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hilfsfunktionen und matrix fix

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This commit is contained in:
Giorgi Kesidis 2025-11-16 22:06:41 +01:00
parent 3f539cbe1d
commit 59cdcafa94
5 changed files with 105 additions and 80 deletions
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79
imageInput.c
View File

@ -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);
char buffer[BUFFER_SIZE];
unsigned short count, width, height;
if (fread(buffer, 1, headerLEN, file) != headerLEN)
{
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)
if (!read_header(file, &count, &width, &height))
{
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++)
{
size_t pixel_count = width * height;
if (fread(series->images[i].buffer, 1, pixel_count, file) != pixel_count)
if (!read_single_image(file, &series->images[i]))
{
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)
return;
if (series->images)
if (series)
{
for (int i = 0; i < series->count; i++)
{
free(series->images[i].buffer);
}
free(series->images);
}
free(series->labels);
free(series);
}
}

11
imageInputTests.c
View File

@ -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
}

4
matrix.c
View File

@ -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;
}

2
matrix.h
View File

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

87
neuralNetworkTests.c
View File

@ -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 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);
@ -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};
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);
@ -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};
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);
@ -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};
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);
@ -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};
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);
@ -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};
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);
@ -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 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]));
@ -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
}