chore: import upstream snapshot with attribution

examples/nearest_neighbors.py

@@ -0,0 +1,59 @@
+try:
+    from sklearn.model_selection import train_test_split
+except ImportError:
+    from sklearn.cross_validation import train_test_split
+from sklearn.datasets import make_classification
+from sklearn.datasets import make_regression
+from scipy.spatial import distance
+
+from mla import knn
+from mla.metrics.metrics import mean_squared_error, accuracy
+
+
+def regression():
+    # Generate a random regression problem
+    X, y = make_regression(
+        n_samples=500,
+        n_features=5,
+        n_informative=5,
+        n_targets=1,
+        noise=0.05,
+        random_state=1111,
+        bias=0.5,
+    )
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.25, random_state=1111
+    )
+
+    model = knn.KNNRegressor(k=5, distance_func=distance.euclidean)
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    print("regression mse", mean_squared_error(y_test, predictions))
+
+
+def classification():
+    X, y = make_classification(
+        n_samples=500,
+        n_features=5,
+        n_informative=5,
+        n_redundant=0,
+        n_repeated=0,
+        n_classes=3,
+        random_state=1111,
+        class_sep=1.5,
+    )
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.1, random_state=1111
+    )
+
+    clf = knn.KNNClassifier(k=5, distance_func=distance.euclidean)
+
+    clf.fit(X_train, y_train)
+    predictions = clf.predict(X_test)
+    print("classification accuracy", accuracy(y_test, predictions))
+
+
+if __name__ == "__main__":
+    regression()
+    classification()