forked from freudenreichan/info2Praktikum-NeuronalesNetz
multiply fix
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c560fd5241
commit
14388a5637
21
matrix.c
21
matrix.c
@ -23,7 +23,8 @@ Matrix createMatrix(unsigned int rows, unsigned int cols)
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void clearMatrix(Matrix *matrix)
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{
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if (matrix == NULL) {
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if (matrix == NULL)
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{
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return;
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}
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@ -75,7 +76,6 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
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for (unsigned int j = 0; j < result.cols; j++)
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{
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result.buffer[i * result.cols + j] = matrix1.buffer[i * matrix1.cols + j] + matrix2.buffer[i * matrix2.cols + j];
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}
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}
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return result;
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@ -85,13 +85,13 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
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{
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Matrix result = {0};
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if(matrix1.rows != matrix2.rows || matrix1.cols != matrix2.cols)
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if (matrix1.cols != matrix2.rows)
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{
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return result;
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}
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result.rows = matrix1.rows;
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result.cols = matrix1.cols;
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result.cols = matrix2.cols;
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result.buffer = malloc(result.rows * result.cols * sizeof(MatrixType));
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// wenn buffer nicht allokiert werden kann dann zurücksetzen und abbrechen
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@ -103,16 +103,19 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
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}
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// Matritzenmultiplikation
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for (unsigned int i = 0; i < result.rows; i++)
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for (int r = 0; r < result.rows; r++) // Zeile in Ergebnis
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{
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for (unsigned int j = 0; j < result.cols; j++)
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for (int m = 0; m < result.cols; m++) // Spalte in Ergebnis
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{
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MatrixType sum = 0;
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for (unsigned int k = 0; k < matrix1.cols; k++)
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for (int n = 0; n < matrix1.cols; n++)
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{
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sum += matrix1.buffer[i * matrix1.cols + k] * matrix2.buffer[k * matrix2.cols + j];
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sum += matrix1.buffer[r * matrix1.cols + n] *
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matrix2.buffer[n * matrix2.cols + m];
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}
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result.buffer[i * result.cols + j] = sum;
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result.buffer[r * result.cols + m] = sum;
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}
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}
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