Consolidate the two existing kNN implementations (TheAlgorithms#8903)

* Add type hints to k_nearest_neighbours.py

* Refactor k_nearest_neighbours.py into class

* Add documentation to k_nearest_neighbours.py

* Use heap-based priority queue for k_nearest_neighbours.py

* Delete knn_sklearn.py

* updating DIRECTORY.md

* Use optional args in k_nearest_neighbours.py for demo purposes

* Fix wrong function arg in k_nearest_neighbours.py

---------

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

Author: 2 people

DIRECTORY.md

@@ -507,7 +507,6 @@
   * [Gradient Descent](machine_learning/gradient_descent.py)
   * [K Means Clust](machine_learning/k_means_clust.py)
   * [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
-  * [Knn Sklearn](machine_learning/knn_sklearn.py)
   * [Linear Discriminant Analysis](machine_learning/linear_discriminant_analysis.py)
   * [Linear Regression](machine_learning/linear_regression.py)
   * Local Weighted Learning

machine_learning/k_nearest_neighbours.py

@@ -1,58 +1,88 @@
+"""
+k-Nearest Neighbours (kNN) is a simple non-parametric supervised learning
+algorithm used for classification. Given some labelled training data, a given
+point is classified using its k nearest neighbours according to some distance
+metric. The most commonly occurring label among the neighbours becomes the label
+of the given point. In effect, the label of the given point is decided by a
+majority vote.
+
+This implementation uses the commonly used Euclidean distance metric, but other
+distance metrics can also be used.
+
+Reference: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
+"""
+
 from collections import Counter
+from heapq import nsmallest
 
 import numpy as np
 from sklearn import datasets
 from sklearn.model_selection import train_test_split
 
-data = datasets.load_iris()
-
-X = np.array(data["data"])
-y = np.array(data["target"])
-classes = data["target_names"]
-
-X_train, X_test, y_train, y_test = train_test_split(X, y)
-
-
-def euclidean_distance(a, b):
-    """
-    Gives the euclidean distance between two points
-    >>> euclidean_distance([0, 0], [3, 4])
-    5.0
-    >>> euclidean_distance([1, 2, 3], [1, 8, 11])
-    10.0
-    """
-    return np.linalg.norm(np.array(a) - np.array(b))
-
-
-def classifier(train_data, train_target, classes, point, k=5):
-    """
-    Classifies the point using the KNN algorithm
-    k closest points are found (ranked in ascending order of euclidean distance)
-    Params:
-    :train_data: Set of points that are classified into two or more classes
-    :train_target: List of classes in the order of train_data points
-    :classes: Labels of the classes
-    :point: The data point that needs to be classified
-
-    >>> X_train = [[0, 0], [1, 0], [0, 1], [0.5, 0.5], [3, 3], [2, 3], [3, 2]]
-    >>> y_train = [0, 0, 0, 0, 1, 1, 1]
-    >>> classes = ['A','B']; point = [1.2,1.2]
-    >>> classifier(X_train, y_train, classes,point)
-    'A'
-    """
-    data = zip(train_data, train_target)
-    # List of distances of all points from the point to be classified
-    distances = []
-    for data_point in data:
-        distance = euclidean_distance(data_point[0], point)
-        distances.append((distance, data_point[1]))
-    # Choosing 'k' points with the least distances.
-    votes = [i[1] for i in sorted(distances)[:k]]
-    # Most commonly occurring class among them
-    # is the class into which the point is classified
-    result = Counter(votes).most_common(1)[0][0]
-    return classes[result]
+
+class KNN:
+    def __init__(
+        self,
+        train_data: np.ndarray[float],
+        train_target: np.ndarray[int],
+        class_labels: list[str],
+    ) -> None:
+        """
+        Create a kNN classifier using the given training data and class labels
+        """
+        self.data = zip(train_data, train_target)
+        self.labels = class_labels
+
+    @staticmethod
+    def _euclidean_distance(a: np.ndarray[float], b: np.ndarray[float]) -> float:
+        """
+        Calculate the Euclidean distance between two points
+        >>> KNN._euclidean_distance(np.array([0, 0]), np.array([3, 4]))
+        5.0
+        >>> KNN._euclidean_distance(np.array([1, 2, 3]), np.array([1, 8, 11]))
+        10.0
+        """
+        return np.linalg.norm(a - b)
+
+    def classify(self, pred_point: np.ndarray[float], k: int = 5) -> str:
+        """
+        Classify a given point using the kNN algorithm
+        >>> train_X = np.array(
+        ...     [[0, 0], [1, 0], [0, 1], [0.5, 0.5], [3, 3], [2, 3], [3, 2]]
+        ... )
+        >>> train_y = np.array([0, 0, 0, 0, 1, 1, 1])
+        >>> classes = ['A', 'B']
+        >>> knn = KNN(train_X, train_y, classes)
+        >>> point = np.array([1.2, 1.2])
+        >>> knn.classify(point)
+        'A'
+        """
+        # Distances of all points from the point to be classified
+        distances = (
+            (self._euclidean_distance(data_point[0], pred_point), data_point[1])
+            for data_point in self.data
+        )
+
+        # Choosing k points with the shortest distances
+        votes = (i[1] for i in nsmallest(k, distances))
+
+        # Most commonly occurring class is the one into which the point is classified
+        result = Counter(votes).most_common(1)[0][0]
+        return self.labels[result]
 
 
 if __name__ == "__main__":
-    print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
+    import doctest
+
+    doctest.testmod()
+
+    iris = datasets.load_iris()
+
+    X = np.array(iris["data"])
+    y = np.array(iris["target"])
+    iris_classes = iris["target_names"]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    iris_point = np.array([4.4, 3.1, 1.3, 1.4])
+    classifier = KNN(X_train, y_train, iris_classes)
+    print(classifier.classify(iris_point, k=3))