def __init__(self):
self.addTrainingSamples()
#read sample data from DB
samples=cloudserver.db.getAllLocationSamples()
pairs=[(s["sample"],s["label"]) for s in samples]
#prepare KNN
self.KNN=KNearestNeighbors(pairs)
#list_of_rooms={}
list_of_rooms_each_lab={}
for room in cloudserver.db.ROOM_DEFINITION:
id=room["id"]
lab=room["lab"]
#if id not in list_of_rooms:
# list_of_rooms[id]=id
if lab not in list_of_rooms_each_lab:
list_of_rooms_each_lab[lab]=[]
list_of_rooms_each_lab[lab]+=[id]
#prepare lab prior, as a list of votes (roomID, #vote)
prior_vote_const=2
self.prior_votes={}
self.prior_votes[0]=[]
for lab in list_of_rooms_each_lab:
if lab>0:
prior=[]
for id in list_of_rooms_each_lab[lab]:
prior.append((id,prior_vote_const))
self.prior_votes[lab]=prior
print("prior votes:")
print(self.prior_votes)
import numpy as np
import matplotlib.pyplot as plt
from sample import sample, getMatrix, confusion_matrix, plot_confusion_matrix
from KNN import zscale, KNearestNeighbors, trainAndGetBestK
############ 3 PERCENT SEPERABLE ##############
data = getMatrix('data/Seperable.csv')
x_train, x_test, y_train, y_test = sample(data)
zscale(x_train)
zscale(x_test)
# UNCOMMENT TO FIND BEST K, WHICH IS K = 1, SCORE = 62.6%
# k,score = trainAndGetBestK(x_train, x_test, y_train, y_test)
# print k, score
knn = KNearestNeighbors(1)
knn.fit(x_train, y_train)
preds = knn.predict(x_test)
cm = confusion_matrix(preds, y_test)
cats = np.unique(y_train)
plot_confusion_matrix(cm, cats)
plt.title('seperable, k = 1, score = 62.6%')
plt.show()
class LocationPredictor:
trainingData = []
trainingLabels = []
def __init__(self):
self.addTrainingSamples()
#read sample data from DB
samples=cloudserver.db.getAllLocationSamples()
pairs=[(s["sample"],s["label"]) for s in samples]
#prepare KNN
self.KNN=KNearestNeighbors(pairs)
#list_of_rooms={}
list_of_rooms_each_lab={}
for room in cloudserver.db.ROOM_DEFINITION:
id=room["id"]
lab=room["lab"]
#if id not in list_of_rooms:
# list_of_rooms[id]=id
if lab not in list_of_rooms_each_lab:
list_of_rooms_each_lab[lab]=[]
list_of_rooms_each_lab[lab]+=[id]
#prepare lab prior, as a list of votes (roomID, #vote)
prior_vote_const=2
self.prior_votes={}
self.prior_votes[0]=[]
for lab in list_of_rooms_each_lab:
if lab>0:
prior=[]
for id in list_of_rooms_each_lab[lab]:
prior.append((id,prior_vote_const))
self.prior_votes[lab]=prior
print("prior votes:")
print(self.prior_votes)
def addSamples(self, sampleFile):
infile = sampleFile + ".txt"
print("Loading from " + infile + "...")
f = open(infile, 'r')
x = f.readlines()
self.trainingData = []
for i in range(len(x)):
y = x[i].split('\t')
last = y[-1].split('\n')
y[-1] = last[0]
y = map(int, y)
self.trainingData.append(y)
infile = sampleFile + "Labels.txt"
print("Loading from " + infile + "...")
f = open(infile, 'r')
x = f.readlines()
self.trainingLabels = []
for j in range(len(x)):
y = x[j]
last = y.split('\n')
y = last[0]
self.trainingLabels.append(y)
assert(len(self.trainingData) > 0)
assert(len(self.trainingData) == len(self.trainingLabels))
for k in range(len(self.trainingData)):
cloudserver.db.addLocationSample(self.trainingLabels[k], self.trainingData[k])
def addTrainingSamples(self):
cloudserver.db.DestroyLocationSamples()
samples=cloudserver.db.getAllLocationSamples()
if (len(samples) > 0):
print(str(len(samples)) + " samples found")
return
print("no samples found")
infile = "trainingFiles/trainingFilesList.txt"
f = open(infile, 'r')
x = f.readlines()
for i in range(len(x)):
filename = x[i]
last = filename.split('\n')
self.addSamples(last[0])
print("added samples from " + last[0])
print "successful reupload"
def personal_classifier(self, ID, sample):
prior=[]
screenName=cloudserver.db.userIDLookup(ID)
if screenName!=None:
usernameAttributes = cloudserver.db.getAttributes(screenName, False)
labInt = usernameAttributes["lab"]
prior=self.prior_votes[labInt]
nearest_votes=self.KNN.get_nearest_pairs(sample)
result_pair=self.KNN.majority_vote(prior+nearest_votes)
return result_pair[0]
def POST(self):
raw_data = web.data()
locs = raw_data.split(',')
if (locs[0] == "REUP"):
infile = "backup.txt"
f = open(infile, 'r')
x = f.readlines()
self.trainingData = []
for i in range(len(x)):
y = x[i].split('\t')
last = y[-1].split('\n')
y[-1] = last[0]
y = map(int, y)
self.trainingData.append(y)
infile = "backuplabels.txt"
f = open(infile, 'r')
x = f.readlines()
self.trainingLabels = []
for j in range(len(x)):
y = x[j]
last = y.split('\n')
y = last[0]
self.trainingLabels.append(y)
infile = "backup2.txt"
f = open(infile, 'r')
x = f.readlines()
for i in range(len(x)):
y = x[i].split('\t')
last = y[-1].split('\n')
y[-1] = last[0]
y = map(int, y)
self.trainingData.append(y)
infile = "backuplabels2.txt"
f = open(infile, 'r')
x = f.readlines()
for j in range(len(x)):
y = x[j]
last = y.split('\n')
y = last[0]
self.trainingLabels.append(y)
print "successful reupload"
return
if (locs[0] == "DES"):
self.trainingData = []
self.trainingLabels = []
return "successful destroy"
l = locs[1:]
if (locs[0] == "GET"):
#outfile = "backup2.txt"
#with open(outfile, 'w') as file:
# file.writelines('\t'.join(str(j) for j in i) + '\n' for i in self.trainingData)
#outfile2 = "backuplabels2.txt"
#with open(outfile2, 'w') as file:
# file.writelines(str(self.rooms[i]) + '\n' for i in self.trainingLabels)
#locs = map(int, l)
#if (len(self.trainingLabels) < self.K):
# ret = "not enough data,"
# ret += str(len(self.trainingLabels))
# return ret
locs = map(int, l)
print('Location Predict request:', locs)
KNN = KNearestNeighbors(
list(zip(self.trainingData, self.trainingLabels)))
pairs = KNN.get_nearest_pairs(locs)
print('Predicted pairs:', pairs)
location = KNN.majority_vote(pairs)
print(location)
return str(location[0]) + ':' + str(location[1]) + ",LOL"
ID = locs[0]
intID = ID
locs = map(int, l)
self.trainingData.append(locs)
self.trainingLabels.append(intID)
print('Submitted ID=', ID)
print('Training sample=', locs)
cloudserver.db.addLocationSample(ID, locs)
return str(cloudserver.db.countLocationSamples()) + " LOL"