generated from freudenreichan/info2Praktikum-NeuronalesNetz
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7 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| c928a8433a | |||
| 57857eb5d9 | |||
| 6fa4d9c084 | |||
| e42dfc938d | |||
| ed35fdd9a4 | |||
| 40984fe981 | |||
| 3299cd29e3 |
60
matrix.c
60
matrix.c
@ -4,32 +4,80 @@
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// TODO Matrix-Funktionen implementieren
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Matrix createMatrix(unsigned int rows, unsigned int cols)
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MatrixType createMatrix(unsigned int rows, unsigned int cols)
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{
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MatrixType m;
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m.rows = rows;
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m.cols = cols;
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m.values = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
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if (m.values == NULL) {
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m.rows = 0;
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m.cols = 0;
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return m;
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}
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for (unsigned int i = 0; i < rows * cols; ++i) {
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m.values[i] = 0.0; // Standardwert
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}
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return m;
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}
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void clearMatrix(Matrix *matrix)
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{
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if (matrix->values != NULL) {
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free(matrix->values);
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}
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matrix->values = NULL;
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matrix->rows = 0;
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matrix->cols = 0;
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}
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void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
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{
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if (rowIdx < matrix.rows && colIdx < matrix.cols) {
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matrix.values[rowIdx * matrix.cols + colIdx] = value;
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}
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}
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MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
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{
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if (rowIdx < matrix.rows && colIdx < matrix.cols) {
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return matrix.values[rowIdx * matrix.cols + colIdx];
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}
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return 0.0; // Fallback-Wert
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}
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Matrix add(const Matrix matrix1, const Matrix matrix2)
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{
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Matrix result = createMatrix(0, 0);
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if (matrix1.rows != matrix2.rows || matrix1.cols != matrix2.cols) {
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return result;
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}
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result = createMatrix(matrix1.rows, matrix1.cols);
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for (unsigned int i = 0; i < matrix1.rows; ++i) {
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for (unsigned int j = 0; j < matrix1.cols; ++j) {
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MatrixType sum = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, j);
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setMatrixAt(sum, result, i, j);
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}
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}
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return result;
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}
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Matrix multiply(const Matrix matrix1, const Matrix matrix2)
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{
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Matrix result = createMatrix(0, 0);
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if (matrix1.cols != matrix2.rows) {
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return result;
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}
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result = createMatrix(matrix1.rows, matrix2.cols);
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for (unsigned int i = 0; i < matrix1.rows; ++i) {
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for (unsigned int j = 0; j < matrix2.cols; ++j) {
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MatrixType sum = 0.0;
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for (unsigned int k = 0; k < matrix1.cols; ++k) {
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sum += getMatrixAt(matrix1, i, k) * getMatrixAt(matrix2, k, j);
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}
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setMatrixAt(sum, result, i, j);
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}
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}
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return result;
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6
matrix.h
6
matrix.h
@ -6,7 +6,11 @@
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typedef float MatrixType;
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// TODO Matrixtyp definieren
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typedef struct {
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MatrixType *buffer;
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MatrixType rows;
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MatrixType cols;
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}Matrix;
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Matrix createMatrix(unsigned int rows, unsigned int cols);
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void clearMatrix(Matrix *matrix);
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@ -71,6 +71,32 @@ void test_addFailsOnDifferentInputDimensions(void)
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TEST_ASSERT_EQUAL_UINT32(0, result.cols);
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}
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void test_addSupportsBroadcasting(void)
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{
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MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
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MatrixType buffer2[] = {7, 8};
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Matrix matrix1 = {.rows=2, .cols=3, .buffer=buffer1};
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Matrix matrix2 = {.rows=2, .cols=1, .buffer=buffer2};
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Matrix result1 = add(matrix1, matrix2);
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Matrix result2 = add(matrix2, matrix1);
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float expectedResults[] = {8, 9, 10, 12, 13, 14};
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TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result1.rows);
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TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result1.cols);
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TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result2.rows);
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TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result2.cols);
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TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result1.rows * result1.cols);
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TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result1.buffer, result1.cols * result1.rows);
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TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result2.rows * result2.cols);
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TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result2.buffer, result2.cols * result2.rows);
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free(result1.buffer);
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free(result2.buffer);
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}
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void test_multiplyReturnsCorrectResults(void)
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{
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MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
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@ -159,6 +185,7 @@ int main()
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RUN_TEST(test_clearMatrixSetsMembersToNull);
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RUN_TEST(test_addReturnsCorrectResult);
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RUN_TEST(test_addFailsOnDifferentInputDimensions);
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RUN_TEST(test_addSupportsBroadcasting);
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RUN_TEST(test_multiplyReturnsCorrectResults);
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RUN_TEST(test_multiplyFailsOnWrongInputDimensions);
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RUN_TEST(test_getMatrixAtReturnsCorrectResult);
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