Rename, create new dataset

big-data/tp1/main.py

@@ -7,15 +7,26 @@ import numpy as np
 
 from perceptron import Perceptron
 
+
 def main():
     nb_data_points = 600
     nb_training_points = int(nb_data_points * 0.8)
 
     # Génération des données d'entraînement et de test
-    data = np.random.rand(nb_data_points, 2)
-    expected = np.array([1 if x[0] + x[1] > 0.7 else 0 for x in data])
+    data1 = np.random.rand(nb_data_points // 2, 2) + 3
+    expected1 = np.array([1 for _ in data1])
+    data0 = np.random.rand(nb_data_points // 2, 2) + 1
+    expected0 = np.array([0 for _ in data0])
+    data = np.concatenate((data1, data0))
+    expected = np.concatenate((expected1, expected0))
+    permutation = np.random.permutation(nb_data_points)
+    data = data[permutation]
+    expected = expected[permutation]
+
+    # Affichage des points
+    plot_dots(data, expected)
+
     training_data = data[:nb_training_points]
-    training_expected = expected[:nb_training_points]
     testing_data = data[nb_training_points:]
     testing_expected = expected[nb_training_points:]
 
@@ -25,9 +36,7 @@ def main():
 
     # Classement sur les données de test
     predicted = [p.predict(x) for x in testing_data]
-
-    # Affichage des points
-    plot_dots(data, expected)
+    print(error_rate(predicted, testing_expected))
 
     # Affichage de la ligne de séparation
     x = np.linspace(0, 1, 50)
@@ -37,7 +46,19 @@ def main():
     plt.show()
 
 
+def error_rate(predicted, expected):
+    """Calculate the error rate of a prediction set"""
+
+    assert len(predicted) == len(expected)
+    errors = 0
+    for i, item in enumerate(predicted):
+        if item != expected[i]:
+            errors += 1
+    return errors / len(predicted)
+
+
 def plot_dots(data, expected):
+    """Plot all dots in the data"""
     # abscisses de la classe 1
     xpoints_1 = [x[0] for i, x in enumerate(data) if expected[i] == 1]
     # ordonnées de la classe 1