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Author SHA1 Message Date
7837be1c3e done? 2025-11-14 17:25:07 +01:00
Simon
44f0bfc16d sync 2025-11-14 13:01:03 +01:00
Simon May
ff21ecab21 nochmal? 2025-11-14 12:36:47 +01:00
Simon May
7f5291deca Merge branch 'main' of https://git.efi.th-nuernberg.de/gitea/hallerni98888/info2Praktikum-NeuronalesNetz 2025-11-11 15:58:58 +01:00
Simon May
dadcdc873d pass 2025-11-11 15:58:53 +01:00
9e7fcca725 matrix.c unitTest PASS 2025-11-11 15:53:35 +01:00
da8dc2a9ef matrixAdd check 2025-11-11 15:34:34 +01:00
07f217f1f4 merge upstream 2025-11-11 13:20:47 +00:00
d4375c31e8 changes? 2025-11-11 14:09:06 +01:00
Simon
67c5110e5c image input prototyp 2025-11-08 18:23:13 +01:00
Simon
e40a5cbd7b matrix pass all tests 2025-11-08 15:33:11 +01:00
42e92f278f Merge branch 'main' of https://git.efi.th-nuernberg.de/gitea/hallerni98888/info2Praktikum-NeuronalesNetz 2025-11-08 15:28:39 +01:00
14388a5637 multiply fix 2025-11-08 15:23:56 +01:00
Simon
ebff958c4e 2nd fail fixed 2025-11-08 15:05:29 +01:00
Simon
5b5e1182bd first fail fixed 2025-11-08 14:59:27 +01:00
Simon
c560fd5241 sync2 2025-11-08 14:46:39 +01:00
e206895359 sync 2025-11-08 14:26:00 +01:00
ddf70dbefd matrix.c prototype 2025-11-08 14:12:40 +01:00
5 changed files with 344 additions and 76 deletions

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@ -1,22 +1,97 @@
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "imageInput.h"
#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)
{
GrayScaleImageSeries *series = NULL;
return series;
}
// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
void clearSeries(GrayScaleImageSeries *series)
{
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "imageInput.h"
#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)
{
FILE *file = fopen(path, "rb");
if (file == NULL)
{
return NULL;
}
// Überprüfe den Header
char fileTag[strlen(FILE_HEADER_STRING)];
fread(fileTag, sizeof(fileTag[0]), strlen(FILE_HEADER_STRING), file);
if (strcmp(fileTag, FILE_HEADER_STRING) != 0)
{
fclose(file);
return NULL;
}
// Lese die Metadaten: Anzahl der Bilder, Breite und Höhe
unsigned short numberOfImages, width, height;
fread(&numberOfImages, sizeof(numberOfImages), 1, file);
fread(&width, sizeof(width), 1, file);
fread(&height, sizeof(height), 1, file);
GrayScaleImageSeries *series = (GrayScaleImageSeries *)malloc(sizeof(GrayScaleImageSeries));
if (series == NULL)
{
fclose(file);
return NULL;
}
series->count = numberOfImages;
series->images = (GrayScaleImage *)malloc(numberOfImages * sizeof(GrayScaleImage));
series->labels = (unsigned char *)malloc(numberOfImages * sizeof(unsigned char));
if (series->images == NULL || series->labels == NULL)
{
free(series);
fclose(file);
return NULL;
}
for (int i = 0; i < numberOfImages; i++)
{
series->images[i].width = width;
series->images[i].height = height;
series->images[i].buffer = (GrayScalePixelType *)malloc(width * height * sizeof(GrayScalePixelType));
if (series->images[i].buffer == NULL)
{
// Fehlerbehandlung: Speicher freigeben, wenn malloc fehlschlägt
for (int j = 0; j < i; j++)
{
free(series->images[j].buffer);
}
free(series->images);
free(series->labels);
free(series);
fclose(file);
return NULL;
}
// Lese die Pixel-Daten und das Label
fread(series->images[i].buffer, sizeof(GrayScalePixelType), width * height, file);
fread(&series->labels[i], sizeof(unsigned char), 1, file);
}
fclose(file);
return series;
}
// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
void clearSeries(GrayScaleImageSeries *series)
{
if (series == NULL)
return;
for (int i = 0; i < series->count; i++)
{
free(series->images[i].buffer); // Speicher für das Bild freigeben
}
free(series->images); // Speicher für die Bild-Array freigeben
free(series->labels); // Speicher für die Labels freigeben
free(series); // Speicher freigeben
}

199
matrix.c
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@ -1,35 +1,166 @@
#include <stdlib.h>
#include <string.h>
#include "matrix.h"
// TODO Matrix-Funktionen implementieren
Matrix createMatrix(unsigned int rows, unsigned int cols)
{
}
void clearMatrix(Matrix *matrix)
{
}
void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
}
MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
}
Matrix add(const Matrix matrix1, const Matrix matrix2)
{
}
Matrix multiply(const Matrix matrix1, const Matrix matrix2)
{
#include <stdlib.h>
#include <string.h>
#include "matrix.h"
// TODO Matrix-Funktionen implementieren
Matrix createMatrix(unsigned int rows, unsigned int cols)
{
Matrix m = {NULL, 0, 0};
if (rows > 0 && cols > 0)
{
m.buffer = malloc(rows * cols * sizeof(int));
m.rows = rows;
m.cols = cols;
}
return m;
}
void clearMatrix(Matrix *matrix)
{
if (matrix == NULL)
{
return;
}
// Speicher freigeben, falls vorhanden
free(matrix->buffer);
matrix->buffer = NULL;
// Metadaten zurücksetzen
matrix->rows = 0;
matrix->cols = 0;
}
void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
matrix.buffer[rowIdx * matrix.cols + colIdx] = value; // setzte Matrix auf den Wert value am Punkt (row col)
}
MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
MatrixType value = 0;
if (rowIdx < matrix.rows && colIdx < matrix.cols)
{
value = matrix.buffer[rowIdx * matrix.cols + colIdx]; // hole Wert value am Punkt (row col)
}
return value;
}
Matrix add(const Matrix matrix1, const Matrix matrix2)
{
Matrix result = {0};
int broadcast_case =
(matrix1.cols == 1 && matrix1.rows == matrix2.rows) ||
(matrix2.cols == 1 && matrix1.rows == matrix2.rows);
if (!broadcast_case && (matrix1.rows != matrix2.rows || matrix1.cols != matrix2.cols))
{
return result;
}
result.rows = matrix1.rows;
result.cols = matrix1.cols;
result.buffer = malloc(result.rows * result.cols * sizeof(MatrixType));
// wenn buffer nicht allokiert werden kann dann zurücksetzen und abbrechen
if (result.buffer == NULL)
{
result.rows = result.cols = 0;
return result;
}
if (matrix1.cols == 1 && matrix1.rows == matrix2.rows) // Broadcasting
{
result.rows = matrix2.rows;
result.cols = matrix2.cols;
for (unsigned int i = 0; i < matrix1.rows; i++)
{
for (unsigned int j = 0; j < result.cols; j++)
{
result.buffer[i * result.cols + j] = matrix1.buffer[i] + matrix2.buffer[i * matrix2.cols + j];
}
}
}
else if (matrix2.cols == 1 && matrix1.rows == matrix2.rows)
{
result.rows = matrix1.rows;
result.cols = matrix1.cols;
for (unsigned int i = 0; i < matrix2.rows; i++)
{
for (unsigned int j = 0; j < result.cols; j++)
{
result.buffer[i * result.cols + j] = matrix1.buffer[i * matrix1.cols + j] + matrix2.buffer[i];
}
}
}
else
{
// Elementweise Addition
for (unsigned int i = 0; i < result.rows; i++)
{
for (unsigned int j = 0; j < result.cols; j++)
{
result.buffer[i * result.cols + j] = matrix1.buffer[i * matrix1.cols + j] + matrix2.buffer[i * matrix2.cols + j];
}
}
}
return result;
}
Matrix multiply(const Matrix matrix1, const Matrix matrix2)
{
Matrix result = {0};
if (matrix1.cols != matrix2.rows)
{
return result;
}
result.rows = matrix1.rows;
result.cols = matrix2.cols;
result.buffer = malloc(result.rows * result.cols * sizeof(MatrixType));
// wenn buffer nicht allokiert werden kann dann zurücksetzen und abbrechen
if (result.buffer == NULL)
{
result.rows = result.cols = 0;
return result;
}
// Matritzenmultiplikation
for (int r = 0; r < result.rows; r++) // Zeile in Ergebnis
{
for (int m = 0; m < result.cols; m++) // Spalte in Ergebnis
{
MatrixType sum = 0;
for (int n = 0; n < matrix1.cols; n++)
{
sum += matrix1.buffer[r * matrix1.cols + n] *
matrix2.buffer[n * matrix2.cols + m];
}
result.buffer[r * result.cols + m] = sum;
}
}
return result;
}

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@ -1,19 +1,26 @@
#ifndef MATRIX_H
#define MATRIX_H
#define UNDEFINED_MATRIX_VALUE 0
typedef float MatrixType;
// TODO Matrixtyp definieren
Matrix createMatrix(unsigned int rows, unsigned int cols);
void clearMatrix(Matrix *matrix);
void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
Matrix add(const Matrix matrix1, const Matrix matrix2);
Matrix multiply(const Matrix matrix1, const Matrix matrix2);
#endif
#ifndef MATRIX_H
#define MATRIX_H
#define UNDEFINED_MATRIX_VALUE 0
typedef float MatrixType;
// TODO Matrixtyp definieren
typedef struct
{
MatrixType *buffer; // Zeiger auf die Matrixdaten
unsigned int rows; // Anzahl der Zeilen
unsigned int cols; // Anzahl der Spalten
} Matrix;
Matrix createMatrix(unsigned int rows, unsigned int cols);
void clearMatrix(Matrix *matrix);
void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
Matrix add(const Matrix matrix1, const Matrix matrix2);
Matrix multiply(const Matrix matrix1, const Matrix matrix2);
#endif

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@ -164,7 +164,7 @@ void test_setMatrixAtFailsOnIndicesOutOfRange(void)
Matrix matrixToTest = {.rows=2, .cols=3, .buffer=buffer};
setMatrixAt(-1, matrixToTest, 2, 3);
TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, sizeof(buffer)/sizeof(MatrixType));
TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, matrixToTest.cols * matrixToTest.rows);
}
void setUp(void) {

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@ -8,7 +8,62 @@
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
{
// TODO
FILE *file = fopen(path, "wb");
if (!file) {
perror("Fehler beim Erstellen der Testdatei");
exit(EXIT_FAILURE);
}
// File header
const char *fileTag = "__info2_neural_network_file_format__";
fwrite(fileTag, strlen(fileTag), 1, file);
if (nn.numberOfLayers == 0)
{
unsigned int zero = 0;
fwrite(&zero, sizeof(unsigned int), 1, file);
fclose(file);
return;
}
// first layer dimension
unsigned int in = nn.layers[0].weights.cols;
unsigned int out = nn.layers[0].weights.rows;
fwrite(&in, sizeof(unsigned int), 1, file);
fwrite(&out, sizeof(unsigned int), 1, file);
// do all layers
for (unsigned int i = 0; i < nn.numberOfLayers; i++)
{
const Layer *L = &nn.layers[i];
// Write weights matrix
fwrite(L->weights.buffer,
sizeof(MatrixType),
L->weights.rows * L->weights.cols,
file);
// Write biases matrix
fwrite(L->biases.buffer,
sizeof(MatrixType),
L->biases.rows * L->biases.cols,
file);
// After layer i, write dimension of next layer
if (i + 1 < nn.numberOfLayers)
{
unsigned int nextOut = nn.layers[i+1].weights.rows;
fwrite(&nextOut, sizeof(unsigned int), 1, file);
}
}
// --- 5. Write terminating zero ---
unsigned int zero = 0;
fwrite(&zero, sizeof(unsigned int), 1, file);
fclose(file);
}
void test_loadModelReturnsCorrectNumberOfLayers(void)