| /* |
| * 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 |
| { |
| |
| |
| /** |
| * Constructor to perform covariance. |
| * input1 <- data |
| * (prior to this lop, input vectors need to attached together using CombineBinary or CombineTertiary) |
| * @throws LopsException |
| */ |
| |
| public CoVariance(Lop input1, DataType dt, ValueType vt) throws LopsException { |
| this(input1, dt, vt, ExecType.MR); |
| } |
| |
| 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) { |
| 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 ); |
| |
| sb.append( getInputs().get(1).prepInputOperand(input2)); |
| sb.append( OPERAND_DELIMITOR ); |
| |
| sb.append( this.prepOutputOperand(output)); |
| |
| return sb.toString(); |
| } |
| |
| /** |
| * 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 ); |
| |
| sb.append( getInputs().get(1).prepInputOperand(input2)); |
| sb.append( OPERAND_DELIMITOR ); |
| |
| sb.append( getInputs().get(2).prepInputOperand(input3)); |
| sb.append( OPERAND_DELIMITOR ); |
| |
| sb.append( this.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) { |
| 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(input_index)); |
| sb.append( OPERAND_DELIMITOR ); |
| |
| sb.append ( this.prepInputOperand(output_index)); |
| |
| return sb.toString(); |
| } |
| |
| } |