输出操作数需要进行缩减，但未启用缩减 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]))

最后一行出现了错误。(输出操作数需要缩减，但未启用缩减)。我该如何解决？我是 Python 的新手，请告诉我是否有更简单的方法。

谢谢

- kiki

---

如果您能添加导入语句（如numpy as np、scipy#s toeplitz等），使得代码可以直接复制、粘贴并运行，那将非常有帮助。 - YXD

我刚刚包含了。谢谢。 - kiki

在引发错误的那一行中，左侧的形状为(512,)，右侧的形状为(512, 512)。你试图将一个512x512的二维数组塞入一个长度为512的一维数组中。 - DSM

Answer 1

你看到的错误是由于你创建的维度不匹配，但你的代码也非常低效，因为它需要循环并且没有充分利用Numpy的自动广播优化。我已经重写了代码以达到你想要的效果:

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])

- chthonicdaemon