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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.compile.JobType;
import org.apache.sysml.parser.Expression.DataType;
import org.apache.sysml.parser.Expression.ValueType;
public class ZeroOut extends Lop
{
private void init(Lop inputMatrix, Lop rowL, Lop rowU, Lop colL, Lop colU, long rowDim, long colDim, DataType dt, ValueType vt, ExecType et) {
this.addInput(inputMatrix);
this.addInput(rowL);
this.addInput(rowU);
this.addInput(colL);
this.addInput(colU);
inputMatrix.addOutput(this);
rowL.addOutput(this);
rowU.addOutput(this);
colL.addOutput(this);
colU.addOutput(this);
boolean breaksAlignment = true;
boolean aligner = false;
boolean definesMRJob = false;
if ( et == ExecType.MR ) {
lps.addCompatibility(JobType.GMR);
lps.addCompatibility(JobType.DATAGEN);
lps.addCompatibility(JobType.MMCJ);
lps.addCompatibility(JobType.MMRJ);
this.lps.setProperties(inputs, et, ExecLocation.Map, breaksAlignment, aligner, definesMRJob);
}
else {
lps.addCompatibility(JobType.INVALID);
this.lps.setProperties(inputs, et, ExecLocation.ControlProgram, breaksAlignment, aligner, definesMRJob);
}
}
public ZeroOut(
Lop input, Lop rowL, Lop rowU, Lop colL, Lop colU, long rowDim, long colDim, DataType dt, ValueType vt, ExecType et)
throws LopsException {
super(Lop.Type.ZeroOut, dt, vt);
init(input, rowL, rowU, colL, colU, rowDim, colDim, dt, vt, et);
}
private String getOpcode() {
return "zeroOut";
}
@Override
public String getInstructions(String input, String rowl, String rowu, String coll, String colu, String output)
throws LopsException
{
StringBuilder sb = new StringBuilder();
sb.append( getExecType() );
sb.append( OPERAND_DELIMITOR );
sb.append( getOpcode() );
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(0).prepInputOperand(input));
sb.append( OPERAND_DELIMITOR );
// rowl, rowu
sb.append( getInputs().get(1).prepScalarInputOperand(getExecType()));
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(2).prepScalarInputOperand(getExecType()));
sb.append( OPERAND_DELIMITOR );
// coll, colu
sb.append( getInputs().get(3).prepScalarInputOperand(getExecType()));
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(4).prepScalarInputOperand(getExecType()));
sb.append( OPERAND_DELIMITOR );
sb.append( this.prepOutputOperand(output));
return sb.toString();
}
@Override
public String getInstructions(int input_index1, int input_index2, int input_index3, int input_index4, int input_index5, int output_index)
throws LopsException {
/*
* Example: B = A[row_l:row_u, col_l:col_u]
* A - input matrix (input_index1)
* row_l - lower bound in row dimension
* row_u - upper bound in row dimension
* col_l - lower bound in column dimension
* col_u - upper bound in column dimension
*
* Since row_l,row_u,col_l,col_u are scalars, values for input_index(2,3,4,5)
* will be equal to -1. They should be ignored and the scalar value labels must
* be derived from input lops.
*/
return getInstructions(
String.valueOf(input_index1), String.valueOf(input_index2),
String.valueOf(input_index3), String.valueOf(input_index4),
String.valueOf(input_index5), String.valueOf(output_index));
}
@Override
public String toString() {
return "ZeroOut";
}
}