Examples of MatrixWritable

de.jungblut.writable.MatrixWritable
Writable class for dense and sparse matrices.
org.apache.hama.commons.io.MatrixWritable
Majorly designed for dense matrices, can be extended for sparse ones as well.
org.apache.hama.ml.writable.MatrixWritable
Majorly designed for dense matrices, can be extended for sparse ones as well.
org.apache.mahout.math.MatrixWritable

Examples of de.jungblut.writable.MatrixWritable

   * Deserializes a new MultinomialNaiveBayesClassifier from the given input
   * stream. Note that "in" will not be closed by this method.
   */
  public static MultinomialNaiveBayes deserialize(DataInput in)
      throws IOException {
    MatrixWritable matrixWritable = new MatrixWritable();
    matrixWritable.readFields(in);
    DoubleVector classProbability = VectorWritable.readVector(in);


    return new MultinomialNaiveBayes(matrixWritable.getMatrix(),
        classProbability);
  }

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Examples of de.jungblut.writable.MatrixWritable

        classProbability);
  }


  public static void serialize(MultinomialNaiveBayes model, DataOutput out)
      throws IOException {
    new MatrixWritable(model.probabilityMatrix).write(out);
    VectorWritable.writeVector(model.classPriorProbability, out);
  }

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Examples of de.jungblut.writable.MatrixWritable

    for (int layer : model.layerSizes) {
      out.writeInt(layer);
    }


    for (DoubleMatrix mat : model.weights) {
      new MatrixWritable(mat).write(out);
    }


    out.writeUTF(model.activationFunction.getClass().getName());


  }

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Examples of de.jungblut.writable.MatrixWritable

      sizes[i] = in.readInt();
    }


    DoubleMatrix[] array = new DoubleMatrix[layers];
    for (int i = 0; i < layers; i++) {
      MatrixWritable mv = new MatrixWritable();
      mv.readFields(in);
      array[i] = mv.getMatrix();
    }
    ActivationFunction func = null;
    try {
      func = (ActivationFunction) Class.forName(in.readUTF()).newInstance();
    } catch (InstantiationException | IllegalAccessException

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Examples of de.jungblut.writable.MatrixWritable

      layers[i] = in.readInt();
    }


    WeightMatrix[] weights = new WeightMatrix[numLayers - 1];
    for (int i = 0; i < weights.length; i++) {
      MatrixWritable wm = new MatrixWritable();
      wm.readFields(in);
      weights[i] = new WeightMatrix(wm.getMatrix());
    }


    ActivationFunction[] funcs = new ActivationFunction[numLayers];
    for (int i = 0; i < numLayers; i++) {
      try {

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Examples of de.jungblut.writable.MatrixWritable

      out.writeInt(l);
    }
    // write the weight matrices
    for (WeightMatrix mat : model.weights) {
      DoubleMatrix weights = mat.getWeights();
      new MatrixWritable(weights).write(out);
    }
    // then write the activation classes
    for (ActivationFunction func : model.activations) {
      out.writeUTF(func.getClass().getName());
    }

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Examples of org.apache.hama.commons.io.MatrixWritable

    // write the number of neurons for each layer
    for (int i = 0; i < this.numberOfLayers; ++i) {
      output.writeInt(this.layerSizeArray[i]);
    }
    for (int i = 0; i < numberOfLayers - 1; ++i) {
      MatrixWritable matrixWritable = new MatrixWritable(this.weightMatrice[i]);
      matrixWritable.write(output);
    }


    // serialize the feature transformer
    Class<? extends FeatureTransformer> featureTransformerCls = this.featureTransformer
        .getClass();

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Examples of org.apache.hama.commons.io.MatrixWritable

      BSPPeer<Text, VectorWritable, Text, VectorWritable, MapWritable> peer,
      DoubleMatrix featureMatrix, IntWritable msgFeatureMatrix, boolean broadcast) 
          throws IOException, SyncException, InterruptedException {
    // send to master peer
    MapWritable msg = new MapWritable();
    MatrixWritable mtx = new MatrixWritable(featureMatrix);
    msg.put(msgFeatureMatrix, mtx);
    String master = peer.getPeerName(peer.getNumPeers()/2);
    peer.send(master, msg);
    peer.sync();


    // normalize
    DoubleMatrix res = null;
    if (peer.getPeerName().equals(master)) {
      res = new DenseDoubleMatrix(featureMatrix.getRowCount(),
                                  featureMatrix.getColumnCount(), 0);
      int incomingMsgCount = 0;
      while ( (msg = peer.getCurrentMessage()) != null) {
        MatrixWritable tmp = (MatrixWritable) msg.get(msgFeatureMatrix);
        res.add(tmp.getMatrix());
        incomingMsgCount++;
      }
      res.divide(incomingMsgCount);
    }


    if (broadcast) {
      if (peer.getPeerName().equals(master)) {
        // broadcast to all
        msg = new MapWritable();
        msg.put(msgFeatureMatrix, new MatrixWritable(res));
        // send to all
        for (String peerName : peer.getAllPeerNames()) {
          peer.send(peerName, msg);
        }
      }

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Examples of org.apache.hama.commons.io.MatrixWritable

    // write the number of neurons for each layer
    for (int i = 0; i < this.numberOfLayers; ++i) {
      output.writeInt(this.layerSizeArray[i]);
    }
    for (int i = 0; i < numberOfLayers - 1; ++i) {
      MatrixWritable matrixWritable = new MatrixWritable(this.weightMatrice[i]);
      matrixWritable.write(output);
    }


    // serialize the feature transformer
    Class<? extends FeatureTransformer> featureTransformerCls = this.featureTransformer
        .getClass();

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Examples of org.apache.hama.ml.writable.MatrixWritable

    // write the number of neurons for each layer
    for (int i = 0; i < this.numberOfLayers; ++i) {
      output.writeInt(this.layerSizeArray[i]);
    }
    for (int i = 0; i < numberOfLayers - 1; ++i) {
      MatrixWritable matrixWritable = new MatrixWritable(this.weightMatrice[i]);
      matrixWritable.write(output);
    }
  }

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