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76767d2f09
* 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
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Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
"""
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k-Nearest Neighbours (kNN) is a simple non-parametric supervised learning
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algorithm used for classification. Given some labelled training data, a given
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point is classified using its k nearest neighbours according to some distance
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metric. The most commonly occurring label among the neighbours becomes the label
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of the given point. In effect, the label of the given point is decided by a
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majority vote.
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This implementation uses the commonly used Euclidean distance metric, but other
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distance metrics can also be used.
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Reference: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
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"""
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from collections import Counter
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from heapq import nsmallest
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import numpy as np
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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class KNN:
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def __init__(
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self,
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train_data: np.ndarray[float],
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train_target: np.ndarray[int],
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class_labels: list[str],
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) -> None:
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"""
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Create a kNN classifier using the given training data and class labels
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"""
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self.data = zip(train_data, train_target)
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self.labels = class_labels
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@staticmethod
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def _euclidean_distance(a: np.ndarray[float], b: np.ndarray[float]) -> float:
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"""
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Calculate the Euclidean distance between two points
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>>> KNN._euclidean_distance(np.array([0, 0]), np.array([3, 4]))
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5.0
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>>> KNN._euclidean_distance(np.array([1, 2, 3]), np.array([1, 8, 11]))
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10.0
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"""
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return np.linalg.norm(a - b)
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def classify(self, pred_point: np.ndarray[float], k: int = 5) -> str:
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"""
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Classify a given point using the kNN algorithm
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>>> train_X = np.array(
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... [[0, 0], [1, 0], [0, 1], [0.5, 0.5], [3, 3], [2, 3], [3, 2]]
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... )
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>>> train_y = np.array([0, 0, 0, 0, 1, 1, 1])
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>>> classes = ['A', 'B']
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>>> knn = KNN(train_X, train_y, classes)
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>>> point = np.array([1.2, 1.2])
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>>> knn.classify(point)
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'A'
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"""
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# Distances of all points from the point to be classified
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distances = (
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(self._euclidean_distance(data_point[0], pred_point), data_point[1])
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for data_point in self.data
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)
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# Choosing k points with the shortest distances
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votes = (i[1] for i in nsmallest(k, distances))
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# Most commonly occurring class is the one into which the point is classified
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result = Counter(votes).most_common(1)[0][0]
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return self.labels[result]
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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iris = datasets.load_iris()
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X = np.array(iris["data"])
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y = np.array(iris["target"])
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iris_classes = iris["target_names"]
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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iris_point = np.array([4.4, 3.1, 1.3, 1.4])
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classifier = KNN(X_train, y_train, iris_classes)
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print(classifier.classify(iris_point, k=3))
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