numpy.matlib

module documentation

(source)

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'): (source) ¶

Return a new matrix of given shape and type, without initializing entries.

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]])

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.

def eye(n, M=None, k=0, dtype=float, order='C'): (source) ¶

Return a matrix with ones on the diagonal and zeros elsewhere.

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.]])

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
`matrix`	I - A `n` x `M` matrix where all elements are equal to zero, except for the `k`-th diagonal, whose values are equal to one.

def identity(n, dtype=None): (source) ¶

Returns the square identity matrix of given size.

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]])

Parameters
n:`int`	Size of the returned identity matrix.
dtype:`data-type`, optional	Data-type of the output. Defaults to `float`.
Returns
`matrix`	out - `n` x `n` matrix with its main diagonal set to one, and all other elements zero.

def ones(shape, dtype=None, order='C'): (source) ¶

Matrix of ones.

Return a matrix of given shape and type, filled with ones.

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.]])

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
`matrix`	out - Matrix of ones of given shape, dtype, and order.

def rand(*args): (source) ¶

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).

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]])

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
`ndarray`	out - The matrix of random values with shape given by `*args`.

def randn(*args): (source) ¶

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.

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 ]])

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
`matrix` of `floats`	Z - A matrix of floating-point samples drawn from the standard normal distribution.

def repmat(a, m, n): (source) ¶

Repeat a 0-D to 2-D array or matrix MxN times.

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]])

Parameters
a:`array_like`	The array or matrix to be repeated.
m:`int`	The number of times `a` is repeated along the first and second axes.
n:`int`	The number of times `a` is repeated along the first and second axes.
Returns
`ndarray`	out - The result of repeating `a`.

def zeros(shape, dtype=None, order='C'): (source) ¶

Return a matrix of given shape and type, filled with zeros.

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.]])

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
`matrix`	out - Zero matrix of given shape, dtype, and order.