All Samples(19) | Call(11) | Derive(0) | Import(8)
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. 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], [ 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):
src/d/o/dolo-HEAD/dolo/numeric/extern/helpers.py dolo(Download)
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):
src/n/u/nupic-linux64-HEAD/lib64/python2.6/site-packages/numpy/tests/test_matlib.py nupic-linux64(Download)
def test_empty(): x = np.matlib.empty((2,)) assert_(isinstance(x, np.matrix)) assert_(x.shape, (1,2))
src/m/i/MissionPlanner-HEAD/Lib/site-packages/numpy/tests/test_matlib.py MissionPlanner(Download)
def test_empty(): x = np.matlib.empty((2,)) assert_(isinstance(x, np.matrix)) assert_(x.shape, (1,2))
src/n/u/numpy-1.8.1/numpy/tests/test_matlib.py numpy(Download)
def test_empty(): x = np.matlib.empty((2,)) assert_(isinstance(x, np.matrix)) assert_(x.shape, (1, 2))