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In [27]:

    
import os,sys,re
import numpy as np
from sklearn.cross_validation import train_test_split
os.chdir('/home/mckc/Clustering///')
direct = os.getcwd()



In [28]:

    
files = os.listdir('/home/mckc/Clustering///')
subject = []
file_name = []



In [29]:

    
import re

def tryint(s):
    try:
        return int(s)
    except:
        return s

def alphanum_key(s):
    """ Turn a string into a list of string and number chunks.
        "z23a" -> ["z", 23, "a"]
    """
    return [ tryint(c) for c in re.split('([0-9]+)', s) ]

def sort_nicely(l):
    """ Sort the given list in the way that humans expect.
    """
    l.sort(key=alphanum_key)



In [30]:

    
sort_nicely(files)



In [31]:

    
for name in files:
    file_name = np.append(file_name,direct+'/' + name+'.jpg')
    print name
    index = name.index('_')
    subject = np.append(subject,name[:index])



In [32]:

    
data = np.vstack((np.array(file_name), np.array(subject)))
data = np.transpose(data)
data = data.astype(str)
train , test = train_test_split(data, test_size=0.25, random_state=42,stratify=subject)



In [5]:

    
test[0,0] = 'File_name'
test[0,1] = 'subject'
np.savetxt("test.csv", test, delimiter=",",fmt='%s')



In [6]:

    
train[0,0] = 'File_name'
train[0,1] = 'subject'
np.savetxt("train.csv", train, delimiter=",",fmt='%s')



In [34]:

    
data[0,0] = 'File_name'
data[0,1] = 'subject'
np.savetxt("file.csv", data, delimiter=",",fmt='%s')



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