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# numpy.matlib.zeros

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```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,(more...)
```

```        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.

--------
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.]])

"""
a = ndarray.__new__(matrix, shape, dtype, order=order)
a.fill(0)
return a
```

```        # Create all matrices in a loop over argli
for argx in argli:
XX = MAT.zeros(argx[3])
dic1 = dict([[x,'0'] for x in range(argx[3][1])])
sXX = dict([[x,COP.deepcopy(dic1)] for x in range(len(argx[2]))])
```
```            Xi_mat = \
np.concatenate((Gamma_mat,Theta_mat,MAT.eye(m_states),\
MAT.zeros((m_states,m_states))),1)
Delta_mat = \
np.concatenate((Psi_mat,MAT.zeros((m_states,m_states)),\
MAT.zeros((m_states,m_states)),\
MAT.zeros((m_states,m_states)),MAT.eye(m_states),1))
```

```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):
```
```    rk = lstsq(m, ones(p).T)[2]
# first the square and full rank case:
if rk == p: result = zeros((p,0))   # note the shape! hopefully octave-like
# then the zero-matrix case (within machine precision):
elif rk == 0: result = eye(p)
```
```            # (must be ones() instead of 1 because of 2d-requirement
if lstsq( m[row,:], ones(1) )[2] == 0 or idrcount >= r:
c = r_[ c, zeros(r) ]
co = r_[ co, idpr[row-idrcount, :] ]
else:     # row is non-zero, and we haven't used all unit vecs
c = r_[ c, idr[idrcount, :] ]
co = r_[ co, zeros(p-r) ]
```

```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):
```
```    rk = lstsq(m, ones(p).T)[2]
# first the square and full rank case:
if rk == p: result = zeros((p,0))   # note the shape! hopefully octave-like
# then the zero-matrix case (within machine precision):
elif rk == 0: result = eye(p)
```
```            # (must be ones() instead of 1 because of 2d-requirement
if lstsq( m[row,:], ones(1) )[2] == 0 or idrcount >= r:
c = r_[ c, zeros(r) ]
co = r_[ co, idpr[row-idrcount, :] ]
else:     # row is non-zero, and we haven't used all unit vecs
c = r_[ c, idr[idrcount, :] ]
co = r_[ co, zeros(p-r) ]
```

```
# Create P and F matrix reflecting ordering of pymaclab
P = np.matlib.zeros([self.nstat,self.nstat])
F = np.matlib.zeros([self.ncon,self.nstat])
listo = [x[0] for x in self.vdic['exo']]+[x[0] for x in self.vdic['endo']]
```
```            jrows = len(jdic)
carry_over_dic = {}
numj = mat.zeros((jrows,jcols))
alldic = {}
alldic.update(self.paramdic)
```
```            hcols = len(hdic[0])
hrows = len(hdic[0])*len(hdic)
numh = mat.zeros((hrows,hcols))
jdic = self.jdic
jdicc = self.jdicc
```
```
#Prepare ln inmatrix (vector)
inmat = mat.zeros((len(tmpli),1))
alldic={}
alldic.update(self.paramdic)
```

```    list_tmp1 = str_tmp1.split(';')
len1 = len(list_tmp1[0].split())
mat1=mat.zeros((len1,len1))
i1=0
for x in list_tmp1:
```

```import sys,os, time
from scipy.linalg import norm
from numpy.matlib import zeros
import scipy as Sci
import scipy.linalg
```
```                    dis_cur  = vstack(((OMPLpath.getSolutionPath().getState(self.currentState)).getX(),(OMPLpath.getSolutionPath().getState(self.currentState)).getY(),(OMPLpath.getSolutionPath().getState(self.currentState)).getZ()))- pose

Vel = zeros([3,1])
Vel[0:3,0] = dis_cur/norm(dis_cur)*0.3              #TUNE THE SPEED LATER

```
```                    self.currentState = self.currentState + 1
dis_cur  = vstack(((OMPLpath.getSolutionPath().getState(self.currentState)).getX(),(OMPLpath.getSolutionPath().getState(self.currentState)).getY()))- pose[0:2]
Vel = zeros([2,1])
# set different speed for basicSim
Vel[0:2,0] = dis_cur/norm(dis_cur)*0.5                    #TUNE THE SPEED LATER
```
```                self.currentState = self.currentState + 1

Node = zeros([2,1])
Node[0,0] = (OMPLpath.getSolutionPath().getState(self.currentState)).getX()
Node[1,0] = (OMPLpath.getSolutionPath().getState(self.currentState)).getY()
```

```import sys,os
from scipy.linalg import norm
from numpy.matlib import zeros
import __is_inside
import time, sys,os
```
```            #    dis_cur  = vstack((V[1,E[1,self.E_current_column]],V[2,E[1,self.E_current_column]]))- vstack((V[1,E[0,self.E_current_column]],V[2,E[0,self.E_current_column]]))

Vel = zeros([2,1])
Vel[0:2,0] = dis_cur/norm(dis_cur)*0.5                    #TUNE THE SPEED LATER
return Vel
```
```

Node = zeros([2,1])
Node[0,0] = V[1,E[1,self.E_current_column]]
Node[1,0] = V[2,E[1,self.E_current_column]]
```

```dim = np.shape(G(1))[0]  # 1 (non-zero value) used to allow for ramps
I = np.matlib.identity(dim)
Z = np.matlib.zeros((dim, dim))

# Store the matrices in suitable indices for later
```

```from numpy import *
from scipy.linalg import norm
from numpy.matlib import zeros
from __is_inside import is_inside

```
```
V = hstack((v, v[:, : 1]))
d = zeros([v.shape[1], 1])
for i in range(0, v.shape[1]):
num = V[1, i+1] - V[1, i]
```

```import scipy.linalg
import numpy.matlib
from numpy.matlib import zeros

########################################################### all the required modules imported ################################################
```
```# Creating the matrix ( signature matrix )and initialised with zeros

cv_signature_images=zeros((total_no_of_images,no_of_eigen_vectors_to_be_taken))
mean_sub_img_array_transpose=numpy.transpose(mean_sub_img_array)  # transpose is taken so it becomes easy for multiplicati

```

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