org.apache.sysml.runtime.instructions.cp.CovarianceCPInstruction Maven / Gradle / Ivy

Show more of this group Show more artifacts with this name
Show all versions of systemml Show documentation
Declarative Machine Learning
There is a newer version: 1.2.0
/*
 * 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.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.functionobjects.COV;
import org.apache.sysml.runtime.instructions.InstructionUtils;
import org.apache.sysml.runtime.matrix.data.MatrixBlock;
import org.apache.sysml.runtime.matrix.operators.COVOperator;
import org.apache.sysml.runtime.matrix.operators.Operator;

public class CovarianceCPInstruction extends BinaryCPInstruction
{
	
	public CovarianceCPInstruction(Operator op, CPOperand in1, CPOperand in2, CPOperand out, String opcode, String istr )
	{
		super(op, in1, in2, out, opcode, istr);
		_cptype = CPINSTRUCTION_TYPE.AggregateBinary;
	}
	
	public CovarianceCPInstruction(Operator op, CPOperand in1, CPOperand in2, CPOperand in3, CPOperand out,
								   String opcode, String istr )
	{
		super(op, in1, in2, in3, out, opcode, istr);
		_cptype = CPINSTRUCTION_TYPE.AggregateBinary;
	}

	/**
	 * 
	 * @param str
	 * @return
	 * @throws DMLRuntimeException
	 */
	public static CovarianceCPInstruction parseInstruction( String str ) 
		throws DMLRuntimeException 
	{
		CPOperand in1 = new CPOperand("", ValueType.UNKNOWN, DataType.UNKNOWN);
		CPOperand in2 = new CPOperand("", ValueType.UNKNOWN, DataType.UNKNOWN);
		CPOperand in3 = null;
		CPOperand out = new CPOperand("", ValueType.UNKNOWN, DataType.UNKNOWN);

		String[] parts = InstructionUtils.getInstructionPartsWithValueType(str);
		String opcode = parts[0];

		if( !opcode.equalsIgnoreCase("cov") ) {
			throw new DMLRuntimeException("CovarianceCPInstruction.parseInstruction():: Unknown opcode " + opcode);
		}
		
		COVOperator cov = new COVOperator(COV.getCOMFnObject());
		if ( parts.length == 4 ) {
			// CP.cov.mVar0.mVar1.mVar2
			parseBinaryInstruction(str, in1, in2, out);
			return new CovarianceCPInstruction(cov, in1, in2, out, opcode, str);
		} else if ( parts.length == 5 ) {
			// CP.cov.mVar0.mVar1.mVar2.mVar3
			in3 = new CPOperand("", ValueType.UNKNOWN, DataType.UNKNOWN);
			parseBinaryInstruction(str, in1, in2, in3, out);
			return new CovarianceCPInstruction(cov, in1, in2, in3, out, opcode, str);
		}
		else {
			throw new DMLRuntimeException("Invalid number of arguments in Instruction: " + str);
		}
	}
	
	@Override
	public void processInstruction(ExecutionContext ec) 
		throws DMLRuntimeException, DMLUnsupportedOperationException
	{	
		MatrixBlock matBlock1 = ec.getMatrixInput(input1.getName());
        MatrixBlock matBlock2 = ec.getMatrixInput(input2.getName());
		String output_name = output.getName(); 
		
		COVOperator cov_op = (COVOperator)_optr;
		CM_COV_Object covobj = new CM_COV_Object();
			
		if ( input3 == null ) 
		{
			// Unweighted: cov.mvar0.mvar1.out
			covobj = matBlock1.covOperations(cov_op, matBlock2);
			
			ec.releaseMatrixInput(input1.getName());
			ec.releaseMatrixInput(input2.getName());
		}
		else 
		{
			// Weighted: cov.mvar0.mvar1.weights.out
	        MatrixBlock wtBlock = ec.getMatrixInput(input3.getName());
			
			covobj = matBlock1.covOperations(cov_op, matBlock2, wtBlock);
			
			ec.releaseMatrixInput(input1.getName());
			ec.releaseMatrixInput(input2.getName());
			ec.releaseMatrixInput(input3.getName());
		}
		
		double val = covobj.getRequiredResult(_optr);
		DoubleObject ret = new DoubleObject(output_name, val);
			
		ec.setScalarOutput(output_name, ret);
	}
}