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426 lines
12 KiB
426 lines
12 KiB
4 years ago
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<?php
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/**
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* PHPExcel_Best_Fit
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*
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* Copyright (c) 2006 - 2015 PHPExcel
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*
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* This library is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* This library is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with this library; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*
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* @category PHPExcel
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* @package PHPExcel_Shared_Trend
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* @copyright Copyright (c) 2006 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
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* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
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* @version ##VERSION##, ##DATE##
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*/
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class PHPExcel_Best_Fit
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{
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/**
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* Indicator flag for a calculation error
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*
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* @var boolean
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**/
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protected $error = false;
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/**
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* Algorithm type to use for best-fit
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*
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* @var string
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**/
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protected $bestFitType = 'undetermined';
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/**
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* Number of entries in the sets of x- and y-value arrays
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*
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* @var int
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**/
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protected $valueCount = 0;
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/**
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* X-value dataseries of values
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*
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* @var float[]
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**/
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protected $xValues = array();
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/**
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* Y-value dataseries of values
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*
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* @var float[]
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**/
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protected $yValues = array();
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/**
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* Flag indicating whether values should be adjusted to Y=0
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*
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* @var boolean
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**/
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protected $adjustToZero = false;
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/**
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* Y-value series of best-fit values
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*
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* @var float[]
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**/
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protected $yBestFitValues = array();
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protected $goodnessOfFit = 1;
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protected $stdevOfResiduals = 0;
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protected $covariance = 0;
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protected $correlation = 0;
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protected $SSRegression = 0;
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protected $SSResiduals = 0;
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protected $DFResiduals = 0;
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protected $f = 0;
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protected $slope = 0;
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protected $slopeSE = 0;
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protected $intersect = 0;
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protected $intersectSE = 0;
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protected $xOffset = 0;
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protected $yOffset = 0;
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public function getError()
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{
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return $this->error;
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}
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public function getBestFitType()
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{
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return $this->bestFitType;
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}
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/**
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* Return the Y-Value for a specified value of X
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*
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* @param float $xValue X-Value
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* @return float Y-Value
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*/
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public function getValueOfYForX($xValue)
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{
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return false;
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}
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/**
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* Return the X-Value for a specified value of Y
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*
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* @param float $yValue Y-Value
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* @return float X-Value
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*/
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public function getValueOfXForY($yValue)
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{
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return false;
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}
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/**
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* Return the original set of X-Values
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*
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* @return float[] X-Values
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*/
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public function getXValues()
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{
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return $this->xValues;
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}
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/**
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* Return the Equation of the best-fit line
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*
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* @param int $dp Number of places of decimal precision to display
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* @return string
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*/
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public function getEquation($dp = 0)
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{
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return false;
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}
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/**
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* Return the Slope of the line
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*
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* @param int $dp Number of places of decimal precision to display
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* @return string
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*/
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public function getSlope($dp = 0)
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{
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if ($dp != 0) {
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return round($this->slope, $dp);
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}
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return $this->slope;
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}
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/**
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* Return the standard error of the Slope
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*
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* @param int $dp Number of places of decimal precision to display
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* @return string
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*/
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public function getSlopeSE($dp = 0)
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{
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if ($dp != 0) {
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return round($this->slopeSE, $dp);
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}
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return $this->slopeSE;
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}
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/**
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* Return the Value of X where it intersects Y = 0
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*
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* @param int $dp Number of places of decimal precision to display
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* @return string
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*/
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public function getIntersect($dp = 0)
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{
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if ($dp != 0) {
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return round($this->intersect, $dp);
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}
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return $this->intersect;
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}
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/**
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* Return the standard error of the Intersect
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*
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* @param int $dp Number of places of decimal precision to display
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* @return string
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*/
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public function getIntersectSE($dp = 0)
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{
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if ($dp != 0) {
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return round($this->intersectSE, $dp);
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}
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return $this->intersectSE;
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}
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/**
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* Return the goodness of fit for this regression
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*
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* @param int $dp Number of places of decimal precision to return
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* @return float
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*/
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public function getGoodnessOfFit($dp = 0)
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{
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if ($dp != 0) {
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return round($this->goodnessOfFit, $dp);
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}
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return $this->goodnessOfFit;
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}
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public function getGoodnessOfFitPercent($dp = 0)
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{
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if ($dp != 0) {
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return round($this->goodnessOfFit * 100, $dp);
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}
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return $this->goodnessOfFit * 100;
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}
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/**
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* Return the standard deviation of the residuals for this regression
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*
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* @param int $dp Number of places of decimal precision to return
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* @return float
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*/
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public function getStdevOfResiduals($dp = 0)
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{
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if ($dp != 0) {
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return round($this->stdevOfResiduals, $dp);
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}
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return $this->stdevOfResiduals;
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}
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public function getSSRegression($dp = 0)
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{
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if ($dp != 0) {
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return round($this->SSRegression, $dp);
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}
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return $this->SSRegression;
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}
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public function getSSResiduals($dp = 0)
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{
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if ($dp != 0) {
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return round($this->SSResiduals, $dp);
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}
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return $this->SSResiduals;
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}
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public function getDFResiduals($dp = 0)
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{
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if ($dp != 0) {
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return round($this->DFResiduals, $dp);
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}
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return $this->DFResiduals;
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}
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public function getF($dp = 0)
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{
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if ($dp != 0) {
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return round($this->f, $dp);
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}
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return $this->f;
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}
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public function getCovariance($dp = 0)
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{
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if ($dp != 0) {
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return round($this->covariance, $dp);
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}
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return $this->covariance;
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}
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public function getCorrelation($dp = 0)
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{
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if ($dp != 0) {
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return round($this->correlation, $dp);
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}
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return $this->correlation;
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}
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public function getYBestFitValues()
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{
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return $this->yBestFitValues;
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}
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protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const)
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{
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$SSres = $SScov = $SScor = $SStot = $SSsex = 0.0;
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foreach ($this->xValues as $xKey => $xValue) {
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$bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
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$SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);
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if ($const) {
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$SStot += ($this->yValues[$xKey] - $meanY) * ($this->yValues[$xKey] - $meanY);
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} else {
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$SStot += $this->yValues[$xKey] * $this->yValues[$xKey];
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}
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$SScov += ($this->xValues[$xKey] - $meanX) * ($this->yValues[$xKey] - $meanY);
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if ($const) {
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$SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
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} else {
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$SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
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}
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}
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$this->SSResiduals = $SSres;
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$this->DFResiduals = $this->valueCount - 1 - $const;
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if ($this->DFResiduals == 0.0) {
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$this->stdevOfResiduals = 0.0;
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} else {
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$this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals);
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}
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if (($SStot == 0.0) || ($SSres == $SStot)) {
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$this->goodnessOfFit = 1;
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} else {
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$this->goodnessOfFit = 1 - ($SSres / $SStot);
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}
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$this->SSRegression = $this->goodnessOfFit * $SStot;
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$this->covariance = $SScov / $this->valueCount;
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$this->correlation = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - pow($sumX, 2)) * ($this->valueCount * $sumY2 - pow($sumY, 2)));
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$this->slopeSE = $this->stdevOfResiduals / sqrt($SSsex);
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$this->intersectSE = $this->stdevOfResiduals * sqrt(1 / ($this->valueCount - ($sumX * $sumX) / $sumX2));
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if ($this->SSResiduals != 0.0) {
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if ($this->DFResiduals == 0.0) {
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$this->f = 0.0;
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} else {
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$this->f = $this->SSRegression / ($this->SSResiduals / $this->DFResiduals);
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}
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} else {
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if ($this->DFResiduals == 0.0) {
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$this->f = 0.0;
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} else {
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$this->f = $this->SSRegression / $this->DFResiduals;
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}
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}
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}
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protected function leastSquareFit($yValues, $xValues, $const)
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{
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// calculate sums
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$x_sum = array_sum($xValues);
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$y_sum = array_sum($yValues);
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$meanX = $x_sum / $this->valueCount;
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$meanY = $y_sum / $this->valueCount;
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$mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
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for ($i = 0; $i < $this->valueCount; ++$i) {
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$xy_sum += $xValues[$i] * $yValues[$i];
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$xx_sum += $xValues[$i] * $xValues[$i];
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$yy_sum += $yValues[$i] * $yValues[$i];
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if ($const) {
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$mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
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$mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
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} else {
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$mBase += $xValues[$i] * $yValues[$i];
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$mDivisor += $xValues[$i] * $xValues[$i];
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}
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}
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// calculate slope
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// $this->slope = (($this->valueCount * $xy_sum) - ($x_sum * $y_sum)) / (($this->valueCount * $xx_sum) - ($x_sum * $x_sum));
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$this->slope = $mBase / $mDivisor;
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// calculate intersect
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// $this->intersect = ($y_sum - ($this->slope * $x_sum)) / $this->valueCount;
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if ($const) {
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$this->intersect = $meanY - ($this->slope * $meanX);
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} else {
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$this->intersect = 0;
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}
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$this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, $meanX, $meanY, $const);
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* Define the regression
|
||
|
|
*
|
||
|
|
* @param float[] $yValues The set of Y-values for this regression
|
||
|
|
* @param float[] $xValues The set of X-values for this regression
|
||
|
|
* @param boolean $const
|
||
|
|
*/
|
||
|
|
public function __construct($yValues, $xValues = array(), $const = true)
|
||
|
|
{
|
||
|
|
// Calculate number of points
|
||
|
|
$nY = count($yValues);
|
||
|
|
$nX = count($xValues);
|
||
|
|
|
||
|
|
// Define X Values if necessary
|
||
|
|
if ($nX == 0) {
|
||
|
|
$xValues = range(1, $nY);
|
||
|
|
$nX = $nY;
|
||
|
|
} elseif ($nY != $nX) {
|
||
|
|
// Ensure both arrays of points are the same size
|
||
|
|
$this->error = true;
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
|
||
|
|
$this->valueCount = $nY;
|
||
|
|
$this->xValues = $xValues;
|
||
|
|
$this->yValues = $yValues;
|
||
|
|
}
|
||
|
|
}
|