jagrit's commit files

examples/cpp/CMakeLists.txt

@@ -0,0 +1,10 @@
+function(build_example SRCFILE)
+  get_filename_component(src_name ${SRCFILE} NAME_WE)
+  set(target "${src_name}")
+  add_executable(${target} ${SRCFILE})
+  target_link_libraries(${target} PRIVATE mlx)
+endfunction(build_example)
+
+build_example(tutorial.cpp)
+build_example(linear_regression.cpp)
+build_example(logistic_regression.cpp)

examples/cpp/linear_regression.cpp

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+#include <chrono>
+#include <cmath>
+#include <iostream>
+
+#include "mlx/mlx.h"
+#include "timer.h"
+
+/**
+ * An example of linear regression with MLX.
+ */
+using namespace mlx::core;
+
+int main() {
+  int num_features = 100;
+  int num_examples = 1'000;
+  int num_iters = 10'000;
+  float learning_rate = 0.01;
+
+  // True parameters
+  auto w_star = random::normal({num_features});
+
+  // The input examples (design matrix)
+  auto X = random::normal({num_examples, num_features});
+
+  // Noisy labels
+  auto eps = 1e-2 * random::normal({num_examples});
+  auto y = matmul(X, w_star) + eps;
+
+  // Initialize random parameters
+  array w = 1e-2 * random::normal({num_features});
+
+  auto loss_fn = [&](array w) {
+    auto yhat = matmul(X, w);
+    return (0.5f / num_examples) * sum(square(yhat - y));
+  };
+
+  auto grad_fn = grad(loss_fn);
+
+  auto tic = timer::time();
+  for (int it = 0; it < num_iters; ++it) {
+    auto grad = grad_fn(w);
+    w = w - learning_rate * grad;
+    eval(w);
+  }
+  auto toc = timer::time();
+
+  auto loss = loss_fn(w);
+  auto error_norm = std::sqrt(sum(square(w - w_star)).item<float>());
+  auto throughput = num_iters / timer::seconds(toc - tic);
+  std::cout << "Loss " << loss << ", |w - w*| = " << error_norm
+            << ", Throughput " << throughput << " (it/s)." << std::endl;
+}