test

import math
import random
import numpy
import scipy as sp
import matplotlib.pyplot as plt
X=[]
Y=[]
XTEST=[]
YTEST=[]
trainnum=100
for i in range(trainnum):
	X.append(random.uniform(-1,1))
	Y.append(math.sin(X[i]))
	XTEST.append(random.uniform(-1,1))
	YTEST.append(math.sin(XTEST[i]))
	#plt.plot(X[trainnum],Y[trainnum],'.')
def error(f, x, y):  
	return sp.sum((f(x)-y)**2)
fp1, residuals, rank, sv, rcond = sp.polyfit(X, Y, 1, full=True) 
f1 = sp.poly1d(fp1)  
plt.plot(X,Y,'.')
plt.show()
print(error(f1, X, Y))  
print("Model parameters: %s" % fp1)  
trainingerrors=[] 
testerrors=[] 
for i in range(20):
	fpi, residuals, rank, sv, rcond = sp.polyfit(X, Y, i, full=True)
	fi=sp.poly1d(fpi)
	fx=sp.linspace(-1,1,5)
	plt.plot(fx, fi(fx), linewidth=1)
	plt.legend(["d=%i" % i], loc="upper left") 
	er=error(fi, X, Y)
	trainingerrors.append(er)
	err=error(fi,XTEST,YTEST)
	testerrors.append(err)
plt.show()
print trainingerrors
print testerrors