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numpy.matlib.empty

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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 C (row-major) or(more...)

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 C (row-major) or
Fortran (column-major) order in memory.

--------
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],
[  7.39337286e-309,   3.22135945e-309]])        #random
>>> np.matlib.empty((2, 2), dtype=int)
matrix([[ 6600475,        0],
[ 6586976, 22740995]])                          #random

"""
return ndarray.__new__(matrix, shape, dtype, order=order)

from numpy import r_, c_, arange, diff, mean, sqrt, log, mat
from numpy import asarray, nan
from numpy.matlib import ones, zeros, rand, eye, empty
from numpy.linalg import eigh, cholesky, solve, lstsq
# (lstsq also as tool to determine rank)
return u[:, rk:]

from numpy.matlib import empty, zeros, eye, mat, asarray
from numpy.linalg import lstsq
def getOrthColumns(m):
idr = eye(r)
idpr = eye(p-r)
c = empty([0,r])    # starting point
co = empty([0, p-r]) # will hold orth-compl.
idrcount = 0
else: return temp

from numpy.matlib import empty, ones, zeros
from numpy import mat, c_, r_
def getDeterministics(nobs, which = 'c', date = 0.5):

from numpy import r_, c_, arange, diff, mean, sqrt, log, mat
from numpy import asarray, nan
from numpy.matlib import ones, zeros, rand, eye, empty
from numpy.linalg import eigh, cholesky, solve, lstsq
# (lstsq also as tool to determine rank)
return u[:, rk:]

from numpy.matlib import empty, zeros, eye, mat, asarray
from numpy.linalg import lstsq
def getOrthColumns(m):
idr = eye(r)
idpr = eye(p-r)
c = empty([0,r])    # starting point
co = empty([0, p-r]) # will hold orth-compl.
idrcount = 0
else: return temp

from numpy.matlib import empty, ones, zeros
from numpy import mat, c_, r_
def getDeterministics(nobs, which = 'c', date = 0.5):

def test_empty():
x = np.matlib.empty((2,))
assert_(isinstance(x, np.matrix))
assert_(x.shape, (1,2))

def test_empty():
x = np.matlib.empty((2,))
assert_(isinstance(x, np.matrix))
assert_(x.shape, (1,2))