completed prepareNeuralNetworkFile

I2_NeuronalerAbsturz/Start_Mac/neuralNetworkTests.c

@@ -5,12 +5,89 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
+/* 
+################
+Aufbau Test File 
+################
+
+
+HEADER
+
+inputDim
+outputDim
+
+-- Layer 1 --
+weights (outputDim * inputDim * MatrixType)
+biases  (outputDim * MatrixType)
+
+outputDim
+
+-- Layer 2 --
+weights
+biases
+
+...
+...
+-- Layer n --
+weights
+biases
+
+outputDim = 0  => Ende 
+*/
+
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *file = fopen(path, "wb");
+    if (file)
+    {
+        const char *fileTag = "__info2_neural_network_file_format__";
+        fwrite(fileTag, 1, strlen(fileTag), file);
+
+        //Stopt loadModel, falls keine Layer vorhanden
+        if (nn.numberOfLayers == 0) 
+        {
+            int zero = 0;
+            fwrite(&zero, sizeof(int), 1, file);
+            fclose(file);
+            return;
+        }
+
+        // input und output dimension schreiben
+        int inputDim = nn.layers[0].weights.cols;
+        int outputDim = nn.layers[0].weights.rows;
+        fwrite(&inputDim, sizeof(int), 1, file);
+        fwrite(&outputDim, sizeof(int), 1, file);
+
+        // erstes Layer schreiben
+        int weightCount = nn.layers[0].weights.rows * nn.layers[0].weights.cols;
+        fwrite(nn.layers[0].weights.buffer, sizeof(MatrixType), weightCount, file);
+
+        int biasesCount = nn.layers[0].biases.rows * nn.layers[0].biases.cols;
+        fwrite(nn.layers[0].biases.buffer, sizeof(MatrixType), biasesCount, file);
+
+        // für weiter Layer nur outputDimension schreiben
+        for (unsigned int i = 1; i < nn.numberOfLayers; i++)
+        {
+            outputDim = nn.layers[i].weights.rows;
+            fwrite(&outputDim, sizeof(int), 1, file);
+
+            weightCount = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+            fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightCount, file);
+
+            biasesCount = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+            fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasesCount, file);
+        }
+
+        // loadModel ließt 0 ein -> Stop
+        int fileEnd = 0;
+        fwrite(&fileEnd, sizeof(int), 1, file);
 }
 
+    fclose(file);
+}
+
+
 void test_loadModelReturnsCorrectNumberOfLayers(void)
 {
     const char *path = "some__nn_test_file.info2";