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main.py
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main.py
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__author__ = 'Jinesh and Vinayak'
import copy
import numpy as np
# import matplotlib
# import matplotlib.pyplot as plt
# import pylab
import math
# from itertools import product
# from mpl_toolkits.mplot3d import Axes3D
# from matplotlib import cm
# import time
# import sys
# import decimal
# import scipy.spatial as spatial
from collections import Counter
import itertools
import sys
sys.setrecursionlimit(50000)
epsilon = 2.82
min_points = 10.0 # float(sys.argv[3])
# points,neighbours,merging distance,position,flag,cluster_id,hot_cold
def dbscan():
# Take input from file (Copy Paste from dbscan code) and sort acc. to x and y axis
# open the dataset
file_name = "vinu_dataset.csv"
i = open(file_name)
lines = i.read().strip().split('\n')
i.close()
# initialize the list in which your dataset will be stored in the form of list
dataset = {}
coord = []
dim_size = 2 # int(sys.argv[1]) #float(sys.argv[2])
min_points = 4.0 # float(sys.argv[3])
# Sort
for i in range(dim_size):
coord.append([])
for i in lines:
line = i.rstrip().split(',')
temp = []
# extract x and y coordinates
for j in line:
temp.append(j.strip())
# print temp
# convert to float if the input is not numeric type
for i in range(dim_size):
temp[i] = float(temp[i])
coord[i].append(temp[i])
# can't use list as keys so converting to tuple
temp = tuple(temp)
# print len(dataset)
# default not visited that's why 0
dataset[temp] = 0
data = list(sorted(dataset.keys(), key=lambda t: t[0]))
data = re_round(data)
# print data
start_box_coord = []
start_box_coord.append(round(min(coord[0]), 2)) # left most point in dataset(0)
start_box_coord.append(round(max(coord[0]), 2)) # right most point in dataset(1)
start_box_coord.append(round(max(coord[1]), 2)) # top most point in dataset(2)
start_box_coord.append(round(min(coord[1]), 2)) # bottom most point in dataset(3)
print start_box_coord
len_x = round(abs(start_box_coord[1] - start_box_coord[0]), 2) # total length of x-axis
len_y = round(abs(start_box_coord[2] - start_box_coord[3]), 2) # total length of y-axis
number_box_x = int(math.ceil(len_x / round((epsilon / math.sqrt(2)), 2))) # epsilon/rt(2)=1.27
number_box_y = int(math.ceil(len_y / round((epsilon / math.sqrt(2)), 2)))
len_x = number_box_x * round(epsilon / math.sqrt(2), 2)
len_y = number_box_y * round(epsilon / math.sqrt(2), 2)
print len_x,len_y
'''fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(coord[0], coord[1], cmap=plt.hot())
plt.plot([start_box_coord[0],start_box_coord[1],start_box_coord[1],start_box_coord[0],start_box_coord[0]], [start_box_coord[2],start_box_coord[2],start_box_coord[3],start_box_coord[3],start_box_coord[2]], 'r-')
#plt.show()'''
no_k_bands = number_box_x
no_l_bands = number_box_y
k_bands = []
l_bands = []
k_bands.append(start_box_coord[0] + round(epsilon / math.sqrt(2), 2))
l_bands.append(start_box_coord[3] + round(epsilon / math.sqrt(2), 2))
for i in range(1, no_k_bands):
temp = k_bands[i - 1] + round((epsilon / math.sqrt(2)), 2)
k_bands.append(round(temp, 2))
for j in range(1, no_l_bands):
temp1 = l_bands[j - 1] + round((epsilon / math.sqrt(2)), 2)
l_bands.append(round(temp1, 2))
# print k_bands,l_bands
# creating the boxes
box_list = list(itertools.product(k_bands, l_bands))
# print box_list
# print(len(box_list))
# 0 for corner,1 for edge,2 for middle
box_details = {}
merge_points = []
open_flag = 0
cluster_id = 0
hot_cold = 0
for i in range(8):
merge_points.append((-999, -999))
box_start_coord = []
box_start_coord.append(k_bands[0])
box_start_coord.append(k_bands[number_box_x - 1])
box_start_coord.append(l_bands[number_box_y - 1])
box_start_coord.append(l_bands[0])
# print box_start_coord
# print start_box_coord
for n in box_list:
# print n
if (n[0] == box_start_coord[0] or n[0] == box_start_coord[1] or n[1] == box_start_coord[2] or n[1] ==
box_start_coord[3]):
# corner cases
# print "####################################################################"
if ((n[0] == box_start_coord[0] and n[1] == box_start_coord[2])):
# print "______________________________________________________"
neighbour = re_round([(n[0] + round((epsilon / math.sqrt(2)), 2), n[1]), (
n[0] + round((epsilon / math.sqrt(2)), 2), n[1] + round((epsilon / math.sqrt(2)), 2)),
(n[0], n[1] + round((epsilon / math.sqrt(2)), 2))])
box_details[n] = [[], neighbour, merge_points, 0, open_flag, cluster_id,
hot_cold] # points,neighbours,merging distance,position,flag,cluster_id,hot_cold
elif ((n[0] == box_start_coord[1] and n[1] == box_start_coord[2])):
# print "++++++++++++++++++++++++++++++++++++++++++++++++++++++"
neighbour = re_round([(n[0] - round((epsilon / math.sqrt(2)), 2), n[1]), (
n[0] - round((epsilon / math.sqrt(2)), 2), n[1] - round((epsilon / math.sqrt(2)), 2)),
(n[0], n[1] - round((epsilon / math.sqrt(2)), 2))])
box_details[n] = [[], neighbour, merge_points, 0, open_flag, cluster_id, hot_cold]
elif ((n[0] == box_start_coord[1] and n[1] == box_start_coord[3])):
# print "&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&"
neighbour = re_round([(n[0], n[1] + round((epsilon / math.sqrt(2)), 2)),
(n[0] - round((epsilon / math.sqrt(2)), 2), n[1]), (
n[0] - round((epsilon / math.sqrt(2)), 2),
n[1] + round((epsilon / math.sqrt(2)), 2))])
box_details[n] = [[], neighbour, merge_points, 0, open_flag, cluster_id, hot_cold]
elif ((n[0] == box_start_coord[0] and n[1] == box_start_coord[3])):
# print "-------------------------------------------------------"
neighbour = re_round([(n[0], n[1] + round((epsilon / math.sqrt(2)), 2)), (
n[0] + round((epsilon / math.sqrt(2)), 2), n[1] + round((epsilon / math.sqrt(2)), 2)),
(n[0] + round((epsilon / math.sqrt(2)), 2), n[1])])
box_details[n] = [[], neighbour, merge_points, 0, open_flag, cluster_id, hot_cold]
else: # edge case
if (n[0] == box_start_coord[0]): # left side
neighbour = re_round([(n[0], n[1] + round((epsilon / math.sqrt(2)), 2)), (
n[0] - round((epsilon / math.sqrt(2)), 2), n[1] + round((epsilon / math.sqrt(2)), 2)),
(n[0] - round((epsilon / math.sqrt(2)), 2), n[1]), (
n[0] - round((epsilon / math.sqrt(2)), 2),
n[1] - round((epsilon / math.sqrt(2)), 2)),
(n[0], n[1] - round((epsilon / math.sqrt(2)), 2))])
box_details[n] = [[], neighbour, merge_points, 1, open_flag, cluster_id, hot_cold]
elif (n[1] == box_start_coord[2]): # top side
neighbour = re_round([(n[0] - round((epsilon / math.sqrt(2)), 2), n[1]), (
n[0] - round((epsilon / math.sqrt(2)), 2), n[1] - round((epsilon / math.sqrt(2)), 2)),
(n[0], n[1] - round((epsilon / math.sqrt(2)), 2)), (
n[0] + round((epsilon / math.sqrt(2)), 2),
n[1] - round((epsilon / math.sqrt(2)), 2)),
(n[0] + round((epsilon / math.sqrt(2)), 2), n[1])])
box_details[n] = [[], neighbour, merge_points, 1, open_flag, cluster_id, hot_cold]
elif (n[0] == box_start_coord[1]): # right side
# print "jinesh"
neighbour = re_round([(n[0], n[1] + round((epsilon / math.sqrt(2)), 2)),
(n[0], n[1] - round((epsilon / math.sqrt(2)), 2)), (
n[0] - round((epsilon / math.sqrt(2)), 2),
n[1] + round((epsilon / math.sqrt(2)), 2)),
(n[0] - round((epsilon / math.sqrt(2)), 2), n[1]), (
n[0] - round((epsilon / math.sqrt(2)), 2),
n[1] - round((epsilon / math.sqrt(2)), 2))])
box_details[n] = [[], neighbour, merge_points, 1, open_flag, cluster_id, hot_cold]
else: # bottom side
# print "jinesh"
neighbour = re_round([(n[0] - round((epsilon / math.sqrt(2)), 2), n[1]),
(n[0] + round((epsilon / math.sqrt(2)), 2), n[1]),
(n[0], n[1] + round((epsilon / math.sqrt(2)), 2)), (
n[0] - round((epsilon / math.sqrt(2)), 2),
n[1] + round((epsilon / math.sqrt(2)), 2)), (
n[0] + round((epsilon / math.sqrt(2)), 2),
n[1] + round((epsilon / math.sqrt(2)), 2))])
box_details[n] = [[], neighbour, merge_points, 1, open_flag, cluster_id, hot_cold]
else:
neighbour = re_round(
[(n[0] - round((epsilon / math.sqrt(2)), 2), n[1] + round((epsilon / math.sqrt(2)), 2)),
(n[0], n[1] + round((epsilon / math.sqrt(2)), 2)),
(n[0] + round((epsilon / math.sqrt(2)), 2), n[1] + round((epsilon / math.sqrt(2)), 2)),
(n[0] - round((epsilon / math.sqrt(2)), 2), n[1]), (n[0] + round((epsilon / math.sqrt(2)), 2), n[1]),
(n[0] - round((epsilon / math.sqrt(2)), 2), n[1] - round((epsilon / math.sqrt(2)), 2)),
(n[0], n[1] - round((epsilon / math.sqrt(2)), 2)),
(n[0] + round((epsilon / math.sqrt(2)), 2), n[1] - round((epsilon / math.sqrt(2)), 2))])
box_details[n] = [[], neighbour, merge_points, 2, open_flag, cluster_id, hot_cold]
# for i in box_details.keys():
# print i," ke neighbours hai ---> ",box_details[i][1]
'''k_bands.append(3.13)
l_bands.append(3.07)
v=list ( itertools.product(k_bands,l_bands) )
print v
l=[]
p=[]
for i in v:
l.append(i[0])
p.append(i[1])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(coord[0], coord[1], cmap=plt.hot())
plt.plot(l,p,'go')
plt.show()'''
count = 0
for j in data:
# print j
count = count + 1
# print coun
# check for mode over here
p = (j[0] - start_box_coord[0])
q = (j[1] - start_box_coord[3])
if p == 0:
p = 1
elif q == 0:
q = 1
else:
p = p
q = q
mov_x = math.ceil(p / round((epsilon / math.sqrt(2)), 2))
mov_y = math.ceil(q / round((epsilon / math.sqrt(2)), 2))
a = (mov_x * round((epsilon / math.sqrt(2)), 2)) + start_box_coord[0]
# print a
b = (mov_y * round((epsilon / math.sqrt(2)), 2)) + start_box_coord[3]
# print b
a = re_round(a)
b = re_round(b)
# print "point :", j
# print "box:",a,b
tp = box_details[a, b][0]
# print "points already present:",tp
check = box_details[a, b][4]
# print "already visited:",check
# print "box contents:",box_details
if (not check):
# print (a,b),j
box_details[a, b][4] = 1
list_t = []
for _ in range(8):
list_t.append(j)
box_details[a, b][2] = copy.deepcopy(list_t)
else:
if (j[0] < box_details[a, b][2][3][0]):
box_details[a, b][2][3] = j
if (j[0] > box_details[a, b][2][4][0]):
box_details[a, b][2][4] = j
if (j[1] > box_details[a, b][2][1][1]):
box_details[a, b][2][1] = j
if (j[1] < box_details[a, b][2][6][1]):
box_details[a, b][2][6] = j
if (j[0]) < (box_details[a, b][2][0][0]) and (j[1] > box_details[a, b][2][0][1]):
box_details[a, b][2][0] = j
if j[0] > box_details[a, b][2][2][0] and j[1] > box_details[a, b][2][2][1]:
box_details[a, b][2][2] = j
if (j[0] < box_details[a, b][2][5][0] and j[1] < box_details[a, b][2][5][1]):
box_details[a, b][2][5] = j
if (j[0] > box_details[a, b][2][7][0] and j[1] < box_details[a, b][2][7][1]):
box_details[a, b][2][7] = j
'''
#print box_details[a,b][2][0]
box_details[a,b][2][1]=j
box_details[a,b][2][2]=j
box_details[a,b][2][3] = j
box_details[a,b][2][4] = j
box_details[a,b][2][5] = j
box_details[a,b][2][6] = j
box_details[a,b][2][7] = j
check=0
'''
# print tp
# print "box contents:",box_details
# print "merging points:",box_details[a,b][2]
tp.append(j)
# print "current points:",tp
box_details[a, b][0] = tp
# print "points in box:",box_details[a,b][0]
# print "box contents:",box_details
print box_details
# print data
# for i in box_details.keys():
# print i,box_details[i][0]
# 0=cold
#cluster_id=count
'''count = 1
hot=1
for i in box_details.keys():
box_details[i][6]=hot
clustering(box_details, i)
box_details[i][5] = count
count = count + 1
print box_details
cluster_list=[]
for i in box_details.keys():
temp=box_details[i][5]
cluster_list.append(temp)
print Counter(cluster_list)
def clustering(box_details, i):
open_flag = box_details[i][4]
hot_flag = box_details[i][6]
if (open_flag and hot_flag):
# Checking for top box
j = (i[0], i[1] + round(epsilon / math.sqrt(2), 2))
if (j in box_details.keys()):
flag = check_up(i, box_details, j)
if (flag):
box_details[j][6] = 1 # hot
box_details[j][5] = box_details[i][5]
clustering(box_details, j)
else:
# checking for j+1
jplus1 = (j[0], j[1] + round(epsilon / math.sqrt(2), 2))
if (jplus1 in box_details.keys()):
flag = check_up(j, box_details, jplus1)
if (flag):
box_details[jplus1][6] = 1 # hot
box_details[jplus1][5] = box_details[j][5]
clustering(box_details, jplus1)
# Checking for up right
j = (i[0] + round(epsilon / math.sqrt(2), 2), i[1] + round(epsilon / math.sqrt(2), 2))
if (j in box_details.keys()):
flag = check_up_right(i, box_details, j)
if (flag):
box_details[j][6] = 1
box_details[j][5] = box_details[i][5]
clustering(box_details, j)
else:
# checking for j+1
jplus1 = (j[0] + round(epsilon / math.sqrt(2), 2), j[1] + round(epsilon / math.sqrt(2), 2))
if (jplus1 in box_details.keys()):
flag = check_up_right(j, box_details, jplus1)
if (flag):
box_details[jplus1][6] = 1
box_details[jplus1][5] = box_details[j][5]
clustering(box_details, jplus1)
# Checking for right
j = (i[0] + round(epsilon / math.sqrt(2), 2), i[1])
if (j in box_details.keys()):
flag=check_right(i, box_details, j)
if (flag):
box_details[j][6] = 1
box_details[j][5] = box_details[i][5]
clustering(box_details, j)
else:
jplus1 = (j[0] + round(epsilon / math.sqrt(2), 2), j[1])
if (jplus1 in box_details.keys()):
flag=check_right(j, box_details, jplus1)
if (flag):
box_details[jplus1][6] = 1
box_details[jplus1][5] = box_details[j][5]
clustering(box_details, jplus1)
# Checking for down right
j = (i[0] + round(epsilon / math.sqrt(2), 2), i[1] - round(epsilon / math.sqrt(2), 2))
if (j in box_details.keys()):
flag=check_down_right(i, box_details, j)
if (flag):
box_details[j][6] = 1
box_details[j][5] = box_details[i][5]
clustering(box_details, j)
else:
# check for j+1
jplus1 = (j[0] + round(epsilon / math.sqrt(2), 2), j[1] - round(epsilon / math.sqrt(2), 2))
if (jplus1 in box_details.keys()):
flag=check_down_right(j, box_details, jplus1)
if (flag):
box_details[jplus1][6] = 1
box_details[jplus1][5] = box_details[j][5]
clustering(box_details, jplus1)
# Checking for down
j = (i[0], i[1] - round(epsilon / math.sqrt(2), 2))
if (j in box_details.keys()):
flag=check_down(i, box_details, j)
if (flag):
box_details[j][6] = 1
box_details[j][5] = box_details[i][5]
clustering(box_details, j)
else:
# check for j+1
jplus1 = (j[0], j[1] - round(epsilon / math.sqrt(2), 2))
if (jplus1 in box_details.keys()):
flag=check_down(j, box_details, jplus1)
if (flag):
box_details[jplus1][6] = 1
box_details[jplus1][5] = box_details[j][5]
clustering(box_details, jplus1)
# Checking for down left
j = (i[0] - round(epsilon / math.sqrt(2), 2), i[1] - round(epsilon / math.sqrt(2), 2))
if (j in box_details.keys()):
flag=check_down_left(i, box_details, j)
if (flag):
box_details[j][6] = 1
box_details[j][5] = box_details[i][5]
clustering(box_details, j)
else:
# check for j+1
jplus1 = (i[0] - round(epsilon / math.sqrt(2), 2), i[1] - round(epsilon / math.sqrt(2), 2))
if (jplus1 in box_details.keys()):
flag=check_down_left(j, box_details, jplus1)
if (flag):
box_details[jplus1][6] = 1
box_details[jplus1][5] = box_details[j][5]
clustering(box_details, jplus1)
# Checking for left
j = (i[0] - round(epsilon / math.sqrt(2), 2), i[1])
if (j in box_details.keys()):
flag=check_left(i, box_details, j)
if (flag):
box_details[j][6] = 1
box_details[j][5] = box_details[i][5]
clustering(box_details, j)
else:
# check for j+1 box
jplus1 = (j[0] - round(epsilon / math.sqrt(2), 2), j[1])
if (jplus1 in box_details.keys()):
flag=check_left(j, box_details, jplus1)
if (flag):
box_details[jplus1][6] = 1
box_details[jplus1][5] = box_details[j][5]
clustering(box_details, jplus1)
# checking for up left
j = (i[0] - round(epsilon / math.sqrt(2), 2), i[1] + round(epsilon / math.sqrt(2), 2))
if (j in box_details.keys()):
flag=check_up_left(i, box_details, j)
if (flag):
box_details[j][6] = 1
box_details[j][5] = box_details[i][5]
clustering(box_details, j)
else:
# check for j+1
jplus1 = (j[0] - round(epsilon / math.sqrt(2), 2), j[1] + round(epsilon / math.sqrt(2), 2))
if (jplus1 in box_details.keys()):
flag=check_up_left(j, box_details, jplus1)
if (flag):
box_details[jplus1][6] = 1
box_details[jplus1][5] = box_details[j][5]
clustering(box_details, jplus1)
'''
def check_up(box_coord, box_details, check_box):
flag = False
top = box_details[box_coord][2][1]
bottom = box_details[check_box][2][6]
dist = euclidean(top,bottom)
print dist
if (dist < epsilon and len(box_details[box_coord][0]) > min_points):
flag = True
return flag
def check_up_right(box_coord, box_details, check_box):
flag = False
top_right = box_details[box_coord][2][2] # add correct values
bottom_left = box_details[check_box][2][5] # add correct values
dist = euclidean(top_right,bottom_left)
print dist
if (dist < epsilon and len(box_details[box_coord][0]) > min_points):
flag = True
return flag
def check_right(box_coord, box_details, check_box):
flag = False
right = box_details[box_coord][2][4] # add correct values
left = box_details[check_box][2][3] # add correct values
dist = euclidean(right,left)
print dist
if (dist < epsilon and len(box_details[box_coord][0]) > min_points):
flag = True
return flag
def check_down_right(box_coord, box_details, check_box):
flag = False
bottom_right = box_details[box_coord][2][7] # add correct values
top_left = box_details[check_box][2][0] # add correct values
dist=euclidean(bottom_right,top_left)
print dist
if (dist < epsilon and len(box_details[box_coord][0]) > min_points):
flag = True
return flag
def check_down(box_coord, box_details, check_box):
flag = False
bottom = box_details[box_coord][2][6] # add correct values
top = box_details[check_box][2][1] # add correct values
dist = euclidean(bottom,top)
print dist
if (dist < epsilon and len(box_details[box_coord][0]) > min_points):
flag = True
return flag
def check_down_left(box_coord, box_details, check_box):
flag = False
bottom_left = box_details[box_coord][2][5] # add correct values
top_right = box_details[check_box][2][2] # add correct values
dist = euclidean(bottom_left,top_right)
print dist
if (dist < epsilon and len(box_details[box_coord][0]) > min_points):
flag = True
return flag
def check_left(box_coord, box_details, check_box):
flag = False
left = box_details[box_coord][2][3] # add correct values
right = box_details[check_box][2][4] # add correct values
dist = euclidean(left,right)
print dist
if (dist < epsilon and len(box_details[box_coord][0]) > min_points):
flag = True
return flag
def check_up_left(box_coord, box_details, check_box):
flag = False
top_left = box_details[box_coord][2][0] # add correct values
bottom_right = box_details[check_box][2][7] # add correct values
dist = euclidean(top_left,bottom_right)
print dist
if (dist < epsilon and len(box_details[box_coord][0]) > min_points):
flag = True
return flag
def euclidean(x, y):
sumSq = 0.0
# add up the squared differences
for i in range(len(x)):
sumSq += (x[i] - y[i]) ** 2
# take the square root of the result
return sumSq ** 0.5
# round off
def re_round(li, _prec=2):
try:
return round(li, _prec)
except TypeError:
return type(li)(re_round(x, _prec) for x in li)
dbscan()