| 1 | #!/usr/bin/env python3 |
| 2 | |
| 3 | # -*- coding: utf-8 -*- |
| 4 | from sklearn import neighbors |
| 5 | from sklearn.datasets import load_iris |
| 6 | irisData = load_iris() |
| 7 | |
| 8 | X = irisData.data |
| 9 | Y = irisData.target |
| 10 | |
| 11 | nb_voisins = 15 |
| 12 | clf = neighbors.KNeighborsClassifier(nb_voisins) |
| 13 | clf.fit(X, Y) |
| 14 | print("kNN prediction on [5.4, 3.2, 1.6, 0.4]:") |
| 15 | print(clf.predict([[5.4, 3.2, 1.6, 0.4]])) |
| 16 | print("kNN probability prediction on [5.4, 3.2, 1.6, 0.4]:") |
| 17 | print(clf.predict_proba([[5.4, 3.2, 1.6, 0.4]])) |
| 18 | print("kNN score on Iris data:") |
| 19 | print(clf.score(X, Y)) |
| 20 | print("kNN prediction error(s) on Iris data:") |
| 21 | Z = clf.predict(X) |
| 22 | print(X[Z != Y]) |