diff --git a/.idea/misc.xml b/.idea/misc.xml
index 12ebf45039801177f1bb66cb906ed35fe5414453..372062d0d327c4cf104ef7d826e76a4a231a586f 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,7 +1,10 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
+ <component name="Black">
+ <option name="sdkName" value="Python 3.9" />
+ </component>
<component name="MarkdownSettingsMigration">
<option name="stateVersion" value="1" />
</component>
- <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10.12 WSL (Ubuntu-20.04): (/home/thib/.virtualenvs/tp-clustering/bin/python)" project-jdk-type="Python SDK" />
+ <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
</project>
\ No newline at end of file
diff --git a/.idea/tp-clustering.iml b/.idea/tp-clustering.iml
index df7c332ff659f1d30cd0f3b3fb8bc69292c6e0fd..74d515a027de98657e9d3d5f0f1831882fd81374 100644
--- a/.idea/tp-clustering.iml
+++ b/.idea/tp-clustering.iml
@@ -4,7 +4,7 @@
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/venv" />
</content>
- <orderEntry type="jdk" jdkName="Python 3.10.12 WSL (Ubuntu-20.04): (/home/thib/.virtualenvs/tp-clustering/bin/python)" jdkType="Python SDK" />
+ <orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>
\ No newline at end of file
diff --git a/decisiontree-iris.py b/decisiontree-iris.py
index 4fe15e37f72a0513e0a7e9e7f256fb6185a62127..c69fd0a6fcc51a09d74c59d3a5a1264a3edc8a77 100644
--- a/decisiontree-iris.py
+++ b/decisiontree-iris.py
@@ -18,7 +18,6 @@ y_iris = df_iris['class']
X_train_iris, X_test_iris, y_train_iris, y_test_iris = train_test_split(X_iris, y_iris, test_size=0.2, random_state=42)
# Construction de l'arbre de décision avec des paramètres spécifiques
-# Vous pouvez jouer avec les valeurs de min_samples_leaf et max_depth
clf_iris = DecisionTreeClassifier(min_samples_leaf=5, max_depth=3)
clf_iris.fit(X_train_iris, y_train_iris)
diff --git a/decisiontree-student.py b/decisiontree-student.py
index c2eaa34d0bd62464fb90ba0438f903b3fef5f6bd..17afbba1a570fd5acd06af0ab237ff7f8f22d375 100644
--- a/decisiontree-student.py
+++ b/decisiontree-student.py
@@ -18,7 +18,6 @@ y = df['success']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Vous pouvez ajuster la taille du test si nécessaire
# Construction de l'arbre de décision avec des paramètres spécifiques
-# Vous pouvez jouer avec les valeurs de min_samples_leaf et max_depth
clf = DecisionTreeClassifier(min_samples_leaf=5, max_depth=3)
clf.fit(X_train, y_train)
diff --git a/perceptron-tp3.py b/perceptron-tp3.py
index 63f17176a95a540d29f6703d41dad7a107141103..da63e76481d186436f75b5a2cbe00e82908155e8 100644
--- a/perceptron-tp3.py
+++ b/perceptron-tp3.py
@@ -1,26 +1,31 @@
-# Author : Capt Thibault , Souza Luz Juliano
+# Author : Capt Thibault, Souza Luz Juliano
# Date : 31.10.2023
-# Project : Perceptron
-# Description : Ce fichier représente notre travail pour le tp du perceptron
+# Project : Perceptron multicouche
+# Description : Ce fichier représente notre travail pour le tp du perceptron multicouche
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
-def upd_weights_hidden(wi_old, learning, t, y, xi):
- return wi_old + learning * (t - y) * y * (1 - y) * xi
+
+def upd_weights_hidden(wi_old, learning, delta, xi):
+ return wi_old + learning * np.outer(xi, delta)
+
def upd_weights_output(wi_old, learning, delta, y):
return wi_old + learning * delta * y
+
def sigmoide(x):
return 1 / (1 + np.exp(-x))
+
def predict(poids_input_hidden, poids_hidden_output, input_data):
- hidden_input = np.dot(poids_input_hidden, input_data)
+ hidden_input = np.dot(input_data, poids_input_hidden)
hidden_output = sigmoide(hidden_input)
- output = sigmoide(np.dot(poids_hidden_output, np.insert(hidden_output, 0, 1)))
+ output = sigmoide(np.dot(np.insert(hidden_output, 0, 1), poids_hidden_output))
return output, hidden_output
+
if __name__ == '__main__':
dataset = pd.read_csv("Data/student-data-train.csv", header=0)
dataset['norm_grade_1'] = (dataset['grade_1'] - dataset['grade_1'].mean()) / dataset['grade_1'].std()
@@ -32,6 +37,7 @@ if __name__ == '__main__':
learning_rate = 1e-2
max_iterations = 2000
+ num_hidden = 10
# Initialize weights for input to hidden layer and hidden to output layer
weights_input_hidden = np.random.rand(num_features + 1, num_hidden) - 0.5
@@ -48,13 +54,14 @@ if __name__ == '__main__':
target = y[i]
# Forward pass
- hidden_output, output = predict(weights_input_hidden, weights_hidden_output, input_data)
+ output, hidden_output = predict(weights_input_hidden, weights_hidden_output, input_data)
# Backpropagation
- delta_output = (target - output) * output * (1 - output)
- weights_hidden_output = upd_weights_output(weights_hidden_output, learning_rate, delta_output, np.insert(hidden_output, 0, 1))
+ delta_output = output * (1 - output) * (target - output)
+ weights_hidden_output = upd_weights_output(weights_hidden_output, learning_rate, delta_output,
+ np.insert(hidden_output, 0, 1))
- delta_hidden = hidden_output * (1 - hidden_output) * delta_output * weights_hidden_output[1:]
+ delta_hidden = hidden_output * (1 - hidden_output) * np.dot(delta_output, weights_hidden_output[1:])
weights_input_hidden = upd_weights_hidden(weights_input_hidden, learning_rate, delta_hidden, input_data)
total_error += (target - output) ** 2 / 2
@@ -65,17 +72,17 @@ if __name__ == '__main__':
correct_classifications = 0
for i in range(len(X)):
input_data = np.insert(X[i], 0, 1)
- cible = y[i]
- sortie = sigmoide(np.dot(poids, input_data))
- pred = 1 if sortie >= 0.5 else 0
- if pred == cible:
+ target = y[i]
+ output, _ = predict(weights_input_hidden, weights_hidden_output, input_data)
+ pred = 1 if output >= 0.5 else 0
+ if pred == target:
correct_classifications += 1
accuracy = correct_classifications / len(X)
print(f"Taux de classifications correctes: {accuracy * 100}%")
# Affichage de la droite de séparation des classes
- w1, w2, b = poids[1], poids[2], poids[0]
+ w1, w2, b = weights_hidden_output[1], weights_hidden_output[2], weights_hidden_output[0]
pente = -w1 / w2
intercept = -b / w2
print(f"Droite de séparation: y = {pente}x + {intercept}")
diff --git a/perceptron.py b/perceptron.py
index 63f9d30a8be50ba8a031e586bceb56f72ac323be..55c3b9fba4f59f7b06927611146fd59bec0049fa 100644
--- a/perceptron.py
+++ b/perceptron.py
@@ -1,4 +1,4 @@
-# Author : Capt Thibault , Souza Luz Juliano
+# Author : Capt Thibault, Souza Luz Juliano
# Date : 31.10.2023
# Project : Perceptron
# Description : Ce fichier représente notre travail pour le tp du perceptron