Merge pull request 'matrixTests korrigiert.' (#4) from schroederen/info2Praktikum-NeuronalesNetz:main into main

Reviewed-on: freudenreichan/info2Praktikum-NeuronalesNetz#4

imageInput.c

@@ -5,59 +5,12 @@
 
 #define BUFFER_SIZE 100
 #define FILE_HEADER_STRING "__info2_image_file_format__"
-#define FILE_HEADER_SIZE (sizeof(FILE_HEADER_STRING)-1)
- 
 
 // TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
-FILE *checkFile(const char *path)
-{
-    FILE *datei = fopen(path,"rb");
-    if (datei == NULL) 
-    {
-        perror("Datei konnte nicht geoeffnet werden");
-        return NULL;
-    }
-    char buffer[FILE_HEADER_SIZE+1];
-    if (fread(buffer,1,FILE_HEADER_SIZE,datei)!=FILE_HEADER_SIZE)
-    {
-        perror("Header konnte nicht eingelessen werden");
-        fclose(datei);
-        return NULL;
-    }
-    buffer[FILE_HEADER_SIZE] = '\0';
-    if (strcmp(buffer,FILE_HEADER_STRING)!=0)
-    {
-        printf("Falscher Dateikopf");
-        //printf("\n%s",buffer);
-        //printf("\n%s",FILE_HEADER_STRING);
-        //printf("\n%d",strcmp(buffer,FILE_HEADER_STRING));
-        fclose(datei);
-        return NULL;
-    }
-    return datei;
-}
 
 // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
-    FILE *datei = checkFile(path);
-    if (datei==NULL)
-    {
-        return NULL;
-    }
-    unsigned short image_count, width, height;
-    fread(&image_count,1,sizeof(unsigned short),datei);
-    fread(&width,1,sizeof(unsigned short),datei);
-    fread(&height,1,sizeof(unsigned short),datei);
-    //printf("%u Bilder und %u mal %u",image_count,width,height);
-    
-    fclose(datei);
-
-
-
-
-
-
     GrayScaleImageSeries *series = NULL;
     
     return series;

imageInput.h

@@ -19,5 +19,5 @@ typedef struct
 
 GrayScaleImageSeries *readImages(const char *path);
 void clearSeries(GrayScaleImageSeries *series);
-FILE *checkFile(const char *path);
+
 #endif

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);

main.c

@@ -1,13 +1,11 @@
 #include <stdio.h>
 #include <stdlib.h>
 #include "imageInput.h"
-//#include "mnistVisualization.h"
+#include "mnistVisualization.h"
-//#include "neuralNetwork.h"
+#include "neuralNetwork.h"
 
 int main(int argc, char *argv[])
 {
-    readImages("mnist_test.info2");
-    /*
     const unsigned int windowWidth = 800;
     const unsigned int windowHeight = 600;
 
@@ -67,5 +65,4 @@ int main(int argc, char *argv[])
     }
 
     return exitCode;
-    */
 }

matrix.c

@@ -4,104 +4,32 @@
 
 // TODO Matrix-Funktionen implementieren
 
-// Matrix erzeugen
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
-    Matrix matrix;
-    matrix.buffer = NULL;
-    matrix.rows = 0;
-    matrix.cols = 0;
     
-
-    // Wenn die Dimensionen gültig sind, Speicher reservieren
-    if (rows > 0 && cols > 0) 
-    {
-        matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
-        if (matrix.buffer != NULL)
-        {
-            matrix.rows = rows;
-            matrix.cols = cols;
-        }
-    }
-    return matrix;
 }
 
-// Matrix Speicher freigeben
 void clearMatrix(Matrix *matrix)
 {
-    if (matrix->buffer != NULL)
+    
-    {
-        free(matrix->buffer);
-        matrix->buffer = NULL;
-    }
-    matrix->rows = 0;
-    matrix->cols = 0;
 }
 
-// Wert setzen
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    if (rowIdx < matrix.rows && colIdx < matrix.cols)
+    
-    {
-        matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
-    }
 }
 
-// Wert auslesen
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    if (rowIdx < matrix.rows && colIdx < matrix.cols)
+    
-    {
-        return matrix.buffer[rowIdx * matrix.cols + colIdx];
-    }
-    return 0; // Fallback
 }
 
-// Matrizen addieren
+Matrix add(const Matrix matrix1, const Matrix matrix2)
-Matrix add(const Matrix m1, const Matrix m2)
 {
-    if (m1.rows != m2.rows || m1.cols != m2.cols)
-    {
-        return createMatrix(0, 0); // Falls Matrix-Dimensionen nicht passen
-    }
     
-    Matrix result = createMatrix(m1.rows, m1.cols);
-    if (result.buffer == NULL) return result;
-
-    for (unsigned int r = 0; r < m1.rows; r++)
-    {
-        for (unsigned int c = 0; c < m1.cols; c++)
-        {
-            result.buffer[r * m1.cols + c] =
-                getMatrixAt(m1, r, c) + getMatrixAt(m2, r, c);
-        }
-    }
-    return result;
 }
 
-// Matrizen multiplizieren
+Matrix multiply(const Matrix matrix1, const Matrix matrix2)
-Matrix multiply(const Matrix m1, const Matrix m2)
 {
-    if (m1.cols != m2.rows)
-    {
-        return createMatrix(0, 0); // Falls Matrix-Dimensionen nicht passen
-    }
     
-    Matrix result = createMatrix(m1.rows, m2.cols);
-    if (result.buffer == NULL) return result;
-
-    for (unsigned int r = 0; r < m1.rows; r++)
-    {
-        for (unsigned int c = 0; c < m2.cols; c++)
-        {
-            MatrixType sum = 0;
-            for (unsigned int k = 0; k < m1.cols; k++)
-            {
-                sum += getMatrixAt(m1, r, k) * getMatrixAt(m2, k, c);
-            }
-            result.buffer[r * m2.cols + c] = sum;
-        }
-    }
-
-    return result;
 }

matrix.h

@@ -6,13 +6,6 @@
 typedef float MatrixType;
 
 // TODO Matrixtyp definieren
-typedef struct 
-{
-    MatrixType *buffer;
-    unsigned int rows;
-    unsigned int cols;
-    
-} Matrix; 
 
 
 Matrix createMatrix(unsigned int rows, unsigned int cols);

matrixTests.c

@@ -71,6 +71,32 @@ void test_addFailsOnDifferentInputDimensions(void)
     TEST_ASSERT_EQUAL_UINT32(0, result.cols);
 }
 
+void test_addSupportsBroadcasting(void)
+{
+    MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
+    MatrixType buffer2[] = {7, 8};
+    Matrix matrix1 = {.rows=2, .cols=3, .buffer=buffer1};
+    Matrix matrix2 = {.rows=2, .cols=1, .buffer=buffer2};
+
+    Matrix result1 = add(matrix1, matrix2);
+    Matrix result2 = add(matrix2, matrix1);
+
+    float expectedResults[] = {8, 9, 10, 12, 13, 14};
+
+    TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result1.rows);
+    TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result1.cols);
+    TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result2.rows);
+    TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result2.cols);
+
+    TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result1.rows * result1.cols);
+    TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result1.buffer, result1.cols * result1.rows);
+    TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result2.rows * result2.cols);
+    TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result2.buffer, result2.cols * result2.rows);
+
+    free(result1.buffer);
+    free(result2.buffer);
+}
+
 void test_multiplyReturnsCorrectResults(void)
 {
     MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
@@ -138,7 +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, matrixToTest.cols * matrixToTest.rows);
+    TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, sizeof(buffer)/sizeof(MatrixType));
 }
 
 void setUp(void) {
@@ -159,6 +185,7 @@ int main()
     RUN_TEST(test_clearMatrixSetsMembersToNull);
     RUN_TEST(test_addReturnsCorrectResult);
     RUN_TEST(test_addFailsOnDifferentInputDimensions);
+    RUN_TEST(test_addSupportsBroadcasting);
     RUN_TEST(test_multiplyReturnsCorrectResults);
     RUN_TEST(test_multiplyFailsOnWrongInputDimensions);
     RUN_TEST(test_getMatrixAtReturnsCorrectResult);

neuralNetworkTests.c

@@ -8,47 +8,7 @@
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO : Fehlerbehandlung
+    // TODO
-  FILE *file = fopen(path, "wb");
-    if (!file) {
-        perror("Fehler beim Öffnen der Datei");
-        return;
-    }
-
-    const char *header = "__info2_neural_network_file_format__";
-    fwrite(header, sizeof(char), strlen(header), file);
-
-    for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
-        Layer layer = nn.layers[i];
-
-        unsigned int inputDim = (i == 0) ? layer.weights.cols : 0; // nur beim ersten Layer
-        unsigned int outputDim = layer.weights.rows;
-
-        if (i == 0) {
-            // erstes Layer: inputDim und outputDim schreiben
-            fwrite(&inputDim, sizeof(unsigned int), 1, file);
-            fwrite(&outputDim, sizeof(unsigned int), 1, file);
-        } else {
-            // nur outputDim für weitere Layer
-            fwrite(&outputDim, sizeof(unsigned int), 1, file);
-        }
-
-        fwrite(layer.weights.buffer, sizeof(MatrixType), layer.weights.rows * layer.weights.cols, file);
-        fwrite(layer.biases.buffer, sizeof(MatrixType), layer.biases.rows * layer.biases.cols, file);
-    }
-
-    unsigned int zero = 0;
-    fwrite(&zero, sizeof(unsigned int), 1, file);
-    fclose(file);
-
-    // Debug
-    printf("prepareNeuralNetworkFile: Datei '%s' erstellt mit %u Layer(n)\n", path, nn.numberOfLayers);
-    for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
-        Layer layer = nn.layers[i];
-        printf("Layer %u: weights (%u x %u), biases (%u x %u)\n", 
-               i, layer.weights.rows, layer.weights.cols, layer.biases.rows, layer.biases.cols);
-    }
-
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)