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Declarative Machine Learning
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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 org.apache.sysml.runtime.matrix.data;
import java.io.IOException;
/**
* Any data output that is intended to support fast serialization / write
* of entire blocks should implement this interface. On write of a matrix block
* we check if the output stream is an implementation of this interface, if
* yes we directly pass the entire block instead of value-by-value.
*
* Known implementation classes:
* - CacheDataOutput (cache serialization into in-memory write buffer)
* - FastBufferedDataOutputStream (cache eviction to local file system)
*
*/
public interface MatrixBlockDataOutput
{
/**
* Writes the double array of a dense block to the data output.
*
* @param len
* @param varr
* @throws IOException
*/
public void writeDoubleArray(int len, double[] varr)
throws IOException;
/**
* Writes the sparse rows array of a sparse block to the data output.
*
* @param rlen
* @param rows
* @throws IOException
*/
public void writeSparseRows(int rlen, SparseBlock rows)
throws IOException;
}