| 6 | return distance |
| 7 | |
| 8 | class KNN: |
| 9 | def __init__(self, k=3): |
| 10 | self.k = k |
| 11 | |
| 12 | def fit(self, X, y): |
| 13 | self.X_train = X |
| 14 | self.y_train = y |
| 15 | |
| 16 | def predict(self, X): |
| 17 | predictions = [self._predict(x) for x in X] |
| 18 | return predictions |
| 19 | |
| 20 | def _predict(self, x): |
| 21 | # compute the distance |
| 22 | distances = [euclidean_distance(x, x_train) for x_train in self.X_train] |
| 23 | |
| 24 | # get the closest k |
| 25 | k_indices = np.argsort(distances)[:self.k] |
| 26 | k_nearest_labels = [self.y_train[i] for i in k_indices] |
| 27 | |
| 28 | # majority voye |
| 29 | most_common = Counter(k_nearest_labels).most_common() |
| 30 | return most_common[0][0] |