B >> 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 )order)ndarray__new__r)shapedtyper r?/opt/alt/python37/lib64/python3.7/site-packages/numpy/matlib.pyempty s$rcCs tjt|||d}|d|S)a 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.]]) )r )r rrfill)rrr arrrones3s) rcCs tjt|||d}|d|S)a 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.]]) )r r)r rrr)rrr rrrrzeros`s( rcCs2tdg|dg|d}t||f|d}||_|S)a 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]]) rr)r)ZarrayrZflat)nrrbrrridentitysrcCstt||||S)a 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.]]) )rnpeye)rMkrrrrrs%rcGs&t|dtr|d}ttjj|S)a 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 r) isinstancetuplerrrandomr)argsrrrrs)cGs&t|dtr|d}ttjj|S)a2 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 r)r r!rrr"r )r#rrrr s.c Cst|}|j}|dkr d\}}n$|dkr:d|jd}}n |j\}}||}||}|d|j|d|||d}|||S)aj 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]]) r)rrr)Z asanyarrayndimrZreshapesizerepeat) rmrr$ZorigrowsZorigcolsZrowsZcolscrrrr 6s%  &)Nr )Nr )Nr )N)Z __future__rrrZnumpyrZnumpy.matrixlib.defmatrixrr __version____all__rrrrfloatrrr r rrrrs & - , $'-2