In [90]:

    

import csv
with open('dataset/wafer_TRAIN', 'rb') as csvfile:
    lines = csv.reader(csvfile)
    for row in lines:
        print ', '.join(row)


    

    




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In [66]:

    

import csv
def loadTrain(filename, trainingSet=[]):
    with open(filename, 'rb') as csvfile:
        lines = csv.reader(csvfile,delimiter=' ')
        dataset = list(lines)
        for x in range(len(dataset)):
            dataset[x] = filter(None, dataset[x])
            dataset[x] = list(map(float, dataset[x]))
            trainingSet.append(dataset[x])


    

In [69]:

    

trainingSet=[]
testSet=[]
loadTrain('dataset/Coffee_TRAIN', trainingSet)
print 'Train: ' + repr(len(trainingSet))
print 'Test: ' + repr(len(testSet))


    

    




Train: 28
Test: 0

In [70]:

    

trainingSet


    

    
Out[70]:





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In [76]:

    

import math
def euclideanDistance(instance1, instance2, length):
# 	print instance1
# 	print instance2
	distance = 0
	for x in range(length):
# 		print x
		if x == 0:
			continue
		distance += pow((instance1[x] - instance2[x]), 2)
# 		print "dist>>",distance
	return math.sqrt(distance)


    

In [77]:

    

data1 = ['a',2, 2, 2]
data2 = ['c',2, 2, 2]
distance = euclideanDistance(data1, data2, 4)
print 'Distance: ' + repr(distance)
# import numpy as np
# seq1 = [1,1, 1, 1]
# seq2 = [0,2, 3, 4]
# seq1 = (np.array(seq1)-np.mean(seq1))/np.std(seq1)
# seq2 = (np.array(seq2)-np.mean(seq2))/np.std(seq2)
# d = Dtw(seq1, seq2)
# d.get_dtw()


    

    




Distance: 0.0

In [78]:

    

import operator 
def getNeighbors(trainingSet, testInstance, k):
	distances = []
	length = len(testInstance)-1
	for x in range(len(trainingSet)):
		dist = euclideanDistance(testInstance, trainingSet[x], length)
		distances.append((trainingSet[x], dist))
	distances.sort(key=operator.itemgetter(1))
	neighbors = []
	for x in range(k):
		neighbors.append(distances[x][0])
	return neighbors


    

In [80]:

    

trainSet = [[2, 2, 2], [4, 4, 4]]
testInstance = [2, 4, 3]
k = 1
neighbors = getNeighbors(trainSet, testInstance, k)
print(neighbors)


    

    




[[4, 4, 4]]

In [81]:

    

import operator
def getResponse(neighbors):
	classVotes = {}
	for x in range(len(neighbors)):
		response = neighbors[x][0]
		if response in classVotes:
			classVotes[response] += 1
		else:
			classVotes[response] = 1
	sortedVotes = sorted(classVotes.iteritems(), key=operator.itemgetter(1), reverse=True)
	return sortedVotes[0][0]


    

In [84]:

    

neighbors = [['b',1,3,2], ['a',2,2,3], ['a',3,3,1]]
response = getResponse(neighbors)
print(response)


    

    




a

In [85]:

    

def getAccuracy(testSet, predictions):
    correct = 0
    for x in range(len(testSet)):
        if testSet[x][0] is predictions[x]:
            correct += 1
    return (correct/float(len(testSet))) * 100.0


    

In [86]:

    

testSet = [['a',1,1,1], ['a',2,2,2], ['b',3,3,3]]
predictions = ['a', 'a', 'a']
accuracy = getAccuracy(testSet, predictions)
print(accuracy)


    

    




66.6666666667

In [34]:

    

import csv
import random
import math
import operator
 
def loadDataset(filename, split, trainingSet=[] , testSet=[]):
	with open(filename, 'rb') as csvfile:
	    lines = csv.reader(csvfile)
	    dataset = list(lines)
	    for x in range(len(dataset)-1):
	        for y in range(4):
	            dataset[x][y] = float(dataset[x][y])
	        if random.random() < split:
	            trainingSet.append(dataset[x])
	        else:
	            testSet.append(dataset[x])
 
 
def euclideanDistance(instance1, instance2, length):
	distance = 0
	for x in range(length):
		distance += pow((instance1[x] - instance2[x]), 2)
	return math.sqrt(distance)
 
def getNeighbors(trainingSet, testInstance, k):
	distances = []
	length = len(testInstance)-1
	for x in range(len(trainingSet)):
		dist = euclideanDistance(testInstance, trainingSet[x], length)
		distances.append((trainingSet[x], dist))
	distances.sort(key=operator.itemgetter(1))
	neighbors = []
	for x in range(k):
		neighbors.append(distances[x][0])
	return neighbors
 
def getResponse(neighbors):
	classVotes = {}
	for x in range(len(neighbors)):
		response = neighbors[x][-1]
		if response in classVotes:
			classVotes[response] += 1
		else:
			classVotes[response] = 1
	sortedVotes = sorted(classVotes.iteritems(), key=operator.itemgetter(1), reverse=True)
	return sortedVotes[0][0]
 
def getAccuracy(testSet, predictions):
	correct = 0
	for x in range(len(testSet)):
		if testSet[x][-1] == predictions[x]:
			correct += 1
	return (correct/float(len(testSet))) * 100.0
	
def main():
	# prepare data
	trainingSet=[]
	testSet=[]
	split = 0.67
	loadDataset('iris.data.txt', split, trainingSet, testSet)
	print 'Train set: ' + repr(len(trainingSet))
	print 'Test set: ' + repr(len(testSet))
	# generate predictions
	predictions=[]
	k = 1
	for x in range(len(testSet)):
		print ">>",testSet[x]
		neighbors = getNeighbors(trainingSet, testSet[x], k)
		print "neighbors >>", neighbors
		result = getResponse(neighbors)
		print "result >>", result
		predictions.append(result)
# 		print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))
	accuracy = getAccuracy(testSet, predictions)
	print('Accuracy: ' + repr(accuracy) + '%')
	
main()


    

    




Train set: 90
Test set: 60
>> [4.9, 3.0, 1.4, 0.2, 'Iris-setosa']
neighbors >> [[4.9, 3.1, 1.5, 0.1, 'Iris-setosa']]
result >> Iris-setosa
>> [4.6, 3.1, 1.5, 0.2, 'Iris-setosa']
neighbors >> [[4.7, 3.2, 1.6, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [4.6, 3.4, 1.4, 0.3, 'Iris-setosa']
neighbors >> [[4.7, 3.2, 1.3, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [4.4, 2.9, 1.4, 0.2, 'Iris-setosa']
neighbors >> [[4.4, 3.2, 1.3, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [5.4, 3.7, 1.5, 0.2, 'Iris-setosa']
neighbors >> [[5.2, 3.5, 1.5, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [4.8, 3.0, 1.4, 0.1, 'Iris-setosa']
neighbors >> [[4.9, 3.1, 1.5, 0.1, 'Iris-setosa']]
result >> Iris-setosa
>> [4.3, 3.0, 1.1, 0.1, 'Iris-setosa']
neighbors >> [[4.4, 3.2, 1.3, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [5.4, 3.4, 1.7, 0.2, 'Iris-setosa']
neighbors >> [[5.2, 3.5, 1.5, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [4.8, 3.4, 1.9, 0.2, 'Iris-setosa']
neighbors >> [[4.8, 3.4, 1.6, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [5.0, 3.0, 1.6, 0.2, 'Iris-setosa']
neighbors >> [[4.9, 3.1, 1.5, 0.1, 'Iris-setosa']]
result >> Iris-setosa
>> [5.4, 3.4, 1.5, 0.4, 'Iris-setosa']
neighbors >> [[5.2, 3.5, 1.5, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [5.0, 3.2, 1.2, 0.2, 'Iris-setosa']
neighbors >> [[4.7, 3.2, 1.3, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [4.9, 3.1, 1.5, 0.1, 'Iris-setosa']
neighbors >> [[4.9, 3.1, 1.5, 0.1, 'Iris-setosa']]
result >> Iris-setosa
>> [4.4, 3.0, 1.3, 0.2, 'Iris-setosa']
neighbors >> [[4.4, 3.2, 1.3, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [5.1, 3.8, 1.9, 0.4, 'Iris-setosa']
neighbors >> [[5.1, 3.8, 1.6, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [4.8, 3.0, 1.4, 0.3, 'Iris-setosa']
neighbors >> [[4.8, 3.1, 1.6, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [4.6, 3.2, 1.4, 0.2, 'Iris-setosa']
neighbors >> [[4.7, 3.2, 1.3, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [5.3, 3.7, 1.5, 0.2, 'Iris-setosa']
neighbors >> [[5.2, 3.5, 1.5, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [5.0, 3.3, 1.4, 0.2, 'Iris-setosa']
neighbors >> [[5.0, 3.4, 1.5, 0.2, 'Iris-setosa']]
result >> Iris-setosa
>> [7.0, 3.2, 4.7, 1.4, 'Iris-versicolor']
neighbors >> [[6.7, 3.1, 4.4, 1.4, 'Iris-versicolor']]
result >> Iris-versicolor
>> [6.9, 3.1, 4.9, 1.5, 'Iris-versicolor']
neighbors >> [[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.5, 2.3, 4.0, 1.3, 'Iris-versicolor']
neighbors >> [[5.5, 2.4, 3.8, 1.1, 'Iris-versicolor']]
result >> Iris-versicolor
>> [6.5, 2.8, 4.6, 1.5, 'Iris-versicolor']
neighbors >> [[6.6, 2.9, 4.6, 1.3, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.7, 2.8, 4.5, 1.3, 'Iris-versicolor']
neighbors >> [[5.6, 3.0, 4.5, 1.5, 'Iris-versicolor']]
result >> Iris-versicolor
>> [6.1, 2.9, 4.7, 1.4, 'Iris-versicolor']
neighbors >> [[6.0, 2.9, 4.5, 1.5, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.8, 2.7, 4.1, 1.0, 'Iris-versicolor']
neighbors >> [[5.8, 2.6, 4.0, 1.2, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.9, 3.2, 4.8, 1.8, 'Iris-versicolor']
neighbors >> [[6.0, 3.0, 4.8, 1.8, 'Iris-virginica']]
result >> Iris-virginica
>> [6.1, 2.8, 4.7, 1.2, 'Iris-versicolor']
neighbors >> [[6.0, 2.9, 4.5, 1.5, 'Iris-versicolor']]
result >> Iris-versicolor
>> [6.6, 3.0, 4.4, 1.4, 'Iris-versicolor']
neighbors >> [[6.7, 3.1, 4.4, 1.4, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.5, 2.4, 3.7, 1.0, 'Iris-versicolor']
neighbors >> [[5.5, 2.4, 3.8, 1.1, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.8, 2.7, 3.9, 1.2, 'Iris-versicolor']
neighbors >> [[5.8, 2.6, 4.0, 1.2, 'Iris-versicolor']]
result >> Iris-versicolor
>> [6.7, 3.1, 4.7, 1.5, 'Iris-versicolor']
neighbors >> [[6.7, 3.1, 4.4, 1.4, 'Iris-versicolor']]
result >> Iris-versicolor
>> [6.3, 2.3, 4.4, 1.3, 'Iris-versicolor']
neighbors >> [[6.2, 2.2, 4.5, 1.5, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.5, 2.5, 4.0, 1.3, 'Iris-versicolor']
neighbors >> [[5.6, 2.5, 3.9, 1.1, 'Iris-versicolor']]
result >> Iris-versicolor
>> [6.1, 3.0, 4.6, 1.4, 'Iris-versicolor']
neighbors >> [[6.0, 2.9, 4.5, 1.5, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.7, 3.0, 4.2, 1.2, 'Iris-versicolor']
neighbors >> [[5.6, 3.0, 4.1, 1.3, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.7, 2.9, 4.2, 1.3, 'Iris-versicolor']
neighbors >> [[5.7, 2.8, 4.1, 1.3, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.1, 2.5, 3.0, 1.1, 'Iris-versicolor']
neighbors >> [[4.9, 2.4, 3.3, 1.0, 'Iris-versicolor']]
result >> Iris-versicolor
>> [6.3, 3.3, 6.0, 2.5, 'Iris-virginica']
neighbors >> [[6.3, 3.4, 5.6, 2.4, 'Iris-virginica']]
result >> Iris-virginica
>> [6.3, 2.9, 5.6, 1.8, 'Iris-virginica']
neighbors >> [[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']]
result >> Iris-virginica
>> [4.9, 2.5, 4.5, 1.7, 'Iris-virginica']
neighbors >> [[5.4, 3.0, 4.5, 1.5, 'Iris-versicolor']]
result >> Iris-versicolor
>> [7.3, 2.9, 6.3, 1.8, 'Iris-virginica']
neighbors >> [[7.2, 3.2, 6.0, 1.8, 'Iris-virginica']]
result >> Iris-virginica
>> [6.7, 2.5, 5.8, 1.8, 'Iris-virginica']
neighbors >> [[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']]
result >> Iris-virginica
>> [6.8, 3.0, 5.5, 2.1, 'Iris-virginica']
neighbors >> [[6.9, 3.1, 5.4, 2.1, 'Iris-virginica']]
result >> Iris-virginica
>> [5.7, 2.5, 5.0, 2.0, 'Iris-virginica']
neighbors >> [[5.8, 2.7, 5.1, 1.9, 'Iris-virginica']]
result >> Iris-virginica
>> [6.4, 3.2, 5.3, 2.3, 'Iris-virginica']
neighbors >> [[6.2, 3.4, 5.4, 2.3, 'Iris-virginica']]
result >> Iris-virginica
>> [6.5, 3.0, 5.5, 1.8, 'Iris-virginica']
neighbors >> [[6.5, 3.0, 5.2, 2.0, 'Iris-virginica']]
result >> Iris-virginica
>> [6.0, 2.2, 5.0, 1.5, 'Iris-virginica']
neighbors >> [[6.3, 2.5, 4.9, 1.5, 'Iris-versicolor']]
result >> Iris-versicolor
>> [5.6, 2.8, 4.9, 2.0, 'Iris-virginica']
neighbors >> [[5.8, 2.7, 5.1, 1.9, 'Iris-virginica']]
result >> Iris-virginica
>> [7.7, 2.8, 6.7, 2.0, 'Iris-virginica']
neighbors >> [[7.6, 3.0, 6.6, 2.1, 'Iris-virginica']]
result >> Iris-virginica
>> [6.7, 3.3, 5.7, 2.1, 'Iris-virginica']
neighbors >> [[6.9, 3.2, 5.7, 2.3, 'Iris-virginica']]
result >> Iris-virginica
>> [6.4, 2.8, 5.6, 2.1, 'Iris-virginica']
neighbors >> [[6.5, 3.0, 5.8, 2.2, 'Iris-virginica']]
result >> Iris-virginica
>> [7.4, 2.8, 6.1, 1.9, 'Iris-virginica']
neighbors >> [[7.1, 3.0, 5.9, 2.1, 'Iris-virginica']]
result >> Iris-virginica
>> [7.9, 3.8, 6.4, 2.0, 'Iris-virginica']
neighbors >> [[7.7, 3.8, 6.7, 2.2, 'Iris-virginica']]
result >> Iris-virginica
>> [6.4, 2.8, 5.6, 2.2, 'Iris-virginica']
neighbors >> [[6.5, 3.0, 5.8, 2.2, 'Iris-virginica']]
result >> Iris-virginica
>> [6.4, 3.1, 5.5, 1.8, 'Iris-virginica']
neighbors >> [[6.5, 3.0, 5.2, 2.0, 'Iris-virginica']]
result >> Iris-virginica
>> [6.7, 3.1, 5.6, 2.4, 'Iris-virginica']
neighbors >> [[6.9, 3.2, 5.7, 2.3, 'Iris-virginica']]
result >> Iris-virginica
>> [6.9, 3.1, 5.1, 2.3, 'Iris-virginica']
neighbors >> [[6.7, 3.0, 5.2, 2.3, 'Iris-virginica']]
result >> Iris-virginica
>> [6.7, 3.3, 5.7, 2.5, 'Iris-virginica']
neighbors >> [[6.9, 3.2, 5.7, 2.3, 'Iris-virginica']]
result >> Iris-virginica
>> [5.9, 3.0, 5.1, 1.8, 'Iris-virginica']
neighbors >> [[6.1, 3.0, 4.9, 1.8, 'Iris-virginica']]
result >> Iris-virginica
Accuracy: 95.0%

In [ ]:

    




    

In [96]:

    

# -*- coding: utf-8 -*-

class Dtw(object):
    
    def __init__(self, seq1, seq2,
                 patterns = [(-1,-1), (-1,0), (0,-1)], 
                 weights = [{(0,0):2}, {(0,0):1}, {(0,0):1}], 
                 band_r=0.01):
        self._seq1 = seq1
        self._seq2 = seq2
        self.len_seq1 = len(seq1)
        self.len_seq2 = len(seq2)
        self.len_pattern = len(patterns)
        self.sum_w = [sum(ws.values()) for ws in weights]
        self._r = int(len(seq1)*band_r)
        assert len(patterns) == len(weights)
        self._patterns = patterns
        self._weights = weights
    
    def get_distance(self, i1, i2):
        return abs(self._seq1[i1] - self._seq2[i2])

    def calculate(self):
        g = list([float('inf')]*self.len_seq2 for i in range(self.len_seq1))
        cost = list([0]*self.len_seq2 for i in range(self.len_seq1))

        g[0][0] = 2*self.get_distance(0, 0)
        for i in range(self.len_seq1):
            for j in range(max(0,i-self._r), min(i+self._r+1, self.len_seq2)):
                for pat_i in range(self.len_pattern):
                    coor = (i+self._patterns[pat_i][0], j+self._patterns[pat_i][1])
                    if coor[0]<0 or coor[1]<0:
                        continue
                    dist = 0
                    for w_coor_offset, d_w in self._weights[pat_i].items():
                        w_coor = (i+w_coor_offset[0], j+w_coor_offset[1])
                        dist += d_w*self.get_distance(w_coor[0], w_coor[1])
                    this_val = g[coor[0]][coor[1]] + dist
                    this_cost = cost[coor[0]][coor[1]] + self.sum_w[pat_i]
                    if this_val < g[i][j]:
                        g[i][j] = this_val
                        cost[i][j] = this_cost
        return g[self.len_seq1-1][self.len_seq2-1]/cost[self.len_seq1-1][self.len_seq2-1], g, cost
    
    def print_table(self, tb):
        print('      '+' '.join(["{:^7d}".format(i) for i in range(self.len_seq2)]))
        for i in range(self.len_seq1):
            str = "{:^4d}: ".format(i)
            for j in range(self.len_seq2):
                str += "{:^7.3f} ".format(tb[i][j])
            print (str)

    def print_g_matrix(self):
        _, tb, _ = self.calculate()
        self.print_table(tb)

    def print_cost_matrix(self):
        _, _, tb = self.calculate()
        self.print_table(tb)
        
    def get_dtw(self):
        ans, _, _ = self.calculate()
        return ans


    

In [138]:

    

import csv
import random
import math
import operator
import numpy as np
 
def loadDataset(filename, data=[]):
    with open(filename, 'rb') as csvfile:
        lines = csv.reader(csvfile,delimiter=' ')
        dataset = list(lines)
        for x in range(len(dataset)):
            dataset[x] = filter(None, dataset[x])
            dataset[x] = list(map(float, dataset[x]))
            data.append(dataset[x])

def euclideanDistance(instance1, instance2, length):
	distance = 0
	for x in range(length):
		if x == 0:
			continue
		distance += pow((instance1[x] - instance2[x]), 2)
	return math.sqrt(distance)
 
def getNeighbors(trainingSet, testInstance, k, pattern, weight):
	distances = []
	length = len(testInstance)
	for x in range(len(trainingSet)):
#  z-normalization
		new_testInstance = (np.array(testInstance)-np.mean(testInstance))/np.std(testInstance)
		new_trainingSet = (np.array(trainingSet[x])-np.mean(trainingSet[x]))/np.std(trainingSet[x])
		d = Dtw(new_testInstance[1:], new_trainingSet[1:], pattern, weight)
		dist = d.get_dtw()
# 		dist = euclideanDistance(testInstance, trainingSet[x], length)
		distances.append((trainingSet[x], dist))
	distances.sort(key=operator.itemgetter(1))
#  	print "dist >>>> ",distances
	neighbors = []
	for x in range(k):
		neighbors.append(distances[x][0])
	return neighbors

def getResponse(neighbors):
	classVotes = {}
	for x in range(len(neighbors)):
		response = neighbors[x][0]
		if response in classVotes:
			classVotes[response] += 1
		else:
			classVotes[response] = 1
	sortedVotes = sorted(classVotes.iteritems(), key=operator.itemgetter(1), reverse=True)
	return sortedVotes[0][0]
 
def getAccuracy(testSet, predictions):
	correct = 0
	for x in range(len(testSet)):
		if testSet[x][0] == predictions[x]:
			correct += 1
	return (correct/float(len(testSet))) * 100.0
	
def knn(train_data, test_data, k, pattern, weight):
	# prepare data
	trainingSet=[]
	testSet=[]
	loadDataset(train_data, trainingSet)
	loadDataset(test_data, testSet)
# 	print 'Train set: ' + repr(len(trainingSet))
# 	print trainingSet
# 	print 'Test set: ' + repr(len(testSet))
# 	print testSet
	# generate predictions
	predictions=[]
	for x in range(len(testSet)):
# 		print ">>",testSet[x]
		neighbors = getNeighbors(trainingSet, testSet[x], k, pattern, weight)
# 		print "neighbors >>", neighbors
		result = getResponse(neighbors)
# 		print "result >>", result
		predictions.append(result)
# 		print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][0]))
	accuracy = getAccuracy(testSet, predictions)
	return accuracy


    

In [139]:

    

PATTERNS_1 = [(0,-1), (-1,-1), (-1,0)]
WEIGHTS_SYM_1 = [{(0,0):1}, {(0,0):2}, {(0,0):1}]


    

In [140]:

    

COUNT = 100
weights = []
for i in range(COUNT+1):
    for j in range(COUNT-i):
        k = COUNT - j
        weights.append([{(0,0):i}, {(0,0):j}, {(0,0):k}])


    

In [141]:

    

TRAIN_DATA = 'dataset/Coffee_TRAIN'
TEST_DATA = 'dataset/Coffee_TEST'
OUTPUT_FILE = 'acc_coffee_0.01band.csv'


    

In [142]:

    

knn(TRAIN_DATA, TEST_DATA, 1, PATTERNS_1, WEIGHTS_SYM_1)


    

    
Out[142]:





92.85714285714286

In [144]:

    

with open(OUTPUT_FILE, "w") as myfile:
    myfile.write("i,j,k,accuracy\n")
for weight in weights:
    i = weight[0][(0,0)]
    j = weight[1][(0,0)]
    k = weight[2][(0,0)]
    print "i:", i, "j:", j,"k:", k
    acc = knn(TRAIN_DATA, TEST_DATA, 1, PATTERNS_1, weight)
    print acc
    with open(OUTPUT_FILE, "a") as myfile:
        myfile.write(str(i)+","+str(j)+","+str(k)+","+str(acc)+"\n")


    

    




i: 0 j: 0 k: 100






    




/opt/conda/lib/python2.7/site-packages/ipykernel/__main__.py:43: RuntimeWarning: divide by zero encountered in double_scalars






    





KeyboardInterruptTraceback (most recent call last)
<ipython-input-144-e160414b8190> in <module>()
      6     k = weight[2][(0,0)]
      7     print "i:", i, "j:", j,"k:", k
----> 8     acc = knn(TRAIN_DATA, TEST_DATA, 1, PATTERNS_1, weight)
      9     print acc
     10     with open("acc_coffee.csv", "a") as myfile:

<ipython-input-138-5a772f659bd1> in knn(train_data, test_data, k, pattern, weight)
     72         for x in range(len(testSet)):
     73 #               print ">>",testSet[x]
---> 74                 neighbors = getNeighbors(trainingSet, testSet[x], k, pattern, weight)
     75 #               print "neighbors >>", neighbors
     76                 result = getResponse(neighbors)

<ipython-input-138-5a772f659bd1> in getNeighbors(trainingSet, testInstance, k, pattern, weight)
     30                 new_trainingSet = (np.array(trainingSet[x])-np.mean(trainingSet[x]))/np.std(trainingSet[x])
     31                 d = Dtw(new_testInstance[1:], new_trainingSet[1:], pattern, weight)
---> 32                 dist = d.get_dtw()
     33 #               dist = euclideanDistance(testInstance, trainingSet[x], length)
     34                 distances.append((trainingSet[x], dist))

<ipython-input-96-0784cecbe65a> in get_dtw(self)
     60 
     61     def get_dtw(self):
---> 62         ans, _, _ = self.calculate()
     63         return ans

<ipython-input-96-0784cecbe65a> in calculate(self)
     28         for i in range(self.len_seq1):
     29             for j in range(max(0,i-self._r), min(i+self._r+1, self.len_seq2)):
---> 30                 for pat_i in range(self.len_pattern):
     31                     coor = (i+self._patterns[pat_i][0], j+self._patterns[pat_i][1])
     32                     if coor[0]<0 or coor[1]<0:

KeyboardInterrupt: 

In [7]:

    

import numpy as np
x = [100,1,2,3]
np.append(x[0], ( np.array(x[1:])-np.mean(x[1:]) )/np.std(x[1:]) )


    

    
Out[7]:





array([ 100.        ,   -1.22474487,    0.        ,    1.22474487])

In [ ]: