Vollständige Version

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "files.associations": {
+        "unity.h": "c"
+    }
+}

imageInput.c

@@ -33,7 +33,6 @@ GrayScaleImageSeries *readImages(const char *path)
         return NULL;
     }
     //liest die Anzahl der Bilder aus
-    series->count = 0;
     fread(&series->count, sizeof(unsigned short),1, data);
     series->images = malloc(series->count * sizeof(GrayScaleImage));
     if (series->images == NULL){

imageInputTests.c

@@ -123,8 +123,7 @@ void setUp(void) {
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) 
+void tearDown(void) {
-{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }
 

neuralNetworkTests.c

@@ -5,27 +5,32 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
+
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
     FILE *file = fopen(path, "wb");
     if (!file)
-        return;
+    {
+     return;   
+    }
 
-    const char *tag = "__info2_neural_network_file_format__";
+    // Schreibe den Datei-Tag
+    const char *tag = "info2_neural_network_file_format";
     fwrite(tag, 1, strlen(tag), file);
 
-    // Überprüfung, ob es Layer gibt
+    // Überprüfe, ob es Layer gibt
     if (nn.numberOfLayers == 0) 
     {
         fclose(file);
         return;
     }
 
+    
     // Schreibe die Eingabe- und Ausgabegrößen des Netzwerks
-    int input = nn.layers[0].weights.cols;
+    int input  = nn.layers[0].weights.cols;
     int output = nn.layers[0].weights.rows;
 
-    fwrite(&input, sizeof(int), 1, file);
+    fwrite(&input,  sizeof(int), 1, file);
     fwrite(&output, sizeof(int), 1, file);
 
     // Schreibe die Layer-Daten
@@ -33,10 +38,12 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
     {
         const Layer *layer = &nn.layers[i];
         int out = layer->weights.rows;
-        int in = layer->weights.cols;
+        int in  = layer->weights.cols;
+
 
         fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, file);
 
+
         fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, file);
 
         if (i + 1 < nn.numberOfLayers)
@@ -45,9 +52,8 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
             fwrite(&nextOut, sizeof(int), 1, file);
         }
     }
+
     fclose(file);
-
-   
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -55,15 +61,15 @@ void test_loadModelReturnsCorrectNumberOfLayers(void)
     const char *path = "some__nn_test_file.info2";
     MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
     MatrixType buffer2[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights1 = {.buffer = buffer1, .rows = 3, .cols = 2};
+    Matrix weights1 = {.buffer=buffer1, .rows=3, .cols=2};
-    Matrix weights2 = {.buffer = buffer2, .rows = 2, .cols = 3};
+    Matrix weights2 = {.buffer=buffer2, .rows=2, .cols=3};
     MatrixType buffer3[] = {1, 2, 3};
     MatrixType buffer4[] = {1, 2};
-    Matrix biases1 = {.buffer = buffer3, .rows = 3, .cols = 1};
+    Matrix biases1 = {.buffer=buffer3, .rows=3, .cols=1};
-    Matrix biases2 = {.buffer = buffer4, .rows = 2, .cols = 1};
+    Matrix biases2 = {.buffer=buffer4, .rows=2, .cols=1};
-    Layer layers[] = {{.weights = weights1, .biases = biases1}, {.weights = weights2, .biases = biases2}};
+    Layer layers[] = {{.weights=weights1, .biases=biases1}, {.weights=weights2, .biases=biases2}};
 
-    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2};
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -79,12 +85,12 @@ void test_loadModelReturnsCorrectWeightDimensions(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
 
-    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -102,12 +108,12 @@ void test_loadModelReturnsCorrectBiasDimensions(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
 
-    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -125,12 +131,12 @@ void test_loadModelReturnsCorrectWeights(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
 
-    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -150,12 +156,12 @@ void test_loadModelReturnsCorrectBiases(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
 
-    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -177,7 +183,7 @@ void test_loadModelFailsOnWrongFileTag(void)
     NeuralNetwork netUnderTest;
     FILE *file = fopen(path, "wb");
 
-    if (file != NULL)
+    if(file != NULL)
     {
         const char *fileTag = "info2_neural_network_file_format";
 
@@ -198,12 +204,12 @@ void test_clearModelSetsMembersToNull(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
 
-    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -220,7 +226,7 @@ void test_clearModelSetsMembersToNull(void)
 
 static void someActivation(Matrix *matrix)
 {
-    for (int i = 0; i < matrix->rows * matrix->cols; i++)
+    for(int i = 0; i < matrix->rows * matrix->cols; i++)
     {
         matrix->buffer[i] = fabs(matrix->buffer[i]);
     }
@@ -231,23 +237,23 @@ void test_predictReturnsCorrectLabels(void)
     const unsigned char expectedLabels[] = {4, 2};
     GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
     GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
-    GrayScaleImage inputImages[] = {{.buffer = imageBuffer1, .width = 2, .height = 2}, {.buffer = imageBuffer2, .width = 2, .height = 2}};
+    GrayScaleImage inputImages[] = {{.buffer=imageBuffer1, .width=2, .height=2}, {.buffer=imageBuffer2, .width=2, .height=2}};
     MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
     MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
     MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22, 23, -24, 25, 26, 27, -28, -29};
-    Matrix weights1 = {.buffer = weightsBuffer1, .rows = 2, .cols = 4};
+    Matrix weights1 = {.buffer=weightsBuffer1, .rows=2, .cols=4};
-    Matrix weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2};
+    Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
-    Matrix weights3 = {.buffer = weightsBuffer3, .rows = 5, .cols = 3};
+    Matrix weights3 = {.buffer=weightsBuffer3, .rows=5, .cols=3};
     MatrixType biasBuffer1[] = {200, 0};
     MatrixType biasBuffer2[] = {0, -100, 0};
     MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0};
-    Matrix biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1};
+    Matrix biases1 = {.buffer=biasBuffer1, .rows=2, .cols=1};
-    Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
+    Matrix biases2 = {.buffer=biasBuffer2, .rows=3, .cols=1};
-    Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
+    Matrix biases3 = {.buffer=biasBuffer3, .rows=5, .cols=1};
-    Layer layers[] = {{.weights = weights1, .biases = biases1, .activation = someActivation},
+    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
-                      {.weights = weights2, .biases = biases2, .activation = someActivation},
+                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
-                      {.weights = weights3, .biases = biases3, .activation = someActivation}};
+                      {.weights=weights3, .biases=biases3, .activation=someActivation}};
-    NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3};
+    NeuralNetwork netUnderTest = {.layers=layers, .numberOfLayers=3};
     unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
     TEST_ASSERT_NOT_NULL(predictedLabels);
     int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0]));
@@ -255,13 +261,11 @@ void test_predictReturnsCorrectLabels(void)
     free(predictedLabels);
 }
 
-void setUp(void)
+void setUp(void) {
-{
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void)
+void tearDown(void) {
-{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }