leonaburime

from __future__ import division
import numpy as np
import math, pdb
from sklearn import linear_model




#http://stackoverflow.com/questions/17784587/gradient-descent-using-python-and-numpy
def genData(numPoints, bias, variance):

leonaburime

import random, pdb
from pprint import pprint

import pandas as pd, numpy as np
import sklearn
from sklearn.cluster import KMeans


labels = {0 : 'apple', 1 : 'banana'}
input = [

leonaburime

from __future__ import division
from pprint import pprint
import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier

#References - http://saravananthirumuruganathan.wordpress.com/2010/05/17/a-detailed-introduction-to-k-nearest-neighbor-knn-algorithm/

#http://www.saedsayad.com/k_nearest_neighbors.htm
input  = [

leonaburime

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

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

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

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

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 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

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

import urllib2, pandas as pd




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