hilfsfunktionen und matrix fix

imageInput.c

@@ -8,6 +8,48 @@
 
 // TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
 
+static int read_header(FILE *file, unsigned short *count, unsigned short *width, unsigned short *height)
+{
+    size_t headerLEN = strlen(FILE_HEADER_STRING);
+    char buffer[BUFFER_SIZE];
+
+    if (headerLEN >= BUFFER_SIZE)
+    {
+        return 0;
+    }
+
+    if (fread(buffer, 1, headerLEN, file) != headerLEN)
+    {
+        return 0;
+    }
+
+    buffer[headerLEN] = '\0';
+
+    if (strcmp(buffer, FILE_HEADER_STRING) != 0)
+    {
+        return 0;
+    }
+
+    if (fread(count, sizeof(unsigned short), 1, file) != 1 || fread(width, sizeof(unsigned short), 1, file) != 1 ||
+        fread(height, sizeof(unsigned short), 1, file) != 1)
+    {
+        return 0;
+    }
+
+    return 1;
+}
+
+static int read_single_image(FILE *file, GrayScaleImage *image)
+{
+    unsigned int number_of_pixel = image->width * image->height;
+
+    if (fread(image->buffer, sizeof(GrayScalePixelType), number_of_pixel, file) != number_of_pixel) // fehler beim lesen
+    {
+        return 0;
+    }
+    return 1;
+}
+
 // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
@@ -17,26 +59,9 @@ GrayScaleImageSeries *readImages(const char *path)
         return 0;
     }
 
-    const size_t headerLEN = strlen(FILE_HEADER_STRING);
+    unsigned short count, width, height;
-    char buffer[BUFFER_SIZE];
 
-    if (fread(buffer, 1, headerLEN, file) != headerLEN)
+    if (!read_header(file, &count, &width, &height))
-    {
-        fclose(file);
-        return 0;
-    }
-    buffer[headerLEN] = '\0';
-
-    if (strcmp(buffer, FILE_HEADER_STRING) != 0)
-    {
-        fclose(file);
-        return 0;
-    }
-
-    unsigned short count, height, width;
-
-    if (fread(&count, sizeof(unsigned short), 1, file) != 1 || fread(&height, sizeof(unsigned short), 1, file) != 1 ||
-        fread(&width, sizeof(unsigned short), 1, file) != 1)
     {
         fclose(file);
         return 0;
@@ -65,7 +90,7 @@ GrayScaleImageSeries *readImages(const char *path)
     {
         series->images[i].width = width;
         series->images[i].height = height;
-        series->images[i].buffer = malloc(width * height);
+        series->images[i].buffer = malloc(width * height * sizeof(GrayScalePixelType));
 
         if (!series->images[i].buffer)
         {
@@ -73,13 +98,8 @@ GrayScaleImageSeries *readImages(const char *path)
             fclose(file);
             return 0;
         }
-    }
 
-    for (int i = 0; i < count; i++)
+        if (!read_single_image(file, &series->images[i]))
-    {
-        size_t pixel_count = width * height;
-
-        if (fread(series->images[i].buffer, 1, pixel_count, file) != pixel_count)
         {
             clearSeries(series);
             fclose(file);
@@ -101,17 +121,14 @@ GrayScaleImageSeries *readImages(const char *path)
 // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {
-    if (series == 0)
+    if (series)
-        return;
-
-    if (series->images)
     {
         for (int i = 0; i < series->count; i++)
         {
             free(series->images[i].buffer);
         }
         free(series->images);
+        free(series->labels);
+        free(series);
     }
-    free(series->labels);
-    free(series);
 }

imageInputTests.c

@@ -54,7 +54,7 @@ void test_readImagesReturnsCorrectImageWidth(void)
     GrayScaleImageSeries *series = NULL;
     const unsigned short expectedWidth = 10;
     const char *path = "testFile.info2";
-    prepareImageFile(path, 8, expectedWidth, 2, 1);
+    prepareImageFile(path, expectedWidth, 8, 2, 1);
     series = readImages(path);
     TEST_ASSERT_NOT_NULL(series);
     TEST_ASSERT_NOT_NULL(series->images);
@@ -70,7 +70,7 @@ void test_readImagesReturnsCorrectImageHeight(void)
     GrayScaleImageSeries *series = NULL;
     const unsigned short expectedHeight = 10;
     const char *path = "testFile.info2";
-    prepareImageFile(path, expectedHeight, 8, 2, 1);
+    prepareImageFile(path, 8, expectedHeight, 2, 1);
     series = readImages(path);
     TEST_ASSERT_NOT_NULL(series);
     TEST_ASSERT_NOT_NULL(series->images);
@@ -119,6 +119,13 @@ void test_readImagesFailsOnWrongFileTag(void)
     remove(path);
 }
 
+// Tests der Hilfsfunktionen
+
+void test_read_header(void)
+{
+
+}
+
 void setUp(void) {
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }

matrix.c

@@ -138,7 +138,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
     fprintf(stderr, "Fehler: Matrizen haben unterschiedliche Größen (%u x %u) und (%u x %u)\n",
             matrix1.rows, matrix1.cols, matrix2.rows, matrix2.cols);
 
-    Matrix empty = {0, 0, NULL};
+    Matrix empty = {NULL, 0, 0};
     return empty;
 }
 
@@ -149,7 +149,7 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
         fprintf(stderr, "Fehler: Matrizen der Dimension (%u x %u) und (%u x %u) koennen nicht multipliziert werden\n",
                 matrix1.rows, matrix1.cols, matrix2.rows, matrix2.cols);
 
-        Matrix empty = {0, 0, NULL};
+        Matrix empty = {NULL, 0, 0};
         return empty;
     }
 

matrix.h

@@ -8,9 +8,9 @@ typedef float MatrixType;
 // TODO Matrixtyp definieren
 
 typedef struct Matrix {
+    MatrixType *buffer;
     unsigned int rows;
     unsigned int cols;
-    MatrixType *buffer;
 }   Matrix;
 
 

neuralNetworkTests.c

@@ -5,10 +5,9 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
-
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+   
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -16,15 +15,15 @@ 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);
@@ -40,12 +39,12 @@ 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);
@@ -63,12 +62,12 @@ 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);
@@ -86,12 +85,12 @@ 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);
@@ -111,12 +110,12 @@ 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);
@@ -138,7 +137,7 @@ 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";
 
@@ -159,12 +158,12 @@ 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);
@@ -181,7 +180,7 @@ 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]);
     }
@@ -192,23 +191,23 @@ 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]));
@@ -216,11 +215,13 @@ 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
 }