Dateien nach "includes/PHPExcel/Classes/PHPExcel/Shared/trend" hochladen
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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 - 2014 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 - 2014 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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/**
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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 - 2014 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
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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);
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} // function _leastSquareFit()
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/**
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* Define the regression
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*
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* @param float[] $yValues The set of Y-values for this regression
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* @param float[] $xValues The set of X-values for this regression
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* @param boolean $const
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*/
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function __construct($yValues, $xValues=array(), $const=True) {
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// Calculate number of points
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$nY = count($yValues);
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$nX = count($xValues);
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// Define X Values if necessary
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if ($nX == 0) {
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$xValues = range(1,$nY);
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$nX = $nY;
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} elseif ($nY != $nX) {
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// Ensure both arrays of points are the same size
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$this->_error = True;
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return False;
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}
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$this->_valueCount = $nY;
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$this->_xValues = $xValues;
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$this->_yValues = $yValues;
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} // function __construct()
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} // class bestFit
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@ -0,0 +1,148 @@
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<?php
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/**
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||||
* PHPExcel
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||||
*
|
||||
* Copyright (c) 2006 - 2014 PHPExcel
|
||||
*
|
||||
* This library is free software; you can redistribute it and/or
|
||||
* modify it under the terms of the GNU Lesser General Public
|
||||
* License as published by the Free Software Foundation; either
|
||||
* version 2.1 of the License, or (at your option) any later version.
|
||||
*
|
||||
* This library is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
||||
* Lesser General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU Lesser General Public
|
||||
* License along with this library; if not, write to the Free Software
|
||||
* 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 - 2014 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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require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
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/**
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* PHPExcel_Exponential_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 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
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*/
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class PHPExcel_Exponential_Best_Fit extends PHPExcel_Best_Fit
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{
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/**
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* Algorithm type to use for best-fit
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* (Name of this trend class)
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*
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* @var string
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**/
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protected $_bestFitType = 'exponential';
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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 $this->getIntersect() * pow($this->getSlope(),($xValue - $this->_Xoffset));
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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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*
|
||||
* @param float $yValue Y-Value
|
||||
* @return float X-Value
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**/
|
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public function getValueOfXForY($yValue) {
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return log(($yValue + $this->_Yoffset) / $this->getIntersect()) / log($this->getSlope());
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} // function getValueOfXForY()
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/**
|
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* Return the Equation of the best-fit line
|
||||
*
|
||||
* @param int $dp Number of places of decimal precision to display
|
||||
* @return string
|
||||
**/
|
||||
public function getEquation($dp=0) {
|
||||
$slope = $this->getSlope($dp);
|
||||
$intersect = $this->getIntersect($dp);
|
||||
|
||||
return 'Y = '.$intersect.' * '.$slope.'^X';
|
||||
} // function getEquation()
|
||||
|
||||
|
||||
/**
|
||||
* Return the Slope of the line
|
||||
*
|
||||
* @param int $dp Number of places of decimal precision to display
|
||||
* @return string
|
||||
**/
|
||||
public function getSlope($dp=0) {
|
||||
if ($dp != 0) {
|
||||
return round(exp($this->_slope),$dp);
|
||||
}
|
||||
return exp($this->_slope);
|
||||
} // function getSlope()
|
||||
|
||||
|
||||
/**
|
||||
* Return the Value of X where it intersects Y = 0
|
||||
*
|
||||
* @param int $dp Number of places of decimal precision to display
|
||||
* @return string
|
||||
**/
|
||||
public function getIntersect($dp=0) {
|
||||
if ($dp != 0) {
|
||||
return round(exp($this->_intersect),$dp);
|
||||
}
|
||||
return exp($this->_intersect);
|
||||
} // function getIntersect()
|
||||
|
||||
|
||||
/**
|
||||
* Execute the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @param float[] $yValues The set of Y-values for this regression
|
||||
* @param float[] $xValues The set of X-values for this regression
|
||||
* @param boolean $const
|
||||
*/
|
||||
private function _exponential_regression($yValues, $xValues, $const) {
|
||||
foreach($yValues as &$value) {
|
||||
if ($value < 0.0) {
|
||||
$value = 0 - log(abs($value));
|
||||
} elseif ($value > 0.0) {
|
||||
$value = log($value);
|
||||
}
|
||||
}
|
||||
unset($value);
|
||||
|
||||
$this->_leastSquareFit($yValues, $xValues, $const);
|
||||
} // function _exponential_regression()
|
||||
|
||||
|
||||
/**
|
||||
* Define the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @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(), $const=True) {
|
||||
if (parent::__construct($yValues, $xValues) !== False) {
|
||||
$this->_exponential_regression($yValues, $xValues, $const);
|
||||
}
|
||||
} // function __construct()
|
||||
|
||||
} // class exponentialBestFit
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
<?php
|
||||
/**
|
||||
* PHPExcel
|
||||
*
|
||||
* Copyright (c) 2006 - 2014 PHPExcel
|
||||
*
|
||||
* This library is free software; you can redistribute it and/or
|
||||
* modify it under the terms of the GNU Lesser General Public
|
||||
* License as published by the Free Software Foundation; either
|
||||
* version 2.1 of the License, or (at your option) any later version.
|
||||
*
|
||||
* This library is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
||||
* Lesser General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU Lesser General Public
|
||||
* License along with this library; if not, write to the Free Software
|
||||
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||
* @version ##VERSION##, ##DATE##
|
||||
*/
|
||||
|
||||
|
||||
require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
|
||||
|
||||
|
||||
/**
|
||||
* PHPExcel_Linear_Best_Fit
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
*/
|
||||
class PHPExcel_Linear_Best_Fit extends PHPExcel_Best_Fit
|
||||
{
|
||||
/**
|
||||
* Algorithm type to use for best-fit
|
||||
* (Name of this trend class)
|
||||
*
|
||||
* @var string
|
||||
**/
|
||||
protected $_bestFitType = 'linear';
|
||||
|
||||
|
||||
/**
|
||||
* Return the Y-Value for a specified value of X
|
||||
*
|
||||
* @param float $xValue X-Value
|
||||
* @return float Y-Value
|
||||
**/
|
||||
public function getValueOfYForX($xValue) {
|
||||
return $this->getIntersect() + $this->getSlope() * $xValue;
|
||||
} // function getValueOfYForX()
|
||||
|
||||
|
||||
/**
|
||||
* Return the X-Value for a specified value of Y
|
||||
*
|
||||
* @param float $yValue Y-Value
|
||||
* @return float X-Value
|
||||
**/
|
||||
public function getValueOfXForY($yValue) {
|
||||
return ($yValue - $this->getIntersect()) / $this->getSlope();
|
||||
} // function getValueOfXForY()
|
||||
|
||||
|
||||
/**
|
||||
* Return the Equation of the best-fit line
|
||||
*
|
||||
* @param int $dp Number of places of decimal precision to display
|
||||
* @return string
|
||||
**/
|
||||
public function getEquation($dp=0) {
|
||||
$slope = $this->getSlope($dp);
|
||||
$intersect = $this->getIntersect($dp);
|
||||
|
||||
return 'Y = '.$intersect.' + '.$slope.' * X';
|
||||
} // function getEquation()
|
||||
|
||||
|
||||
/**
|
||||
* Execute the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @param float[] $yValues The set of Y-values for this regression
|
||||
* @param float[] $xValues The set of X-values for this regression
|
||||
* @param boolean $const
|
||||
*/
|
||||
private function _linear_regression($yValues, $xValues, $const) {
|
||||
$this->_leastSquareFit($yValues, $xValues,$const);
|
||||
} // function _linear_regression()
|
||||
|
||||
|
||||
/**
|
||||
* Define the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @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(), $const=True) {
|
||||
if (parent::__construct($yValues, $xValues) !== False) {
|
||||
$this->_linear_regression($yValues, $xValues, $const);
|
||||
}
|
||||
} // function __construct()
|
||||
|
||||
} // class linearBestFit
|
||||
|
|
@ -0,0 +1,120 @@
|
|||
<?php
|
||||
/**
|
||||
* PHPExcel
|
||||
*
|
||||
* Copyright (c) 2006 - 2014 PHPExcel
|
||||
*
|
||||
* This library is free software; you can redistribute it and/or
|
||||
* modify it under the terms of the GNU Lesser General Public
|
||||
* License as published by the Free Software Foundation; either
|
||||
* version 2.1 of the License, or (at your option) any later version.
|
||||
*
|
||||
* This library is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
||||
* Lesser General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU Lesser General Public
|
||||
* License along with this library; if not, write to the Free Software
|
||||
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||
* @version ##VERSION##, ##DATE##
|
||||
*/
|
||||
|
||||
|
||||
require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
|
||||
|
||||
|
||||
/**
|
||||
* PHPExcel_Logarithmic_Best_Fit
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
*/
|
||||
class PHPExcel_Logarithmic_Best_Fit extends PHPExcel_Best_Fit
|
||||
{
|
||||
/**
|
||||
* Algorithm type to use for best-fit
|
||||
* (Name of this trend class)
|
||||
*
|
||||
* @var string
|
||||
**/
|
||||
protected $_bestFitType = 'logarithmic';
|
||||
|
||||
|
||||
/**
|
||||
* Return the Y-Value for a specified value of X
|
||||
*
|
||||
* @param float $xValue X-Value
|
||||
* @return float Y-Value
|
||||
**/
|
||||
public function getValueOfYForX($xValue) {
|
||||
return $this->getIntersect() + $this->getSlope() * log($xValue - $this->_Xoffset);
|
||||
} // function getValueOfYForX()
|
||||
|
||||
|
||||
/**
|
||||
* Return the X-Value for a specified value of Y
|
||||
*
|
||||
* @param float $yValue Y-Value
|
||||
* @return float X-Value
|
||||
**/
|
||||
public function getValueOfXForY($yValue) {
|
||||
return exp(($yValue - $this->getIntersect()) / $this->getSlope());
|
||||
} // function getValueOfXForY()
|
||||
|
||||
|
||||
/**
|
||||
* Return the Equation of the best-fit line
|
||||
*
|
||||
* @param int $dp Number of places of decimal precision to display
|
||||
* @return string
|
||||
**/
|
||||
public function getEquation($dp=0) {
|
||||
$slope = $this->getSlope($dp);
|
||||
$intersect = $this->getIntersect($dp);
|
||||
|
||||
return 'Y = '.$intersect.' + '.$slope.' * log(X)';
|
||||
} // function getEquation()
|
||||
|
||||
|
||||
/**
|
||||
* Execute the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @param float[] $yValues The set of Y-values for this regression
|
||||
* @param float[] $xValues The set of X-values for this regression
|
||||
* @param boolean $const
|
||||
*/
|
||||
private function _logarithmic_regression($yValues, $xValues, $const) {
|
||||
foreach($xValues as &$value) {
|
||||
if ($value < 0.0) {
|
||||
$value = 0 - log(abs($value));
|
||||
} elseif ($value > 0.0) {
|
||||
$value = log($value);
|
||||
}
|
||||
}
|
||||
unset($value);
|
||||
|
||||
$this->_leastSquareFit($yValues, $xValues, $const);
|
||||
} // function _logarithmic_regression()
|
||||
|
||||
|
||||
/**
|
||||
* Define the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @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(), $const=True) {
|
||||
if (parent::__construct($yValues, $xValues) !== False) {
|
||||
$this->_logarithmic_regression($yValues, $xValues, $const);
|
||||
}
|
||||
} // function __construct()
|
||||
|
||||
} // class logarithmicBestFit
|
||||
|
|
@ -0,0 +1,224 @@
|
|||
<?php
|
||||
/**
|
||||
* PHPExcel
|
||||
*
|
||||
* Copyright (c) 2006 - 2014 PHPExcel
|
||||
*
|
||||
* This library is free software; you can redistribute it and/or
|
||||
* modify it under the terms of the GNU Lesser General Public
|
||||
* License as published by the Free Software Foundation; either
|
||||
* version 2.1 of the License, or (at your option) any later version.
|
||||
*
|
||||
* This library is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
||||
* Lesser General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU Lesser General Public
|
||||
* License along with this library; if not, write to the Free Software
|
||||
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||
* @version ##VERSION##, ##DATE##
|
||||
*/
|
||||
|
||||
|
||||
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php';
|
||||
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/JAMA/Matrix.php';
|
||||
|
||||
|
||||
/**
|
||||
* PHPExcel_Polynomial_Best_Fit
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
*/
|
||||
class PHPExcel_Polynomial_Best_Fit extends PHPExcel_Best_Fit
|
||||
{
|
||||
/**
|
||||
* Algorithm type to use for best-fit
|
||||
* (Name of this trend class)
|
||||
*
|
||||
* @var string
|
||||
**/
|
||||
protected $_bestFitType = 'polynomial';
|
||||
|
||||
/**
|
||||
* Polynomial order
|
||||
*
|
||||
* @protected
|
||||
* @var int
|
||||
**/
|
||||
protected $_order = 0;
|
||||
|
||||
|
||||
/**
|
||||
* Return the order of this polynomial
|
||||
*
|
||||
* @return int
|
||||
**/
|
||||
public function getOrder() {
|
||||
return $this->_order;
|
||||
} // function getOrder()
|
||||
|
||||
|
||||
/**
|
||||
* Return the Y-Value for a specified value of X
|
||||
*
|
||||
* @param float $xValue X-Value
|
||||
* @return float Y-Value
|
||||
**/
|
||||
public function getValueOfYForX($xValue) {
|
||||
$retVal = $this->getIntersect();
|
||||
$slope = $this->getSlope();
|
||||
foreach($slope as $key => $value) {
|
||||
if ($value != 0.0) {
|
||||
$retVal += $value * pow($xValue, $key + 1);
|
||||
}
|
||||
}
|
||||
return $retVal;
|
||||
} // function getValueOfYForX()
|
||||
|
||||
|
||||
/**
|
||||
* Return the X-Value for a specified value of Y
|
||||
*
|
||||
* @param float $yValue Y-Value
|
||||
* @return float X-Value
|
||||
**/
|
||||
public function getValueOfXForY($yValue) {
|
||||
return ($yValue - $this->getIntersect()) / $this->getSlope();
|
||||
} // function getValueOfXForY()
|
||||
|
||||
|
||||
/**
|
||||
* Return the Equation of the best-fit line
|
||||
*
|
||||
* @param int $dp Number of places of decimal precision to display
|
||||
* @return string
|
||||
**/
|
||||
public function getEquation($dp=0) {
|
||||
$slope = $this->getSlope($dp);
|
||||
$intersect = $this->getIntersect($dp);
|
||||
|
||||
$equation = 'Y = '.$intersect;
|
||||
foreach($slope as $key => $value) {
|
||||
if ($value != 0.0) {
|
||||
$equation .= ' + '.$value.' * X';
|
||||
if ($key > 0) {
|
||||
$equation .= '^'.($key + 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
return $equation;
|
||||
} // function getEquation()
|
||||
|
||||
|
||||
/**
|
||||
* Return the Slope of the line
|
||||
*
|
||||
* @param int $dp Number of places of decimal precision to display
|
||||
* @return string
|
||||
**/
|
||||
public function getSlope($dp=0) {
|
||||
if ($dp != 0) {
|
||||
$coefficients = array();
|
||||
foreach($this->_slope as $coefficient) {
|
||||
$coefficients[] = round($coefficient,$dp);
|
||||
}
|
||||
return $coefficients;
|
||||
}
|
||||
return $this->_slope;
|
||||
} // function getSlope()
|
||||
|
||||
|
||||
public function getCoefficients($dp=0) {
|
||||
return array_merge(array($this->getIntersect($dp)),$this->getSlope($dp));
|
||||
} // function getCoefficients()
|
||||
|
||||
|
||||
/**
|
||||
* Execute the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @param int $order Order of Polynomial for this 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 $const
|
||||
*/
|
||||
private function _polynomial_regression($order, $yValues, $xValues, $const) {
|
||||
// calculate sums
|
||||
$x_sum = array_sum($xValues);
|
||||
$y_sum = array_sum($yValues);
|
||||
$xx_sum = $xy_sum = 0;
|
||||
for($i = 0; $i < $this->_valueCount; ++$i) {
|
||||
$xy_sum += $xValues[$i] * $yValues[$i];
|
||||
$xx_sum += $xValues[$i] * $xValues[$i];
|
||||
$yy_sum += $yValues[$i] * $yValues[$i];
|
||||
}
|
||||
/*
|
||||
* This routine uses logic from the PHP port of polyfit version 0.1
|
||||
* written by Michael Bommarito and Paul Meagher
|
||||
*
|
||||
* The function fits a polynomial function of order $order through
|
||||
* a series of x-y data points using least squares.
|
||||
*
|
||||
*/
|
||||
for ($i = 0; $i < $this->_valueCount; ++$i) {
|
||||
for ($j = 0; $j <= $order; ++$j) {
|
||||
$A[$i][$j] = pow($xValues[$i], $j);
|
||||
}
|
||||
}
|
||||
for ($i=0; $i < $this->_valueCount; ++$i) {
|
||||
$B[$i] = array($yValues[$i]);
|
||||
}
|
||||
$matrixA = new Matrix($A);
|
||||
$matrixB = new Matrix($B);
|
||||
$C = $matrixA->solve($matrixB);
|
||||
|
||||
$coefficients = array();
|
||||
for($i = 0; $i < $C->m; ++$i) {
|
||||
$r = $C->get($i, 0);
|
||||
if (abs($r) <= pow(10, -9)) {
|
||||
$r = 0;
|
||||
}
|
||||
$coefficients[] = $r;
|
||||
}
|
||||
|
||||
$this->_intersect = array_shift($coefficients);
|
||||
$this->_slope = $coefficients;
|
||||
|
||||
$this->_calculateGoodnessOfFit($x_sum,$y_sum,$xx_sum,$yy_sum,$xy_sum);
|
||||
foreach($this->_xValues as $xKey => $xValue) {
|
||||
$this->_yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
|
||||
}
|
||||
} // function _polynomial_regression()
|
||||
|
||||
|
||||
/**
|
||||
* Define the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @param int $order Order of Polynomial for this 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 $const
|
||||
*/
|
||||
function __construct($order, $yValues, $xValues=array(), $const=True) {
|
||||
if (parent::__construct($yValues, $xValues) !== False) {
|
||||
if ($order < $this->_valueCount) {
|
||||
$this->_bestFitType .= '_'.$order;
|
||||
$this->_order = $order;
|
||||
$this->_polynomial_regression($order, $yValues, $xValues, $const);
|
||||
if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) {
|
||||
$this->_error = True;
|
||||
}
|
||||
} else {
|
||||
$this->_error = True;
|
||||
}
|
||||
}
|
||||
} // function __construct()
|
||||
|
||||
} // class polynomialBestFit
|
||||
|
|
@ -0,0 +1,142 @@
|
|||
<?php
|
||||
/**
|
||||
* PHPExcel
|
||||
*
|
||||
* Copyright (c) 2006 - 2014 PHPExcel
|
||||
*
|
||||
* This library is free software; you can redistribute it and/or
|
||||
* modify it under the terms of the GNU Lesser General Public
|
||||
* License as published by the Free Software Foundation; either
|
||||
* version 2.1 of the License, or (at your option) any later version.
|
||||
*
|
||||
* This library is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
||||
* Lesser General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU Lesser General Public
|
||||
* License along with this library; if not, write to the Free Software
|
||||
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||
* @version ##VERSION##, ##DATE##
|
||||
*/
|
||||
|
||||
|
||||
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php';
|
||||
|
||||
|
||||
/**
|
||||
* PHPExcel_Power_Best_Fit
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
*/
|
||||
class PHPExcel_Power_Best_Fit extends PHPExcel_Best_Fit
|
||||
{
|
||||
/**
|
||||
* Algorithm type to use for best-fit
|
||||
* (Name of this trend class)
|
||||
*
|
||||
* @var string
|
||||
**/
|
||||
protected $_bestFitType = 'power';
|
||||
|
||||
|
||||
/**
|
||||
* Return the Y-Value for a specified value of X
|
||||
*
|
||||
* @param float $xValue X-Value
|
||||
* @return float Y-Value
|
||||
**/
|
||||
public function getValueOfYForX($xValue) {
|
||||
return $this->getIntersect() * pow(($xValue - $this->_Xoffset),$this->getSlope());
|
||||
} // function getValueOfYForX()
|
||||
|
||||
|
||||
/**
|
||||
* Return the X-Value for a specified value of Y
|
||||
*
|
||||
* @param float $yValue Y-Value
|
||||
* @return float X-Value
|
||||
**/
|
||||
public function getValueOfXForY($yValue) {
|
||||
return pow((($yValue + $this->_Yoffset) / $this->getIntersect()),(1 / $this->getSlope()));
|
||||
} // function getValueOfXForY()
|
||||
|
||||
|
||||
/**
|
||||
* Return the Equation of the best-fit line
|
||||
*
|
||||
* @param int $dp Number of places of decimal precision to display
|
||||
* @return string
|
||||
**/
|
||||
public function getEquation($dp=0) {
|
||||
$slope = $this->getSlope($dp);
|
||||
$intersect = $this->getIntersect($dp);
|
||||
|
||||
return 'Y = '.$intersect.' * X^'.$slope;
|
||||
} // function getEquation()
|
||||
|
||||
|
||||
/**
|
||||
* Return the Value of X where it intersects Y = 0
|
||||
*
|
||||
* @param int $dp Number of places of decimal precision to display
|
||||
* @return string
|
||||
**/
|
||||
public function getIntersect($dp=0) {
|
||||
if ($dp != 0) {
|
||||
return round(exp($this->_intersect),$dp);
|
||||
}
|
||||
return exp($this->_intersect);
|
||||
} // function getIntersect()
|
||||
|
||||
|
||||
/**
|
||||
* Execute the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @param float[] $yValues The set of Y-values for this regression
|
||||
* @param float[] $xValues The set of X-values for this regression
|
||||
* @param boolean $const
|
||||
*/
|
||||
private function _power_regression($yValues, $xValues, $const) {
|
||||
foreach($xValues as &$value) {
|
||||
if ($value < 0.0) {
|
||||
$value = 0 - log(abs($value));
|
||||
} elseif ($value > 0.0) {
|
||||
$value = log($value);
|
||||
}
|
||||
}
|
||||
unset($value);
|
||||
foreach($yValues as &$value) {
|
||||
if ($value < 0.0) {
|
||||
$value = 0 - log(abs($value));
|
||||
} elseif ($value > 0.0) {
|
||||
$value = log($value);
|
||||
}
|
||||
}
|
||||
unset($value);
|
||||
|
||||
$this->_leastSquareFit($yValues, $xValues, $const);
|
||||
} // function _power_regression()
|
||||
|
||||
|
||||
/**
|
||||
* Define the regression and calculate the goodness of fit for a set of X and Y data values
|
||||
*
|
||||
* @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(), $const=True) {
|
||||
if (parent::__construct($yValues, $xValues) !== False) {
|
||||
$this->_power_regression($yValues, $xValues, $const);
|
||||
}
|
||||
} // function __construct()
|
||||
|
||||
} // class powerBestFit
|
||||
|
|
@ -0,0 +1,156 @@
|
|||
<?php
|
||||
/**
|
||||
* PHPExcel
|
||||
*
|
||||
* Copyright (c) 2006 - 2014 PHPExcel
|
||||
*
|
||||
* This library is free software; you can redistribute it and/or
|
||||
* modify it under the terms of the GNU Lesser General Public
|
||||
* License as published by the Free Software Foundation; either
|
||||
* version 2.1 of the License, or (at your option) any later version.
|
||||
*
|
||||
* This library is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
||||
* Lesser General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU Lesser General Public
|
||||
* License along with this library; if not, write to the Free Software
|
||||
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||
* @version ##VERSION##, ##DATE##
|
||||
*/
|
||||
|
||||
|
||||
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/linearBestFitClass.php';
|
||||
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/logarithmicBestFitClass.php';
|
||||
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/exponentialBestFitClass.php';
|
||||
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/powerBestFitClass.php';
|
||||
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/polynomialBestFitClass.php';
|
||||
|
||||
|
||||
/**
|
||||
* PHPExcel_trendClass
|
||||
*
|
||||
* @category PHPExcel
|
||||
* @package PHPExcel_Shared_Trend
|
||||
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||
*/
|
||||
class trendClass
|
||||
{
|
||||
const TREND_LINEAR = 'Linear';
|
||||
const TREND_LOGARITHMIC = 'Logarithmic';
|
||||
const TREND_EXPONENTIAL = 'Exponential';
|
||||
const TREND_POWER = 'Power';
|
||||
const TREND_POLYNOMIAL_2 = 'Polynomial_2';
|
||||
const TREND_POLYNOMIAL_3 = 'Polynomial_3';
|
||||
const TREND_POLYNOMIAL_4 = 'Polynomial_4';
|
||||
const TREND_POLYNOMIAL_5 = 'Polynomial_5';
|
||||
const TREND_POLYNOMIAL_6 = 'Polynomial_6';
|
||||
const TREND_BEST_FIT = 'Bestfit';
|
||||
const TREND_BEST_FIT_NO_POLY = 'Bestfit_no_Polynomials';
|
||||
|
||||
/**
|
||||
* Names of the best-fit trend analysis methods
|
||||
*
|
||||
* @var string[]
|
||||
**/
|
||||
private static $_trendTypes = array( self::TREND_LINEAR,
|
||||
self::TREND_LOGARITHMIC,
|
||||
self::TREND_EXPONENTIAL,
|
||||
self::TREND_POWER
|
||||
);
|
||||
/**
|
||||
* Names of the best-fit trend polynomial orders
|
||||
*
|
||||
* @var string[]
|
||||
**/
|
||||
private static $_trendTypePolyOrders = array( self::TREND_POLYNOMIAL_2,
|
||||
self::TREND_POLYNOMIAL_3,
|
||||
self::TREND_POLYNOMIAL_4,
|
||||
self::TREND_POLYNOMIAL_5,
|
||||
self::TREND_POLYNOMIAL_6
|
||||
);
|
||||
|
||||
/**
|
||||
* Cached results for each method when trying to identify which provides the best fit
|
||||
*
|
||||
* @var PHPExcel_Best_Fit[]
|
||||
**/
|
||||
private static $_trendCache = array();
|
||||
|
||||
|
||||
public static function calculate($trendType=self::TREND_BEST_FIT, $yValues, $xValues=array(), $const=True) {
|
||||
// Calculate number of points in each dataset
|
||||
$nY = count($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
|
||||
trigger_error("trend(): Number of elements in coordinate arrays do not match.", E_USER_ERROR);
|
||||
}
|
||||
|
||||
$key = md5($trendType.$const.serialize($yValues).serialize($xValues));
|
||||
// Determine which trend method has been requested
|
||||
switch ($trendType) {
|
||||
// Instantiate and return the class for the requested trend method
|
||||
case self::TREND_LINEAR :
|
||||
case self::TREND_LOGARITHMIC :
|
||||
case self::TREND_EXPONENTIAL :
|
||||
case self::TREND_POWER :
|
||||
if (!isset(self::$_trendCache[$key])) {
|
||||
$className = 'PHPExcel_'.$trendType.'_Best_Fit';
|
||||
self::$_trendCache[$key] = new $className($yValues,$xValues,$const);
|
||||
}
|
||||
return self::$_trendCache[$key];
|
||||
break;
|
||||
case self::TREND_POLYNOMIAL_2 :
|
||||
case self::TREND_POLYNOMIAL_3 :
|
||||
case self::TREND_POLYNOMIAL_4 :
|
||||
case self::TREND_POLYNOMIAL_5 :
|
||||
case self::TREND_POLYNOMIAL_6 :
|
||||
if (!isset(self::$_trendCache[$key])) {
|
||||
$order = substr($trendType,-1);
|
||||
self::$_trendCache[$key] = new PHPExcel_Polynomial_Best_Fit($order,$yValues,$xValues,$const);
|
||||
}
|
||||
return self::$_trendCache[$key];
|
||||
break;
|
||||
case self::TREND_BEST_FIT :
|
||||
case self::TREND_BEST_FIT_NO_POLY :
|
||||
// If the request is to determine the best fit regression, then we test each trend line in turn
|
||||
// Start by generating an instance of each available trend method
|
||||
foreach(self::$_trendTypes as $trendMethod) {
|
||||
$className = 'PHPExcel_'.$trendMethod.'BestFit';
|
||||
$bestFit[$trendMethod] = new $className($yValues,$xValues,$const);
|
||||
$bestFitValue[$trendMethod] = $bestFit[$trendMethod]->getGoodnessOfFit();
|
||||
}
|
||||
if ($trendType != self::TREND_BEST_FIT_NO_POLY) {
|
||||
foreach(self::$_trendTypePolyOrders as $trendMethod) {
|
||||
$order = substr($trendMethod,-1);
|
||||
$bestFit[$trendMethod] = new PHPExcel_Polynomial_Best_Fit($order,$yValues,$xValues,$const);
|
||||
if ($bestFit[$trendMethod]->getError()) {
|
||||
unset($bestFit[$trendMethod]);
|
||||
} else {
|
||||
$bestFitValue[$trendMethod] = $bestFit[$trendMethod]->getGoodnessOfFit();
|
||||
}
|
||||
}
|
||||
}
|
||||
// Determine which of our trend lines is the best fit, and then we return the instance of that trend class
|
||||
arsort($bestFitValue);
|
||||
$bestFitType = key($bestFitValue);
|
||||
return $bestFit[$bestFitType];
|
||||
break;
|
||||
default :
|
||||
return false;
|
||||
}
|
||||
} // function calculate()
|
||||
|
||||
} // class trendClass
|
||||
Loading…
Reference in New Issue