Java tutorial
/* * Copyright (C) 2015 Seoul National University * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package edu.snu.dolphin.bsp.examples.ml.sub; import edu.snu.dolphin.bsp.examples.ml.data.LinearModel; import edu.snu.dolphin.bsp.examples.ml.data.LinearRegSummary; import org.apache.mahout.math.DenseVector; import org.apache.mahout.math.Vector; import org.apache.reef.io.serialization.Codec; import javax.inject.Inject; import java.io.*; public class LinearRegSummaryCodec implements Codec<LinearRegSummary> { @Inject public LinearRegSummaryCodec() { } @Override public byte[] encode(final LinearRegSummary sgdSummary) { final LinearModel model = sgdSummary.getModel(); final ByteArrayOutputStream baos = new ByteArrayOutputStream(Integer.SIZE // count + Double.SIZE // loss + Integer.SIZE // parameter size + Double.SIZE * model.getParameters().size()); try (final DataOutputStream daos = new DataOutputStream(baos)) { daos.writeInt(sgdSummary.getCount()); daos.writeDouble(sgdSummary.getLoss()); daos.writeInt(model.getParameters().size()); for (int i = 0; i < model.getParameters().size(); i++) { daos.writeDouble(model.getParameters().get(i)); } } catch (final IOException e) { throw new RuntimeException(e.getCause()); } return baos.toByteArray(); } @Override public LinearRegSummary decode(final byte[] data) { final ByteArrayInputStream bais = new ByteArrayInputStream(data); final LinearModel model; final int count; final double loss; try (final DataInputStream dais = new DataInputStream(bais)) { count = dais.readInt(); loss = dais.readDouble(); final int vecSize = dais.readInt(); final Vector v = new DenseVector(vecSize); for (int i = 0; i < vecSize; i++) { v.set(i, dais.readDouble()); } model = new LinearModel(v); } catch (final IOException e) { throw new RuntimeException(e.getCause()); } return new LinearRegSummary(model, count, loss); } }