style: run clang-format and configure pre-commit hooks

examples/pytorch_integration.cpp

@@ -3,59 +3,57 @@
  * @brief Example: train a small neural network with FCES via libtorch.
  */
 
+#include "fces/optimizer.hpp"
 #include <iostream>
 #include <torch/torch.h>
-#include "fces/optimizer.hpp"
 
 struct TinyNet : torch::nn::Module {
-    torch::nn::Linear fc1{nullptr}, fc2{nullptr};
+  torch::nn::Linear fc1{nullptr}, fc2{nullptr};
 
-    TinyNet() {
-        fc1 = register_module("fc1", torch::nn::Linear(10, 32));
-        fc2 = register_module("fc2", torch::nn::Linear(32, 1));
-    }
+  TinyNet() {
+    fc1 = register_module("fc1", torch::nn::Linear(10, 32));
+    fc2 = register_module("fc2", torch::nn::Linear(32, 1));
+  }
 
-    torch::Tensor forward(torch::Tensor x) {
-        x = torch::relu(fc1->forward(x));
-        return fc2->forward(x);
-    }
+  torch::Tensor forward(torch::Tensor x) {
+    x = torch::relu(fc1->forward(x));
+    return fc2->forward(x);
+  }
 };
 
 int main() {
-    auto model = std::make_shared<TinyNet>();
+  auto model = std::make_shared<TinyNet>();
 
-    std::vector<torch::Tensor> params;
-    for (auto& p : model->parameters()) params.push_back(p);
+  std::vector<torch::Tensor> params;
+  for (auto &p : model->parameters())
+    params.push_back(p);
 
-    fces::FCESOptimizer optimizer(
-        params,
-        fces::FCESConfig{}
-            .set_lr(1.6e-3f)
-            .set_population_size(200)
-            .set_total_steps(1000)
-    );
+  fces::FCESOptimizer optimizer(params, fces::FCESConfig{}
+                                            .set_lr(1.6e-3f)
+                                            .set_population_size(200)
+                                            .set_total_steps(1000));
 
-    // Generate synthetic regression data
-    auto x_train = torch::randn({100, 10});
-    auto y_train = torch::sin(x_train.sum(1, true));
+  // Generate synthetic regression data
+  auto x_train = torch::randn({100, 10});
+  auto y_train = torch::sin(x_train.sum(1, true));
 
-    for (int epoch = 0; epoch < 100; ++epoch) {
-        optimizer.zero_grad();
-        auto pred = model->forward(x_train);
-        auto loss = torch::mse_loss(pred, y_train);
-        loss.backward();
-        optimizer.step();
-        optimizer.update_fitness(loss.item<float>());
+  for (int epoch = 0; epoch < 100; ++epoch) {
+    optimizer.zero_grad();
+    auto pred = model->forward(x_train);
+    auto loss = torch::mse_loss(pred, y_train);
+    loss.backward();
+    optimizer.step();
+    optimizer.update_fitness(loss.item<float>());
 
-        if (epoch % 10 == 0) {
-            std::cout << "Epoch " << epoch
-                      << " | Loss: " << loss.item<float>() << std::endl;
-        }
+    if (epoch % 10 == 0) {
+      std::cout << "Epoch " << epoch << " | Loss: " << loss.item<float>()
+                << std::endl;
     }
+  }
 
-    std::cout << "\nTraining complete. Final loss: "
-              << torch::mse_loss(model->forward(x_train), y_train).item<float>()
-              << std::endl;
+  std::cout << "\nTraining complete. Final loss: "
+            << torch::mse_loss(model->forward(x_train), y_train).item<float>()
+            << std::endl;
 
-    return 0;
+  return 0;
 }