Java DenseVector.getData方法代码示例

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
@Override
public VectorParameters derivative(Model model, double[] data) {
	final double[][] as = model.getLayerActivations(data);

	final double[] d2 = new double[as[1].length];
	for (int i = 0; i < d2.length; i++) {
		d2[i] = -(data[i] - as[1][i]) * (as[1][i] * (1 - as[1][i]));
	}

	final DenseVector wd = (DenseVector) model.w1.transMult(new DenseVector(d2),
			new DenseVector(model.w1.numColumns()));
	final double[] d1 = wd.getData();
	for (int i = 0; i < d1.length; i++) {
		d1[i] *= (as[0][i] * (1 - as[0][i]));
	}

	return null;
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * Solve the general problem A x = L B x. 
 * The returned eigenvalues are not ordered.
 *
 * @param A symmetric matrix A
 * @param B symmetric matrix B
 * @return The eigenvectors x and eigenvalues L.
 */
public static IndependentPair<Matrix, double[]> symmetricGeneralisedEigenvectors(Matrix A, Matrix B) {
	if ((A.getRowDimension() != A.getColumnDimension()) || (B.getRowDimension() != B.getColumnDimension())) 
    	throw new IllegalArgumentException("Input matrices must be square");
	
	int dim = A.getRowDimension();
	DenseMatrix vecs = new DenseMatrix(A.getArray());
	DenseVector W = new DenseVector(dim);
    
	sygvd(1, "V", "U", vecs, new DenseMatrix(B.getArray()), W);
    
	Matrix evecs = new Matrix(dim, dim);
	final double[][] evecsData = evecs.getArray();
	final double[] vecsData = vecs.getData();
	for (int r=0; r<dim; r++)
		for (int c=0; c<dim; c++)
			evecsData[r][c] = vecsData[r + c * dim];
	
    return new IndependentPair<Matrix, double[]>(evecs, W.getData());
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * Solve the general problem A x = L B x. 
 * The returned eigenvalues ordered in a decreasing manner.
 *
 * @param A symmetric matrix A
 * @param B symmetric matrix B
 * @return The eigenvectors x and eigenvalues L.
 */
public static IndependentPair<Matrix, double[]> symmetricGeneralisedEigenvectorsSorted(Matrix A, Matrix B) {
	if ((A.getRowDimension() != A.getColumnDimension()) || (B.getRowDimension() != B.getColumnDimension())) 
    	throw new IllegalArgumentException("Input matrices must be square");
	
	int dim = A.getRowDimension();
	DenseMatrix vecs = new DenseMatrix(A.getArray());
	DenseVector W = new DenseVector(dim);
    
	sygvd(1, "V", "U", vecs, new DenseMatrix(B.getArray()), W);
    
	Matrix evecs = new Matrix(dim, dim);
	final double[][] evecsData = evecs.getArray();
	final double[] vecsData = vecs.getData();
	final double[] valsData = W.getData();
	
	int [] indices = ArrayUtils.range(0, valsData.length-1);
	ArrayUtils.parallelQuicksortDescending(valsData, indices);
	
	for (int r=0; r<dim; r++)
		for (int c=0; c<dim; c++)
			evecsData[r][c] = vecsData[r + indices[c] * dim];
	
    return new IndependentPair<Matrix, double[]>(evecs, valsData);
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
double[] feedforward(double[] input) {
	final DenseVector iv = new DenseVector(input, false);
	final DenseVector a1 = sigmoid(w1.multAdd(iv, b1.copy()));
	final DenseVector a2 = sigmoid(w2.multAdd(a1, b2.copy()));

	return a2.getData();
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * Compute the generalised eigenvalues, L, of the problem A x = L B x.
 * The returned eigenvalues are not ordered.
 *
 * @param A symmetric Matrix A; only the upper triangle is used.
 * @param B symmetric Matrix B; only the upper triangle is used.
 * @return the eigenvalues L.
 */
public static double[] symmetricGeneralisedEigenvalues(Matrix A, Matrix B) {
    if ((A.getRowDimension() != A.getColumnDimension()) || (B.getRowDimension() != B.getColumnDimension())) 
    	throw new IllegalArgumentException("Input matrices must be square");
    
    DenseVector W = new DenseVector(A.getRowDimension());
    sygvd(1, "N", "U", new DenseMatrix(A.getArray()), new DenseMatrix(B.getArray()), W);
    
    return W.getData();
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
void assign(DenseVector feat) {
  mFeatValues = new float[feat.size() + 1];
  double data[] = feat.getData();
  for (int i = 0; i < feat.size(); ++i)
    mFeatValues[i+1] = (float)data[i];
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
double[][] getLayerActivations(double[] input) {
	final DenseVector iv = new DenseVector(input, false);
	final DenseVector a1 = sigmoid(w1.multAdd(iv, b1.copy()));
	final DenseVector a2 = sigmoid(w2.multAdd(a1, b2.copy()));
	return new double[][] { a1.getData(), a2.getData() };
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
private DenseVector sigmoid(Vector vector) {
	final DenseVector out = (DenseVector) vector.copy();

	final double[] xd = out.getData();

	for (int i = 0; i < xd.length; i++)
		xd[i] = 1 / (1 + Math.exp(-xd[i]));

	return out;
}