Add for real the previous commit needed file.

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/TP1/exo2/TP1prog3.py b/TP1/exo2/TP1prog3.py
new file mode 100755 (executable)
index 0000000..4daa73f
--- /dev/null
+++ b/TP1/exo2/TP1prog3.py
@@ -0,0 +1,34 @@
+#!/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])