Added more distance_metrics for vector space and also for boolean vector.

Examples/Distance_metric_example.py

@@ -0,0 +1,66 @@
+from MLlib.distance_metrics import Distance_metrics
+import numpy as np
+
+data = np.genfromtxt('dataset/salaryinp.csv', delimiter=',')
+X1 = np.array(data[:len(data)//2])
+X2 = np.array(data[len(data)//2:])
+
+
+Euc = Distance_metrics.Euclidean_Distance(X1=X1, X2=X2)
+print("Euclidean Distance: ", Euc)
+
+Mah = Distance_metrics.Manhattan_Distance(X1=X1, X2=X2)
+print("Manhattan Distance: ", Mah)
+
+Che = Distance_metrics.Chebyshev_Distance(X1=X1, X2=X2)
+print("Chebyshev Distance: ", Che)
+
+# order will be as per user's requirement
+Mink = Distance_metrics.Minkowski_Distance(X1=X1, X2=X2, p=3)
+print("Minkowski Distance: ", Mink)
+
+# order and weight will be as per user's requirement
+WMink = Distance_metrics.WMinkowski_Distance(X1=X1, X2=X2, p=3, w=0.5)
+print("Weighted Minkowski Distance: ", WMink)
+
+# Variance to be provided by user
+V = np.random.randint(0, 10, size=(len(X1), 1))
+SEuc = Distance_metrics.sEuclidean_distance(X1=X1, X2=X2, V=V)
+print("Standardized Euclidean Distance: ", SEuc)
+
+Maha = Distance_metrics.Mahalanobis_distance(X1=X1, X2=X2)
+print("Mahalanobis Distance: ", Maha)
+
+# Generating Boolean data
+# User can generate boolean data as per requirement
+X1 = np.random.randint(0, 2, size=(100, 5))
+X2 = np.random.randint(0, 2, size=(100, 5))
+
+Ham = Distance_metrics.Hamming_Distance(X1=X1, X2=X2)
+print("Hamming Distance: ", Ham)
+
+Dist_met = Distance_metrics()
+
+Jacc = Dist_met.Jaccard_Distance(X1=X1, X2=X2)
+print("Jaccard Distance: ", Jacc)
+
+Match_dis = Dist_met.Matching_Distance(X1=X1, X2=X2)
+print("Matching Distance: ", Match_dis)
+
+Dice = Dist_met.Dice_Distance(X1=X1, X2=X2)
+print("Dice Distance: ", Dice)
+
+Kuls = Dist_met.Kulsinki_Distance(X1=X1, X2=X2)
+print("Kulsinki Distance: ", Kuls)
+
+Rog = Dist_met.Rogers_Tanimoto_Distance(X1=X1, X2=X2)
+print("Rogers Tanimoto Distance: ", Rog)
+
+Rus = Dist_met.Russell_Rao_Distance(X1=X1, X2=X2)
+print("Russell Rao Distance: ", Rus)
+
+Sok = Dist_met.Sokal_Sneath_Distance(X1=X1, X2=X2)
+print("Sokal Sneath Distance: ", Sok)
+
+Sok_M = Dist_met.Sokal_Michener_Distance(X1=X1, X2=X2)
+print("Sokal Michener Distance: ", Sok_M)