# -*- coding: utf-8 -*-
"""
Created on Mon Sep 09 00:57:33 2013
@author: Tejay Cardon
visualize pla
"""
from utils import points2weights
from pla import runPla
import numpy as np
sum = 0.0
runs = 10
d = 10
for i in range(0, runs):
print "Running test # " + str(i)
f = points2weights(np.random.random((2, 2)) * 2 - 1)
x = np.insert(np.random.random((d, 2)) * 2 - 1, 0, np.ones((1, d)), axis=1)
trainings, w = runPla(x, f, show=True)
sum += trainings
print "Average of " + str(sum / runs) + " iterations"
@author: Tejay Cardon
HW2-5
"""
import numpy as np
import LinearRegression as lr
from utils import points2weights
wrongIn = 0.0
runs = 1000
d = 100
for i in range(0,runs):
print "Running test # " + str(i)
f = points2weights(np.random.random((2,2)) * 2 - 1)
x = np.insert(np.random.random((d,2)) * 2 - 1,0,np.ones((1,d)), axis=1)
truth = np.sign(np.dot(x,f))
i,o,w = lr.runLR(x, truth)
wrongIn += i
print "Average of " + str(wrongIn/runs) + " wrong in sample per run"
fractionWrong = (wrongIn/runs)/d
print "%f incorrect on average in sample"%(fractionWrong)
a = abs(fractionWrong - 0)
b = abs(fractionWrong - 0.001)
c = abs(fractionWrong - 0.01)
d = abs(fractionWrong - 0.1)
if min([a,b,c,d]) == a :