starting point

DBSCAN_Main.py

@@ -17,6 +17,8 @@
 print(elapsed)
 ###
 """
+
+
 # this is a fast approach, it takes 4.5 seconds for reading and creating the whole dataset
 # its work 223 times faster
 t = time.time()
@@ -25,7 +27,7 @@
 elapsed = time.time() - t
 print("creating data time: ",elapsed)
 
-
+"""
 #the sklearn clustering takes 117 seconds to accomplish
 t = time.time()
 clustering = DBSCAN(eps=3, min_samples=2).fit(vectorsArray)
@@ -34,4 +36,11 @@
 
 #my implementation
 
+"""
+
+
+
+
+
+
 

MyDBSCAN.py

@@ -0,0 +1,20 @@
+import numpy as np
+import math
+
+
+def myDBSCAN(data, minPoints, eps):
+    return 0
+
+
+
+
+def rangeQuery(data, point, eps):
+    return 0
+
+
+def calcEuclideanDistance(p1 ,p2):
+    sm = 0
+    for i in range(len(p1)):
+        sm = sm + (p1[i]-p2[i]) * (p1[i]-p2[i])
+    return sm
+