leonaburime

p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
p,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g
e,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m

leonaburime

from __future__ import division
import random, datafile, decisionTree, collections

#Must import datafile.py and decisionTree.py

def randomSelection(arr, n_items):
	return random.sample(arr, n_items)

	
class randomForest:

leonaburime

import numpy as np, pandas as pd 
from pprint import pprint 
from copy import copy
import math,urllib2,datafile,pdb

#This tutorial is derived from ...
#http://nbviewer.ipython.org/github/gumption/Python_for_Data_Science/blob/master/4_Python_Simple_Decision_Tree.ipynb

#Data set will be taken from 
data= datafile.get('mushroom')

leonaburime

import urllib2, pandas as pd




d = {
	'mushroom' :{
		'features':  [
			'class','cap-shape', 'cap-surface', 'cap-color', 
			'bruises?','odor','gill-attachment',

leonaburime

from __future__ import division
import pandas as pd, numpy as np,datafile,math,pdb,itertools
from pprint import pprint
from collections import Counter 

#Will need to import datafile.py and correct dataset for this program

class naiveBayes:
	def __init__(self,name='play', testSize=0): 
		

leonaburime

package KMeans;

import org.apache.commons.lang3.ArrayUtils;
import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.stat.StatUtils;

import java.lang.reflect.Array;
import java.util.*;

leonaburime

package NeuralNetwork;

import org.apache.commons.lang3.*;
import org.apache.commons.math3.linear.*;
import org.apache.commons.math3.stat.StatUtils;

import javax.swing.*;
import java.util.*;

/**

leonaburime

package com.example.kNearestNeighbor;

//To use third party libraries simply download the jar
//and put it in the Program Files/jre/jdk#.#(version number)/lib/ext for example

//Can get the jar file for guava at https://code.google.com/p/guava-libraries/
import com.google.common.base.Functions;
import com.google.common.collect.Ordering;

//Can get the apache commons math file at

leonaburime

import numpy as np
import math
from pprint import pprint
import pdb



#Linearly separable 
_or = {
	'X': [[0,0],[0,1],[1,0],[1,1]],

leonaburime

import numpy as np
import dataMunge, pdb
from sklearn import linear_model

input = [
	[95, 85],
	[85, 95],
	[80, 70],
	[70, 65],
	[60, 70]