completed prepareNeuralNetworkFile

.gitignore

@@ -1,21 +1,7 @@
-I2_Wortsalat/Start_Mac/wordsalad_initial
-I2_Wortsalat/Start_Mac/.DS_Store
-I2_Wortsalat/.DS_Store
-I2_Wortsalat/Start_Mac/input.o
-.gitignore
-I2_Wortsalat/Start_Mac/game.o
-I2_Wortsalat/Start_Mac/runTests
-.o
-.a
-.idea/editor.xml
-.idea/vcs.xml
-.idea/workspace.xml
-I2_Wortsalat/.idea/editor.xml
-I2_Wortsalat/.idea/I2_Wortsalat.iml
-I2_Wortsalat/.idea/modules.xml
-I2_Wortsalat/Start_Mac/main.o
-I2_Wortsalat/Start_Mac/.DS_Store
-I2_Wortsalat/Start_Mac/.DS_Store
-I2_Wortsalat/Start_Mac/wordsalad
-I2_Wortsalat/Start_Mac/graphicalGame.o
-.DS_Store
+# Alles ignorieren
+*
+
+# Ausnahmen: bestimmte Dateitypen in allen Verzeichnissen
+!**/*.c
+!**/*.h
+!**/*Makefile

I2_NeuronalerAbsturz/Start_Mac/imageInputTests.c

@@ -13,10 +13,15 @@ static void prepareImageFile(const char *path, unsigned short int width, unsigne
     if(file != NULL)
     {
         const char *fileTag = "__info2_image_file_format__";
-        GrayScalePixelType *zeroBuffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
+        GrayScalePixelType *buffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
 
-        if(zeroBuffer != NULL)
+        if(buffer != NULL)
         {
+            for(unsigned int i = 0; i < (numberOfImages * width * height); i++)
+            {
+                buffer[i] = (GrayScalePixelType)i;
+            }
+
             fwrite(fileTag, sizeof(fileTag[0]), strlen(fileTag), file);
             fwrite(&numberOfImages, sizeof(numberOfImages), 1, file);
             fwrite(&width, sizeof(width), 1, file);
@@ -24,11 +29,11 @@ static void prepareImageFile(const char *path, unsigned short int width, unsigne
 
             for(int i = 0; i < numberOfImages; i++)
             {
-                fwrite(zeroBuffer, sizeof(GrayScalePixelType), width * height, file);
+                fwrite(buffer, sizeof(GrayScalePixelType), width * height, file);
                 fwrite(&label, sizeof(unsigned char), 1, file);
             }
 
-            free(zeroBuffer);
+            free(buffer);
         }
 
         fclose(file);
@@ -119,6 +124,27 @@ void test_readImagesFailsOnWrongFileTag(void)
     remove(path);
 }
 
+void test_readImagesReadsCorrectGrayScales(void)
+{
+    GrayScaleImageSeries *series = NULL;
+    const char *path = "testFile.info2";
+    
+    prepareImageFile(path, 8, 8, 1, 1);
+    series = readImages(path);
+
+    TEST_ASSERT_NOT_NULL(series);
+    TEST_ASSERT_NOT_NULL(series->images);
+    TEST_ASSERT_EQUAL_UINT16(1, series->count);
+
+    for (unsigned int i = 0; i < (8 * 8); i++)
+    {
+        TEST_ASSERT_EQUAL_UINT8((GrayScalePixelType)i, series->images->buffer[i]);
+    }
+
+    clearSeries(series);
+    remove(path);
+}
+
 void setUp(void) {
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
@@ -138,6 +164,7 @@ int main()
     RUN_TEST(test_readImagesReturnsCorrectLabels);
     RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
     RUN_TEST(test_readImagesFailsOnWrongFileTag);
+    RUN_TEST(test_readImagesReadsCorrectGrayScales);
 
     return UNITY_END();
 }

I2_NeuronalerAbsturz/Start_Mac/makefile

@@ -40,8 +40,8 @@ mnistVisualization.o: mnistVisualization.c
 matrixTests: matrix.o matrixTests.c
 	$(CC) $(CFLAGS) -I$(unityfolder) -o runMatrixTests matrixTests.c matrix.o $(BINARIES)/libunity.a
 
-neuralNetworkTests: neuralNetwork.o neuralNetworkTests.c
-	$(CC) $(CFLAGS) -I$(unityfolder) -o runNeuralNetworkTests neuralNetworkTests.c neuralNetwork.o $(BINARIES)/libunity.a
+neuralNetworkTests: neuralNetwork.o neuralNetworkTests.c matrix.o
+	$(CC) $(CFLAGS) -I$(unityfolder) -o runNeuralNetworkTests neuralNetworkTests.c neuralNetwork.o matrix.o $(BINARIES)/libunity.a
 
 imageInputTests: imageInput.o imageInputTests.c
 	$(CC) $(CFLAGS) -I$(unityfolder) -o runImageInputTests imageInputTests.c imageInput.o $(BINARIES)/libunity.a

I2_NeuronalerAbsturz/Start_Mac/matrix.c

@@ -42,7 +42,7 @@ void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned
 {
     if (matrix.buffer != NULL)
     {
-        if (rowIdx < matrix.rows || colIdx < matrix.cols)
+        if (rowIdx < matrix.rows && colIdx < matrix.cols)
         {
             matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
         }
@@ -63,7 +63,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
     Matrix result;
 
-    if (matrix1.buffer == NULL || matrix2.buffer == NULL || matrix1.rows != matrix2.rows || matrix1.cols != matrix2.cols) 
+    if (matrix1.buffer == NULL || matrix2.buffer == NULL || matrix1.rows != matrix2.rows)
     {
         result.rows = 0;
         result.cols = 0;
@@ -71,17 +71,51 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
         return result;
     }
 
-    result = createMatrix(matrix1.rows, matrix1.cols);
-
-    for (int i = 0; i < matrix1.rows; i++) 
+    if (matrix1.cols == matrix2.cols)
     {
-        for (int j = 0; j < matrix1.cols; j++) 
+        result = createMatrix(matrix1.rows, matrix1.cols);
+        for (int i = 0; i < matrix1.rows; i++)
         {
-            MatrixType value = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, j);
-            setMatrixAt(value, result, i, j);
+            for (int j = 0; j < matrix1.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, j);
+                setMatrixAt(value, result, i, j);
+            }
         }
+        return result;
     }
 
+    if (matrix1.cols == 1 && matrix2.cols > 1)
+    {
+        result = createMatrix(matrix1.rows, matrix2.cols);
+        for (int i = 0; i < matrix1.rows; i++)
+        {
+            for (int j = 0; j < matrix2.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, 0) + getMatrixAt(matrix2, i, j);
+                setMatrixAt(value, result, i, j);
+            }
+        }
+        return result;
+    }
+    else if (matrix2.cols == 1 && matrix1.cols > 1)
+    {
+        result = createMatrix(matrix1.rows, matrix1.cols);
+        for (int i = 0; i < matrix1.rows; i++)
+        {
+            for (int j = 0; j < matrix1.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, 0);
+                setMatrixAt(value, result, i, j);
+            }
+        }
+        return result;
+    }
+
+    //Fall: Unterschiedliche Spaltenanzahl, beide ungleich 1
+    result.rows = 0;
+    result.cols = 0;
+    result.buffer = NULL;
     return result;
 }
 

I2_NeuronalerAbsturz/Start_Mac/matrix.h

@@ -9,9 +9,9 @@ typedef float MatrixType;
 
 typedef struct
 {
+    MatrixType *buffer;
     int rows;
     int cols;
-    MatrixType *buffer;
 } Matrix;
 
 

I2_NeuronalerAbsturz/Start_Mac/matrixTests.c

@@ -141,6 +141,32 @@ void test_setMatrixAtFailsOnIndicesOutOfRange(void)
     TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, matrixToTest.cols * matrixToTest.rows);
 }
 
+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 setUp(void) {
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
@@ -165,6 +191,7 @@ int main()
     RUN_TEST(test_getMatrixAtFailsOnIndicesOutOfRange);
     RUN_TEST(test_setMatrixAtSetsCorrectValue);
     RUN_TEST(test_setMatrixAtFailsOnIndicesOutOfRange);
+    RUN_TEST(test_addSupportsBroadcasting);
 
     return UNITY_END();
 }

I2_NeuronalerAbsturz/Start_Mac/neuralNetworkTests.c

@@ -5,12 +5,89 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
+/* 
+################
+Aufbau Test File 
+################
+
+
+HEADER
+
+inputDim
+outputDim
+
+-- Layer 1 --
+weights (outputDim * inputDim * MatrixType)
+biases  (outputDim * MatrixType)
+
+outputDim
+
+-- Layer 2 --
+weights
+biases
+
+...
+...
+-- Layer n --
+weights
+biases
+
+outputDim = 0  => Ende 
+*/
+
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *file = fopen(path, "wb");
+    if (file)
+    {
+        const char *fileTag = "__info2_neural_network_file_format__";
+        fwrite(fileTag, 1, strlen(fileTag), file);
+
+        //Stopt loadModel, falls keine Layer vorhanden
+        if (nn.numberOfLayers == 0) 
+        {
+            int zero = 0;
+            fwrite(&zero, sizeof(int), 1, file);
+            fclose(file);
+            return;
+        }
+
+        // input und output dimension schreiben
+        int inputDim = nn.layers[0].weights.cols;
+        int outputDim = nn.layers[0].weights.rows;
+        fwrite(&inputDim, sizeof(int), 1, file);
+        fwrite(&outputDim, sizeof(int), 1, file);
+
+        // erstes Layer schreiben
+        int weightCount = nn.layers[0].weights.rows * nn.layers[0].weights.cols;
+        fwrite(nn.layers[0].weights.buffer, sizeof(MatrixType), weightCount, file);
+
+        int biasesCount = nn.layers[0].biases.rows * nn.layers[0].biases.cols;
+        fwrite(nn.layers[0].biases.buffer, sizeof(MatrixType), biasesCount, file);
+
+        // für weiter Layer nur outputDimension schreiben
+        for (unsigned int i = 1; i < nn.numberOfLayers; i++)
+        {
+            outputDim = nn.layers[i].weights.rows;
+            fwrite(&outputDim, sizeof(int), 1, file);
+
+            weightCount = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+            fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightCount, file);
+
+            biasesCount = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+            fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasesCount, file);
+        }
+
+        // loadModel ließt 0 ein -> Stop
+        int fileEnd = 0;
+        fwrite(&fileEnd, sizeof(int), 1, file);
 }
 
+    fclose(file);
+}
+
+
 void test_loadModelReturnsCorrectNumberOfLayers(void)
 {
     const char *path = "some__nn_test_file.info2";