Kommentare angepasst

imageInput.c

@@ -6,96 +6,17 @@
 #define BUFFER_SIZE 100
 #define FILE_HEADER_STRING "__info2_image_file_format__"
 
+// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
 
 // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
-    // Initialisiert einen Zeiger zur struct und reserviert Speicherplatz
-    GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));    
-    if(series == NULL){
-        printf("Es ist nicht genügend Speicher übrig");
-        return NULL;
-    }
-
-    FILE * data = fopen(path, "rb");
-    if (data == NULL){
-        printf("Die Datei konnte nicht gelesen werden");
-        return NULL;
-    }
-
-    // Überprüfung, ob die Datei einen Header hat
-    char header[BUFFER_SIZE];
-    fread(header, strlen(FILE_HEADER_STRING), 1, data);
-    header[strlen(FILE_HEADER_STRING)] ='\0';
-    if(strncmp(header, FILE_HEADER_STRING, strlen(FILE_HEADER_STRING) )!= 0){
-        printf("Die Datei hat keinen Header");
-        fclose(data);
-        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){
-        printf("Es ist nicht genügend Speicher übrig");
-        fclose(data);
-        return NULL;
-    }
-
-    //liest die Höhe und Breite der Bilder aus
-    unsigned short height = 0, width = 0;
-    fread(&width, sizeof(unsigned short), 1, data);
-    fread(&height, sizeof(unsigned short), 1, data);
+    GrayScaleImageSeries *series = NULL;
     
-    //reserviert Speicher für die Labels, die aber erst nach jedem Bild eingelesen werden
-    series->labels = malloc(sizeof(unsigned char) * series->count);
-    if (series->labels == NULL){
-        printf("Es ist nicht genügend Speicher übrig");
-        free(series->images);
-        fclose(data);
-        return NULL;
-    }
-    
-    //liest jedes Bild einzeln aus und speichert es in images
-    for(int counter_picture = 0 ; counter_picture < series->count; counter_picture++){
-        
-        // für jedes Bild muss vorher eine Größe festgelegt werden, die jedoch in diesem Fall immer gleich ist
-        series->images[counter_picture].width = width;
-        series->images[counter_picture].height =height;
-        unsigned int size_picture = height * width;
-
-        //reservieren des Speichers für Buffer, der die einzelnen Pixels speichert
-        series->images[counter_picture].buffer = malloc(size_picture* sizeof(GrayScalePixelType));
-        if (series->images[counter_picture].buffer == NULL){
-            printf("Es ist nicht genügend Speicher übrig");
-            free(series->images);
-            free(series);
-            fclose(data);
-            return NULL;
-        }
-
-        //einlesen der einzelnen Pixel in buffer
-        for(int counter_pixels = 0; counter_pixels < size_picture; counter_pixels++){
-            fread(&series->images[counter_picture].buffer[counter_pixels], sizeof(unsigned char), 1, data);
-        } 
-        
-        //einlesen der Labels
-        fread(&series->labels[counter_picture], sizeof(unsigned char), 1, data);        
-        
-    }
-
-    fclose(data);
     return series;
 }
 
 // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {
-    //erst den Speicherplatz der Pixel freigeben
-    for(int number= 0; number < series->count; number++){
-        free(series->images[number].buffer);
-    }
-    // dann die Bilder freigeben
-    free(series-> images);
-    free(series);
 }

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

@@ -5,49 +5,10 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
+
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    FILE *file = fopen(path, "wb");
-    if (!file)
-        return;
-
-    const char *tag = "__info2_neural_network_file_format__";
-    fwrite(tag, 1, strlen(tag), file);
-
-    // Überprüfung, 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 output = nn.layers[0].weights.rows;
-
-    fwrite(&input, sizeof(int), 1, file);
-    fwrite(&output, sizeof(int), 1, file);
-
-    // Schreibe die Layer-Daten
-    for (int i = 0; i < nn.numberOfLayers; i++)
-    {
-        const Layer *layer = &nn.layers[i];
-        int out = layer->weights.rows;
-        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)
-        {
-            int nextOut = nn.layers[i + 1].weights.rows;
-            fwrite(&nextOut, sizeof(int), 1, file);
-        }
-    }
-    fclose(file);
-
-   
+    // TODO
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -55,15 +16,15 @@ 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 weights2 = {.buffer = buffer2, .rows = 2, .cols = 3};
+    Matrix weights1 = {.buffer=buffer1, .rows=3, .cols=2};
+    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 biases2 = {.buffer = buffer4, .rows = 2, .cols = 1};
-    Layer layers[] = {{.weights = weights1, .biases = biases1}, {.weights = weights2, .biases = biases2}};
+    Matrix biases1 = {.buffer=buffer3, .rows=3, .cols=1};
+    Matrix biases2 = {.buffer=buffer4, .rows=2, .cols=1};
+    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 +40,12 @@ 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};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    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 +63,12 @@ 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};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    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 +86,12 @@ 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};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    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 +111,12 @@ 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};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    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 +138,7 @@ 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 +159,12 @@ 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};
-    Layer layers[] = {{.weights = weights, .biases = biases}};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    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 +181,7 @@ 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 +192,23 @@ 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 weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2};
-    Matrix weights3 = {.buffer = weightsBuffer3, .rows = 5, .cols = 3};
+    Matrix weights1 = {.buffer=weightsBuffer1, .rows=2, .cols=4};
+    Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
+    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 biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
-    Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
-    Layer layers[] = {{.weights = weights1, .biases = biases1, .activation = someActivation},
-                      {.weights = weights2, .biases = biases2, .activation = someActivation},
-                      {.weights = weights3, .biases = biases3, .activation = someActivation}};
-    NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3};
+    Matrix biases1 = {.buffer=biasBuffer1, .rows=2, .cols=1};
+    Matrix biases2 = {.buffer=biasBuffer2, .rows=3, .cols=1};
+    Matrix biases3 = {.buffer=biasBuffer3, .rows=5, .cols=1};
+    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
+                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
+                      {.weights=weights3, .biases=biases3, .activation=someActivation}};
+    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 +216,11 @@ 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
 }