neuralNetwork fixed

neuralNetwork.c

@@ -170,7 +170,7 @@ NeuralNetwork loadModel(const char *path)
 
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
-    Matrix matrix = {NULL, 0, 0};
+    Matrix matrix = {0, 0, NULL};
 
     if(count > 0 && images != NULL)
     {

neuralNetworkTests.c

@@ -5,10 +5,46 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
+static void writeLayer(FILE *file, const Matrix weights, const Matrix biases, unsigned int inputDim)
+{
+    unsigned int outputDim = (unsigned int)weights.rows;
+    fwrite(&outputDim, sizeof(unsigned int), 1, file);
+    if (weights.buffer != NULL)
+        fwrite(weights.buffer, sizeof(MatrixType), outputDim * inputDim, file);
+
+    if (biases.buffer != NULL)
+        fwrite(biases.buffer, sizeof(MatrixType), outputDim, file);
+}
+
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *file = fopen(path, "wb");
+    if (!file) return;
+
+    const char tag[] = "__info2_neural_network_file_format__";
+    fwrite(tag, sizeof(char), strlen(tag), file);
+
+    if (nn.numberOfLayers == 0)
+    {
+        unsigned int zero = 0;
+        fwrite(&zero, sizeof(unsigned int), 1, file);
+        fclose(file);
+        return;
+    }
+
+    unsigned int inputDim = (unsigned int)nn.layers[0].weights.cols;
+    fwrite(&inputDim, sizeof(unsigned int), 1, file);
+
+    for (int i = 0; i < nn.numberOfLayers; i++)
+    {
+        writeLayer(file, nn.layers[i].weights, nn.layers[i].biases, inputDim);
+        inputDim = (unsigned int)nn.layers[i].weights.rows;
+    }
+    unsigned int zero = 0;
+    fwrite(&zero, sizeof(unsigned int), 1, file);
+
+    fclose(file);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)