433 lines
10 KiB
PHP
433 lines
10 KiB
PHP
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<?php
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/**
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* PHPExcel
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*
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* Copyright (c) 2006 - 2012 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 - 2012 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 1.7.8, 2012-10-12
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*/
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/**
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* PHPExcel_Best_Fit
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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 - 2012 PHPExcel (http://www.codeplex.com/PHPExcel)
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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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return $this->_error;
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} // function getBestFitType()
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public function getBestFitType() {
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return $this->_bestFitType;
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} // function getBestFitType()
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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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return False;
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} // function getValueOfYForX()
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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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return False;
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} // function getValueOfXForY()
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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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return $this->_xValues;
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} // function getValueOfXForY()
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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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return False;
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} // function getEquation()
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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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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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} // function getSlope()
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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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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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} // function getSlopeSE()
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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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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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} // function getIntersect()
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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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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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} // function getIntersectSE()
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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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if ($dp != 0) {
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return round($this->_goodnessOfFit,$dp);
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}
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return $this->_goodnessOfFit;
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} // function getGoodnessOfFit()
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public function getGoodnessOfFitPercent($dp=0) {
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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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} // function getGoodnessOfFitPercent()
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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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if ($dp != 0) {
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return round($this->_stdevOfResiduals,$dp);
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}
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return $this->_stdevOfResiduals;
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} // function getStdevOfResiduals()
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public function getSSRegression($dp=0) {
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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 $this->_SSRegression;
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} // function getSSRegression()
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public function getSSResiduals($dp=0) {
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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 $this->_SSResiduals;
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} // function getSSResiduals()
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public function getDFResiduals($dp=0) {
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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 $this->_DFResiduals;
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} // function getDFResiduals()
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public function getF($dp=0) {
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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 $this->_F;
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} // function getF()
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public function getCovariance($dp=0) {
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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 $this->_covariance;
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} // function getCovariance()
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public function getCorrelation($dp=0) {
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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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} // function getCorrelation()
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public function getYBestFitValues() {
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return $this->_yBestFitValues;
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} // function getYBestFitValues()
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protected function _calculateGoodnessOfFit($sumX,$sumY,$sumX2,$sumY2,$sumXY,$meanX,$meanY, $const) {
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$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 / $this->_DFResiduals;
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}
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}
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} // function _calculateGoodnessOfFit()
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protected function _leastSquareFit($yValues, $xValues, $const) {
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// calculate sums
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$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
|
||
|
|
// $this->_intersect = ($y_sum - ($this->_slope * $x_sum)) / $this->_valueCount;
|
||
|
|
if ($const) {
|
||
|
|
$this->_intersect = $meanY - ($this->_slope * $meanX);
|
||
|
|
} else {
|
||
|
|
$this->_intersect = 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
$this->_calculateGoodnessOfFit($x_sum,$y_sum,$xx_sum,$yy_sum,$xy_sum,$meanX,$meanY,$const);
|
||
|
|
} // function _leastSquareFit()
|
||
|
|
|
||
|
|
|
||
|
|
/**
|
||
|
|
* 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 $const
|
||
|
|
*/
|
||
|
|
function __construct($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;
|
||
|
|
} // function __construct()
|
||
|
|
|
||
|
|
} // class bestFit
|