| 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) |
