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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.instructions.cp;
import org.apache.sysml.lops.SortKeys;
import org.apache.sysml.parser.Expression.DataType;
import org.apache.sysml.parser.Expression.ValueType;
import org.apache.sysml.runtime.DMLRuntimeException;
import org.apache.sysml.runtime.DMLUnsupportedOperationException;
import org.apache.sysml.runtime.controlprogram.context.ExecutionContext;
import org.apache.sysml.runtime.instructions.InstructionUtils;
import org.apache.sysml.runtime.matrix.data.MatrixBlock;
import org.apache.sysml.runtime.matrix.operators.Operator;
import org.apache.sysml.runtime.matrix.operators.SimpleOperator;
public class QuantileSortCPInstruction extends UnaryCPInstruction
{
/*
* This class supports two variants of sort operation on a 1-dimensional input matrix.
* The two variants are weighted
and unweighted
.
* Example instructions:
* sort:mVar1:mVar2 (input=mVar1, output=mVar2)
* sort:mVar1:mVar2:mVar3 (input=mVar1, weights=mVar2, output=mVar3)
*
*/
public QuantileSortCPInstruction(Operator op, CPOperand in, CPOperand out, String opcode, String istr){
this(op, in, null, out, opcode, istr);
}
public QuantileSortCPInstruction(Operator op, CPOperand in1, CPOperand in2, CPOperand out, String opcode, String istr){
super(op, in1, in2, out, opcode, istr);
_cptype = CPINSTRUCTION_TYPE.QSort;
}
public static QuantileSortCPInstruction parseInstruction ( String str )
throws DMLRuntimeException
{
CPOperand in1 = new CPOperand("", ValueType.UNKNOWN, DataType.UNKNOWN);
CPOperand in2 = null;
CPOperand out = new CPOperand("", ValueType.UNKNOWN, DataType.UNKNOWN);
String[] parts = InstructionUtils.getInstructionPartsWithValueType(str);
String opcode = parts[0];
if ( opcode.equalsIgnoreCase(SortKeys.OPCODE) ) {
if ( parts.length == 3 ) {
// Example: sort:mVar1:mVar2 (input=mVar1, output=mVar2)
parseUnaryInstruction(str, in1, out);
return new QuantileSortCPInstruction(new SimpleOperator(null), in1, out, opcode, str);
}
else if ( parts.length == 4 ) {
// Example: sort:mVar1:mVar2:mVar3 (input=mVar1, weights=mVar2, output=mVar3)
in2 = new CPOperand("", ValueType.UNKNOWN, DataType.UNKNOWN);
parseUnaryInstruction(str, in1, in2, out);
return new QuantileSortCPInstruction(new SimpleOperator(null), in1, in2, out, opcode, str);
}
else {
throw new DMLRuntimeException("Invalid number of operands in instruction: " + str);
}
}
else {
throw new DMLRuntimeException("Unknown opcode while parsing a QuantileSortCPInstruction: " + str);
}
}
@Override
public void processInstruction(ExecutionContext ec)
throws DMLUnsupportedOperationException, DMLRuntimeException
{
//acquire inputs matrices
MatrixBlock matBlock = ec.getMatrixInput(input1.getName());
MatrixBlock wtBlock = null;
if (input2 != null) {
wtBlock = ec.getMatrixInput(input2.getName());
}
//process core instruction
MatrixBlock resultBlock = (MatrixBlock) matBlock.sortOperations(wtBlock, new MatrixBlock());
//release inputs
ec.releaseMatrixInput(input1.getName());
if (input2 != null)
ec.releaseMatrixInput(input2.getName());
//set and release output
ec.setMatrixOutput(output.getName(), resultBlock);
}
}