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import warnings |
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warnings.warn("Importing from numpy.matlib is deprecated since 1.19.0. " |
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"The matrix subclass is not the recommended way to represent " |
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"matrices or deal with linear algebra (see " |
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"https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). " |
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"Please adjust your code to use regular ndarray. ", |
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PendingDeprecationWarning, stacklevel=2) |
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import numpy as np |
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from numpy.matrixlib.defmatrix import matrix, asmatrix |
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from numpy import * |
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__version__ = np.__version__ |
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__all__ = np.__all__[:] |
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__all__ += ['rand', 'randn', 'repmat'] |
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def empty(shape, dtype=None, order='C'): |
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"""Return a new matrix of given shape and type, without initializing entries. |
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Parameters |
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---------- |
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shape : int or tuple of int |
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Shape of the empty matrix. |
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dtype : data-type, optional |
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Desired output data-type. |
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order : {'C', 'F'}, optional |
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Whether to store multi-dimensional data in row-major |
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(C-style) or column-major (Fortran-style) order in |
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memory. |
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See Also |
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-------- |
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numpy.empty : Equivalent array function. |
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matlib.zeros : Return a matrix of zeros. |
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matlib.ones : Return a matrix of ones. |
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Notes |
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----- |
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Unlike other matrix creation functions (e.g. `matlib.zeros`, |
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`matlib.ones`), `matlib.empty` does not initialize the values of the |
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matrix, and may therefore be marginally faster. However, the values |
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stored in the newly allocated matrix are arbitrary. For reproducible |
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behavior, be sure to set each element of the matrix before reading. |
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Examples |
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-------- |
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>>> import numpy.matlib |
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>>> np.matlib.empty((2, 2)) # filled with random data |
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matrix([[ 6.76425276e-320, 9.79033856e-307], # random |
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[ 7.39337286e-309, 3.22135945e-309]]) |
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>>> np.matlib.empty((2, 2), dtype=int) |
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matrix([[ 6600475, 0], # random |
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[ 6586976, 22740995]]) |
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""" |
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return ndarray.__new__(matrix, shape, dtype, order=order) |
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def ones(shape, dtype=None, order='C'): |
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""" |
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Matrix of ones. |
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Return a matrix of given shape and type, filled with ones. |
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Parameters |
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---------- |
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shape : {sequence of ints, int} |
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Shape of the matrix |
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dtype : data-type, optional |
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The desired data-type for the matrix, default is np.float64. |
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order : {'C', 'F'}, optional |
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Whether to store matrix in C- or Fortran-contiguous order, |
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default is 'C'. |
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Returns |
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------- |
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out : matrix |
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Matrix of ones of given shape, dtype, and order. |
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See Also |
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-------- |
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ones : Array of ones. |
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matlib.zeros : Zero matrix. |
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Notes |
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----- |
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If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``, |
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`out` becomes a single row matrix of shape ``(1,N)``. |
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Examples |
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-------- |
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>>> np.matlib.ones((2,3)) |
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matrix([[1., 1., 1.], |
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[1., 1., 1.]]) |
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>>> np.matlib.ones(2) |
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matrix([[1., 1.]]) |
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""" |
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a = ndarray.__new__(matrix, shape, dtype, order=order) |
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a.fill(1) |
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return a |
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def zeros(shape, dtype=None, order='C'): |
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""" |
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Return a matrix of given shape and type, filled with zeros. |
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Parameters |
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---------- |
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shape : int or sequence of ints |
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Shape of the matrix |
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dtype : data-type, optional |
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The desired data-type for the matrix, default is float. |
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order : {'C', 'F'}, optional |
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Whether to store the result in C- or Fortran-contiguous order, |
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default is 'C'. |
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Returns |
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------- |
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out : matrix |
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Zero matrix of given shape, dtype, and order. |
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See Also |
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-------- |
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numpy.zeros : Equivalent array function. |
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matlib.ones : Return a matrix of ones. |
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Notes |
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----- |
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If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``, |
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`out` becomes a single row matrix of shape ``(1,N)``. |
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Examples |
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-------- |
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>>> import numpy.matlib |
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>>> np.matlib.zeros((2, 3)) |
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matrix([[0., 0., 0.], |
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[0., 0., 0.]]) |
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>>> np.matlib.zeros(2) |
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matrix([[0., 0.]]) |
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""" |
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a = ndarray.__new__(matrix, shape, dtype, order=order) |
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a.fill(0) |
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return a |
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def identity(n,dtype=None): |
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""" |
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Returns the square identity matrix of given size. |
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Parameters |
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---------- |
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n : int |
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Size of the returned identity matrix. |
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dtype : data-type, optional |
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Data-type of the output. Defaults to ``float``. |
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Returns |
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------- |
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out : matrix |
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`n` x `n` matrix with its main diagonal set to one, |
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and all other elements zero. |
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See Also |
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-------- |
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numpy.identity : Equivalent array function. |
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matlib.eye : More general matrix identity function. |
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Examples |
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-------- |
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>>> import numpy.matlib |
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>>> np.matlib.identity(3, dtype=int) |
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matrix([[1, 0, 0], |
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[0, 1, 0], |
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[0, 0, 1]]) |
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""" |
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a = array([1]+n*[0], dtype=dtype) |
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b = empty((n, n), dtype=dtype) |
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b.flat = a |
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return b |
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def eye(n,M=None, k=0, dtype=float, order='C'): |
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""" |
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Return a matrix with ones on the diagonal and zeros elsewhere. |
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Parameters |
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---------- |
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n : int |
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Number of rows in the output. |
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M : int, optional |
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Number of columns in the output, defaults to `n`. |
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k : int, optional |
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Index of the diagonal: 0 refers to the main diagonal, |
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a positive value refers to an upper diagonal, |
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and a negative value to a lower diagonal. |
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dtype : dtype, optional |
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Data-type of the returned matrix. |
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order : {'C', 'F'}, optional |
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Whether the output should be stored in row-major (C-style) or |
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column-major (Fortran-style) order in memory. |
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.. versionadded:: 1.14.0 |
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Returns |
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------- |
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I : matrix |
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A `n` x `M` matrix where all elements are equal to zero, |
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except for the `k`-th diagonal, whose values are equal to one. |
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See Also |
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-------- |
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numpy.eye : Equivalent array function. |
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identity : Square identity matrix. |
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Examples |
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-------- |
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>>> import numpy.matlib |
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>>> np.matlib.eye(3, k=1, dtype=float) |
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matrix([[0., 1., 0.], |
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[0., 0., 1.], |
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[0., 0., 0.]]) |
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""" |
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return asmatrix(np.eye(n, M=M, k=k, dtype=dtype, order=order)) |
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def rand(*args): |
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""" |
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Return a matrix of random values with given shape. |
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Create a matrix of the given shape and propagate it with |
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random samples from a uniform distribution over ``[0, 1)``. |
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Parameters |
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---------- |
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\\*args : Arguments |
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Shape of the output. |
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If given as N integers, each integer specifies the size of one |
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dimension. |
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If given as a tuple, this tuple gives the complete shape. |
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Returns |
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------- |
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out : ndarray |
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The matrix of random values with shape given by `\\*args`. |
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See Also |
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-------- |
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randn, numpy.random.RandomState.rand |
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Examples |
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-------- |
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>>> np.random.seed(123) |
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>>> import numpy.matlib |
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>>> np.matlib.rand(2, 3) |
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matrix([[0.69646919, 0.28613933, 0.22685145], |
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[0.55131477, 0.71946897, 0.42310646]]) |
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>>> np.matlib.rand((2, 3)) |
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matrix([[0.9807642 , 0.68482974, 0.4809319 ], |
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[0.39211752, 0.34317802, 0.72904971]]) |
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If the first argument is a tuple, other arguments are ignored: |
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>>> np.matlib.rand((2, 3), 4) |
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matrix([[0.43857224, 0.0596779 , 0.39804426], |
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[0.73799541, 0.18249173, 0.17545176]]) |
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""" |
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if isinstance(args[0], tuple): |
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args = args[0] |
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return asmatrix(np.random.rand(*args)) |
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def randn(*args): |
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""" |
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Return a random matrix with data from the "standard normal" distribution. |
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`randn` generates a matrix filled with random floats sampled from a |
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univariate "normal" (Gaussian) distribution of mean 0 and variance 1. |
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Parameters |
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---------- |
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\\*args : Arguments |
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Shape of the output. |
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If given as N integers, each integer specifies the size of one |
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dimension. If given as a tuple, this tuple gives the complete shape. |
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Returns |
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------- |
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Z : matrix of floats |
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A matrix of floating-point samples drawn from the standard normal |
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distribution. |
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See Also |
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-------- |
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rand, numpy.random.RandomState.randn |
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Notes |
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----- |
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For random samples from the normal distribution with mean ``mu`` and |
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standard deviation ``sigma``, use:: |
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sigma * np.matlib.randn(...) + mu |
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Examples |
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-------- |
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>>> np.random.seed(123) |
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>>> import numpy.matlib |
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>>> np.matlib.randn(1) |
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matrix([[-1.0856306]]) |
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>>> np.matlib.randn(1, 2, 3) |
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matrix([[ 0.99734545, 0.2829785 , -1.50629471], |
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[-0.57860025, 1.65143654, -2.42667924]]) |
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Two-by-four matrix of samples from the normal distribution with |
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mean 3 and standard deviation 2.5: |
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>>> 2.5 * np.matlib.randn((2, 4)) + 3 |
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matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462], |
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[2.76322758, 6.72847407, 1.40274501, 1.8900451 ]]) |
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""" |
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if isinstance(args[0], tuple): |
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args = args[0] |
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return asmatrix(np.random.randn(*args)) |
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def repmat(a, m, n): |
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""" |
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Repeat a 0-D to 2-D array or matrix MxN times. |
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Parameters |
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---------- |
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a : array_like |
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The array or matrix to be repeated. |
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m, n : int |
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The number of times `a` is repeated along the first and second axes. |
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Returns |
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------- |
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out : ndarray |
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The result of repeating `a`. |
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Examples |
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-------- |
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>>> import numpy.matlib |
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>>> a0 = np.array(1) |
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>>> np.matlib.repmat(a0, 2, 3) |
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array([[1, 1, 1], |
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[1, 1, 1]]) |
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>>> a1 = np.arange(4) |
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>>> np.matlib.repmat(a1, 2, 2) |
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array([[0, 1, 2, 3, 0, 1, 2, 3], |
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[0, 1, 2, 3, 0, 1, 2, 3]]) |
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>>> a2 = np.asmatrix(np.arange(6).reshape(2, 3)) |
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>>> np.matlib.repmat(a2, 2, 3) |
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matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2], |
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[3, 4, 5, 3, 4, 5, 3, 4, 5], |
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[0, 1, 2, 0, 1, 2, 0, 1, 2], |
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[3, 4, 5, 3, 4, 5, 3, 4, 5]]) |
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""" |
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a = asanyarray(a) |
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ndim = a.ndim |
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if ndim == 0: |
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origrows, origcols = (1, 1) |
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elif ndim == 1: |
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origrows, origcols = (1, a.shape[0]) |
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else: |
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origrows, origcols = a.shape |
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rows = origrows * m |
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cols = origcols * n |
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c = a.reshape(1, a.size).repeat(m, 0).reshape(rows, origcols).repeat(n, 0) |
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return c.reshape(rows, cols) |
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