Merge pull request #184 from eternal-f1ame/master

Updated distance_metrics.py

MLlib/distance_metrics.py

@@ -4,7 +4,7 @@ class Distance_metrics:
     Calculate distance between each corresponding points
     of two arrays using different distance metrics
     """
-    def Eucledian_distance(self,X1,X2):
+    def Eucledian_Distance(X1,X2):
         """"
         Returns the list of eucledian distance
         between two corresponding points of 
@@ -18,13 +18,10 @@ def Eucledian_distance(self,X1,X2):
         X2:ndarray(dtype=int,axis=1)
         input array with more than 1 dimension
 
-        distance:list
-        list containing eucledian distance of points
-
         RETURNS
         =========
 
-        float
+        distance:list
         Returns the list of eucledian distance
         between two corresponding points of 
         two arrays
@@ -36,7 +33,7 @@ def Eucledian_distance(self,X1,X2):
             distance.append(np.sqrt(single))
         return(distance)
 
-    def Manhattan_distance(self,X1,X2):
+    def Manhattan_Distance(X1,X2):
         """"
         Returns the list of manhattan distance
         between two corresponding points of 
@@ -50,13 +47,10 @@ def Manhattan_distance(self,X1,X2):
         X2:ndarray(dtype=int,axis=1)
         input array with more than 1 dimension
 
-        distance:list
-        list containing manhattan distance of points
-
         RETURNS
         =========
 
-        float
+        distance:list
         Returns the list of manhattan distance
         between two corresponding points of 
         two arrays
@@ -68,7 +62,7 @@ def Manhattan_distance(self,X1,X2):
             distance.append(single)
         return(distance)
 
-    def Chebyshev_Distance(self,X1,X2):
+    def Chebyshev_Distance(X1,X2):
         """"
         Returns the list of chebyshev distance
         between two corresponding points of 
@@ -82,13 +76,10 @@ def Chebyshev_Distance(self,X1,X2):
         X2:ndarray(dtype=int,axis=1)
         input array with more than 1 dimension
 
-        distance:list
-        list containing chebyshev distance of points
-
         RETURNS
         =========
 
-        float
+        distance:list
         Returns the list of chebyshev distance
         between two corresponding points of 
         two arrays
@@ -100,7 +91,70 @@ def Chebyshev_Distance(self,X1,X2):
             distance.append(single)
         return(distance)
 
+    def Minkowski_Distance(X1,X2,p):
+        """"
+        Returns list of minkowski distance of order 'p'
+        between two corresponding vectors of
+        two arrays
+
+        PARAMETERS
+        ==========
+        X1:ndarray(dtype=int,axis=1)
+        input array with more than 1 dimension
+
+        X2:ndarray(dtype=int,axis=1)
+        input array with more than 1 dimension
 
+        p:float
+        input order value between 1 and 2 inclusive
 
+        RETURNS
+        =========
+
+        distance:list
+        Returns the list of minkowski distance
+        between two corresponding vectors of 
+        two arrays
+        """
+        distance=[]
+        for i in range(len(X1)):
+            single=0
+            single=np.sum((abs(X1[i]-X2[i]))**p)
+            distance.append((single)**(1/p))
+        return(distance)
 
+    def WMinkowski_Distance(X1,X2,p,W):
+        """"
+        Returns list of weighted minkowski distance of order 'p'
+        between two corresponding vectors weighted by W of
+        two arrays
+
+        PARAMETERS
+        ==========
+        X1:ndarray(dtype=int,axis=1)
+        input array with more than 1 dimension
+
+        X2:ndarray(dtype=int,axis=1)
+        input array with more than 1 dimension
+
+        p:float
+        input order value between 1 and 2 inclusive
+
+        W:array(dtype=int,axis=1)
+        input 1 dimensional array
+
+        RETURNS
+        =========
+
+        distance:list
+        Returns the list of weighted minkowski distance
+        between two corresponding vectors of 
+        two arrays
+        """
+        distance=[]
+        for i in range(len(X1)):
+            single=0
+            single=np.sum((abs(W*(X1[i]-X2[i])))**p)
+            distance.append((single)**(1/p))
+        return(distance)