From 3da12420337f2a3c002a6f62551e6d21ecbaa866 Mon Sep 17 00:00:00 2001
From: KOSASIH <kosasihg88@gmail.com>
Date: Thu, 9 May 2024 14:29:54 +0700
Subject: [PATCH] Create ai_models.py

---
 ai/ai_models.py | 65 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 65 insertions(+)
 create mode 100644 ai/ai_models.py

diff --git a/ai/ai_models.py b/ai/ai_models.py
new file mode 100644
index 0000000..0fa6639
--- /dev/null
+++ b/ai/ai_models.py
@@ -0,0 +1,65 @@
+# ai_models.py
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+import numpy as np
+
+class PlayerPerformanceModel(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(PlayerPerformanceModel, self).__init__()
+        self.fc1 = nn.Linear(input_dim, hidden_dim) 
+        self.fc2 = nn.Linear(hidden_dim, output_dim) 
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x)) 
+        x = self.fc2(x)
+        return x
+
+class GameOutcomeModel(RandomForestRegressor):
+    def __init__(self, n_estimators=100, random_state=42):
+        super(GameOutcomeModel, self).__init__(n_estimators=n_estimators, random_state=random_state)
+
+class MetricModel(LinearRegression):
+    def __init__(self):
+        super(MetricModel, self).__init__()
+
+def load_data(file_path):
+    data = pd.read_csv(file_path)
+    X = data.drop(['player_id', 'game_id', 'outcome'], axis=1)
+    y = data['outcome']
+    return X, y
+
+def preprocess_data(X, y):
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X)
+    return X_scaled, y
+
+def train_player_performance_model(X, y, epochs=100, batch_size=32):
+    model = PlayerPerformanceModel(input_dim=X.shape[1], hidden_dim=128, output_dim=1)
+    criterion = nn.MSELoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+    for epoch in range(epochs):
+        for i in range(0, len(X), batch_size):
+            x_batch = X[i:i+batch_size]
+            y_batch = y[i:i+batch_size]
+            optimizer.zero_grad()
+            outputs = model(x_batch)
+            loss = criterion(outputs, y_batch)
+            loss.backward()
+            optimizer.step()
+    return model
+
+def train_game_outcome_model(X, y):
+    model = GameOutcomeModel()
+    model.fit(X, y)
+    return model
+
+def train_metric_model(X, y):
+    model = MetricModel()
+    model.fit(X, y)
+    return model