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matlib.py
import warnings # 2018-05-29, PendingDeprecationWarning added to matrix.__new__ # 2020-01-23, numpy 1.19.0 PendingDeprecatonWarning warnings.warn("Importing from numpy.matlib is deprecated since 1.19.0. " "The matrix subclass is not the recommended way to represent " "matrices or deal with linear algebra (see " "https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). " "Please adjust your code to use regular ndarray. ", PendingDeprecationWarning, stacklevel=2) import numpy as np from numpy.matrixlib.defmatrix import matrix, asmatrix # Matlib.py contains all functions in the numpy namespace with a few # replacements. See doc/source/reference/routines.matlib.rst for details. # Need * as we're copying the numpy namespace. from numpy import * # noqa: F403 __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], # random [ 7.39337286e-309, 3.22135945e-309]]) >>> np.matlib.empty((2, 2), dtype=int) matrix([[ 6600475, 0], # random [ 6586976, 22740995]]) """ 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, 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)) 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.RandomState.rand Examples -------- >>> np.random.seed(123) >>> import numpy.matlib >>> np.matlib.rand(2, 3) matrix([[0.69646919, 0.28613933, 0.22685145], [0.55131477, 0.71946897, 0.42310646]]) >>> np.matlib.rand((2, 3)) matrix([[0.9807642 , 0.68482974, 0.4809319 ], [0.39211752, 0.34317802, 0.72904971]]) If the first argument is a tuple, other arguments are ignored: >>> np.matlib.rand((2, 3), 4) matrix([[0.43857224, 0.0596779 , 0.39804426], [0.73799541, 0.18249173, 0.17545176]]) """ 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, numpy.random.RandomState.randn Notes ----- For random samples from the normal distribution with mean ``mu`` and standard deviation ``sigma``, use:: sigma * np.matlib.randn(...) + mu Examples -------- >>> np.random.seed(123) >>> import numpy.matlib >>> np.matlib.randn(1) matrix([[-1.0856306]]) >>> np.matlib.randn(1, 2, 3) matrix([[ 0.99734545, 0.2829785 , -1.50629471], [-0.57860025, 1.65143654, -2.42667924]]) Two-by-four matrix of samples from the normal distribution with mean 3 and standard deviation 2.5: >>> 2.5 * np.matlib.randn((2, 4)) + 3 matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462], [2.76322758, 6.72847407, 1.40274501, 1.8900451 ]]) """ 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)
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