#!/usr/bin/env pythonfrom sys import argv, exit, stdout, stderrfrom random import randintmethod = 0global nglobal dataset_filenamesubset_filename = ""rest_filename = ""def exit_with_help():	print """\Usage: %s [options] dataset number [output1] [output2]This script selects a subset of the given dataset.options:-s method : method of selection (default 0)     0 -- stratified selection (classification only)     1 -- random selectionoutput1 : the subset (optional)output2 : rest of the data (optional)If output1 is omitted, the subset will be printed on the screen.""" % argv[0]	exit(1)def process_options():	global method, n	global dataset_filename, subset_filename, rest_filename		argc = len(argv)	if argc < 3:		exit_with_help()	i = 1	while i < len(argv):		if argv[i][0] != "-":			break		if argv[i] == "-s":			i = i + 1			method = int(argv[i])			if method < 0 or method > 1:				print "Unknown selection method %d" % (method)				exit_with_help()		i = i + 1	dataset_filename = argv[i]	n = int(argv[i+1])	if i+2 < argc:		subset_filename = argv[i+2]	if i+3 < argc:		rest_filename = argv[i+3]def main():	class Label:		def __init__(self, label, index, selected):			self.label = label			self.index = index			self.selected = selected		def __cmp__(self, other):			return cmp(self.label, other.label)	process_options()		# get labels	i = 0	labels = []	f = open(dataset_filename, 'r')	for line in f:		labels.append(Label(float((line.split())[0]), i, 0))		i = i + 1	f.close()	l = i		# determine where to output	if subset_filename != "":		file1 = open(subset_filename, 'w')	else:		file1 = stdout	split = 0	if rest_filename != "":		split = 1			file2 = open(rest_filename, 'w')		# select the subset	warning = 0	if method == 0: # stratified		labels.sort()		label_end = labels[l-1].label + 1		labels.append(Label(label_end, l, 0))		begin = 0		label = labels[begin].label		for i in range(l+1):			new_label = labels[i].label			if new_label != label:				nr_class = i - begin				k = i*n/l - begin*n/l				# at least one instance per class				if k == 0:					k = 1					warning = warning + 1				for j in range(nr_class):					if randint(0, nr_class-j-1) < k:						labels[begin+j].selected = 1						k = k - 1				begin = i				label = new_label	elif method == 1: # random		k = n		for i in range(l):			if randint(0,l-i-1) < k:				labels[i].selected = 1				k = k - 1			i = i + 1	# output	i = 0	if method == 0:		labels.sort(lambda x, y: cmp(int(x.index), int(y.index)))	f = open(dataset_filename, 'r')	for line in f:		if labels[i].selected == 1:			file1.write(line)		else:			if split == 1:				file2.write(line)		i = i + 1	if warning > 0:		stderr.write("""\Warning:1. You may have regression data. Please use -s 1.2. Classification data unbalanced or too small. We select at least 1 per class.   The subset thus contains %d instances.""" % (n+warning))	# cleanup	f.close()	file1.close()	if split == 1:		file2.close()main()