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crimecluster.py
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import numpy as np
import csv
import sys
import random as rando
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from sklearn.cluster import KMeans
class KMeans(object):
# K is the K in KMeans
def __init__(self, K):
self.K = K
# X is a (n x j) array where j is the number of columns of interest for each of the n points.
def fit(self, X):
n = len(X)
self.n = n
centroidmeans = np.array([rando.choice(X)])
point_nearest_centroid_distance = np.repeat(0.0,self.n)
#print(centroidmeans)
while(len(centroidmeans) != self.K):
for i in range(0,self.n):
init_diffs_vecs = np.repeat(0.0,len(centroidmeans))
for k in range(0,len(centroidmeans)):
diffs_vec = X[i] - centroidmeans[k]
diffs_vec[0] = min(abs(diffs_vec[0]), 24-abs(diffs_vec[0]))
# print("SDCZ", diffs_vec)
# print("SDCZUPINHYO",np.linalg.norm(diffs_vec))
init_diffs_vecs[k] = np.linalg.norm(diffs_vec)
# print("HELLO HELLO HELLO", init_diffs_vecs)
point_nearest_centroid_distance[i] = min(init_diffs_vecs)
# print(i)
newarr = np.square(point_nearest_centroid_distance)
# print("DSF",point_nearest_centroid_distance)
newarr = newarr/np.sum(newarr)
# print("QSEZ",newarr)
# print(np.sum(newarr))
# print("HERE IT IS",newarr)
newarr2 = np.repeat(0.0,len(newarr))
for i in range(0,len(newarr2)):
newarr2[i] = sum(newarr[0:i])
newarr2 = [0] + newarr2
rando_number = rando.uniform(0,1)
l = 0
r = len(newarr2)
while (r - l > 1):
mid = ((l + r) // 2)
if (newarr2[mid] > rando_number):
r = mid
else:
l = mid
#print(X[l])
centroidmeans = np.vstack((centroidmeans,X[l]))
#centroidmeans = [X[l]]+centroidmeans
# print("::::::::::")
# print(centroidmeans)
# centroidmeans = X[0:self.K]
clustermat = np.repeat(0,self.n)
validator = centroidmeans*3
countervar = 0
while(not np.array_equal(validator,centroidmeans)):
# print(countervar)
validator = centroidmeans
for i in range(0,self.n):
normvals = np.repeat(0.0,self.K)
for k in range(0,self.K):
diffs_vec = X[i] - centroidmeans[k]
diffs_vec[0] = min(abs(diffs_vec[0]), 24-abs(diffs_vec[0]))
# print("HIFODSHOFI", diffs_vec, X[i], centroidmeans[k])
normvals[k] = np.linalg.norm(diffs_vec)
# print("ZZZZZZZZZZZ",normvals)
clustermat[i] = np.argmin(normvals)
# print(";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;")
# print(clustermat)
centroidmeans1 = np.zeros((self.K,3))#X[0:self.K]*0
count = np.repeat(0,self.K)
# print(centroidmeans1)
for i in range(0,self.n):
centroidmeans1[clustermat[i]] += X[i]
count[clustermat[i]] += 1
for s in range(0,self.K):
centroidmeans1[s] = centroidmeans1[s]/count[s]
if(np.linalg.norm(centroidmeans1[s]) > 0):
centroidmeans[s] = centroidmeans1[s]
countervar += 1
# print(centroidmeans)
self.clustermat = clustermat
self.centroidmeans = centroidmeans
self.X = X
self.get_error()
# This should return the arrays for K images. Each image should represent the mean of each of the fitted clusters.
def get_centroids(self,matr):
for i in range(0,self.K):
print("Mean of class ",i,"!")
meani = [self.centroidmeans[i][0]*12,self.centroidmeans[i][1]*maxdist,self.centroidmeans[i][2]*maxdist]
print(meani)
matr.append(meani)
return
def get_error(self):
sumsq = 0
for i in range(0,len(self.X)):
diffs_vec = self.X[i] - self.centroidmeans[self.clustermat[i]]
diffs_vec[0] = min(abs(diffs_vec[0]), 24-abs(diffs_vec[0]))
sumsq += (np.linalg.norm(diffs_vec))**2
print("The average sum of squares error is ",sumsq/len(self.X),"!")
return
# img_array should be a 2D (square) numpy array.
# Note, you are welcome to change this function (including its arguments and return values) to suit your needs.
# However, we do ask that any images in your writeup be grayscale images, just as in this example.
# def create_image_from_array(self, img_array):
# plt.figure()
# plt.imshow(img_array, cmap='Greys_r')
# plt.show()
# return
with open("march2017barenecessities.csv") as f:
rd = csv.reader(f)
Z = [r for r in rd]
#np.array([[15,29.79038,-95.4325],[0,29.79273,-95.4077],[18,29.79273,-95.4077],[19,29.80084,-95.4036],[19,29.8007,-95.4142],[15,29.69769,-95.4372],[11,29.63324,-95.4708],[8,29.67679,-95.4339],[7,29.78868,-95.4235],[14,29.64234,-95.3429],[12,29.64216,-95.3661],[13,29.64267,-95.2698],[0,29.61826,-95.4223],[23,29.67869,-95.5662]])
#data in format (lat,long,time)
Z = Z[1:len(Z)]
# print(X[0:5])
for i in range (len(Z)):
Z[i][0] = float(Z[i][0])
Z[i][1] = float(Z[i][1])
Z[i][2] = float(Z[i][2])
# # print(X[0:5])
# Y = Z
# X = []
# for i in range(0,len(Y)):
# if Y[i][0] <= 30:
# X.append([Y[i][2],Y[i][0],Y[i][1]])
# # X[i][0] = Y[i][2]
# # X[i][1] = Y[i][0]
# # X[i][2] = Y[i][1]
# #Calculating maximum distance
# print('check1')
# # arrayofdist = []
# # for i in range(0,len(X)):
# # for j in range(i+1,len(X)):
# # arrayofdist = arrayofdist + [((X[i][1]-X[j][1])**2 + (X[i][2]-X[j][2])**2)**(1/2)]
# lat = []
# lon = []
# for i in range(0,len(X)):
# lat.append(X[i][1])
# lon.append(X[i][2])
# # print(max(lat),min(lat),max(lon),min(lon))
# print('check2')
# maxdist = ((max(lat)-min(lat))**2 + (max(lon)-min(lon))**2)**(1/2)
# # max(arrayofdist)
# print(maxdist)
# for i in range(0,len(X)):
# X[i][0] = X[i][0]/12
# X[i][1] = X[i][1]/maxdist
# X[i][2] = X[i][2]/maxdist
# print('check3')
# # print(";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;")
# # print(type(int(sys.argv[1])))
# KMeansClassifier = KMeans(K=int(sys.argv[1]))
# print('check4')
# KMeansClassifier.fit(X)
# print('check5')
# list_of_means = []
# KMeansClassifier.get_centroids(list_of_means)
# ###### Plotting
# plt.plot(maxdist*np.transpose(X)[1],maxdist*np.transpose(X)[2],'bo')
# plt.plot(np.transpose(list_of_means)[1],np.transpose(list_of_means)[2],'ro')
# plt.show()
# # kmeans = KMeans(n_clusters = , init = "k-means++")
# # kmeans.labels_
# # kmeans.cluster_centers_
with open("mar17.csv") as f:
rd = csv.reader(f)
Q = [r for r in rd]
burglary1 = []
autotheft1 = []
theft1 = []
rape1 = []
robbery1 = []
aggravatedassault1 = []
murder1 = []
for i in range(0,len(Z)):
if(Q[i]==["Burglary"]):
burglary1.append(Z[i])
elif(Q[i]==["Auto Theft"]):
autotheft1.append(Z[i])
elif(Q[i]==["Theft"]):
theft1.append(Z[i])
elif(Q[i]==["Rape"]):
rape1.append(Z[i])
elif(Q[i]==["Robbery"]):
robbery1.append(Z[i])
elif(Q[i]==["Aggravated Assault"]):
aggravatedassault1.append(Z[i])
elif(Q[i]==["Murder"]):
murder1.append(Z[i])
burglary = []
autotheft = []
theft = []
rape = []
robbery = []
aggravatedassault = []
murder = []
for i in range(0,len(burglary1)):
if burglary1[i][0] <= 30:
burglary.append([burglary1[i][2],burglary1[i][0],burglary1[i][1]])
for i in range(0,len(autotheft1)):
if autotheft1[i][0] <= 30:
autotheft.append([autotheft1[i][2],autotheft1[i][0],autotheft1[i][1]])
for i in range(0,len(theft1)):
if theft1[i][0] <= 30:
theft.append([theft1[i][2],theft1[i][0],theft1[i][1]])
for i in range(0,len(rape1)):
if rape1[i][0] <= 30:
rape.append([rape1[i][2],rape1[i][0],rape1[i][1]])
for i in range(0,len(robbery1)):
if robbery1[i][0] <= 30:
robbery.append([robbery1[i][2],robbery1[i][0],robbery1[i][1]])
for i in range(0,len(aggravatedassault1)):
if aggravatedassault1[i][0] <= 30:
aggravatedassault.append([aggravatedassault1[i][2],aggravatedassault1[i][0],aggravatedassault1[i][1]])
for i in range(0,len(murder1)):
if murder1[i][0] <= 30:
murder.append([murder1[i][2],murder1[i][0],murder1[i][1]])
# ###BURGLARY
# print('Burglary 1')
# lat = []
# lon = []
# for i in range(0,len(burglary)):
# lat.append(burglary[i][1])
# lon.append(burglary[i][2])
# print('Burglary 2')
# print(len(burglary))
# maxdist = ((max(lat)-min(lat))**2 + (max(lon)-min(lon))**2)**(1/2)
# print(maxdist)
# for i in range(0,len(burglary)):
# burglary[i][0] = burglary[i][0]/12
# burglary[i][1] = burglary[i][1]/maxdist
# burglary[i][2] = burglary[i][2]/maxdist
# print('Burglary 3')
# KMeansClassifier = KMeans(K=int(sys.argv[1]))
# print('Burglary 4')
# KMeansClassifier.fit(burglary)
# print('Burglary 5')
# list_of_means = []
# KMeansClassifier.get_centroids(list_of_means)
# ###### Plotting
# plt.plot(maxdist*np.transpose(burglary)[1],maxdist*np.transpose(burglary)[2],'bo')
# plt.plot(np.transpose(list_of_means)[1],np.transpose(list_of_means)[2],'ro')
# plt.show()
# ###Auto Theft
# print('Auto Theft 1')
# lat = []
# lon = []
# for i in range(0,len(autotheft)):
# lat.append(autotheft[i][1])
# lon.append(autotheft[i][2])
# print('Auto Theft 2')
# maxdist = ((max(lat)-min(lat))**2 + (max(lon)-min(lon))**2)**(1/2)
# print(maxdist)
# for i in range(0,len(autotheft)):
# autotheft[i][0] = autotheft[i][0]/12
# autotheft[i][1] = autotheft[i][1]/maxdist
# autotheft[i][2] = autotheft[i][2]/maxdist
# print('Auto Theft 3')
# KMeansClassifier = KMeans(K=int(sys.argv[1]))
# print('Auto Theft 4')
# KMeansClassifier.fit(autotheft)
# print('Auto Theft 5')
# list_of_means = []
# KMeansClassifier.get_centroids(list_of_means)
# ###### Plotting
# plt.plot(maxdist*np.transpose(autotheft)[1],maxdist*np.transpose(autotheft)[2],'bo')
# plt.plot(np.transpose(list_of_means)[1],np.transpose(list_of_means)[2],'ro')
# plt.show()
# ###Theft
# print('Auto Theft 1')
# lat = []
# lon = []
# for i in range(0,len(theft)):
# lat.append(theft[i][1])
# lon.append(theft[i][2])
# print('Theft 2')
# maxdist = ((max(lat)-min(lat))**2 + (max(lon)-min(lon))**2)**(1/2)
# print(maxdist)
# for i in range(0,len(theft)):
# theft[i][0] = theft[i][0]/12
# theft[i][1] = theft[i][1]/maxdist
# theft[i][2] = theft[i][2]/maxdist
# print('Theft 3')
# KMeansClassifier = KMeans(K=int(sys.argv[1]))
# print('Theft 4')
# KMeansClassifier.fit(theft)
# print('Theft 5')
# list_of_means = []
# KMeansClassifier.get_centroids(list_of_means)
# ###### Plotting
# plt.plot(maxdist*np.transpose(theft)[1],maxdist*np.transpose(theft)[2],'bo')
# plt.plot(np.transpose(list_of_means)[1],np.transpose(list_of_means)[2],'ro')
# plt.show()
# ###Rape
# print('Rape 1')
# lat = []
# lon = []
# for i in range(0,len(rape)):
# lat.append(rape[i][1])
# lon.append(rape[i][2])
# print('Rape 2')
# maxdist = ((max(lat)-min(lat))**2 + (max(lon)-min(lon))**2)**(1/2)
# print(maxdist)
# for i in range(0,len(rape)):
# rape[i][0] = rape[i][0]/12
# rape[i][1] = rape[i][1]/maxdist
# rape[i][2] = rape[i][2]/maxdist
# print('Rape 3')
# KMeansClassifier = KMeans(K=int(sys.argv[1]))
# print('Rape 4')
# KMeansClassifier.fit(rape)
# print('Rape 5')
# list_of_means = []
# KMeansClassifier.get_centroids(list_of_means)
# ###### Plotting
# plt.plot(maxdist*np.transpose(rape)[1],maxdist*np.transpose(rape)[2],'bo')
# plt.plot(np.transpose(list_of_means)[1],np.transpose(list_of_means)[2],'ro')
# plt.show()
# ###Robbery
# print('Robbery 1')
# lat = []
# lon = []
# for i in range(0,len(robbery)):
# lat.append(robbery[i][1])
# lon.append(robbery[i][2])
# print('Robbery 2')
# maxdist = ((max(lat)-min(lat))**2 + (max(lon)-min(lon))**2)**(1/2)
# print(maxdist)
# for i in range(0,len(robbery)):
# robbery[i][0] = robbery[i][0]/12
# robbery[i][1] = robbery[i][1]/maxdist
# robbery[i][2] = robbery[i][2]/maxdist
# print('Robbery 3')
# KMeansClassifier = KMeans(K=int(sys.argv[1]))
# print('Robbery 4')
# KMeansClassifier.fit(robbery)
# print('Robbery 5')
# list_of_means = []
# KMeansClassifier.get_centroids(list_of_means)
# ###### Plotting
# plt.plot(maxdist*np.transpose(robbery)[1],maxdist*np.transpose(robbery)[2],'bo')
# plt.plot(np.transpose(list_of_means)[1],np.transpose(list_of_means)[2],'ro')
# plt.show()
# ###Aggravated Assault
# print('Aggravated Assault 1')
# lat = []
# lon = []
# for i in range(0,len(aggravatedassault)):
# lat.append(aggravatedassault[i][1])
# lon.append(aggravatedassault[i][2])
# print('Aggravated Assault 2')
# maxdist = ((max(lat)-min(lat))**2 + (max(lon)-min(lon))**2)**(1/2)
# print(maxdist)
# for i in range(0,len(aggravatedassault)):
# aggravatedassault[i][0] = aggravatedassault[i][0]/12
# aggravatedassault[i][1] = aggravatedassault[i][1]/maxdist
# aggravatedassault[i][2] = aggravatedassault[i][2]/maxdist
# print('Aggravated Assault 3')
# KMeansClassifier = KMeans(K=int(sys.argv[1]))
# print('Aggravated Assault 4')
# KMeansClassifier.fit(aggravatedassault)
# print('Aggravated Assault 5')
# list_of_means = []
# KMeansClassifier.get_centroids(list_of_means)
# ###### Plotting
# plt.plot(maxdist*np.transpose(aggravatedassault)[1],maxdist*np.transpose(aggravatedassault)[2],'bo')
# plt.plot(np.transpose(list_of_means)[1],np.transpose(list_of_means)[2],'ro')
# plt.show()
# ###Murder
# print('Murder 1')
# lat = []
# lon = []
# for i in range(0,len(murder)):
# lat.append(murder[i][1])
# lon.append(murder[i][2])
# print('Murder 2')
# maxdist = ((max(lat)-min(lat))**2 + (max(lon)-min(lon))**2)**(1/2)
# print(maxdist)
# for i in range(0,len(murder)):
# murder[i][0] = murder[i][0]/12
# murder[i][1] = murder[i][1]/maxdist
# murder[i][2] = murder[i][2]/maxdist
# print('Murder 3')
# KMeansClassifier = KMeans(K=int(sys.argv[1]))
# print('Murder 4')
# KMeansClassifier.fit(murder)
# print('Murder 5')
# list_of_means = []
# KMeansClassifier.get_centroids(list_of_means)
# ###### Plotting
# plt.plot(maxdist*np.transpose(murder)[1],maxdist*np.transpose(murder)[2],'bo')
# plt.plot(np.transpose(list_of_means)[1],np.transpose(list_of_means)[2],'ro')
# plt.show()