请将下面的matlab代码转换为python代码：function OneError=One_error(Outputs,test_target) [num_class,num_instance]=size(Outputs); temp_Outputs=[]; temp_test_target=[]; for i=1:num_instance temp=test_target(:,i); if((sum(temp)~=num_class)&(sum(temp)~=-num_class)) temp_Outputs=[temp_Outputs,Outputs(:,i)]; temp_test_target=[temp_test_target,temp]; end end Outputs=temp_Outputs; test_target=temp_test_target; [num_class,num_instance]=size(Outputs); Label=cell(num_instance,1); not_Label=cell(num_instance,1); Label_size=zeros(1,num_instance); for i=1:num_instance temp=test_target(:,i); Label_size(1,i)=sum(temp==ones(num_class,1)); for j=1:num_class if(temp(j)==1) Label{i,1}=[Label{i,1},j]; else not_Label{i,1}=[not_Label{i,1},j]; end end end oneerr=0; for i=1:num_instance indicator=0; temp=Outputs(:,i); [maximum,index]=max(temp); for j=1:num_class if(temp(j)==maximum) if(ismember(j,Label{i,1})) indicator=1; break; end end end if(indicator==0) oneerr=oneerr+1; end end OneError=oneerr/num_instance;

以下是将matlab代码转换为python代码的结果：

import numpy as np

def One_error(Outputs, test_target):
    num_class, num_instance = Outputs.shape
    temp_Outputs = np.empty((num_class, 0))
    temp_test_target = np.empty((num_class, 0))
    for i in range(num_instance):
        temp = test_target[:, i]
        if (np.sum(temp) != num_class) and (np.sum(temp) != -num_class):
            temp_Outputs = np.concatenate((temp_Outputs, Outputs[:, i].reshape(num_class, 1)), axis=1)
            temp_test_target = np.concatenate((temp_test_target, temp.reshape(num_class, 1)), axis=1)
    Outputs = temp_Outputs
    test_target = temp_test_target
    num_class, num_instance = Outputs.shape
    Label = [None] * num_instance
    not_Label = [None] * num_instance
    Label_size = np.zeros((1, num_instance))
    for i in range(num_instance):
        temp = test_target[:, i]
        Label_size[0, i] = np.sum(temp == np.ones((num_class, 1)))
        for j in range(num_class):
            if temp[j] == 1:
                if Label[i] is None:
                    Label[i] = [j + 1]
                else:
                    Label[i].append(j + 1)
            else:
                if not_Label[i] is None:
                    not_Label[i] = [j + 1]
                else:
                    not_Label[i].append(j + 1)
    oneerr = 0
    for i in range(num_instance):
        indicator = 0
        temp = Outputs[:, i]
        maximum = np.max(temp)
        index = np.argmax(temp)
        for j in range(num_class):
            if temp[j] == maximum:
                if (j + 1) in Label[i]:
                    indicator = 1
                    break
        if indicator == 0:
            oneerr += 1
    OneError = oneerr / num_instance
    return OneError

需要注意的是，matlab中的数组索引从1开始，而python中的数组索引从0开始，因此在转换过程中需要将所有的索引减1。