From 880514b55f5835f0ac0a9b547e00b7995097fbb1 Mon Sep 17 00:00:00 2001
From: D2A62006 <david06.bruenner@gmail.com>
Date: Thu, 27 Nov 2025 11:54:47 +0100
Subject: [PATCH] implement prepareNeuralNetworkFile()

---
 neuralNetwork.c      |  2 +-
 neuralNetwork.sh     |  1 +
 neuralNetworkTests.c | 36 ++++++++++++++++++++++++++++++++++--
 3 files changed, 36 insertions(+), 3 deletions(-)
 create mode 100644 neuralNetwork.sh

diff --git a/neuralNetwork.c b/neuralNetwork.c
index bd8f164..36fca85 100644
--- a/neuralNetwork.c
+++ b/neuralNetwork.c
@@ -164,7 +164,7 @@ NeuralNetwork loadModel(const char *path)
 
         assignActivations(model);
     }
-
+    printf("%d\n", model.numberOfLayers);
     return model;
 }
 
diff --git a/neuralNetwork.sh b/neuralNetwork.sh
new file mode 100644
index 0000000..7e56cdd
--- /dev/null
+++ b/neuralNetwork.sh
@@ -0,0 +1 @@
+make clean && make && make neuralNetworkTests 
diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c
index 21ab370..1a993db 100644
--- a/neuralNetworkTests.c
+++ b/neuralNetworkTests.c
@@ -8,9 +8,41 @@
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
-}
+    FILE *file = fopen(path, "wb");
 
+    if(file != NULL){
+        const char *fileTag = "__info2_neural_network_file_format__";
+        
+        // Write file header
+        fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+        
+        // Write the input dimension of the first layer
+        if(nn.numberOfLayers > 0){
+            fwrite(&nn.layers[0].weights.cols, sizeof(int), 1, file);
+        }
+        
+        // Write dimensions and data for each layer
+        for(int i = 0; i < nn.numberOfLayers; i++){
+            
+            // Write output dimension (rows of weights)
+            fwrite(&nn.layers[i].weights.rows, sizeof(int), 1, file);
+            
+            // Write weight matrix data
+            int weightSize = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+            fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightSize, file);
+            
+            // Write bias matrix data
+            int biasSize = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+            fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasSize, file);
+        }
+        
+        // Write terminating 0 to signal end of layers
+        int zero = 0;
+        fwrite(&zero, sizeof(int), 1, file);
+        
+        fclose(file);
+    }
+}
 void test_loadModelReturnsCorrectNumberOfLayers(void)
 {
     const char *path = "some__nn_test_file.info2";