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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.functionobjects;
import org.apache.sysml.runtime.DMLRuntimeException;
import org.apache.sysml.runtime.instructions.cp.CM_COV_Object;
import org.apache.sysml.runtime.instructions.cp.Data;
import org.apache.sysml.runtime.instructions.cp.KahanObject;
/**
* GENERAL NOTE:
* * 05/28/2014: We decided to do handle weights consistently to SPSS in an operation-specific manner,
* i.e., we (1) round instead of casting where required (e.g. count), and (2) consistently use
* fractional weight values elsewhere. In case a count-base interpretation of weights is needed, just
* ensure rounding before calling CM/COV/KahanPlus.
*
*/
public class COV extends ValueFunction
{
private static final long serialVersionUID = 1865050401811477181L;
private static COV singleObj = null;
private KahanPlus _plus = null;
public static COV getCOMFnObject() {
if ( singleObj == null )
singleObj = new COV();
return singleObj;
}
private COV() {
_plus = KahanPlus.getKahanPlusFnObject();
}
/**
* General case for arbitrary weights w2
*
* @param in1 input data
* @param u ?
* @param v ?
* @param w2 ?
* @return result
* @throws DMLRuntimeException if DMLRuntimeException occurs
*/
public Data execute(Data in1, double u, double v, double w2)
throws DMLRuntimeException
{
CM_COV_Object cov1=(CM_COV_Object) in1;
if(cov1.isCOVAllZeros())
{
cov1.w=w2;
cov1.mean.set(u, 0);
cov1.mean_v.set(v, 0);
cov1.c2.set(0,0);
return cov1;
}
double w = cov1.w + w2;
double du=u-cov1.mean._sum;
double dv=v-cov1.mean_v._sum;
cov1.mean=(KahanObject) _plus.execute(cov1.mean, w2*du/w);
cov1.mean_v=(KahanObject) _plus.execute(cov1.mean_v, w2*dv/w);
cov1.c2=(KahanObject) _plus.execute(cov1.c2, cov1.w*w2/w*du*dv);
cov1.w=w;
return cov1;
}
/**
* Special case for weights w2==1
*
* @param in1 ?
* @param u ?
* @param v ?
* @return result
*/
@Override
public Data execute(Data in1, double u, double v)
throws DMLRuntimeException
{
CM_COV_Object cov1=(CM_COV_Object) in1;
if(cov1.isCOVAllZeros())
{
cov1.w=1L;
cov1.mean.set(u, 0);
cov1.mean_v.set(v, 0);
cov1.c2.set(0,0);
return cov1;
}
double w=cov1.w+1;
double du=u-cov1.mean._sum;
double dv=v-cov1.mean_v._sum;
cov1.mean=(KahanObject) _plus.execute(cov1.mean, du/w);
cov1.mean_v=(KahanObject) _plus.execute(cov1.mean_v, dv/w);
cov1.c2=(KahanObject) _plus.execute(cov1.c2, cov1.w/w*du*dv);
cov1.w=w;
return cov1;
}
@Override
public Data execute(Data in1, Data in2) throws DMLRuntimeException
{
CM_COV_Object cov1=(CM_COV_Object) in1;
CM_COV_Object cov2=(CM_COV_Object) in2;
if(cov1.isCOVAllZeros())
{
cov1.w=cov2.w;
cov1.mean.set(cov2.mean);
cov1.mean_v.set(cov2.mean_v);
cov1.c2.set(cov2.c2);
return cov1;
}
if(cov2.isCOVAllZeros())
return cov1;
double w = cov1.w + cov2.w;
double du=cov2.mean._sum-cov1.mean._sum;
double dv=cov2.mean_v._sum-cov1.mean_v._sum;
cov1.mean=(KahanObject) _plus.execute(cov1.mean, cov2.w*du/w);
cov1.mean_v=(KahanObject) _plus.execute(cov1.mean_v, cov2.w*dv/w);
cov1.c2=(KahanObject) _plus.execute(cov1.c2, cov2.c2._sum, cov2.c2._correction);
cov1.c2=(KahanObject) _plus.execute(cov1.c2, cov1.w*cov2.w/w*du*dv);
cov1.w=w;
return cov1;
}
}