protected static Matrix[] trimBiases(Matrix M[]) { for(int i = 0; i < M.length; i++) { M[i] = trimBiases(M[i]); } return M; }
@Override public void buildClassifier(Instances D) throws Exception { testCapabilities(D); if (getDebug()) System.out.println("Build RBM(s) ... "); String ops[] = this.getOptions(); dbm = new DBM(ops); dbm.setE(m_E); if (getDebug()) { Matrix tW[] = dbm.getWs(); System.out.println("X = \n"+ MatrixUtils.toString(X_)); Matrix W[] = trimBiases(dbm.getWs()); if (getDebug()) System.out.println("You have chosen to use BPNN (good!)"); if (getDebug()) { Matrix tW[] = W;
/** * Description to display in the GUI. * * @return the description */ @Override public String globalInfo() { return "A Deep Back-Propagation Neural Network. " + "For more information see:\n" + getTechnicalInformation().toString(); }
public static void main(String args[]) throws Exception { ProblemTransformationMethod.evaluation(new DBPNN(), args); }
@Override public void buildClassifier(Instances D) throws Exception { testCapabilities(D); if (getDebug()) System.out.println("Build RBM(s) ... "); String ops[] = this.getOptions(); dbm = new DBM(ops); dbm.setE(m_E); if (getDebug()) { Matrix tW[] = dbm.getWs(); System.out.println("X = \n"+ MatrixUtils.toString(X_)); Matrix W[] = trimBiases(dbm.getWs()); if (getDebug()) System.out.println("You have chosen to use BPNN (good!)"); if (getDebug()) { Matrix tW[] = W;
/** * Description to display in the GUI. * * @return the description */ @Override public String globalInfo() { return "A Deep Back-Propagation Neural Network. " + "For more information see:\n" + getTechnicalInformation().toString(); }
public static void main(String args[]) throws Exception { ProblemTransformationMethod.evaluation(new DBPNN(), args); }
protected static Matrix[] trimBiases(Matrix M[]) { for(int i = 0; i < M.length; i++) { M[i] = trimBiases(M[i]); } return M; }