Added k-Nearest-Neighbor Algorithm (#89)

* K-Nearest-Neighbors

* Delete knn.py

* Added K-Nearest-Neighbor Algorithm

* Update classification_algos.rst

* Update classification_algos.rst

code/knn.py

@@ -0,0 +1,74 @@
+from collections import Counter
+from math import sqrt
+
+def euclidean_distance(point1, point2):
+    distance = 0
+    for i in range(len(point1)):
+        distance +=(point1[i] - point2[i]) ** 2
+    return sqrt(distance)
+
+def mean(labels):
+    return sum(labels) / len(labels)
+
+def mode(labels):
+    return Counter(labels).most_common(1)[0][0]
+
+def KNN(training_data, target, k, func):
+    neighbors= []
+    # For each example in the training_data
+    for index, data in enumerate(training_data):
+        # distance between the target data and the current example from the data.
+        distance = euclidean_distance(data[:-1], target)
+        neighbors.append((distance, index))
+
+    sorted_neighbors = sorted(neighbors)
+
+    #Pick the first K entries from the sorted list
+    k_nearest = sorted_neighbors[:k]
+
+    # Get the labels of the selected K entries
+    k_nearest_labels = [training_data[i][1] for distance, i in k_nearest]
+
+    # If regression return the mean & if classification return the mode of the K labels.
+    return k_nearest , func(k_nearest_labels)
+
+def main():
+    """
+    # Regression Data(Column 0 : Height(inch), Column 1: Weight(lb))
+    """
+    reg_data = [
+       [73.84, 241.89],
+       [68.78, 162.31],
+       [74.11, 212.74],
+       [71.73, 220.04],
+       [69.88, 206.34],
+       [67.25, 152.21],
+       [63.45, 156.39]
+    ]
+
+    target_data = [70]
+    reg_k_nearest_neighbors, reg_prediction = KNN(
+        reg_data, target_data, k=3, func=mean
+    )
+    print(reg_prediction)
+    '''
+    # Classification Data( Column 0: age, Column 1:like paragliding  or not )
+    '''
+    clf_data = [
+       [26, 1],
+       [20, 1],
+       [22, 1],
+       [19, 1],
+       [28, 0],
+       [33, 0],
+       [30, 0],
+       [50, 0],
+    ]
+    target_data2 = [32]
+    clf_k_nearest_neighbors, clf_prediction = KNN(
+        clf_data, target_data2, k=3, func=mode
+    )
+    print(clf_prediction)
+
+if __name__ == '__main__':
+    main()

docs/classification_algos.rst

@@ -24,7 +24,7 @@ ID3 decision tree: `code example <https://github.com/bfortuner/ml-cheatsheet/blo
 K-Nearest Neighbor
 ==================
 
-Be the first to `contribute! <https://github.com/bfortuner/ml-cheatsheet>`__
+k-Nearest-Neighbor Algorithm: `code example <https://github.com/bfortuner/ml-cheatsheet/blob/master/code/knn.py>`__
 
 Logistic Regression
 ===================