numpy.matlib

module documentation

Undocumented

Function empty Return a new matrix of given shape and type, without initializing entries.
Function eye Return a matrix with ones on the diagonal and zeros elsewhere.
Function identity Returns the square identity matrix of given size.
Function ones Matrix of ones.
Function rand Return a matrix of random values with given shape.
Function randn Return a random matrix with data from the "standard normal" distribution.
Function repmat Repeat a 0-D to 2-D array or matrix MxN times.
Function zeros Return a matrix of given shape and type, filled with zeros.
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]])
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.

New in version 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.]])
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]])
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.]])
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]])
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 N(μ, σ2), 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 N(3, 6.25):

>>> 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 ]])
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]])
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.]])