+ comapred real parameters and sampled

src/sampling.py

@@ -25,14 +25,13 @@
     class_samples = []
 
     for variable_params in parameters[y]:
-
         mean, std = variable_params
         samples = np.random.normal(mean, std, SAMPLES)
         class_samples.append(samples)
 
     class_samples = np.column_stack(class_samples)
     sampled_data.append(class_samples)
-    sampled_labels.extend([y]*SAMPLES)
+    sampled_labels.extend([y] * SAMPLES)
 
 # On concatène les échantillons
 sampled_data = np.vstack(sampled_data)
@@ -56,7 +55,28 @@
     print(f"Mean: {mean_sampled}")
     print(f"Std: {std_sampled}")
 
-    vars = [0, 1, 2, 3]    
+    vars = [0, 1, 2, 3]
+
+    plt.figure()
+    plt.title(f"Courbe des distribution de probabilité sachant Y={c}")
+
+    for var in vars:
+        plt.plot(
+            np.linspace(means[var] - 10, means[var] + 10, 1000),
+            normal_pdf(means[var], stds[var])(np.linspace(means[var] - 10, means[var] + 10, 1000)),
+            label=f"X_{var} réelle",
+        )
+        plt.plot(
+            np.linspace(mean_sampled[var] - 10, mean_sampled[var] + 10, 1000),
+            normal_pdf(mean_sampled[var], std_sampled[var])(
+                np.linspace(mean_sampled[var] - 10, mean_sampled[var] + 10, 1000)
+            ),
+            label=f"X_{var} échantillonnée",
+        )
+
+    plt.legend()
+    plt.savefig(f"src/res/sample_compare_Y_{c}")
+    plt.close()
 
 
 print("\n\n")
@@ -66,7 +86,9 @@
 X_train = pd.DataFrame(X_train)
 X_test = pd.DataFrame(X_test)
 
-sampled_params = {c: list(zip(np.mean(X_train[y_train == c], axis=0), np.std(X_train[y_train == c], axis=0))) for c in classes}
+sampled_params = {
+    c: list(zip(np.mean(X_train[y_train == c], axis=0), np.std(X_train[y_train == c], axis=0))) for c in classes
+}
 
 # --- Notre implémentation de Naive Bayes ---
 print("Notre Naive Bayes")
@@ -103,11 +125,3 @@
 # --------------------------------------------
 
 # ---------------------------------------------------------------------------
-
-
-
-
-
-
-
-