@69 59 """
jpayne@69 60 return ndarray.__new__(matrix, shape, dtype, order=order)
jpayne@69 61
jpayne@69 62 def ones(shape, dtype=None, order='C'):
jpayne@69 63 """
jpayne@69 64 Matrix of ones.
jpayne@69 65
jpayne@69 66 Return a matrix of given shape and type, filled with ones.
jpayne@69 67
jpayne@69 68 Parameters
jpayne@69 69 ----------
jpayne@69 70 shape : {sequence of ints, int}
jpayne@69 71 Shape of the matrix
jpayne@69 72 dtype : data-type, optional
jpayne@69 73 The desired data-type for the matrix, default is np.float64.
jpayne@69 74 order : {'C', 'F'}, optional
jpayne@69 75 Whether to store matrix in C- or Fortran-contiguous order,
jpayne@69 76 default is 'C'.
jpayne@69 77
jpayne@69 78 Returns
jpayne@69 79 -------
jpayne@69 80 out : matrix
jpayne@69 81 Matrix of ones of given shape, dtype, and order.
jpayne@69 82
jpayne@69 83 See Also
jpayne@69 84 --------
jpayne@69 85 ones : Array of ones.
jpayne@69 86 matlib.zeros : Zero matrix.
jpayne@69 87
jpayne@69 88 Notes
jpayne@69 89 -----
jpayne@69 90 If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
jpayne@69 91 `out` becomes a single row matrix of shape ``(1,N)``.
jpayne@69 92
jpayne@69 93 Examples
jpayne@69 94 --------
jpayne@69 95 >>> np.matlib.ones((2,3))
jpayne@69 96 matrix([[1., 1., 1.],
jpayne@69 97 [1., 1., 1.]])
jpayne@69 98
jpayne@69 99 >>> np.matlib.ones(2)
jpayne@69 100 matrix([[1., 1.]])
jpayne@69 101
jpayne@69 102 """
jpayne@69 103 a = ndarray.__new__(matrix, shape, dtype, order=order)
jpayne@69 104 a.fill(1)
jpayne@69 105 return a
jpayne@69 106
jpayne@69 107 def zeros(shape, dtype=None, order='C'):
jpayne@69 108 """
jpayne@69 109 Return a matrix of given shape and type, filled with zeros.
jpayne@69 110
jpayne@69 111 Parameters
jpayne@69 112 ----------
jpayne@69 113 shape : int or sequence of ints
jpayne@69 114 Shape of the matrix
jpayne@69 115 dtype : data-type, optional
jpayne@69 116 The desired data-type for the matrix, default is float.
jpayne@69 117 order : {'C', 'F'}, optional
jpayne@69 118 Whether to store the result in C- or Fortran-contiguous order,
jpayne@69 119 default is 'C'.
jpayne@69 120
jpayne@69 121 Returns
jpayne@69 122 -------
jpayne@69 123 out : matrix
jpayne@69 124 Zero matrix of given shape, dtype, and order.
jpayne@69 125
jpayne@69 126 See Also
jpayne@69 127 --------
jpayne@69 128 numpy.zeros : Equivalent array function.
jpayne@69 129 matlib.ones : Return a matrix of ones.
jpayne@69 130
jpayne@69 131 Notes
jpayne@69 132 -----
jpayne@69 133 If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
jpayne@69 134 `out` becomes a single row matrix of shape ``(1,N)``.
jpayne@69 135
jpayne@69 136 Examples
jpayne@69 137 --------
jpayne@69 138 >>> import numpy.matlib
jpayne@69 139 >>> np.matlib.zeros((2, 3))
jpayne@69 140 matrix([[0., 0., 0.],
jpayne@69 141 [0., 0., 0.]])
jpayne@69 142
jpayne@69 143 >>> np.matlib.zeros(2)
jpayne@69 144 matrix([[0., 0.]])
jpayne@69 145
jpayne@69 146 """
jpayne@69 147 a = ndarray.__new__(matrix, shape, dtype, order=order)
jpayne@69 148 a.fill(0)
jpayne@69 149 return a
jpayne@69 150
jpayne@69 151 def identity(n,dtype=None):
jpayne@69 152 """
jpayne@69 153 Returns the square identity matrix of given size.
jpayne@69 154
jpayne@69 155 Parameters
jpayne@69 156 ----------
jpayne@69 157 n : int
jpayne@69 158 Size of the returned identity matrix.
jpayne@69 159 dtype : data-type, optional
jpayne@69 160 Data-type of the output. Defaults to ``float``.
jpayne@69 161
jpayne@69 162 Returns
jpayne@69 163 -------
jpayne@69 164 out : matrix
jpayne@69 165 `n` x `n` matrix with its main diagonal set to one,
jpayne@69 166 and all other elements zero.
jpayne@69 167
jpayne@69 168 See Also
jpayne@69 169 --------
jpayne@69 170 numpy.identity : Equivalent array function.
jpayne@69 171 matlib.eye : More general matrix identity function.
jpayne@69 172
jpayne@69 173 Examples
jpayne@69 174 --------
jpayne@69 175 >>> import numpy.matlib
jpayne@69 176 >>> np.matlib.identity(3, dtype=int)
jpayne@69 177 matrix([[1, 0, 0],
jpayne@69 178 [0, 1, 0],
jpayne@69 179 [0, 0, 1]])
jpayne@69 180
jpayne@69 181 """
jpayne@69 182 a = array([1]+n*[0], dtype=dtype)
jpayne@69 183 b = empty((n, n), dtype=dtype)
jpayne@69 184 b.flat = a
jpayne@69 185 return b
jpayne@69 186
jpayne@69 187 def eye(n,M=None, k=0, dtype=float, order='C'):
jpayne@69 188 """
jpayne@69 189 Return a matrix with ones on the diagonal and zeros elsewhere.
jpayne@69 190
jpayne@69 191 Parameters
jpayne@69 192 ----------
jpayne@69 193 n : int
jpayne@69 194 Number of rows in the output.
jpayne@69 195 M : int, optional
jpayne@69 196 Number of columns in the output, defaults to `n`.
jpayne@69 197 k : int, optional
jpayne@69 198 Index of the diagonal: 0 refers to the main diagonal,
jpayne@69 199 a positive value refers to an upper diagonal,
jpayne@69 200 and a negative value to a lower diagonal.
jpayne@69 201 dtype : dtype, optional
jpayne@69 202 Data-type of the returned matrix.
jpayne@69 203 order : {'C', 'F'}, optional
jpayne@69 204 Whether the output should be stored in row-major (C-style) or
jpayne@69 205 column-major (Fortran-style) order in memory.
jpayne@69 206
jpayne@69 207 .. versionadded:: 1.14.0
jpayne@69 208
jpayne@69 209 Returns
jpayne@69 210 -------
jpayne@69 211 I : matrix
jpayne@69 212 A `n` x `M` matrix where all elements are equal to zero,
jpayne@69 213 except for the `k`-th diagonal, whose values are equal to one.
jpayne@69 214
jpayne@69 215 See Also
jpayne@69 216 --------
jpayne@69 217 numpy.eye : Equivalent array function.
jpayne@69 218 identity : Square identity matrix.
jpayne@69 219
jpayne@69 220 Examples
jpayne@69 221 --------
jpayne@69 222 >>> import numpy.matlib
jpayne@69 223 >>> np.matlib.eye(3, k=1, dtype=float)
jpayne@69 224 matrix([[0., 1., 0.],
jpayne@69 225 [0., 0., 1.],
jpayne@69 226 [0., 0., 0.]])
jpayne@69 227
jpayne@69 228 """
jpayne@69 229 return asmatrix(np.eye(n, M=M, k=k, dtype=dtype, order=order))
jpayne@69 230
jpayne@69 231 def rand(*args):
jpayne@69 232 """
jpayne@69 233 Return a matrix of random values with given shape.
jpayne@69 234
jpayne@69 235 Create a matrix of the given shape and propagate it with
jpayne@69 236 random samples from a uniform distribution over ``[0, 1)``.
jpayne@69 237
jpayne@69 238 Parameters
jpayne@69 239 ----------
jpayne@69 240 \\*args : Arguments
jpayne@69 241 Shape of the output.
jpayne@69 242 If given as N integers, each integer specifies the size of one
jpayne@69 243 dimension.
jpayne@69 244 If given as a tuple, this tuple gives the complete shape.
jpayne@69 245
jpayne@69 246 Returns
jpayne@69 247 -------
jpayne@69 248 out : ndarray
jpayne@69 249 The matrix of random values with shape given by `\\*args`.
jpayne@69 250
jpayne@69 251 See Also
jpayne@69 252 --------
jpayne@69 253 randn, numpy.random.RandomState.rand
jpayne@69 254
jpayne@69 255 Examples
jpayne@69 256 --------
jpayne@69 257 >>> np.random.seed(123)
jpayne@69 258 >>> import numpy.matlib
jpayne@69 259 >>> np.matlib.rand(2, 3)
jpayne@69 260 matrix([[0.69646919, 0.28613933, 0.22685145],
jpayne@69 261 [0.55131477, 0.71946897, 0.42310646]])
jpayne@69 262 >>> np.matlib.rand((2, 3))
jpayne@69 263 matrix([[0.9807642 , 0.68482974, 0.4809319 ],
jpayne@69 264 [0.39211752, 0.34317802, 0.72904971]])
jpayne@69 265
jpayne@69 266 If the first argument is a tuple, other arguments are ignored:
jpayne@69 267
jpayne@69 268 >>> np.matlib.rand((2, 3), 4)
jpayne@69 269 matrix([[0.43857224, 0.0596779 , 0.39804426],
jpayne@69 270 [0.73799541, 0.18249173, 0.17545176]])
jpayne@69 271
jpayne@69 272 """
jpayne@69 273 if isinstance(args[0], tuple):
jpayne@69 274 args = args[0]
jpayne@69 275 return asmatrix(np.random.rand(*args))
jpayne@69 276
jpayne@69 277 def randn(*args):
jpayne@69 278 """
jpayne@69 279 Return a random matrix with data from the "standard normal" distribution.
jpayne@69 280
jpayne@69 281 `randn` generates a matrix filled with random floats sampled from a
jpayne@69 282 univariate "normal" (Gaussian) distribution of mean 0 and variance 1.
jpayne@69 283
jpayne@69 284 Parameters
jpayne@69 285 ----------
jpayne@69 286 \\*args : Arguments
jpayne@69 287 Shape of the output.
jpayne@69 288 If given as N integers, each integer specifies the size of one
jpayne@69 289 dimension. If given as a tuple, this tuple gives the complete shape.
jpayne@69 290
jpayne@69 291 Returns
jpayne@69 292 -------
jpayne@69 293 Z : matrix of floats
jpayne@69 294 A matrix of floating-point samples drawn from the standard normal
jpayne@69 295 distribution.
jpayne@69 296
jpayne@69 297 See Also
jpayne@69 298 --------
jpayne@69 299 rand, numpy.random.RandomState.randn
jpayne@69 300
jpayne@69 301 Notes
jpayne@69 302 -----
jpayne@69 303 For random samples from the normal distribution with mean ``mu`` and
jpayne@69 304 standard deviation ``sigma``, use::
jpayne@69 305
jpayne@69 306 sigma * np.matlib.randn(...) + mu
jpayne@69 307
jpayne@69 308 Examples
jpayne@69 309 --------
jpayne@69 310 >>> np.random.seed(123)
jpayne@69 311 >>> import numpy.matlib
jpayne@69 312 >>> np.matlib.randn(1)
jpayne@69 313 matrix([[-1.0856306]])
jpayne@69 314 >>> np.matlib.randn(1, 2, 3)
jpayne@69 315 matrix([[ 0.99734545, 0.2829785 , -1.50629471],
jpayne@69 316 [-0.57860025, 1.65143654, -2.42667924]])
jpayne@69 317
jpayne@69 318 Two-by-four matrix of samples from the normal distribution with
jpayne@69 319 mean 3 and standard deviation 2.5:
jpayne@69 320
jpayne@69 321 >>> 2.5 * np.matlib.randn((2, 4)) + 3
jpayne@69 322 matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462],
jpayne@69 323 [2.76322758, 6.72847407, 1.40274501, 1.8900451 ]])
jpayne@69 324
jpayne@69 325 """
jpayne@69 326 if isinstance(args[0], tuple):
jpayne@69 327 args = args[0]
jpayne@69 328 return asmatrix(np.random.randn(*args))
jpayne@69 329
jpayne@69 330 def repmat(a, m, n):
jpayne@69 331 """
jpayne@69 332 Repeat a 0-D to 2-D array or matrix MxN times.
jpayne@69 333
jpayne@69 334 Parameters
jpayne@69 335 ----------
jpayne@69 336 a : array_like
jpayne@69 337 The array or matrix to be repeated.
jpayne@69 338 m, n : int
jpayne@69 339 The number of times `a` is repeated along the first and second axes.
jpayne@69 340
jpayne@69 341 Returns
jpayne@69 342 -------
jpayne@69 343 out : ndarray
jpayne@69 344 The result of repeating `a`.
jpayne@69 345
jpayne@69 346 Examples
jpayne@69 347 --------
jpayne@69 348 >>> import numpy.matlib
jpayne@69 349 >>> a0 = np.array(1)
jpayne@69 350 >>> np.matlib.repmat(a0, 2, 3)
jpayne@69 351 array([[1, 1, 1],
jpayne@69 352 [1, 1, 1]])
jpayne@69 353
jpayne@69 354 >>> a1 = np.arange(4)
jpayne@69 355 >>> np.matlib.repmat(a1, 2, 2)
jpayne@69 356 array([[0, 1, 2, 3, 0, 1, 2, 3],
jpayne@69 357 [0, 1, 2, 3, 0, 1, 2, 3]])
jpayne@69 358
jpayne@69 359 >>> a2 = np.asmatrix(np.arange(6).reshape(2, 3))
jpayne@69 360 >>> np.matlib.repmat(a2, 2, 3)
jpayne@69 361 matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2],
jpayne@69 362 [3, 4, 5, 3, 4, 5, 3, 4, 5],
jpayne@69 363 [0, 1, 2, 0, 1, 2, 0, 1, 2],
jpayne@69 364 [3, 4, 5, 3, 4, 5, 3, 4, 5]])
jpayne@69 365
jpayne@69 366 """
jpayne@69 367 a = asanyarray(a)
jpayne@69 368 ndim = a.ndim
jpayne@69 369 if ndim == 0:
jpayne@69 370 origrows, origcols = (1, 1)
jpayne@69 371 elif ndim == 1:
jpayne@69 372 origrows, origcols = (1, a.shape[0])
jpayne@69 373 else:
jpayne@69 374 origrows, origcols = a.shape
jpayne@69 375 rows = origrows * m
jpayne@69 376 cols = origcols * n
jpayne@69 377 c = a.reshape(1, a.size).repeat(m, 0).reshape(rows, origcols).repeat(n, 0)
jpayne@69 378 return c.reshape(rows, cols)