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217 lines
7 KiB
C#
217 lines
7 KiB
C#
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/*
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* Copyright 2007 ZXing authors
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using System;
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namespace com.google.zxing.common.reedsolomon
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{
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/// <summary> <p>Implements Reed-Solomon decoding, as the name implies.</p>
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///
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/// <p>The algorithm will not be explained here, but the following references were helpful
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/// in creating this implementation:</p>
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///
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/// <ul>
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/// <li>Bruce Maggs.
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/// <a href="http://www.cs.cmu.edu/afs/cs.cmu.edu/project/pscico-guyb/realworld/www/rs_decode.ps">
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/// "Decoding Reed-Solomon Codes"</a> (see discussion of Forney's Formula)</li>
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/// <li>J.I. Hall. <a href="www.mth.msu.edu/~jhall/classes/codenotes/GRS.pdf">
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/// "Chapter 5. Generalized Reed-Solomon Codes"</a>
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/// (see discussion of Euclidean algorithm)</li>
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/// </ul>
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///
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/// <p>Much credit is due to William Rucklidge since portions of this code are an indirect
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/// port of his C++ Reed-Solomon implementation.</p>
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///
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/// </summary>
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/// <author> Sean Owen
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/// </author>
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/// <author> William Rucklidge
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/// </author>
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/// <author> sanfordsquires
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/// </author>
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/// <author>www.Redivivus.in (suraj.supekar@redivivus.in) - Ported from ZXING Java Source
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/// </author>
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public sealed class ReedSolomonDecoder
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{
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//UPGRADE_NOTE: Final was removed from the declaration of 'field '. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1003'"
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private GF256 field;
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public ReedSolomonDecoder(GF256 field)
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{
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this.field = field;
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}
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/// <summary> <p>Decodes given set of received codewords, which include both data and error-correction
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/// codewords. Really, this means it uses Reed-Solomon to detect and correct errors, in-place,
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/// in the input.</p>
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///
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/// </summary>
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/// <param name="received">data and error-correction codewords
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/// </param>
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/// <param name="twoS">number of error-correction codewords available
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/// </param>
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/// <throws> ReedSolomonException if decoding fails for any reason </throws>
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public void decode(int[] received, int twoS)
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{
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GF256Poly poly = new GF256Poly(field, received);
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int[] syndromeCoefficients = new int[twoS];
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bool dataMatrix = field.Equals(GF256.DATA_MATRIX_FIELD);
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bool noError = true;
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for (int i = 0; i < twoS; i++)
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{
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// Thanks to sanfordsquires for this fix:
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int eval = poly.evaluateAt(field.exp(dataMatrix?i + 1:i));
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syndromeCoefficients[syndromeCoefficients.Length - 1 - i] = eval;
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if (eval != 0)
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{
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noError = false;
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}
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}
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if (noError)
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{
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return ;
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}
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GF256Poly syndrome = new GF256Poly(field, syndromeCoefficients);
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GF256Poly[] sigmaOmega = runEuclideanAlgorithm(field.buildMonomial(twoS, 1), syndrome, twoS);
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GF256Poly sigma = sigmaOmega[0];
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GF256Poly omega = sigmaOmega[1];
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int[] errorLocations = findErrorLocations(sigma);
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int[] errorMagnitudes = findErrorMagnitudes(omega, errorLocations, dataMatrix);
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for (int i = 0; i < errorLocations.Length; i++)
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{
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int position = received.Length - 1 - field.log(errorLocations[i]);
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if (position < 0)
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{
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throw new ReedSolomonException("Bad error location");
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}
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received[position] = GF256.addOrSubtract(received[position], errorMagnitudes[i]);
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}
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}
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private GF256Poly[] runEuclideanAlgorithm(GF256Poly a, GF256Poly b, int R)
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{
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// Assume a's degree is >= b's
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if (a.Degree < b.Degree)
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{
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GF256Poly temp = a;
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a = b;
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b = temp;
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}
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GF256Poly rLast = a;
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GF256Poly r = b;
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GF256Poly sLast = field.One;
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GF256Poly s = field.Zero;
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GF256Poly tLast = field.Zero;
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GF256Poly t = field.One;
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// Run Euclidean algorithm until r's degree is less than R/2
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while (r.Degree >= R / 2)
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{
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GF256Poly rLastLast = rLast;
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GF256Poly sLastLast = sLast;
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GF256Poly tLastLast = tLast;
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rLast = r;
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sLast = s;
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tLast = t;
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// Divide rLastLast by rLast, with quotient in q and remainder in r
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if (rLast.Zero)
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{
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// Oops, Euclidean algorithm already terminated?
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throw new ReedSolomonException("r_{i-1} was zero");
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}
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r = rLastLast;
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GF256Poly q = field.Zero;
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int denominatorLeadingTerm = rLast.getCoefficient(rLast.Degree);
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int dltInverse = field.inverse(denominatorLeadingTerm);
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while (r.Degree >= rLast.Degree && !r.Zero)
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{
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int degreeDiff = r.Degree - rLast.Degree;
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int scale = field.multiply(r.getCoefficient(r.Degree), dltInverse);
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q = q.addOrSubtract(field.buildMonomial(degreeDiff, scale));
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r = r.addOrSubtract(rLast.multiplyByMonomial(degreeDiff, scale));
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}
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s = q.multiply(sLast).addOrSubtract(sLastLast);
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t = q.multiply(tLast).addOrSubtract(tLastLast);
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}
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int sigmaTildeAtZero = t.getCoefficient(0);
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if (sigmaTildeAtZero == 0)
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{
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throw new ReedSolomonException("sigmaTilde(0) was zero");
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}
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int inverse = field.inverse(sigmaTildeAtZero);
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GF256Poly sigma = t.multiply(inverse);
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GF256Poly omega = r.multiply(inverse);
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return new GF256Poly[]{sigma, omega};
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}
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private int[] findErrorLocations(GF256Poly errorLocator)
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{
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// This is a direct application of Chien's search
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int numErrors = errorLocator.Degree;
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if (numErrors == 1)
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{
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// shortcut
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return new int[]{errorLocator.getCoefficient(1)};
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}
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int[] result = new int[numErrors];
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int e = 0;
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for (int i = 1; i < 256 && e < numErrors; i++)
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{
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if (errorLocator.evaluateAt(i) == 0)
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{
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result[e] = field.inverse(i);
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e++;
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}
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}
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if (e != numErrors)
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{
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throw new ReedSolomonException("Error locator degree does not match number of roots");
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}
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return result;
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}
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private int[] findErrorMagnitudes(GF256Poly errorEvaluator, int[] errorLocations, bool dataMatrix)
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{
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// This is directly applying Forney's Formula
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int s = errorLocations.Length;
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int[] result = new int[s];
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for (int i = 0; i < s; i++)
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{
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int xiInverse = field.inverse(errorLocations[i]);
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int denominator = 1;
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for (int j = 0; j < s; j++)
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{
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if (i != j)
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{
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denominator = field.multiply(denominator, GF256.addOrSubtract(1, field.multiply(errorLocations[j], xiInverse)));
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}
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}
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result[i] = field.multiply(errorEvaluator.evaluateAt(xiInverse), field.inverse(denominator));
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// Thanks to sanfordsquires for this fix:
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if (dataMatrix)
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{
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result[i] = field.multiply(result[i], xiInverse);
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}
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}
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return result;
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}
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}
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}
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