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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.LopProperties.ExecLocation;
import org.apache.sysml.lops.LopProperties.ExecType;
import org.apache.sysml.lops.WeightedCrossEntropy.WCeMMType;
import org.apache.sysml.lops.compile.JobType;
import org.apache.sysml.parser.Expression.DataType;
import org.apache.sysml.parser.Expression.ValueType;
public class WeightedCrossEntropyR extends Lop
{
public static final String OPCODE = "redwcemm";
private WCeMMType _wcemmType = null;
private boolean _cacheU = false;
private boolean _cacheV = false;
public WeightedCrossEntropyR(Lop input1, Lop input2, Lop input3, Lop input4, DataType dt, ValueType vt, WCeMMType wt, boolean cacheU, boolean cacheV, ExecType et)
throws LopsException
{
super(Lop.Type.WeightedCeMM, dt, vt);
addInput(input1); //X
addInput(input2); //U
addInput(input3); //V
addInput(input4); //optional
input1.addOutput(this);
input2.addOutput(this);
input3.addOutput(this);
input4.addOutput(this);
//setup mapmult parameters
_wcemmType = wt;
_cacheU = cacheU;
_cacheV = cacheV;
setupLopProperties(et);
}
private void setupLopProperties( ExecType et )
throws LopsException
{
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, ExecType.MR, 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 "Operation = WeightedCrossEntropyR";
}
@Override
public String getInstructions(int input1, int input2, int input3, int input4, int output)
{
return getInstructions(
String.valueOf(input1),
String.valueOf(input2),
String.valueOf(input3),
String.valueOf(input4),
String.valueOf(output));
}
@Override
public String getInstructions(String input1, String input2, String input3, String input4, String output)
{
StringBuilder sb = new StringBuilder();
final ExecType et = getExecType();
sb.append(et);
sb.append(Lop.OPERAND_DELIMITOR);
sb.append(OPCODE);
sb.append(Lop.OPERAND_DELIMITOR);
sb.append( getInputs().get(0).prepInputOperand(input1));
sb.append(Lop.OPERAND_DELIMITOR);
sb.append( getInputs().get(1).prepInputOperand(input2));
sb.append(Lop.OPERAND_DELIMITOR);
sb.append( getInputs().get(2).prepInputOperand(input3));
sb.append(Lop.OPERAND_DELIMITOR);
if ( (et == ExecType.MR) && (getInputs().get(3).getDataType() == DataType.SCALAR) ) {
sb.append( getInputs().get(3).prepScalarInputOperand(et));
}
else {
sb.append( getInputs().get(3).prepInputOperand(input4));
}
sb.append(Lop.OPERAND_DELIMITOR);
sb.append( prepOutputOperand(output));
sb.append(Lop.OPERAND_DELIMITOR);
sb.append(_wcemmType);
sb.append(Lop.OPERAND_DELIMITOR);
sb.append(_cacheU);
sb.append(Lop.OPERAND_DELIMITOR);
sb.append(_cacheV);
return sb.toString();
}
@Override
public boolean usesDistributedCache() {
return (_cacheU || _cacheV);
}
@Override
public int[] distributedCacheInputIndex()
{
if( !_cacheU && !_cacheV )
return new int[]{-1};
else if( _cacheU && !_cacheV )
return new int[]{2};
else if( !_cacheU && _cacheV )
return new int[]{3};
else
return new int[]{2,3};
}
}