K nearest neighbours algo

knnAlgorithem.py

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+#Importing the required modules
+import numpy as np
+from scipy.stats import mode
+
+#Euclidean Distance
+def eucledian(p1,p2):
+    dist = np.sqrt(np.sum((p1-p2)**2))
+    return dist
+
+#Function to calculate KNN
+def predict(x_train, y , x_input, k):
+    op_labels = []
+
+    #Loop through the Datapoints to be classified
+    for item in x_input: 
+
+        #Array to store distances
+        point_dist = []
+
+        #Loop through each training Data
+        for j in range(len(x_train)): 
+            distances = eucledian(np.array(x_train[j,:]) , item) 
+            #Calculating the distance
+            point_dist.append(distances) 
+        point_dist = np.array(point_dist) 
+
+        #Sorting the array while preserving the index
+        #Keeping the first K datapoints
+        dist = np.argsort(point_dist)[:k] 
+
+        #Labels of the K datapoints from above
+        labels = y[dist]
+
+        #Majority voting
+        lab = mode(labels) 
+        lab = lab.mode[0]
+        op_labels.append(lab)
+
+    return op_labels