输出操作数需要归约，但没有启用归约 Python答案

【问题标题】：output operand requires a reduction, but reduction is not enabled Python输出操作数需要归约，但没有启用归约 Python
【发布时间】：2013-02-21 09:22:21
【问题描述】：

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]))

最后一行给了我错误。（输出操作数需要归约，但没有启用归约）我如何解决它？我是 python 新手，如果有更简单的方法，请告诉我

谢谢

【问题讨论】：

如果您可以添加导入语句（numpy as np、scipy#s toeplitz 等）以便代码复制、粘贴和按原样运行，将会有很大帮助。
我刚刚包括在内。谢谢
在引发错误的行中，左侧的形状为(512,)，右侧的形状为(512, 512)。您正在尝试将 512x512 二维数组填充到 512 长的一维数组中。

标签： python arrays numpy scipy

【解决方案1】：

您看到的错误是因为您创建的内容的尺寸不匹配，但是您的代码在所有循环中效率也很低，并且没有最佳地利用 Numpy 的自动广播。我已经重写了代码来做你想做的事：

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])

【讨论】：