import numpy as np
import pandas as pd
from array import *

#make an array of 100 points x uniform distrib., btw 0-1 interval
#with y as a polynomial plus a Gaussian random variable with STD = 0.5

x = np.arange(0,1,0.01)

z = np.random.normal(loc=0.0, scale=.5, size=None)

for i in x:
	y = 2+3*x+z
array = np.array([y])
#print array

#performs the svd calculation for array calculated above

U, s, V = np.linalg.svd(array, full_matrices=True)
U.shape, V.shape, s.shape

print U
print s
print V

#Bootstrap method

def bootstrap_resample(X, n=None):
	if n == None:
		n = len(X)

	resample_i = np.floor(np.random.rand(n)*len(X)).astype(int)
	X_resample = X[resample_i]
	return X_resample

X = arange(10000)
X = resample = bootstrap_resample(X, n=5000)
print 'original mean:', X.mean()
print 'resampled mean:', X_resample.mean()



#write output to a txt and csv file
np.savetxt('svdmatrix.txt', array, fmt='%2.5f', delimiter=',')
np.savetxt('svdmatrix.csv', array, fmt='%2.5f', delimiter=',')