public void getUnNormalizedScores (Matrix2 lambdas, FeatureVector fv, double[] scores) { for (int li = 0; li < 2; li++) { scores[li] = lambdas.value (li, numSGDFeatures) + lambdas.rowDotProduct (li, fv, numSGDFeatures,null); } }
public void printParams (MaxEnt me) { double[] parameters = me.getParameters(); int numFeatures = parameters.length/2; Matrix2 matrix2 = new Matrix2(parameters,2,numFeatures); for (int i=0; i<2; i++) { System.out.print(i + ": "); for (int j=0; j<numFeatures; j++) { System.out.print(j + "=" + matrix2.value(new int[] {i,j}) + " "); } System.out.println(); } }
protected DenseVector getDenseVectorOf (int ri, Matrix2 matrix) { int dims[] = new int [2]; matrix.getDimensions(dims); DenseVector vec = new DenseVector (dims[1]); for (int i=0; i < dims[1]; i++) { vec.setValue (i, matrix.value(ri,i)); } return vec; }
protected DenseVector getDenseVectorOf (int ri, Matrix2 matrix) { int dims[] = new int [2]; matrix.getDimensions(dims); DenseVector vec = new DenseVector (dims[1]); for (int i=0; i < dims[1]; i++) { vec.setValue (i, matrix.value(ri,i)); } return vec; }
public void getUnNormalizedScores (Matrix2 lambdas, FeatureVector fv, double[] scores) { int defaultFeatureIndex = pipe.getDataAlphabet().size(); assert (fv.getAlphabet () == pipe.getDataAlphabet ()); for (int li = 0; li < 2; li++) { scores[li] = lambdas.value (li, defaultFeatureIndex) + lambdas.rowDotProduct (li, fv, defaultFeatureIndex,null); } }