223 lines
6.8 KiB
PHP
223 lines
6.8 KiB
PHP
<?php
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require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php';
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require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/JAMA/Matrix.php';
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/**
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* PHPExcel_Polynomial_Best_Fit
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*
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* Copyright (c) 2006 - 2015 PHPExcel
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*
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* This library is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* This library is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with this library; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*
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* @category PHPExcel
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* @package PHPExcel_Shared_Trend
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* @copyright Copyright (c) 2006 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
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* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
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* @version ##VERSION##, ##DATE##
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*/
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class PHPExcel_Polynomial_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 = 'polynomial';
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/**
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* Polynomial order
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*
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* @protected
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* @var int
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**/
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protected $order = 0;
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/**
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* Return the order of this polynomial
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*
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* @return int
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**/
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public function getOrder()
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{
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return $this->order;
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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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$retVal = $this->getIntersect();
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$slope = $this->getSlope();
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foreach ($slope as $key => $value) {
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if ($value != 0.0) {
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$retVal += $value * pow($xValue, $key + 1);
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}
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}
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return $retVal;
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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 ($yValue - $this->getIntersect()) / $this->getSlope();
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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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$slope = $this->getSlope($dp);
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$intersect = $this->getIntersect($dp);
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$equation = 'Y = ' . $intersect;
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foreach ($slope as $key => $value) {
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if ($value != 0.0) {
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$equation .= ' + ' . $value . ' * X';
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if ($key > 0) {
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$equation .= '^' . ($key + 1);
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}
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}
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}
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return $equation;
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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 ($dp != 0) {
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$coefficients = array();
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foreach ($this->_slope as $coefficient) {
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$coefficients[] = round($coefficient, $dp);
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}
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return $coefficients;
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}
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return $this->_slope;
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}
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public function getCoefficients($dp = 0)
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{
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return array_merge(array($this->getIntersect($dp)), $this->getSlope($dp));
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}
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/**
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* Execute the regression and calculate the goodness of fit for a set of X and Y data values
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*
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* @param int $order Order of Polynomial for this regression
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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 $const
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*/
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private function polynomialRegression($order, $yValues, $xValues, $const)
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{
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// calculate sums
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$x_sum = array_sum($xValues);
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$y_sum = array_sum($yValues);
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$xx_sum = $xy_sum = 0;
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for ($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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}
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/*
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* This routine uses logic from the PHP port of polyfit version 0.1
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* written by Michael Bommarito and Paul Meagher
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*
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* The function fits a polynomial function of order $order through
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* a series of x-y data points using least squares.
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*
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*/
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for ($i = 0; $i < $this->valueCount; ++$i) {
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for ($j = 0; $j <= $order; ++$j) {
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$A[$i][$j] = pow($xValues[$i], $j);
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}
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}
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for ($i=0; $i < $this->valueCount; ++$i) {
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$B[$i] = array($yValues[$i]);
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}
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$matrixA = new Matrix($A);
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$matrixB = new Matrix($B);
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$C = $matrixA->solve($matrixB);
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$coefficients = array();
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for ($i = 0; $i < $C->m; ++$i) {
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$r = $C->get($i, 0);
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if (abs($r) <= pow(10, -9)) {
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$r = 0;
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}
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$coefficients[] = $r;
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}
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$this->intersect = array_shift($coefficients);
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$this->_slope = $coefficients;
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$this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum);
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foreach ($this->xValues as $xKey => $xValue) {
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$this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
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}
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}
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/**
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* Define the regression and calculate the goodness of fit for a set of X and Y data values
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*
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* @param int $order Order of Polynomial for this regression
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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 $const
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*/
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public function __construct($order, $yValues, $xValues = array(), $const = true)
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{
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if (parent::__construct($yValues, $xValues) !== false) {
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if ($order < $this->valueCount) {
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$this->bestFitType .= '_'.$order;
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$this->order = $order;
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$this->polynomialRegression($order, $yValues, $xValues, $const);
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if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) {
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$this->_error = true;
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}
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} else {
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$this->_error = true;
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}
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}
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}
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}
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