polishing

include/adam.hpp

@@ -27,11 +27,9 @@
 // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 // THE SOFTWARE.
 // */
-class AdamOptimizer
-{
-    /*
-    arXiv preprint arXiv:1412.6980 , Diederik P Kingma, Jimmy Ba
 
+ /*
+  
         Adam maintains two moving averages for each parameter:
 
         First Moment Estimate (Mean):
@@ -54,6 +52,9 @@ class AdamOptimizer
 
 
     */
+class AdamOptimizer
+{
+
 public:
     double lr;      // Learning rate
     double beta1;   // Exponential decay rate for the first moment
@@ -81,30 +82,30 @@ class AdamOptimizer
         }
     }
 
-    // Step function to update parameters
+
     void step()
     {
-        t++; // Increment time step
+        t++; //   time step
         for (auto &param : params)
         {
             double g = param->grad; // Gradient of the parameter
 
-            // Update first moment estimate (mean)
+            //   first moment estimate (mean)
             m[param.get()] = beta1 * m[param.get()] + (1.0 - beta1) * g;
 
-            // Update second moment estimate (uncentered variance)
+            //   second moment estimate (uncentered variance)
             v[param.get()] = beta2 * v[param.get()] + (1.0 - beta2) * g * g;
 
             // Compute bias-corrected estimates
             double m_hat = m[param.get()] / (1.0 - std::pow(beta1, t));
             double v_hat = v[param.get()] / (1.0 - std::pow(beta2, t));
 
-            // Update parameter
+
             param->data -= lr * m_hat / (std::sqrt(v_hat) + epsilon);
         }
     }
 
-    // Zero gradients for the next step
+
     void zero_grad()
     {
         for (auto &param : params)

include/data_utils.hpp

@@ -51,20 +51,20 @@ using vv_double = std::vector<std::vector<double>>;
 
 inline 
 void display_data(const ColRows& inputs, const ColRows& targets, const std::vector<std::string>& column_names ) {
-    // Print column headers
+    //   column headers
     for (const auto& col_name : column_names) {
         std::cout << std::setw(15) << std::left << col_name;
     }
     std::cout << std::setw(15) << std::left << "target";
     std::cout << "\n";
 
-    // Print separator line
+    //   separator line
     for (size_t i = 0; i < column_names.size() + 1; ++i) {
         std::cout << std::setw(15) << std::setfill('-') << "" << std::setfill(' ');
     }
     std::cout << "\n";
 
-    // Print rows of data
+    //   rows of data
     for (size_t i = 0; i < inputs.size(); ++i) {
         for (const auto& value : inputs[i]) {
             std::cout << std::setw(15) << std::left << value->data;
@@ -78,7 +78,7 @@ void display_data(const ColRows& inputs, const ColRows& targets, const std::vect
         std::cout << "\n";
     }
 
-    // Print final separator line
+     
     std::cout << "========================\n";
 }
 
@@ -105,7 +105,7 @@ inline void write_to_csv(const std::vector<std::vector<T>> &data, const std::str
 {
     std::ofstream file(filename);
 
-    // Check if the file is open
+
     if (!file.is_open())
     {
         std::cerr << "Error: Could not open file " << filename << " for writing.\n";
@@ -195,7 +195,7 @@ inline vv_double convert_to_double_with_encoding(const vv_string &data, bool has
             }
         }
 
-        // Encode the target column using the encoding map
+
         std::string target_value = data[i].back();
         if (target_encoding_map.find(target_value) == target_encoding_map.end())
         {

include/micrograd.hpp

@@ -45,8 +45,8 @@ THE SOFTWARE.
 // #include "dataset.hpp"
 #include "loss.hpp"
 #include "csv.hpp"
-#include "column.hpp"
-#include "csv_table.hpp"
+// #include "column.hpp"
+// #include "csv_table.hpp"
 #include "mlp.hpp"
 #include "sgd.hpp"
 #include "console_utils.hpp"