Update Main Models/bai-2.0/README.md

Main Models/bai-2.0/README.md

@@ -1,43 +1,82 @@
-# bai-2.0 (338787 parametre)
-
-## EEG üzerinden duygu sınıflandırması yapan ilk kararlı modellerimizden olan "bai-2.0" modeli.
-
-#### NOT: Gerçek zamanlı EEG veri takibi uygulamasına modeli entegre ederseniz, gerçek zamanlı olarak duygu tahmini yapabilmektedir. Uygulamaya erişebilmek için: https://github.com/neurazum/Realtime-EEG-Monitoring
-
-## -----------------------------------------------------------------------------------
-
-# bai-2.0 (338787 parameters)
-
-## "bai-2.0" model, one of the first stable models that classifies emotions via EEG.
-
-#### NOTE: If you integrate the model into a real-time EEG data tracking application, it can predict emotions in real time. To access the application: https://github.com/neurazum/Realtime-EEG-Monitoring
-
-**Doğruluk/Accuracy: %97.93621013133207**
-
-## -----------------------------------------------------------------------------------
-
-# Kullanım / Usage:
-
-```python
-import numpy as np
-import pandas as pd
-from sklearn.preprocessing import StandardScaler
-from tensorflow.keras.models import load_model
-import matplotlib.pyplot as plt
-
-model_path = 'model-path'
-
-model = load_model(model_path)
-
-model_name = model_path.split('/')[-1].split('.')[0]
-
-plt.figure(figsize=(10, 6))
-plt.title(f'Duygu Tahmini ({model_name}.0)')
-plt.xlabel('Zaman')
-plt.ylabel('Sınıf')
-plt.legend(loc='upper right')
-plt.grid(True)
-plt.show()
-model.summary()
-
+# bai-2.0 (338787 parametre)
+
+## EEG üzerinden duygu sınıflandırması yapan ilk kararlı modellerimizden olan "bai-2.0" modeli.
+
+#### NOT: Gerçek zamanlı EEG veri takibi uygulamasına modeli entegre ederseniz, gerçek zamanlı olarak duygu tahmini yapabilmektedir. Uygulamaya erişebilmek için: https://github.com/neurazum/Realtime-EEG-Monitoring
+
+## -----------------------------------------------------------------------------------
+
+# bai-2.0 (338787 parameters)
+
+## "bai-2.0" model, one of the first stable models that classifies emotions via EEG.
+
+#### NOTE: If you integrate the model into a real-time EEG data tracking application, it can predict emotions in real time. To access the application: https://github.com/neurazum/Realtime-EEG-Monitoring
+
+**Doğruluk/Accuracy: %97.93621013133207**
+
+## -----------------------------------------------------------------------------------
+
+# Kullanım / Usage
+
+```python
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import load_model
+import matplotlib.pyplot as plt
+
+model_path = 'model-path'
+
+model = load_model(model_path)
+
+model_name = model_path.split('/')[-1].split('.')[0]
+
+plt.figure(figsize=(10, 6))
+plt.title(f'Duygu Tahmini ({model_name}.0)')
+plt.xlabel('Zaman')
+plt.ylabel('Sınıf')
+plt.legend(loc='upper right')
+plt.grid(True)
+plt.show()
+model.summary()
+```
+
+# Tahmin / Prediction
+
+```python
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import load_model
+
+model_path = 'model-path'
+
+model = load_model(model_path)
+
+scaler = StandardScaler()
+
+predictions = model.predict(X_new_reshaped)
+predicted_labels = np.argmax(predictions, axis=1)
+
+label_mapping = {'NEGATIVE': 0, 'NEUTRAL': 1, 'POSITIVE': 2}
+label_mapping_reverse = {v: k for k, v in label_mapping.items()}
+
+#new_input = np.array([[23, 465, 12, 9653] * 637])
+new_input = np.random.rand(1, 2548)  # 1 örnek ve 2548 özellik
+new_input_scaled = scaler.fit_transform(new_input)
+new_input_reshaped = new_input_scaled.reshape((new_input_scaled.shape[0], 1, new_input_scaled.shape[1]))
+
+new_prediction = model.predict(new_input_reshaped)
+predicted_label = np.argmax(new_prediction, axis=1)[0]
+predicted_emotion = label_mapping_reverse[predicted_label]
+
+if predicted_emotion == 'NEGATIVE':
+    predicted_emotion = 'Negatif'
+elif predicted_emotion == 'NEUTRAL':
+    predicted_emotion = 'Nötr'
+elif predicted_emotion == 'POSITIVE':
+    predicted_emotion = 'Pozitif'
+
+print(f'Giriş Verileri: {new_input}')
+print(f'Tahmin Edilen Duygu: {predicted_emotion}')
 ```