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KNN (K Nearest Neighbors) in Python - ML From Scratch 01

In this Machine Learning from Scratch Tutorial, we are going to implement the K Nearest Neighbors (KNN) algorithm, using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm.

All algorithms from this course can be found on GitHub together with example tests.

Implementation

import numpy as np from collections import Counter def euclidean_distance(x1, x2): return np.sqrt(np.sum((x1 - x2)**2)) class KNN: def __init__(self, k=3): self.k = k def fit(self, X, y): self.X_train = X self.y_train = y def predict(self, X): y_pred = [self._predict(x) for x in X] return np.array(y_pred) def _predict(self, x): # Compute distances between x and all examples in the training set distances = [euclidean_distance(x, x_train) for x_train in self.X_train] # Sort by distance and return indices of the first k neighbors k_idx = np.argsort(distances)[:self.k] # Extract the labels of the k nearest neighbor training samples k_neighbor_labels = [self.y_train[i] for i in k_idx] # return the most common class label most_common = Counter(k_neighbor_labels).most_common(1) return most_common[0][0]

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