#!/usr/bin/env pythonimport randomfrom svm import *def do_cross_validation(prob_x, prob_y, param, nr_fold):	"Do cross validation for a given SVM problem."	total_correct = 0	total_error = sumv = sumy = sumvv = sumyy = sumvy = 0.	prob_l = len(prob_y)	for i in range(prob_l):		j = random.randrange(i,prob_l);		prob_x[i], prob_x[j] = prob_x[j], prob_x[i]		prob_y[i], prob_y[j] = prob_y[j], prob_y[i]	for i in range(nr_fold):		begin = i * prob_l / nr_fold		end = (i + 1) * prob_l / nr_fold		subprob = svm_problem(prob_y[:begin] + prob_y[end:], prob_x[:begin] + prob_x[end:])		if param.svm_type == EPSILON_SVR or param.svm_type == NU_SVR:			submodel = svm_model(subprob, param)			error = 0.0			for j in range(begin, end):				v = submodel.predict(prob_x[j])				y = prob_y[j]				error = error + (v - y) * (v - y)				sumv = sumv + v				sumy = sumy + y				sumvv = sumvv + v * v				sumyy = sumyy + y * y				sumvy = sumvy + v * y			print "Mean squared error = %g" % (error / (end - begin))			total_error = total_error + error		else:			submodel = svm_model(subprob, param)			correct = 0			for j in range(begin, end):				v = submodel.predict(prob_x[j])				if v == prob_y[j]: correct = correct + 1			print "Accuracy = %g%% (%d/%d)" % (100.0 * correct / (end - begin), correct, (end - begin))			total_correct = total_correct + correct	if param.svm_type == EPSILON_SVR or param.svm_type == NU_SVR:		print "Cross Validation Mean squared error = %g" % (total_error / prob_l)		print "Cross Validation Squared correlation coefficient = %g" % (((prob_l * sumvy - sumv * sumy) * (prob_l * sumvy - sumv * sumy)) / ((prob_l * sumvv - sumv * sumv) * (prob_l * sumyy - sumy * sumy)))	else:		print "Cross Validation Accuracy = %g%%" % (100.0 * total_correct / prob_l)