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

Reviewed-on: freudenreichan/info2Praktikum-NeuronalesNetz#4

imageInput.c

@@ -8,127 +8,15 @@
 
 // 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)
 {
-    FILE *file = fopen(path, "rb");
+    GrayScaleImageSeries *series = NULL;
-    if (!file)
+    
-    {
-        return 0;
-    }
-
-    unsigned short count, width, height;
-
-    if (!read_header(file, &count, &width, &height))
-    {
-        fclose(file);
-        return 0;
-    }
-
-    GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));
-
-    if (!series)
-    {
-        fclose(file);
-        return 0;
-    }
-
-    series->count = count;
-    series->images = malloc(count * sizeof(GrayScaleImage));
-    series->labels = malloc(count * sizeof(unsigned char));
-
-    if (!series->images || !series->labels)
-    {
-        clearSeries(series);
-        fclose(file);
-        return 0;
-    }
-
-    for (int i = 0; i < count; i++)
-    {
-        series->images[i].width = width;
-        series->images[i].height = height;
-        series->images[i].buffer = malloc(width * height * sizeof(GrayScalePixelType));
-
-        if (!series->images[i].buffer)
-        {
-            clearSeries(series);
-            fclose(file);
-            return 0;
-        }
-
-        if (!read_single_image(file, &series->images[i]))
-        {
-            clearSeries(series);
-            fclose(file);
-            return 0;
-        }
-
-        if (fread(&series->labels[i], 1, 1, file) != 1)
-        {
-            clearSeries(series);
-            fclose(file);
-            return 0;
-        }
-    }
-
-    fclose(file);
     return series;
 }
 
 // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {
-    if (series)
-    {
-        for (int i = 0; i < series->count; i++)
-        {
-            free(series->images[i].buffer);
-        }
-        free(series->images);
-        free(series->labels);
-        free(series);
-    }
 }

imageInputTests.c

@@ -119,13 +119,6 @@ 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

@@ -1,178 +1,35 @@
 #include <stdlib.h>
 #include <string.h>
 #include "matrix.h"
-#include <stdio.h>
 
 // TODO Matrix-Funktionen implementieren
 
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
-    Matrix matrix;
+    
-
-    if (rows == 0 || cols == 0)
-    {
-        matrix.rows = 0;
-        matrix.cols = 0;
-        matrix.buffer = NULL;
-        return matrix;
-    }
-
-    matrix.rows = rows;
-    matrix.cols = cols;
-
-    matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
-
-    if (matrix.buffer == NULL)
-    {
-
-        matrix.rows = 0;
-        matrix.cols = 0;
-
-        return matrix;
-    }
-
-    for (int i = 0; i < rows; i++)
-    {
-        for (int j = 0; j < cols; j++)
-        {
-            matrix.buffer[i * matrix.cols + j] = UNDEFINED_MATRIX_VALUE;
-        }
-    }
-    return matrix;
 }
 
 void clearMatrix(Matrix *matrix)
 {
-    if (matrix->buffer != NULL)
+    
-    {
-        free(matrix->buffer);
-        matrix->buffer = NULL;
-    }
-
-    matrix->rows = 0;
-    matrix->cols = 0;
 }
 
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    if (rowIdx >= matrix.rows || colIdx >= matrix.cols)
+    
-    {
-        fprintf(stderr, "Fehler: Ungültiger Index (%u, %u) bei Matrixgröße %u x %u\n", rowIdx, colIdx, matrix.rows, matrix.cols);
-        return; // abbruch falls fehler
-    }
-
-    matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
 }
 
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    if (rowIdx >= matrix.rows || colIdx >= matrix.cols)
+    
-    {
-        fprintf(stderr, "Fehler: Ungültiger Index (%u, %u) bei Matrixgröße %u x %u\n", rowIdx, colIdx, matrix.rows, matrix.cols);
-        return UNDEFINED_MATRIX_VALUE;
-    }
-
-    return matrix.buffer[rowIdx * matrix.cols + colIdx];
 }
 
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
-    if (matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols) // gleiche Dimension
-    {
-        Matrix result = createMatrix(matrix1.rows, matrix1.cols);
-
-        if (result.buffer == NULL)
-        {
-            fprintf(stderr, "Fehler: Speicher konnte nicht reserviert werden!\n");
-            return result;
-        }
-
-        for (int i = 0; i < matrix1.rows; i++)
-        {
-            for (int j = 0; j < matrix1.cols; j++)
-            {
-                result.buffer[i * result.cols + j] = matrix1.buffer[i * matrix1.cols + j] + matrix2.buffer[i * matrix2.cols + j];
-            }
-        }
-        return result;
-    }
-        if (matrix1.rows == matrix2.rows && matrix2.cols == 1) // Matrix 2 hat eine Spalte
-        {
-            Matrix result = createMatrix(matrix1.rows, matrix1.cols);
-
-            if(result.buffer == NULL)
-            {
-                fprintf(stderr, "Fehler: Speicher konnte nicht reserviert werden!\n");
-                return result;
-            }
-            for (int i = 0; i < matrix1.rows; i++)
-            {
-                for (int j = 0; j < matrix1.cols; j++)
-                {
-                    result.buffer[i * result.cols + j] = matrix1.buffer[i * matrix1.cols + j] + matrix2.buffer[i];
-                }
-            }
-            return result;
-        }
-
-        if (matrix1.rows == matrix2.rows && matrix1.cols == 1) // Matrix 1 hat eine Spalte
-        {
-            Matrix result = createMatrix(matrix2.rows, matrix2.cols);
-
-            if(result.buffer == NULL)
-            {
-                fprintf(stderr, "Fehler: Speicher konnte nicht reserviert werden!\n");
-                return result;
-            }
-            for (int i = 0; i < matrix2.rows; i++)
-            {
-                for (int j = 0; j < matrix2.cols; j++)
-                {
-                    result.buffer[i * result.cols + j] = matrix1.buffer[i] + matrix2.buffer[i * matrix2.cols + j];
-                }
-            }
-            return result;
-        }
     
-    // passt nicht
-    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 = {NULL, 0, 0};
-    return empty;
 }
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
-    if (matrix1.cols != matrix2.rows)
+    
-    {
+}
-        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 = {NULL, 0, 0};
-        return empty;
-    }
-
-    Matrix result = createMatrix(matrix1.rows, matrix2.cols);
-
-    if (result.buffer == NULL)
-    {
-        fprintf(stderr, "Fehler: Speicher konnte nicht reserviert werden!\n");
-        return result;
-    }
-
-    for (int i = 0; i < matrix1.rows; i++)
-    {
-        for (int j = 0; j < matrix2.cols; j++)
-        {
-            MatrixType sum = 0.0;
-
-            for (int k = 0; k < matrix1.cols; k++)
-            {
-                sum += matrix1.buffer[i * matrix1.cols + k] * matrix2.buffer[k * matrix2.cols + j];
-            }
-            result.buffer[i * result.cols + j] = sum;
-        }
-    }
-    return result;
-}

matrix.h

@@ -7,13 +7,6 @@ typedef float MatrixType;
 
 // TODO Matrixtyp definieren
 
-typedef struct Matrix {
-    MatrixType *buffer;
-    unsigned int rows;
-    unsigned int cols;
-}   Matrix;
-
-
 
 Matrix createMatrix(unsigned int rows, unsigned int cols);
 void clearMatrix(Matrix *matrix);

matrixTests.c

@@ -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, matrixToTest.cols * matrixToTest.rows);
+    TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, sizeof(buffer)/sizeof(MatrixType));
 }
 
 void setUp(void) {

neuralNetworkTests.c

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