输出操作数需要减少，但是减少未启用Python

Question

import numpy as np 
from numpy.linalg import solve,norm,cond,inv,pinv 
import math 
import matplotlib.pyplot as plt 
from scipy.linalg import toeplitz 
from numpy.random import rand 

c = np.zeros(512) 
c[0] = 2 
c[1] = -1 
a = c 
A = toeplitz(c,a) 

cond_A = cond(A,2) 

# creating 10 random vectors 512 x 1 
b = rand(10,512) 

# making b into unit vector 
for i in range (10): 
    b[i]= b[i]/norm(b[i],2) 

# creating 10 random del_b vectors 
del_b = [rand(10,512), rand(10,512), rand(10,512), rand(10,512), rand(10,512), rand(10,512), rand(10,512), rand(10,512), rand(10,512), rand(10,512)] 

# del_b = 10 sets of 10 vectors (512x1) whose norm is 0.01,0.02 ~0.1 
for i in range(10): 
    for j in range(10): 
     del_b[i][j] = del_b[i][j]/(norm(del_b[i][j],2)/((float(j+1)/100))) 

x_in = [np.zeros(512), np.zeros(512), np.zeros(512), np.zeros(512), np.zeros(512), np.zeros(512), np.zeros(512), np.zeros(512), np.zeros(512), np.zeros(512)] 

x2 = np.zeros((10,10,512)) 
for i in range(10): 
    x_in[i] = A.transpose()*b[i] 

for i in range(10): 
    for j in range(10): 
     x2[i][j] = ((A.transpose()*(b[i]+del_b[i][j])) 

最后一行给我错误。 （输出操作数需要减少，但是减少未启用） 我该如何解决它？ 我是新来的蟒蛇并请让我知道，如果没有做到这一点

由于更简单的方法

Answer 1

你看到的错误是因为在您创建什么尺寸不匹配的，但你的代码在循环播放时效率也很低，并且不能最大限度地利用Numpy的自动广播。我已经重写了代码做什么，似乎你想要的：

import numpy as np 
from numpy.linalg import solve,norm,cond,inv,pinv 
import math 
import matplotlib.pyplot as plt 
from scipy.linalg import toeplitz 
from numpy.random import rand 

# These should probably get more sensible names 
Nvec = 10 # number of vectors in b 
Nlevels = 11 # number of perturbation norm levels 
Nd = 512 # dimension of the vector space 

c = np.zeros(Nd) 
c[0] = 2 
c[1] = -1 
a = c 

# NOTE: I'm assuming you want A to be a matrix 
A = np.asmatrix(toeplitz(c, a)) 

cond_A = cond(A,2) 

# create Nvec random vectors Nd x 1 
# Note: packing the vectors in the columns makes the next step easier 
b = rand(Nd, Nvec) 

# normalise each column of b to be a unit vector 
b /= norm(b, axis=0) 

# create Nlevels of Nd x Nvec random del_b vectors 
del_b = rand(Nd, Nvec, Nlevels) 

# del_b = 10 sets of 10 vectors (512x1) whose norm is 0.01,0.02 ~0.1 
targetnorms = np.linspace(0.01, 0.1, Nlevels) 
# cause the norms in the Nlevels dimension to be equal to the target norms 
del_b /= norm(del_b, axis=0)[None, :, :]/targetnorms[None, None, :] 

# Straight linear transformation - make sure you actually want the transpose 
x_in = A.T*b 

# same linear transformation on perturbed versions of b 
x2 = np.zeros((Nd, Nvec, Nlevels)) 
for i in range(Nlevels): 
    x2[:, :, i] = A.T*(b + del_b[:, :, i])