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* .pre-commit-config.yaml: mypy directories that pass
* updating DIRECTORY.md
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59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
from collections import Counter
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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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data = datasets.load_iris()
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X = np.array(data["data"])
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y = np.array(data["target"])
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classes = data["target_names"]
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X_train, X_test, y_train, y_test = train_test_split(X, y)
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def euclidean_distance(a, b):
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"""
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Gives the euclidean distance between two points
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>>> euclidean_distance([0, 0], [3, 4])
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5.0
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>>> euclidean_distance([1, 2, 3], [1, 8, 11])
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10.0
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"""
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return np.linalg.norm(np.array(a) - np.array(b))
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def classifier(train_data, train_target, classes, point, k=5):
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"""
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Classifies the point using the KNN algorithm
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k closest points are found (ranked in ascending order of euclidean distance)
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Params:
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:train_data: Set of points that are classified into two or more classes
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:train_target: List of classes in the order of train_data points
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:classes: Labels of the classes
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:point: The data point that needs to be classified
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>>> X_train = [[0, 0], [1, 0], [0, 1], [0.5, 0.5], [3, 3], [2, 3], [3, 2]]
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>>> y_train = [0, 0, 0, 0, 1, 1, 1]
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>>> classes = ['A','B']; point = [1.2,1.2]
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>>> classifier(X_train, y_train, classes,point)
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'A'
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"""
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data = zip(train_data, train_target)
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# List of distances of all points from the point to be classified
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distances = []
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for data_point in data:
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distance = euclidean_distance(data_point[0], point)
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distances.append((distance, data_point[1]))
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# Choosing 'k' points with the least distances.
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votes = [i[1] for i in sorted(distances)[:k]]
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# Most commonly occurring class among them
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# is the class into which the point is classified
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result = Counter(votes).most_common(1)[0][0]
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return classes[result]
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if __name__ == "__main__":
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print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
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