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PUSH1 0x80
PUSH1 0x40
MSTORE
CALLVALUE
DUP1
ISZERO
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JUMPI
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DUP1

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# complete particle filter
# Anuj Bansal
#

# --------------
# USER INSTRUCTIONS
#
# Now you will put everything together.
#
# First make sure that your sense and move functions

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# A program that will iteratively update and
# predict based on the location measurements 
# and inferred motions shown below. 

def update(mean1, var1, mean2, var2):
    new_mean = float(var2 * mean1 + var1 * mean2) / (var1 + var2)
    new_var = 1./(1./var1 + 1./var2)
    return [new_mean, new_var]

def predict(mean1, var1, mean2, var2):

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# The function localize takes the following arguments:
#
# colors:
#        2D list, each entry either 'R' (for red cell) or 'G' (for green cell)
#
# measurements:
#        list of measurements taken by the robot, each entry either 'R' or 'G'
#
# motions:
#        list of actions taken by the robot, each entry of the form [dy,dx],

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# -*- coding: utf-8 -*-
"""
Created on Mon Oct 12 19:42:55 2015

@author: user
"""

import os
import sys
import pandas as pd

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import csv
import random
import math
from sklearn.naive_bayes import GaussianNB

def loadCsv(filename):
	lines = csv.reader(open(filename,'rb'))
	dataset = list(lines)
	for i in range(len(dataset)):
		dataset[i] = [float(x) for x in dataset[i]]