public static Create ( int rows, int columns, Func |
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rows | int | |
columns | int | |
init | Func |
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return |
public static Matrix <float> GenerateMatrix(Matrix <float> Sample) { int rows, columns; DimsOfMatrix(Sample.ToTypeString(), out rows, out columns); int[] maxIndex = new int[columns]; float[] maxValue = new float[columns]; Func <int, int, float> Filter = (int j, int i) => j == maxIndex[i] ? 1 : 0; for (int i = 0; i < columns; i++) { for (int j = 0; j < rows; j++) { maxIndex[i] = maxValue[i] < Sample[j, i] ? j : maxIndex[i]; maxValue[i] = maxValue[i] < Sample[j, i] ? Sample[j, i] : maxValue[i]; } // work on it further // the plan is simle// classical trajectoreis, supervised learning } // here we need to figure out how to extract dimenstions from a matrix return(DenseMatrix.Create(rows, columns, Filter)); }
// Matrix Multiplications public static Matrix <float> GenerateMatrix(int rows, int colums) { return(DenseMatrix.Create(rows, colums, 0)); }