python 和 matlab

Python Matlab 功能 str2 = str1.replace(old,new[,count]) dstStr= strrep(origStr,oldSubstr,newSubStr)
filepath = 'E:/data/1.jpg';
filepath2 = strrep(filepath,'data','lfw/data');
disp(filepath2); 替换子串

随机打乱:

Matlabs:

nsample = 10;if ~exist('rand_ind.mat','file')    rand_ind = randperm(nsample);    save('rand_ind.mat','rand_ind');else    load('rand_ind.mat');% load rand_ind    disp('load successfully!')enddisp(rand_ind);

Python:

import randomimport osimport numpy as nprand_ind = range(10)if not os.path.exists('rand_ind.npy'):    random.shuffle(rand_ind) # 操作该对象本身    np.save('rand_ind.npy',rand_ind)else:    rand_ind = np.load('rand_ind.npy')    print 'load successfully!'print rand_ind

在进行实验的时候，我们经常希望对数据集进行随机的打乱，但是又希望这种随机打乱的结果具有可重现性。下面给出matlab和python的实现方法：

Matlab:

nsample = 10;feat_len = 1000;train_data = rand(nsample,feat_len);%随机生成一个样本矩阵,含有10个样本，特征维数为1000if ~exist('rand_ind.mat','file')    rand_ind = randperm(nsample);    save('rand_ind.mat','rand_ind');else    load('rand_ind.mat');% load rand_ind    disp('load successfully!')end% 将样本按照生成的随机序列打乱train_data = train_data(rand_ind,:);disp(rand_ind);

Python:

import randomimport osimport numpy as npnsample = 10feat_len = 1000train_data = np.random.random((nsample,feat_len))print train_data.shaperand_ind = range(nsample)if not os.path.exists('rand_ind.npy'):    random.shuffle(rand_ind) # 操作该对象本身    np.save('rand_ind.npy',rand_ind)else:    rand_ind = np.load('rand_ind.npy')    print 'load successfully!'print rand_indtrain_data2 = np.zeros(train_data.shape,dtype=train_data.dtype)print train_data2.shapeprint rand_ind[1]for i in xrange(len(rand_ind)):    train_data2[i,:] = train_data[rand_ind[i],:]

对于list的话，可以使用列表推导式，如；

mylist = ['a','b','c','d','e']myorder = [3,2,0,1,4]mylist =[mylist[i] for i in myorder]print mylist