def __rmul__(self, other):
if isinstance(other, (N.ndarray, list, tuple)):
if self.trans:
return self._left_matrix_multiplication(self, asmatrix(other).T).T
else:
return self._right_matrix_multiplication(self, asmatrix(other))
if isscalar(other) or not hasattr(other, '__rmul__'):
return self._copy(self.ent_table, self.att_table, a=self.a*other)
return NotImplemented
def __mul__(self, other):
if isinstance(other, NormalizedMatrix):
if self.stamp == other.stamp and self.trans ^ other.trans:
return self._cross_prod()
else:
return NotImplemented
if isinstance(other, (N.ndarray, list, tuple)):
# This promotes 1-D vectors to row vectors
if self.trans:
return self._right_matrix_multiplication(self, asmatrix(other).T).T
else:
return self._left_matrix_multiplication(self, asmatrix(other))
if isscalar(other) or not hasattr(other, '__rmul__'):
return self._copy(self.ent_table, self.att_table, a=self.a*other)
return NotImplemented
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 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, random.randn
Notes
-----
For random samples from :math:`N(\\mu, \\sigma^2)`, 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 :math:`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 ]])
"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.randn(*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, 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
"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.randn(*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, 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
"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.randn(*args))
def __rdiv__(self, other):
if isscalar(other):
return self._copy(other / self.ent_table,
[other / t for t in self.att_table])
if isinstance(other, (N.ndarray, list, tuple)):
other = asmatrix(other)
if other.shape[1] == self.shape[1] and other.shape[0] == 1:
return self._copy(
other[:, :self.ent_table.shape[1]] / self.ent_table, [
other[:, self.indexes[i][0]:self.indexes[i][1]] / t
for i, t in enumerate(self.att_table)
])
return NotImplemented
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 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.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 __idiv__(self, other):
if isscalar(other):
self.ent_table = self.ent_table / other
self.att_table = [t / other for t in self.att_table]
return self
if isinstance(other, (N.ndarray, list, tuple)):
other = asmatrix(other)
if other.shape[1] == self.shape[1] and other.shape[0] == 1:
self.ent_table = self.ent_table / other[:, :self.ent_table.
shape[1]]
self.att_table = [
t / other[:, self.indexes[i][0]:self.indexes[i][1]]
for i, t in enumerate(self.att_table)
]
return self
return NotImplemented
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.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
"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.rand(*args))
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.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
"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.rand(*args))
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 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 eye(n, M=None, k=0, dtype=float):
"""
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.]])
"""
return asmatrix(np.eye(n, M, k, dtype))
def eye(n,M=None, k=0, dtype=float):
"""
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.]])
"""
return asmatrix(np.eye(n, M, k, dtype))