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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.lops;
import org.apache.sysml.lops.Aggregate.OperationTypes;
import org.apache.sysml.lops.LopProperties.ExecLocation;
import org.apache.sysml.lops.LopProperties.ExecType;
import org.apache.sysml.lops.compile.JobType;
import org.apache.sysml.parser.Expression.*;
public class CumulativeOffsetBinary extends Lop
{
private OperationTypes _op;
private double _initValue = 0;
public CumulativeOffsetBinary(Lop data, Lop offsets, DataType dt, ValueType vt, OperationTypes op, ExecType et)
throws LopsException
{
super(Lop.Type.CumulativeOffsetBinary, dt, vt);
checkSupportedOperations(op);
_op = op;
init(data, offsets, dt, vt, et);
}
public CumulativeOffsetBinary(Lop data, Lop offsets, DataType dt, ValueType vt, double init, OperationTypes op, ExecType et)
throws LopsException
{
super(Lop.Type.CumulativeOffsetBinary, dt, vt);
checkSupportedOperations(op);
_op = op;
//in case of Spark, CumulativeOffset includes CumulativeSplit and hence needs the init value
_initValue = init;
init(data, offsets, dt, vt, et);
}
private void init(Lop input1, Lop input2, DataType dt, ValueType vt, ExecType et)
{
this.addInput(input1);
this.addInput(input2);
input1.addOutput(this);
input2.addOutput(this);
if( et == ExecType.MR )
{
//setup MR parameters
boolean breaksAlignment = true;
boolean aligner = false;
boolean definesMRJob = false;
lps.addCompatibility(JobType.GMR);
lps.addCompatibility(JobType.DATAGEN);
lps.setProperties(inputs, et, ExecLocation.Reduce, breaksAlignment, aligner, definesMRJob);
}
else //Spark/CP
{
//setup Spark parameters
boolean breaksAlignment = false;
boolean aligner = false;
boolean definesMRJob = false;
lps.addCompatibility(JobType.INVALID);
lps.setProperties( inputs, et, ExecLocation.ControlProgram, breaksAlignment, aligner, definesMRJob );
}
}
@Override
public String toString() {
return "CumulativeOffsetBinary";
}
private static void checkSupportedOperations(OperationTypes op)
throws LopsException
{
//sanity check for supported aggregates
if( !(op == OperationTypes.KahanSum || op == OperationTypes.Product ||
op == OperationTypes.Min || op == OperationTypes.Max) )
{
throw new LopsException("Unsupported aggregate operation type: "+op);
}
}
private String getOpcode() {
switch( _op ) {
case KahanSum: return "bcumoffk+";
case Product: return "bcumoff*";
case Min: return "bcumoffmin";
case Max: return "bcumoffmax";
default: return null;
}
}
@Override
public String getInstructions(int input_index1, int input_index2, int output_index) {
return getInstructions(String.valueOf(input_index1),
String.valueOf(input_index2), String.valueOf(output_index));
}
@Override
public String getInstructions(String input1, String input2, String output)
{
StringBuilder sb = new StringBuilder();
sb.append( getExecType() );
sb.append( OPERAND_DELIMITOR );
sb.append( getOpcode() );
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(0).prepInputOperand(input1) );
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(1).prepInputOperand(input2) );
sb.append( OPERAND_DELIMITOR );
sb.append( this.prepOutputOperand(output) );
if( getExecType() == ExecType.SPARK ) {
sb.append( OPERAND_DELIMITOR );
sb.append( _initValue );
}
return sb.toString();
}
}