#401 : Support for namespaces
This commit is contained in:
Progi1984
parent
f546be620e
commit
c866be3c7a
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@ -1765,7 +1765,7 @@ class Statistical
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/**
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/**
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* GROWTH
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* GROWTH
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*
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*
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* Returns values along a predicted emponential trend
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* Returns values along a predicted emponential Trend
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*
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*
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* @param array of mixed Data Series Y
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* @param array of mixed Data Series Y
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* @param array of mixed Data Series X
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* @param array of mixed Data Series X
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@ -3404,7 +3404,7 @@ class Statistical
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/**
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/**
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* TREND
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* TREND
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*
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*
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* Returns values along a linear trend
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* Returns values along a linear Trend
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*
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*
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* @param array of mixed Data Series Y
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* @param array of mixed Data Series Y
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* @param array of mixed Data Series X
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* @param array of mixed Data Series X
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@ -63,8 +63,8 @@ class Excel5 extends BaseReader implements IReader
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// ParseXL definitions
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// ParseXL definitions
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const XLS_BIFF8 = 0x0600;
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const XLS_BIFF8 = 0x0600;
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const XLS_BIFF7 = 0x0500;
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const XLS_BIFF7 = 0x0500;
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const XLS_WorkbookGlobals = 0x0005;
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const XLS_WORKBOOKGLOBALS = 0x0005;
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const XLS_Worksheet = 0x0010;
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const XLS_WORKSHEET = 0x0010;
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// record identifiers
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// record identifiers
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const XLS_TYPE_FORMULA = 0x0006;
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const XLS_TYPE_FORMULA = 0x0006;
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@ -1688,14 +1688,14 @@ class Excel5 extends BaseReader implements IReader
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$substreamType = self::getInt2d($recordData, 2);
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$substreamType = self::getInt2d($recordData, 2);
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switch ($substreamType) {
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switch ($substreamType) {
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case self::XLS_WorkbookGlobals:
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case self::XLS_WORKBOOKGLOBALS:
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$version = self::getInt2d($recordData, 0);
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$version = self::getInt2d($recordData, 0);
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if (($version != self::XLS_BIFF8) && ($version != self::XLS_BIFF7)) {
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if (($version != self::XLS_BIFF8) && ($version != self::XLS_BIFF7)) {
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throw new Exception('Cannot read this Excel file. Version is too old.');
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throw new Exception('Cannot read this Excel file. Version is too old.');
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}
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}
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$this->version = $version;
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$this->version = $version;
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break;
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break;
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case self::XLS_Worksheet:
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case self::XLS_WORKSHEET:
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// do not use this version information for anything
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// do not use this version information for anything
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// it is unreliable (OpenOffice doc, 5.8), use only version information from the global stream
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// it is unreliable (OpenOffice doc, 5.8), use only version information from the global stream
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break;
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break;
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@ -0,0 +1,427 @@
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<?php
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namespace PHPExcel\Shared\Trend;
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/**
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* bestFit
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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 BestFit
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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 / $this->DFResiduals;
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}
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}
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||||||
|
}
|
||||||
|
|
||||||
|
protected function leastSquareFit($yValues, $xValues, $const)
|
||||||
|
{
|
||||||
|
// calculate sums
|
||||||
|
$x_sum = array_sum($xValues);
|
||||||
|
$y_sum = array_sum($yValues);
|
||||||
|
$meanX = $x_sum / $this->valueCount;
|
||||||
|
$meanY = $y_sum / $this->valueCount;
|
||||||
|
$mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
|
||||||
|
for ($i = 0; $i < $this->valueCount; ++$i) {
|
||||||
|
$xy_sum += $xValues[$i] * $yValues[$i];
|
||||||
|
$xx_sum += $xValues[$i] * $xValues[$i];
|
||||||
|
$yy_sum += $yValues[$i] * $yValues[$i];
|
||||||
|
|
||||||
|
if ($const) {
|
||||||
|
$mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
|
||||||
|
$mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
|
||||||
|
} else {
|
||||||
|
$mBase += $xValues[$i] * $yValues[$i];
|
||||||
|
$mDivisor += $xValues[$i] * $xValues[$i];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// calculate slope
|
||||||
|
// $this->slope = (($this->valueCount * $xy_sum) - ($x_sum * $y_sum)) / (($this->valueCount * $xx_sum) - ($x_sum * $x_sum));
|
||||||
|
$this->slope = $mBase / $mDivisor;
|
||||||
|
|
||||||
|
// 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);
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,138 @@
|
||||||
|
<?php
|
||||||
|
|
||||||
|
namespace PHPExcel\Shared\Trend;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* PHPExcel_Exponential_Best_Fit
|
||||||
|
*
|
||||||
|
* Copyright (c) 2006 - 2015 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 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||||
|
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||||
|
* @version ##VERSION##, ##DATE##
|
||||||
|
*/
|
||||||
|
class ExponentialBestFit extends BestFit
|
||||||
|
{
|
||||||
|
/**
|
||||||
|
* Algorithm type to use for best-fit
|
||||||
|
* (Name of this Trend class)
|
||||||
|
*
|
||||||
|
* @var string
|
||||||
|
**/
|
||||||
|
protected $bestFitType = 'exponential';
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Return the Y-Value for a specified value of X
|
||||||
|
*
|
||||||
|
* @param float $xValue X-Value
|
||||||
|
* @return float Y-Value
|
||||||
|
**/
|
||||||
|
public function getValueOfYForX($xValue)
|
||||||
|
{
|
||||||
|
return $this->getIntersect() * pow($this->getSlope(), ($xValue - $this->xOffset));
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Return the X-Value for a specified value of Y
|
||||||
|
*
|
||||||
|
* @param float $yValue Y-Value
|
||||||
|
* @return float X-Value
|
||||||
|
**/
|
||||||
|
public function getValueOfXForY($yValue)
|
||||||
|
{
|
||||||
|
return log(($yValue + $this->yOffset) / $this->getIntersect()) / log($this->getSlope());
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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';
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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);
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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);
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
private function exponentialRegression($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);
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
public function __construct($yValues, $xValues = array(), $const = true)
|
||||||
|
{
|
||||||
|
if (parent::__construct($yValues, $xValues) !== false) {
|
||||||
|
$this->exponentialRegression($yValues, $xValues, $const);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,102 @@
|
||||||
|
<?php
|
||||||
|
|
||||||
|
namespace PHPExcel\Shared\Trend;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* PHPExcel_Linear_Best_Fit
|
||||||
|
*
|
||||||
|
* Copyright (c) 2006 - 2015 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 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||||
|
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||||
|
* @version ##VERSION##, ##DATE##
|
||||||
|
*/
|
||||||
|
class LinearBestFit extends BestFit
|
||||||
|
{
|
||||||
|
/**
|
||||||
|
* 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;
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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();
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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';
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
private function linearRegression($yValues, $xValues, $const)
|
||||||
|
{
|
||||||
|
$this->leastSquareFit($yValues, $xValues, $const);
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
public function __construct($yValues, $xValues = array(), $const = true)
|
||||||
|
{
|
||||||
|
if (parent::__construct($yValues, $xValues) !== false) {
|
||||||
|
$this->linearRegression($yValues, $xValues, $const);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,110 @@
|
||||||
|
<?php
|
||||||
|
|
||||||
|
namespace PHPExcel\Shared\Trend;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* PHPExcel_Logarithmic_Best_Fit
|
||||||
|
*
|
||||||
|
* Copyright (c) 2006 - 2015 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 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||||
|
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||||
|
* @version ##VERSION##, ##DATE##
|
||||||
|
*/
|
||||||
|
class LogarithmicBestFit extends BestFit
|
||||||
|
{
|
||||||
|
/**
|
||||||
|
* 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);
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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());
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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)';
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
private function logarithmicRegression($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);
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
public function __construct($yValues, $xValues = array(), $const = true)
|
||||||
|
{
|
||||||
|
if (parent::__construct($yValues, $xValues) !== false) {
|
||||||
|
$this->logarithmicRegression($yValues, $xValues, $const);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,221 @@
|
||||||
|
<?php
|
||||||
|
|
||||||
|
namespace PHPExcel\Shared\Trend;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* PHPExcel_Polynomial_Best_Fit
|
||||||
|
*
|
||||||
|
* Copyright (c) 2006 - 2015 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 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||||
|
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||||
|
* @version ##VERSION##, ##DATE##
|
||||||
|
*/
|
||||||
|
class PolynomialBestFit extends BestFit
|
||||||
|
{
|
||||||
|
/**
|
||||||
|
* 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;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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();
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $this->_slope;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
public function getCoefficients($dp = 0)
|
||||||
|
{
|
||||||
|
return array_merge(array($this->getIntersect($dp)), $this->getSlope($dp));
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
private function polynomialRegression($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);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
public function __construct($order, $yValues, $xValues = array(), $const = true)
|
||||||
|
{
|
||||||
|
if (parent::__construct($yValues, $xValues) !== false) {
|
||||||
|
if ($order < $this->valueCount) {
|
||||||
|
$this->bestFitType .= '_'.$order;
|
||||||
|
$this->order = $order;
|
||||||
|
$this->polynomialRegression($order, $yValues, $xValues, $const);
|
||||||
|
if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) {
|
||||||
|
$this->_error = true;
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
$this->_error = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,138 @@
|
||||||
|
<?php
|
||||||
|
|
||||||
|
namespace PHPExcel\Shared\Trend;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \PHPExcel\Shared\Trend\powerBestFit
|
||||||
|
*
|
||||||
|
* Copyright (c) 2006 - 2015 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 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
|
||||||
|
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
|
||||||
|
* @version ##VERSION##, ##DATE##
|
||||||
|
*/
|
||||||
|
class PowerBestFit extends BestFit
|
||||||
|
{
|
||||||
|
/**
|
||||||
|
* 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());
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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()));
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
private function powerRegression($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);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 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 $const
|
||||||
|
*/
|
||||||
|
public function __construct($yValues, $xValues = array(), $const = true)
|
||||||
|
{
|
||||||
|
if (parent::__construct($yValues, $xValues) !== false) {
|
||||||
|
$this->powerRegression($yValues, $xValues, $const);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,143 @@
|
||||||
|
<?php
|
||||||
|
|
||||||
|
namespace PHPExcel\Shared\Trend;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* PHPExcel_\PHPExcel\Shared\Trend\Trend
|
||||||
|
*
|
||||||
|
* Copyright (c) 2006 - 2015 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
|
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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 Trend
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{
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const TREND_LINEAR = 'Linear';
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const TREND_LOGARITHMIC = 'Logarithmic';
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const TREND_EXPONENTIAL = 'Exponential';
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const TREND_POWER = 'Power';
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const TREND_POLYNOMIAL_2 = 'Polynomial_2';
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const TREND_POLYNOMIAL_3 = 'Polynomial_3';
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const TREND_POLYNOMIAL_4 = 'Polynomial_4';
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|
const TREND_POLYNOMIAL_5 = 'Polynomial_5';
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|
const TREND_POLYNOMIAL_6 = 'Polynomial_6';
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|
const TREND_BEST_FIT = 'Bestfit';
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|
const TREND_BEST_FIT_NO_POLY = 'Bestfit_no_Polynomials';
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|
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|
/**
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|
* Names of the best-fit Trend analysis methods
|
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|
*
|
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|
* @var string[]
|
||||||
|
**/
|
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|
private static $trendTypes = array(
|
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|
self::TREND_LINEAR,
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|
self::TREND_LOGARITHMIC,
|
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|
self::TREND_EXPONENTIAL,
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|
self::TREND_POWER
|
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|
);
|
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|
|
||||||
|
/**
|
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|
* Names of the best-fit Trend polynomial orders
|
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|
*
|
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|
* @var string[]
|
||||||
|
**/
|
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|
private static $trendTypePolynomialOrders = array(
|
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|
self::TREND_POLYNOMIAL_2,
|
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|
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 bestFit[]
|
||||||
|
**/
|
||||||
|
private static $trendCache = array();
|
||||||
|
|
||||||
|
|
||||||
|
public static function calculate($trendType = self::TREND_BEST_FIT, $yValues = array(), $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])) {
|
||||||
|
$className = '\PHPExcel\Shared\Trend\\'.$trendType.'BestFit';
|
||||||
|
self::$trendCache[$key] = new $className($yValues, $xValues, $const);
|
||||||
|
}
|
||||||
|
return self::$trendCache[$key];
|
||||||
|
case self::TREND_POLYNOMIAL_2:
|
||||||
|
case self::TREND_POLYNOMIAL_3:
|
||||||
|
case self::TREND_POLYNOMIAL_4:
|
||||||
|
case self::TREND_POLYNOMIAL_5:
|
||||||
|
case self::TREND_POLYNOMIAL_6:
|
||||||
|
if (!isset(self::$trendCache[$key])) {
|
||||||
|
$order = substr($trendType, -1);
|
||||||
|
self::$trendCache[$key] = new PolynomialBestFit($order, $yValues, $xValues, $const);
|
||||||
|
}
|
||||||
|
return self::$trendCache[$key];
|
||||||
|
case self::TREND_BEST_FIT:
|
||||||
|
case self::TREND_BEST_FIT_NO_POLY:
|
||||||
|
// If the request is to determine the best fit regression, then we test each Trend line in turn
|
||||||
|
// Start by generating an instance of each available Trend method
|
||||||
|
foreach (self::$trendTypes as $trendMethod) {
|
||||||
|
$className = '\PHPExcel\Shared\Trend\\'.$trendType.'BestFit';
|
||||||
|
$bestFit[$trendMethod] = new $className($yValues, $xValues, $const);
|
||||||
|
$bestFitValue[$trendMethod] = $bestFit[$trendMethod]->getGoodnessOfFit();
|
||||||
|
}
|
||||||
|
if ($trendType != self::TREND_BEST_FIT_NO_POLY) {
|
||||||
|
foreach (self::$trendTypePolynomialOrders as $trendMethod) {
|
||||||
|
$order = substr($trendMethod, -1);
|
||||||
|
$bestFit[$trendMethod] = new PolynomialBestFit($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];
|
||||||
|
default:
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue