mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-12-18 09:10:16 +00:00
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
"""
|
|
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
|
|
|
|
|
|
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 float(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__":
|
|
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))
|