Java Code Examples for no.uib.cipr.matrix.Vector#set()

@Override
public Vector transSolve(Vector b, Vector x) {
    if (!(x instanceof DenseVector))
        return super.transSolve(b, x);

    x.set(b);

    double[] xd = ((DenseVector) x).getData();

    for (int i = numRows - 1; i >= 0; --i) {

        // Get row i
        SparseVector row = LU.getRow(i);
        int[] index = row.getIndex();
        double[] data = row.getData();

        // At this stage, x[i] is known, so move it over to the right
        // hand side for the remaining equations
        for (int j = 0; j < diagind[i]; ++j)
            xd[index[j]] -= data[j] * xd[i];

    }

    return x;
}
 

@Override
public Vector transSolve(Vector b, Vector x) {
    if (!(x instanceof DenseVector))
        return super.transSolve(b, x);

    x.set(b);

    double[] xd = ((DenseVector) x).getData();

    for (int i = numRows - 1; i >= 0; --i)

        // At this stage, x[i] is known, so move it over to the right hand
        // side for the remaining equations
        for (int j = rowptr[i]; j < diagind[i]; ++j)
            xd[colind[j]] -= data[j] * xd[i];

    return x;
}
 

@Override
public Vector transSolve(Vector b, Vector x) {
    if (!(x instanceof DenseVector))
        return super.transSolve(b, x);

    x.set(b);

    double[] xd = ((DenseVector) x).getData();

    for (int i = 0; i < numRows; ++i) {

        // Solve for the current entry
        xd[i] /= data[diagind[i]];

        // Move this known solution over to the right hand side for the
        // remaining equations
        for (int j = diagind[i] + 1; j < rowptr[i + 1]; ++j)
            xd[colind[j]] -= data[j] * xd[i];
    }

    return x;
}
 

private void loadVector(final Vector vector, final File file) throws IOException {
	final Iterator<String> lines = Util.readFileLineByLine(file);
	int row = 0;
	while (lines.hasNext()) {

		final String data = lines.next();
		vector.set(row, Double.valueOf(data));
		row++;
	}
}
 

private void loadVector(Vector vector, File file) throws IOException
{
  Iterator<String> lines = Util.readFileLineByLine(file);
  int row=0;
  while (lines.hasNext()) {

    String data = lines.next();
    vector.set(row, Double.valueOf(data));
    row++;
  }
}
 

@Override
public Vector transSolve(Vector b, Vector x) {
    if (!(x instanceof DenseVector))
        return super.transSolve(b, x);

    x.set(b);

    double[] xd = ((DenseVector) x).getData();

    for (int i = 0; i < numRows; ++i) {

        // Get row i
        SparseVector row = LU.getRow(i);
        int[] index = row.getIndex();
        int used = row.getUsed();
        double[] data = row.getData();

        // Solve for the current entry
        xd[i] /= data[diagind[i]];

        // Move this known solution over to the right hand side for the
        // remaining equations
        for (int j = diagind[i] + 1; j < used; ++j)
            xd[index[j]] -= data[j] * xd[i];
    }

    return x;
}
 

public Vector apply(Vector b, Vector x) {
    u[0].set(x);
    f[0].set(b);

    transpose = false;
    cycle(0);

    return x.set(u[0]);
}
 

public Vector transApply(Vector b, Vector x) {
    u[0].set(x);
    f[0].set(b);

    transpose = true;
    cycle(0);

    return x.set(u[0]);
}
 

public TaggerEmbeddings(final File modelFolder, final double beta, final int maxTagsPerWord,
		final CutoffsDictionaryInterface cutoffs) throws IOException {
	super(cutoffs, beta, loadCategories(new File(modelFolder, "categories")), maxTagsPerWord);
	try {
		final FilenameFilter embeddingsFileFilter = new PatternFilenameFilter("embeddings.*");

		// If we're using POS tags or lexical features, load l.
		this.posFeatures = loadSparseFeatures(new File(modelFolder + "/postags"));
		this.lexicalFeatures = loadSparseFeatures(new File(modelFolder + "/frequentwords"));

		// Load word embeddings.
		embeddingsFeatures = loadEmbeddings(true, modelFolder.listFiles(embeddingsFileFilter));

		// Load embeddings for capitalization and suffix features.
		discreteFeatures = new HashMap<>();
		discreteFeatures.putAll(loadEmbeddings(false, new File(modelFolder, "capitals")));
		discreteFeatures.putAll(loadEmbeddings(false, new File(modelFolder, "suffix")));
		totalFeatures = (embeddingsFeatures.get(unknownLower).length + discreteFeatures.get(unknownSuffix).length
				+ discreteFeatures.get(capsLower).length + posFeatures.size() + lexicalFeatures.size())
				* (2 * contextWindow + 1);

		// Load the list of categories used by the model.
		categoryToIndex = new HashMap<>();
		for (int i = 0; i < lexicalCategories.size(); i++) {
			categoryToIndex.put(lexicalCategories.get(i), i);
		}

		// Load the weight matrix used by the classifier.
		weightMatrix = new DenseMatrix(lexicalCategories.size(), totalFeatures);
		loadMatrix(weightMatrix, new File(modelFolder, "classifier"));

		weightMatrixRows = new ArrayList<>(lexicalCategories.size());
		for (int i = 0; i < lexicalCategories.size(); i++) {
			final Vector row = new DenseVector(totalFeatures);
			for (int j = 0; j < totalFeatures; j++) {
				row.set(j, weightMatrix.get(i, j));
			}
			weightMatrixRows.add(row);
		}

		bias = new DenseVector(lexicalCategories.size());

		loadVector(bias, new File(modelFolder, "bias"));

	} catch (final Exception e) {
		throw new RuntimeException(e);
	}
}
 

public TaggerEmbeddings(File modelFolder, int maxSentenceLength, double beta, int maxTagsPerWord) {
  try {
    FilenameFilter embeddingsFileFilter = new PatternFilenameFilter("embeddings.*");

    // If we're using POS tags or lexical features, load l.
    this.posFeatures = loadSparseFeatures(new File(modelFolder + "/postags"));     
    this.lexicalFeatures = loadSparseFeatures(new File(modelFolder + "/frequentwords"));     
    
    // Load word embeddings.
    embeddingsFeatures = loadEmbeddings(true, modelFolder.listFiles(embeddingsFileFilter));
    
    // Load embeddings for capitalization and suffix features.
    discreteFeatures = new HashMap<String, double[]>();
    discreteFeatures.putAll(loadEmbeddings(false, new File(modelFolder, "capitals")));
    discreteFeatures.putAll(loadEmbeddings(false, new File(modelFolder, "suffix")));
    totalFeatures = 
      (embeddingsFeatures.get(unknownLower).length + 
          discreteFeatures.get(unknownSuffix).length + 
          discreteFeatures.get(capsLower).length +
          posFeatures.size()  + lexicalFeatures.size())       * (2 * contextWindow + 1);
    
    // Load the list of categories used by the model.
    lexicalCategories = loadCategories(new File(modelFolder, "categories"));
    
    // Load the weight matrix used by the classifier.
    weightMatrix = new DenseMatrix(lexicalCategories.size(), totalFeatures);
    loadMatrix(weightMatrix, new File(modelFolder, "classifier"));

    weightMatrixRows = new ArrayList<Vector>(lexicalCategories.size());
    for (int i=0; i<lexicalCategories.size(); i++) {
      Vector row = new DenseVector(totalFeatures);
      for (int j=0; j<totalFeatures; j++) {
        row.set(j, weightMatrix.get(i, j));
      }
      weightMatrixRows.add(row);
    }

    bias = new DenseVector(lexicalCategories.size());
    this.beta = beta;
    this.maxTagsPerWord = maxTagsPerWord;

    int maxCategoryID = 0;
    for (Category c : lexicalCategories) {
      maxCategoryID = Math.max(maxCategoryID, c.getID());
    }
    
    this.tagDict = ImmutableMap.copyOf(loadTagDictionary(modelFolder));
    
    terminalFactory = new SyntaxTreeNodeFactory(maxSentenceLength, maxCategoryID);
    loadVector(bias, new File(modelFolder, "bias"));

  }
  catch (Exception e) {
    throw new RuntimeException(e);
  }
}
 

public Vector apply(Vector b, Vector x) {
    return x.set(b);
}
 

public Vector transApply(Vector b, Vector x) {
    return x.set(b);
}