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libreoffice/chart2/source/inc/RegressionCalculationHelper.hxx
Daniel Baumann 8e63e14cf6
Adding upstream version 4:25.2.3.
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
2025-06-22 16:20:04 +02:00

134 lines
3.6 KiB
C++

/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
* This file is part of the LibreOffice project.
*
* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/.
*
* This file incorporates work covered by the following license notice:
*
* 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 .
*/
#pragma once
#include <com/sun/star/uno/Sequence.hxx>
#include <cmath>
#include <utility>
#include <vector>
namespace chart::RegressionCalculationHelper
{
typedef std::pair< std::vector< double >, std::vector< double > > tDoubleVectorPair;
/** takes the given x- and y-values and copies them into the resulting pair,
which contains x-values in the first element and the y-values in the second
one. All tuples for which aPred is false are not copied.
<p>The function below provide a set of useful predicates that can be
used to pass as parameter aPred.</p>
*/
template< class Pred >
tDoubleVectorPair
cleanup( const css::uno::Sequence< double > & rXValues,
const css::uno::Sequence< double > & rYValues,
Pred aPred )
{
tDoubleVectorPair aResult;
sal_Int32 nSize = std::min( rXValues.getLength(), rYValues.getLength());
for( sal_Int32 i=0; i<nSize; ++i )
{
if( aPred( rXValues[i], rYValues[i] ))
{
aResult.first.push_back( rXValues[i] );
aResult.second.push_back( rYValues[i] );
}
}
return aResult;
}
class isValid
{
public:
bool operator()( double x, double y )
{ return ! ( std::isnan( x ) ||
std::isnan( y ) ||
std::isinf( x ) ||
std::isinf( y ) );
}
};
class isValidAndXPositive
{
public:
bool operator()( double x, double y )
{ return ! ( std::isnan( x ) ||
std::isnan( y ) ||
std::isinf( x ) ||
std::isinf( y ) ||
x <= 0.0 );
}
};
class isValidAndYPositive
{
public:
bool operator()( double x, double y )
{ return ! ( std::isnan( x ) ||
std::isnan( y ) ||
std::isinf( x ) ||
std::isinf( y ) ||
y <= 0.0 );
}
};
class isValidAndYNegative
{
public:
bool operator()( double x, double y )
{ return ! ( std::isnan( x ) ||
std::isnan( y ) ||
std::isinf( x ) ||
std::isinf( y ) ||
y >= 0.0 );
}
};
class isValidAndBothPositive
{
public:
bool operator()( double x, double y )
{ return ! ( std::isnan( x ) ||
std::isnan( y ) ||
std::isinf( x ) ||
std::isinf( y ) ||
x <= 0.0 ||
y <= 0.0 );
}
};
class isValidAndXPositiveAndYNegative
{
public:
bool operator()( double x, double y )
{ return ! ( std::isnan( x ) ||
std::isnan( y ) ||
std::isinf( x ) ||
std::isinf( y ) ||
x <= 0.0 ||
y >= 0.0 );
}
};
} // namespace chart::RegressionCalculationHelper
/* vim:set shiftwidth=4 softtabstop=4 expandtab: */