[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

colors = ["red", "green", "blue"]

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
	11	colors = ["red", "green", "blue"]
	12
	13	nb_voisins = 15
	14	clf = neighbors.KNeighborsClassifier(nb_voisins)
	15	clf.fit(X, Y)
	16	print("kNN prediction on [5.4, 3.2, 1.6, 0.4]:")
	17	print(clf.predict([[5.4, 3.2, 1.6, 0.4]]))
	18	print("kNN probability prediction on [5.4, 3.2, 1.6, 0.4]:")
	19	print(clf.predict_proba([[5.4, 3.2, 1.6, 0.4]]))
	20	print("kNN score on Iris data:")
	21	print(clf.score(X, Y))
	22	print("kNN prediction error(s) on Iris data:")
	23	Z = clf.predict(X)
	24	print(X[Z != Y])