X-Git-Url: https://git.piment-noir.org/?p=TP_AA.git;a=blobdiff_plain;f=TP1%2Fexo2%2FTP1prog3.py;fp=TP1%2Fexo2%2FTP1prog3.py;h=4daa73f4dc91a3e9bb1d555b53a97f4e738a2fe6;hp=0000000000000000000000000000000000000000;hb=4e5e7ce89fa5c28e59588c06956be7517d232a11;hpb=d5d653ea02f22bd8ca9fa8e853a843cd55a9805a

diff --git a/TP1/exo2/TP1prog3.py b/TP1/exo2/TP1prog3.py
new file mode 100755
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--- /dev/null
+++ b/TP1/exo2/TP1prog3.py
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+#!/usr/bin/env python3
+
+# -*- coding: utf-8 -*-
+import random
+from sklearn import neighbors
+from sklearn.cross_validation import train_test_split
+from sklearn.datasets import load_iris
+
+irisData = load_iris()
+
+X = irisData.data
+Y = irisData.target
+
+# print(help(train_test_split))
+X_train, X_test, Y_train, Y_test = train_test_split(
+    X, Y, test_size=0.3, random_state=random.seed())
+# print(len(X_train))
+# print(len(X_test))
+# print(len(X_train[Y_train == 0]))
+# print(len(X_train[Y_train == 1]))
+# print(len(X_train[Y_train == 2]))
+
+nb_voisins = 15
+clf = neighbors.KNeighborsClassifier(nb_voisins)
+clf.fit(X_train, Y_train)
+# 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 test data:")
+print(clf.score(X_test, Y_test))
+print("kNN prediction error(s) on Iris test data:")
+Z = clf.predict(X_test)
+print(X_test[Z != Y_test])