phase 1

model/constant_predictor.py

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+import tensorflow as tf
+import numpy as np
+
+def build_model():
+    model = tf.keras.Sequential([
+        tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)),
+        tf.keras.layers.Dense(8, activation='relu'),
+        tf.keras.layers.Dense(1, activation='linear')
+    ])
+    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
+    return model
+
+def train_model(X_train, y_train):
+    model = build_model()
+    model.fit(X_train, y_train, epochs=100, batch_size=8, verbose=0)
+    return model
+
+# 사용 예시:
+# input: [physical, stamina, tech, accuracy, reading]
+# X_train = np.array([[12.5, 26, 0.41, 0.1, 0.0], ...])
+# y_train = np.array([11.0, ...])

model/dataset.json

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+[]

model/train.py

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+import json
+import numpy as np
+import tensorflow as tf
+
+with open('model/dataset.json', 'r') as f:
+    data = json.load(f)
+
+X = np.array([d['x'] for d in data])
+y = np.array([d['y'] for d in data])
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(32, activation='relu', input_shape=(4,)),
+    tf.keras.layers.Dense(16, activation='relu'),
+    tf.keras.layers.Dense(1)
+])
+model.compile(optimizer='adam', loss='mse')
+model.fit(X, y, epochs=200, verbose=0)
+model.save('model/constant_model.keras')
+print("Model saved to model/constant_model.keras")