[TP_AA.git] / TP1 / exo2 / TP1prog2.py

#!/usr/bin/env python3

# -*- coding: utf-8 -*-
from sklearn import neighbors
from sklearn.datasets import load_iris
irisData = load_iris()

X = irisData.data
Y = irisData.target

nb_voisins = 15
clf = neighbors.KNeighborsClassifier(nb_voisins)
clf.fit(X, Y)
print("kNN prediction on [5.4, 3.2, 1.6, 0.4]:")
print(clf.predict([[5.4, 3.2, 1.6, 0.4]]))
print("kNN probability prediction on [5.4, 3.2, 1.6, 0.4]:")
print(clf.predict_proba([[5.4, 3.2, 1.6, 0.4]]))
print("kNN score on Iris data:")
print(clf.score(X, Y))
print("kNN prediction error(s) on Iris data:")
Z = clf.predict(X)
print(X[Z != Y])
Commit	Line	Data
7761b833 JB	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
7761b833 JB	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])