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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;
/**
* Lop to compute covariance between two 1D matrices
*
*/
public class CoVariance extends Lop
{
public CoVariance(Lop input1, DataType dt, ValueType vt, ExecType et) throws LopsException {
super(Lop.Type.CoVariance, dt, vt);
init(input1, null, null, et);
}
public CoVariance(Lop input1, Lop input2, DataType dt, ValueType vt, ExecType et) throws LopsException {
this(input1, input2, null, dt, vt, et);
}
public CoVariance(Lop input1, Lop input2, Lop input3, DataType dt, ValueType vt, ExecType et) throws LopsException {
super(Lop.Type.CoVariance, dt, vt);
init(input1, input2, input3, et);
}
private void init(Lop input1, Lop input2, Lop input3, ExecType et)
throws LopsException
{
/*
* When et = MR: covariance lop will have a single input lop, which
* denote the combined input data -- output of combinebinary, if unweighed;
* and output combineteriaty (if weighted).
*
* When et = CP: covariance lop must have at least two input lops, which
* denote the two input columns on which covariance is computed. It also
* takes an optional third arguments, when weighted covariance is computed.
*/
addInput(input1);
input1.addOutput(this);
boolean breaksAlignment = false;
boolean aligner = false;
boolean definesMRJob = true;
if ( et == ExecType.MR )
{
lps.addCompatibility(JobType.CM_COV);
lps.setProperties(inputs, et, ExecLocation.MapAndReduce, breaksAlignment, aligner, definesMRJob);
}
else //CP/SPARK
{
definesMRJob = false;
if ( input2 == null ) {
throw new LopsException(this.printErrorLocation() + "Invalid inputs to covariance lop.");
}
addInput(input2);
input2.addOutput(this);
if ( input3 != null ) {
addInput(input3);
input3.addOutput(this);
}
lps.addCompatibility(JobType.INVALID);
lps.setProperties(inputs, et, ExecLocation.ControlProgram, breaksAlignment, aligner, definesMRJob);
}
}
@Override
public String toString() {
return "Operation = coVariance";
}
/**
* Function two generate CP instruction to compute unweighted covariance.
* input1 -> input column 1
* input2 -> input column 2
*/
@Override
public String getInstructions(String input1, String input2, String output) {
return getInstructions(input1, input2, null, output);
}
/**
* Function two generate CP instruction to compute weighted covariance.
* input1 -> input column 1
* input2 -> input column 2
* input3 -> weights
*/
@Override
public String getInstructions(String input1, String input2, String input3, String output) {
StringBuilder sb = new StringBuilder();
sb.append( getExecType() );
sb.append( Lop.OPERAND_DELIMITOR );
sb.append( "cov" );
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(0).prepInputOperand(input1));
sb.append( OPERAND_DELIMITOR );
if( input2 != null ) {
sb.append( getInputs().get(1).prepInputOperand(input2));
sb.append( OPERAND_DELIMITOR );
}
if( input3 != null ) {
sb.append( getInputs().get(2).prepInputOperand(input3));
sb.append( OPERAND_DELIMITOR );
}
sb.append( prepOutputOperand(output));
return sb.toString();
}
/**
* Function to generate MR version of covariance instruction.
* input_index -> denote the "combined" input columns and weights,
* when applicable.
*/
@Override
public String getInstructions(int input_index, int output_index) {
return getInstructions(String.valueOf(input_index), null, null, String.valueOf(output_index));
}
}