forked from freudenreichan/info2Praktikum-NeuronalesNetz
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4
Commits
4a1b6cbb40
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KTobi2
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786aa2e6d8 | ||
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58df4199b5 | ||
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fb18b75b60 | ||
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7f3c6d1d3f |
+7
-1
@@ -1,4 +1,10 @@
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mnist
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mnist
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runTests
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runTests
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*.o
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*.o
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*.exe
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*.exe
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.vscode/settings.json
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.vscode/launch.json
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.vscode/settings.json
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.vscode/settings.json
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runImageInputTests
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testFile.info2
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+29
-3
@@ -15,10 +15,36 @@ GrayScaleImage readImage()
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// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
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// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
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GrayScaleImageSeries *readImages(const char *path)
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GrayScaleImageSeries *readImages(const char *path)
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{
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{
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unsigned short * numImages;
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unsigned short * breiteBilder;
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unsigned short * laengeBilder;
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GrayScaleImageSeries *series = NULL;
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GrayScaleImageSeries *series = NULL;
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FILE *file = fopen("mnist_test.info2","rb");
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FILE *file = fopen(*path,"rb");
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char headOfFile;
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char * headOfFile;
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series = malloc();
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fread(headOfFile, sizeof(FILE_HEADER_STRING),1, file); //liest den header ein und überprüft ob korrekte datei
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if(strcmp(headOfFile, FILE_HEADER_STRING) != 0)
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return NULL;
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// liest numIMages, breite und länge der Bilder ein
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fseek(file, sizeof(FILE_HEADER_STRING), SEEK_SET);
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fread(numImages, sizeof(short), 1, file);
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fseek(file, sizeof(short), SEEK_CUR);
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fread(breiteBilder, sizeof(short), 1, file);
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fseek(file, sizeof(short), SEEK_CUR);
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fread(laengeBilder, sizeof(short), 1, file);
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series = malloc(*numImages * *breiteBilder * *laengeBilder * sizeof(short));
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for(int i = 0; i < numImages; i++)
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{
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}
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return series;
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return series;
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}
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}
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@@ -57,12 +57,12 @@ imageInputTests: imageInput.o imageInputTests.c $(unityfolder)/unity.c
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# --------------------------
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# --------------------------
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# Clean
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# Clean
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# --------------------------
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# --------------------------
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#clean:
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clean:
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#ifeq ($(OS),Windows_NT)
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ifeq ($(OS),Windows_NT)
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# del /f *.o *.exe
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del /f *.o *.exe
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#else
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else
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# rm -f *.o mnist runMatrixTests runNeuralNetworkTests runImageInputTests
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rm -f *.o mnist runMatrixTests runNeuralNetworkTests runImageInputTests
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#endif
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endif
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# clean für windows
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clean für windows
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clean:
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clean:
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rm -f *.o *.exe
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rm -f *.o *.exe
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@@ -41,25 +41,28 @@ MatrixType getMatrixAt(const Matrix matrix,
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return value;
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return value;
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}
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}
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Matrix add(const Matrix matrix1, const Matrix matrix2) {
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Matrix add(const Matrix matrix1, const Matrix matrix2) {
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Matrix result;
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const int cols1 = matrix1.cols;
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const int rows1 = matrix1.rows;
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const int cols2 = matrix2.cols;
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const int rows2 = matrix2.rows;
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const int colsEqu = (matrix1.cols == matrix2.cols) ? 1 : 0;
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const int rowsEqu = (matrix1.rows == matrix2.rows) ? 1 : 0;
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if(colsEqu && rowsEqu)
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{
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Matrix result = createMatrix(matrix1.rows, matrix1.cols);
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for(int i = 0; i < rows1; i++)
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{
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for (int j = 0; j < cols1; j++)
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{
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int valueM1 = getMatrixAt(matrix1, i, j);
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int valueM2 =getMatrixAt(matrix2, i, j);
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int sum = valueM1 + valueM2;
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setMatrixAt(sum, result,i,j);
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}
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}
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// Ergebnismatrix
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return result;
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Matrix result;
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}
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// Broadcasting nur bei Vektor und Matrix, Fehlermeldung bei zwei unpassender
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// Matrix
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if (matrix1.rows != matrix2.rows) {
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// check, which one is smaller
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// realloc
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}
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if (matrix1.cols != matrix2.cols) {
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}
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// Speicher reservieren
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// Matrix addieren
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return result;
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}
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}
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Matrix multiply(const Matrix matrix1, const Matrix matrix2) { return matrix1; }
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Matrix multiply(const Matrix matrix1, const Matrix matrix2) { return matrix1; }
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