출력 피연산자는 감소가 필요하지만 감소는 활성화되어 있지 않습니다. 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])) 

마지막 줄에서 오류가 발생합니다. (출력 피연산자는 감소가 필요하지만 감소는 활성화되지 않습니다) 어떻게 수정합니까? 내가 파이썬에 새로 온 사람이

감사를 할 수있는 쉬운 방법이 있다면 알려 주시기 바랍니다

Answer 1

당신이보고있는 오류 때문에 사용자가 만든 어떤 크기의 불일치이지만, 당신의 코드는 Numpy의 자동 방송을 최적의 방법으로 사용하지 않고 모든 루핑 작업을하는 데에도 상당히 비효율적입니다. 나는 당신이 원하는 것 무엇을 할 수있는 코드를 다시 작성했습니다

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])