public static Matrix2D Process(Matrix2D _mat, boolean removeNZRows) { MatrixDescriptor desc = _mat.getMatrixDescriptor(); // Calculates the mean System.out.println("Calculating Mean and Standar Deviation"); Matrix2D meanSDMat = RateOfMean.ColumnMeanAndStandardDeviation(_mat, removeNZRows); // Calculate the standard score Matrix2D standardScore = new Matrix2D(desc); int numColumns = desc.getNumCols(); for (int column = 0; column < numColumns; column++) { System.out.println("Processing column " + column + " of " + numColumns); float _mean = meanSDMat.GetElement(RateOfMean.Mean, desc.getColumnAt(column)); float _sd = meanSDMat.GetElement(RateOfMean.StandardDeviation, desc.getColumnAt(column)); boolean denZero = Float.compare(0, _sd) == 0; for (int row = 0; row < desc.getNumRows(); row++) { float z = 0; if (!denZero) z = (_mat.GetElement(row, column) - _mean) / _sd; standardScore.SetElement(desc.getRowAt(row), desc.getColumnAt(column), z); } } return standardScore; }
private static Matrix2D SquaredVariance(Matrix2D _mat, Matrix2D _mean) { MatrixDescriptor _meanDesc = _mean.getMatrixDescriptor(); MatrixDescriptor _matDesc = _mat.getMatrixDescriptor(); Matrix2D squaredVariance = new Matrix2D(_matDesc); for (int column = 0; column < _matDesc.getNumCols(); column++) { float columnMean = _mean.GetElement(0, column); for (int row = 0; row < _matDesc.getNumRows(); row++) { float res = _mat.GetElement(row, column) - columnMean; squaredVariance.SetElement(_matDesc.getRowAt(row), _matDesc.getColumnAt(column), res * res); } } return squaredVariance; }
public static Matrix3D Process(Matrix3D _cube, Definitions.Algorithm _algorithm) { Matrix3D res = new Matrix3D(); Set<Map.Entry<String, Matrix2D>> records = _cube.getIterator(); // Mean and standard deviation System.out.println("Processing Mean and SD"); Matrix2D _meanMat = RateOfMean.ColumnDepthMeanAndStandardDeviation(_cube, true, _algorithm); System.out.println("Processing layers..."); int c = 0; boolean print = false; boolean print2 = false; for (Map.Entry<String, Matrix2D> entry : records) { Matrix2D layer = entry.getValue(); MatrixDescriptor layerDesc = layer.getMatrixDescriptor(); Matrix2D layerSS = new Matrix2D(layerDesc); // Matrix2D layerSS = layer.DoGPUStandardScore(_meanMat); for (int col = 0; col < layerDesc.getNumCols(); col++) { String colName = layerDesc.getColumnAt(col); float _mean = _meanMat.GetElement(RateOfMean.Mean, layerDesc.getColumnAt(col)); float _sd = _meanMat.GetElement(RateOfMean.StandardDeviation, colName); for (int row = 0; row < layerDesc.getNumRows(); row++) { float _vv = layer.GetElement(row, col); if (_vv >= 8 && print == false) { print = true; float _ss = (layer.GetElement(row, col) - _mean) / _sd; System.out.println(_ss); } layerSS.SetElement( layerDesc.getRowAt(row), layerDesc.getColumnAt(col), (layer.GetElement(row, col) - _mean) / _sd); } } if (print == true && print2 == false) { print2 = true; layerSS.Debug(); } res.AddLayer(entry.getKey(), layerSS); System.out.println("Standard Score of matrix " + c + " of " + records.size()); c++; } return res; }