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agomez |
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#include "surveydistribution.h" |
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#include "readfunc.h" |
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#include "readword.h" |
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#include "readaggregation.h" |
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#include "errorhandler.h" |
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#include "mathfunc.h" |
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#include "areatime.h" |
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#include "stock.h" |
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#include "suitfuncptrvector.h" |
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#include "gadget.h" |
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#include "global.h" |
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SurveyDistribution::SurveyDistribution(CommentStream& infile, const AreaClass* const Area,
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const TimeClass* const TimeInfo, Keeper* const keeper, double weight, const char* name)
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: Likelihood(SURVEYDISTRIBUTIONLIKELIHOOD, weight, name), alptr(0) {
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int i, j;
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char text[MaxStrLength];
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strncpy(text, "", MaxStrLength);
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int numarea = 0, numage = 0, numlen = 0;
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char datafilename[MaxStrLength];
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char aggfilename[MaxStrLength];
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strncpy(datafilename, "", MaxStrLength);
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strncpy(aggfilename, "", MaxStrLength);
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ifstream datafile;
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CommentStream subdata(datafile);
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readWordAndValue(infile, "datafile", datafilename);
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timeindex = 0;
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fittype = new char[MaxStrLength];
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strncpy(fittype, "", MaxStrLength);
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liketype = new char[MaxStrLength];
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strncpy(liketype, "", MaxStrLength);
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//read in area aggregation from file
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readWordAndValue(infile, "areaaggfile", aggfilename);
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datafile.open(aggfilename, ios::in);
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handle.checkIfFailure(datafile, aggfilename);
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handle.Open(aggfilename);
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numarea = readAggregation(subdata, areas, areaindex);
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handle.Close();
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datafile.close();
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datafile.clear();
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//Must change from outer areas to inner areas.
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for (i = 0; i < areas.Nrow(); i++)
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for (j = 0; j < areas.Ncol(i); j++)
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areas[i][j] = Area->getInnerArea(areas[i][j]);
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//read in length aggregation from file
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readWordAndValue(infile, "lenaggfile", aggfilename);
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datafile.open(aggfilename, ios::in);
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handle.checkIfFailure(datafile, aggfilename);
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handle.Open(aggfilename);
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numlen = readLengthAggregation(subdata, lengths, lenindex);
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handle.Close();
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datafile.close();
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datafile.clear();
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LgrpDiv = new LengthGroupDivision(lengths);
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if (LgrpDiv->Error())
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handle.logMessage(LOGFAIL, "Error in surveydistribution - failed to create length group");
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//read in age aggregation from file
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readWordAndValue(infile, "ageaggfile", aggfilename);
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datafile.open(aggfilename, ios::in);
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handle.checkIfFailure(datafile, aggfilename);
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handle.Open(aggfilename);
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numage = readAggregation(subdata, ages, ageindex);
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handle.Close();
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datafile.close();
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datafile.clear();
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//read in the stocknames
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i = 0;
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infile >> text >> ws;
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if (strcasecmp(text, "stocknames") != 0)
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handle.logFileUnexpected(LOGFAIL, "stocknames", text);
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infile >> text;
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while (!infile.eof() && (strcasecmp(text, "fittype") != 0)) {
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infile >> ws;
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stocknames.resize(new char[strlen(text) + 1]);
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strcpy(stocknames[i++], text);
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infile >> text;
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}
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if (stocknames.Size() == 0)
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handle.logFileMessage(LOGFAIL, "\nError in surveydistribution - failed to read stocks");
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handle.logMessage(LOGMESSAGE, "Read stock data - number of stocks", stocknames.Size());
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infile >> fittype >> ws;
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fitnumber = 0;
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if (strcasecmp(fittype, "linearfit") == 0) {
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fitnumber = 1;
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} else if (strcasecmp(fittype, "powerfit") == 0) {
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fitnumber = 2;
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} else
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handle.logFileMessage(LOGFAIL, "\nError in surveydistribution - unrecognised fittype", fittype);
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parameters.resize(2, keeper);
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infile >> text >> ws;
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if (strcasecmp(text, "parameters") != 0)
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handle.logFileUnexpected(LOGFAIL, "parameters", text);
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parameters.read(infile, TimeInfo, keeper);
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suit.resize(LgrpDiv->numLengthGroups(), 0.0);
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infile >> text >> ws;
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if ((strcasecmp(text, "function") == 0)) {
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//read suitability function
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infile >> text >> ws;
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SuitFuncPtrVector tempsuitfunc;
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tempsuitfunc.readSuitFunction(infile, text, TimeInfo, keeper);
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suitfunction = tempsuitfunc[0];
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if (suitfunction->usesPredLength())
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suitfunction->setPredLength(0.0);
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for (i = 0; i < LgrpDiv->numLengthGroups(); i++) {
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if (suitfunction->usesPreyLength())
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suitfunction->setPreyLength(LgrpDiv->meanLength(i));
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suit[i] = suitfunction->calculate();
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}
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} else if (strcasecmp(text, "suitfile") == 0) {
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//read values from file
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for (i = 0; i < LgrpDiv->numLengthGroups(); i++)
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infile >> suit[i];
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} else {
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handle.logFileMessage(LOGFAIL, "\nError in surveydistribution - unrecognised suitability", text);
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}
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//JMB - changed to make the reading of epsilon optional
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infile >> ws;
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char c = infile.peek();
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if ((c == 'e') || (c == 'E'))
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readWordAndVariable(infile, "epsilon", epsilon);
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else
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epsilon = 1.0;
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if (epsilon < verysmall) {
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handle.logFileMessage(LOGWARN, "epsilon should be a positive integer - set to default value 1");
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epsilon = 1.0;
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}
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readWordAndValue(infile, "likelihoodtype", liketype);
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likenumber = 0;
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if (strcasecmp(liketype, "pearson") == 0)
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likenumber = 1;
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else if (strcasecmp(liketype, "multinomial") == 0)
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likenumber = 2;
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else if (strcasecmp(liketype, "gamma") == 0)
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likenumber = 3;
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else if (strcasecmp(liketype, "log") == 0)
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likenumber = 4;
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else
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handle.logFileMessage(LOGFAIL, "\nError in surveydistribution - unrecognised likelihoodtype", liketype);
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//read the survey distribution data from the datafile
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datafile.open(datafilename, ios::in);
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handle.checkIfFailure(datafile, datafilename);
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handle.Open(datafilename);
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readDistributionData(subdata, TimeInfo, numarea, numage, numlen);
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handle.Close();
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datafile.close();
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datafile.clear();
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//prepare for next likelihood component
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infile >> ws;
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if (!infile.eof()) {
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infile >> text >> ws;
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if (strcasecmp(text, "[component]") != 0)
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handle.logFileUnexpected(LOGFAIL, "[component]", text);
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}
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}
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void SurveyDistribution::readDistributionData(CommentStream& infile,
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const TimeClass* TimeInfo, int numarea, int numage, int numlen) {
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double tmpnumber;
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char tmparea[MaxStrLength], tmpage[MaxStrLength], tmplen[MaxStrLength];
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strncpy(tmparea, "", MaxStrLength);
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strncpy(tmpage, "", MaxStrLength);
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strncpy(tmplen, "", MaxStrLength);
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int keepdata, timeid, areaid, ageid, lenid;
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int i, year, step, count, reject;
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//Check the number of columns in the inputfile
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if (countColumns(infile) != 6)
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handle.logFileMessage(LOGFAIL, "wrong number of columns in inputfile - should be 6");
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year = step = count = reject = 0;
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while (!infile.eof()) {
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keepdata = 1;
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infile >> year >> step >> tmparea >> tmpage >> tmplen >> tmpnumber >> ws;
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//crude check to see if something has gone wrong and avoid infinite loops
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if (strlen(tmparea) == 0)
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handle.logFileMessage(LOGFAIL, "failed to read data from file");
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//if tmparea is in areaindex keep data, else dont keep the data
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areaid = -1;
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for (i = 0; i < areaindex.Size(); i++)
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if (strcasecmp(areaindex[i], tmparea) == 0)
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areaid = i;
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if (areaid == -1)
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keepdata = 0;
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//if tmpage is in ageindex keep data, else dont keep the data
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ageid = -1;
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for (i = 0; i < ageindex.Size(); i++)
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if (strcasecmp(ageindex[i], tmpage) == 0)
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ageid = i;
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if (ageid == -1)
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keepdata = 0;
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//if tmplen is in lenindex keep data, else dont keep the data
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lenid = -1;
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for (i = 0; i < lenindex.Size(); i++)
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if (strcasecmp(lenindex[i], tmplen) == 0)
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lenid = i;
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if (lenid == -1)
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keepdata = 0;
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//check if the year and step are in the simulation
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timeid = -1;
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if ((TimeInfo->isWithinPeriod(year, step)) && (keepdata == 1)) {
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for (i = 0; i < Years.Size(); i++)
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if ((Years[i] == year) && (Steps[i] == step))
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timeid = i;
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if (timeid == -1) {
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Years.resize(1, year);
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Steps.resize(1, step);
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timeid = (Years.Size() - 1);
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obsDistribution.resize();
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modelDistribution.resize();
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| 242 : |
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likelihoodValues.AddRows(1, numarea, 0.0);
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| 243 : |
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for (i = 0; i < numarea; i++) {
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| 244 : |
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obsDistribution[timeid].resize(new DoubleMatrix(numage, numlen, 0.0));
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| 245 : |
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modelDistribution[timeid].resize(new DoubleMatrix(numage, numlen, 0.0));
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}
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}
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| 248 : |
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} else
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keepdata = 0;
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| 251 : |
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| 252 : |
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| 253 : |
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if (keepdata == 1) {
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| 254 : |
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//survey distribution data is required, so store it
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| 255 : |
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count++;
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| 256 : |
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(*obsDistribution[timeid][areaid])[ageid][lenid] = tmpnumber;
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| 257 : |
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} else
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| 258 : |
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reject++; //count number of rejected data points read from file
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| 259 : |
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}
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| 260 : |
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| 261 : |
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AAT.addActions(Years, Steps, TimeInfo);
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| 262 : |
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if (count == 0)
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| 263 : |
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handle.logMessage(LOGWARN, "Warning in surveydistribution - found no data in the data file for", this->getName());
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| 264 : |
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| 265 : |
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if (Steps.Size() > 0) {
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| 266 : |
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//JMB - to be comparable, this should only take place on the same step in each year
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| 267 : |
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step = Steps[0];
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| 268 : |
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timeid = 0;
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| 269 : |
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for (i = 1; i < Steps.Size(); i++)
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| 270 : |
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if (Steps[i] != step)
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| 271 : |
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timeid++;
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| 272 : |
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| 273 : |
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if (timeid != 0)
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| 274 : |
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handle.logMessage(LOGWARN, "Warning in surveydistribution - differing timesteps for", this->getName());
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| 275 : |
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}
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| 276 : |
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| 277 : |
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if (reject != 0)
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| 278 : |
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handle.logMessage(LOGMESSAGE, "Discarded invalid surveydistribution data - number of invalid entries", reject);
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| 279 : |
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handle.logMessage(LOGMESSAGE, "Read surveydistribution data file - number of entries", count);
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| 280 : |
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}
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| 281 : |
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| 282 : |
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SurveyDistribution::~SurveyDistribution() {
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| 283 : |
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int i, j;
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| 284 : |
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for (i = 0; i < stocknames.Size(); i++)
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| 285 : |
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delete[] stocknames[i];
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| 286 : |
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for (i = 0; i < areaindex.Size(); i++)
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| 287 : |
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delete[] areaindex[i];
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| 288 : |
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for (i = 0; i < ageindex.Size(); i++)
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| 289 : |
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delete[] ageindex[i];
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| 290 : |
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for (i = 0; i < lenindex.Size(); i++)
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| 291 : |
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delete[] lenindex[i];
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| 292 : |
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| 293 : |
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if (suitfunction != NULL) {
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| 294 : |
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delete suitfunction;
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| 295 : |
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suitfunction = NULL;
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| 296 : |
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}
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| 297 : |
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for (i = 0; i < obsDistribution.Nrow(); i++) {
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| 298 : |
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for (j = 0; j < obsDistribution.Ncol(i); j++) {
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| 299 : |
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delete obsDistribution[i][j];
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| 300 : |
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delete modelDistribution[i][j];
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| 301 : |
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}
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| 302 : |
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}
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| 303 : |
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| 304 : |
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if (aggregator != 0)
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| 305 : |
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delete aggregator;
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| 306 : |
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delete LgrpDiv;
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| 307 : |
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delete[] fittype;
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| 308 : |
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delete[] liketype;
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| 309 : |
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}
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| 310 : |
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| 311 : |
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void SurveyDistribution::Reset(const Keeper* const keeper) {
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| 312 : |
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Likelihood::Reset(keeper);
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| 313 : |
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if (isZero(weight))
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| 314 : |
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handle.logMessage(LOGWARN, "Warning in surveydistribution - zero weight for", this->getName());
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| 315 : |
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int i, j;
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| 316 : |
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for (i = 0; i < modelDistribution.Nrow(); i++)
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| 317 : |
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for (j = 0; j < modelDistribution.Ncol(i); j++)
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| 318 : |
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(*modelDistribution[i][j]).setToZero();
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| 319 : |
|
|
if (handle.getLogLevel() >= LOGMESSAGE)
|
| 320 : |
|
|
handle.logMessage(LOGMESSAGE, "Reset surveydistribution component", this->getName());
|
| 321 : |
|
|
}
|
| 322 : |
|
|
|
| 323 : |
|
|
void SurveyDistribution::Print(ofstream& outfile) const {
|
| 324 : |
|
|
int i;
|
| 325 : |
|
|
outfile << "\nSurvey Distribution " << this->getName() << " - likelihood value " << likelihood
|
| 326 : |
|
|
<< "\n\tFunction " << liketype << "\n\tStock names:";
|
| 327 : |
|
|
for (i = 0; i < stocknames.Size(); i++)
|
| 328 : |
|
|
outfile << sep << stocknames[i];
|
| 329 : |
|
|
outfile << endl;
|
| 330 : |
|
|
aggregator->Print(outfile);
|
| 331 : |
|
|
outfile.flush();
|
| 332 : |
|
|
}
|
| 333 : |
|
|
|
| 334 : |
|
|
void SurveyDistribution::printLikelihood(ofstream& outfile, const TimeClass* const TimeInfo) {
|
| 335 : |
|
|
|
| 336 : |
|
|
if (!AAT.atCurrentTime(TimeInfo))
|
| 337 : |
|
|
return;
|
| 338 : |
|
|
|
| 339 : |
|
|
int i, area, age, len;
|
| 340 : |
|
|
timeindex = -1;
|
| 341 : |
|
|
for (i = 0; i < Years.Size(); i++)
|
| 342 : |
|
|
if ((Years[i] == TimeInfo->getYear()) && (Steps[i] == TimeInfo->getStep()))
|
| 343 : |
|
|
timeindex = i;
|
| 344 : |
|
|
if (timeindex == -1)
|
| 345 : |
|
|
handle.logMessage(LOGFAIL, "Error in surveydistribution - invalid timestep");
|
| 346 : |
|
|
|
| 347 : |
|
|
for (area = 0; area < modelDistribution.Ncol(timeindex); area++) {
|
| 348 : |
|
|
for (age = 0; age < modelDistribution[timeindex][area]->Nrow(); age++) {
|
| 349 : |
|
|
for (len = 0; len < modelDistribution[timeindex][area]->Ncol(age); len++) {
|
| 350 : |
|
|
outfile << setw(lowwidth) << Years[timeindex] << sep << setw(lowwidth)
|
| 351 : |
|
|
<< Steps[timeindex] << sep << setw(printwidth) << areaindex[area] << sep
|
| 352 : |
|
|
<< setw(printwidth) << ageindex[age] << sep << setw(printwidth)
|
| 353 : |
|
|
<< lenindex[len] << sep << setprecision(largeprecision) << setw(largewidth);
|
| 354 : |
|
|
|
| 355 : |
|
|
//JMB crude filter to remove the 'silly' values from the output
|
| 356 : |
|
|
if ((*modelDistribution[timeindex][area])[age][len] < rathersmall)
|
| 357 : |
|
|
outfile << 0 << endl;
|
| 358 : |
|
|
else
|
| 359 : |
|
|
outfile << (*modelDistribution[timeindex][area])[age][len] << endl;
|
| 360 : |
|
|
}
|
| 361 : |
|
|
}
|
| 362 : |
|
|
}
|
| 363 : |
|
|
}
|
| 364 : |
|
|
|
| 365 : |
|
|
void SurveyDistribution::setFleetsAndStocks(FleetPtrVector& Fleets, StockPtrVector& Stocks) {
|
| 366 : |
|
|
|
| 367 : |
|
|
int i, j, k, found, minage, maxage;
|
| 368 : |
|
|
StockPtrVector stocks;
|
| 369 : |
|
|
|
| 370 : |
|
|
for (i = 0; i < stocknames.Size(); i++) {
|
| 371 : |
|
|
found = 0;
|
| 372 : |
|
|
for (j = 0; j < Stocks.Size(); j++) {
|
| 373 : |
|
|
if (strcasecmp(stocknames[i], Stocks[j]->getName()) == 0) {
|
| 374 : |
|
|
found++;
|
| 375 : |
|
|
stocks.resize(Stocks[j]);
|
| 376 : |
|
|
}
|
| 377 : |
|
|
}
|
| 378 : |
|
|
if (found == 0)
|
| 379 : |
|
|
handle.logMessage(LOGFAIL, "Error in surveydistribution - failed to match stock", stocknames[i]);
|
| 380 : |
|
|
}
|
| 381 : |
|
|
|
| 382 : |
|
|
for (i = 0; i < stocks.Size(); i++)
|
| 383 : |
|
|
for (j = 0; j < stocks.Size(); j++)
|
| 384 : |
|
|
if ((strcasecmp(stocks[i]->getName(), stocks[j]->getName()) == 0) && (i != j))
|
| 385 : |
|
|
handle.logMessage(LOGFAIL, "Error in surveydistribution - repeated stock", stocks[i]->getName());
|
| 386 : |
|
|
|
| 387 : |
|
|
//check areas, ages and lengths
|
| 388 : |
|
|
if (handle.getLogLevel() >= LOGWARN) {
|
| 389 : |
|
|
for (j = 0; j < areas.Nrow(); j++) {
|
| 390 : |
|
|
found = 0;
|
| 391 : |
|
|
for (i = 0; i < stocks.Size(); i++)
|
| 392 : |
|
|
for (k = 0; k < areas.Ncol(j); k++)
|
| 393 : |
|
|
if (stocks[i]->isInArea(areas[j][k]))
|
| 394 : |
|
|
found++;
|
| 395 : |
|
|
if (found == 0)
|
| 396 : |
|
|
handle.logMessage(LOGWARN, "Warning in surveydistribution - stock not defined on all areas");
|
| 397 : |
|
|
}
|
| 398 : |
|
|
|
| 399 : |
|
|
minage = 9999;
|
| 400 : |
|
|
maxage = 0;
|
| 401 : |
|
|
for (i = 0; i < ages.Nrow(); i++) {
|
| 402 : |
|
|
for (j = 0; j < ages.Ncol(i); j++) {
|
| 403 : |
|
|
minage = min(ages[i][j], minage);
|
| 404 : |
|
|
maxage = max(ages[i][j], maxage);
|
| 405 : |
|
|
}
|
| 406 : |
|
|
}
|
| 407 : |
|
|
|
| 408 : |
|
|
found = 0;
|
| 409 : |
|
|
for (i = 0; i < stocks.Size(); i++)
|
| 410 : |
|
|
if (minage >= stocks[i]->minAge())
|
| 411 : |
|
|
found++;
|
| 412 : |
|
|
if (found == 0)
|
| 413 : |
|
|
handle.logMessage(LOGWARN, "Warning in surveydistribution - minimum age less than stock age");
|
| 414 : |
|
|
|
| 415 : |
|
|
found = 0;
|
| 416 : |
|
|
for (i = 0; i < stocks.Size(); i++)
|
| 417 : |
|
|
if (maxage <= stocks[i]->maxAge())
|
| 418 : |
|
|
found++;
|
| 419 : |
|
|
if (found == 0)
|
| 420 : |
|
|
handle.logMessage(LOGWARN, "Warning in surveydistribution - maximum age greater than stock age");
|
| 421 : |
|
|
|
| 422 : |
|
|
found = 0;
|
| 423 : |
|
|
for (i = 0; i < stocks.Size(); i++)
|
| 424 : |
|
|
if (LgrpDiv->maxLength(0) > stocks[i]->getLengthGroupDiv()->minLength())
|
| 425 : |
|
|
found++;
|
| 426 : |
|
|
if (found == 0)
|
| 427 : |
|
|
handle.logMessage(LOGWARN, "Warning in surveydistribution - minimum length group less than stock length");
|
| 428 : |
|
|
|
| 429 : |
|
|
found = 0;
|
| 430 : |
|
|
for (i = 0; i < stocks.Size(); i++)
|
| 431 : |
|
|
if (LgrpDiv->minLength(LgrpDiv->numLengthGroups()) < stocks[i]->getLengthGroupDiv()->maxLength())
|
| 432 : |
|
|
found++;
|
| 433 : |
|
|
if (found == 0)
|
| 434 : |
|
|
handle.logMessage(LOGWARN, "Warning in surveydistribution - maximum length group greater than stock length");
|
| 435 : |
|
|
}
|
| 436 : |
|
|
|
| 437 : |
|
|
aggregator = new StockAggregator(stocks, LgrpDiv, areas, ages);
|
| 438 : |
|
|
}
|
| 439 : |
|
|
|
| 440 : |
|
|
void SurveyDistribution::calcIndex(const TimeClass* const TimeInfo) {
|
| 441 : |
|
|
//written by kgf 13/10 98
|
| 442 : |
|
|
|
| 443 : |
|
|
int area, age, len;
|
| 444 : |
|
|
if (suitfunction != NULL) {
|
| 445 : |
|
|
suitfunction->updateConstants(TimeInfo);
|
| 446 : |
|
|
if ((timeindex == 0) || (suitfunction->didChange(TimeInfo))) {
|
| 447 : |
|
|
if (suitfunction->usesPredLength())
|
| 448 : |
|
|
suitfunction->setPredLength(0.0);
|
| 449 : |
|
|
|
| 450 : |
|
|
for (len = 0; len < LgrpDiv->numLengthGroups(); len++) {
|
| 451 : |
|
|
if (suitfunction->usesPreyLength())
|
| 452 : |
|
|
suitfunction->setPreyLength(LgrpDiv->meanLength(len));
|
| 453 : |
|
|
|
| 454 : |
|
|
suit[len] = suitfunction->calculate();
|
| 455 : |
|
|
}
|
| 456 : |
|
|
}
|
| 457 : |
|
|
}
|
| 458 : |
|
|
|
| 459 : |
|
|
parameters.Update(TimeInfo);
|
| 460 : |
|
|
switch (fitnumber) {
|
| 461 : |
|
|
case 1:
|
| 462 : |
|
|
for (area = 0; area < areas.Nrow(); area++)
|
| 463 : |
|
|
for (age = (*alptr)[area].minAge(); age <= (*alptr)[area].maxAge(); age++)
|
| 464 : |
|
|
for (len = (*alptr)[area].minLength(age); len < (*alptr)[area].maxLength(age); len++)
|
| 465 : |
|
|
(*modelDistribution[timeindex][area])[age][len] = parameters[0] * suit[len] * (((*alptr)[area][age][len]).N + parameters[1]);
|
| 466 : |
|
|
break;
|
| 467 : |
|
|
case 2:
|
| 468 : |
|
|
for (area = 0; area < areas.Nrow(); area++)
|
| 469 : |
|
|
for (age = (*alptr)[area].minAge(); age <= (*alptr)[area].maxAge(); age++)
|
| 470 : |
|
|
for (len = (*alptr)[area].minLength(age); len < (*alptr)[area].maxLength(age); len++)
|
| 471 : |
|
|
(*modelDistribution[timeindex][area])[age][len] = parameters[0] * suit[len] * pow(((*alptr)[area][age][len]).N, parameters[1]);
|
| 472 : |
|
|
break;
|
| 473 : |
|
|
default:
|
| 474 : |
|
|
handle.logMessage(LOGWARN, "Warning in surveydistribution - unrecognised fittype", fittype);
|
| 475 : |
|
|
break;
|
| 476 : |
|
|
}
|
| 477 : |
|
|
}
|
| 478 : |
|
|
|
| 479 : |
|
|
void SurveyDistribution::addLikelihood(const TimeClass* const TimeInfo) {
|
| 480 : |
|
|
|
| 481 : |
|
|
if ((!(AAT.atCurrentTime(TimeInfo))) || (isZero(weight)))
|
| 482 : |
|
|
return;
|
| 483 : |
|
|
|
| 484 : |
|
|
int i;
|
| 485 : |
|
|
timeindex = -1;
|
| 486 : |
|
|
for (i = 0; i < Years.Size(); i++)
|
| 487 : |
|
|
if ((Years[i] == TimeInfo->getYear()) && (Steps[i] == TimeInfo->getStep()))
|
| 488 : |
|
|
timeindex = i;
|
| 489 : |
|
|
if (timeindex == -1)
|
| 490 : |
|
|
handle.logMessage(LOGFAIL, "Error in surveydistribution - invalid timestep");
|
| 491 : |
|
|
|
| 492 : |
|
|
double l = 0.0;
|
| 493 : |
|
|
aggregator->Sum();
|
| 494 : |
|
|
if (handle.getLogLevel() >= LOGMESSAGE)
|
| 495 : |
|
|
handle.logMessage(LOGMESSAGE, "Calculating likelihood score for surveydistribution component", this->getName());
|
| 496 : |
|
|
|
| 497 : |
|
|
alptr = &aggregator->getSum();
|
| 498 : |
|
|
this->calcIndex(TimeInfo);
|
| 499 : |
|
|
switch (likenumber) {
|
| 500 : |
|
|
case 1:
|
| 501 : |
|
|
l = calcLikPearson();
|
| 502 : |
|
|
break;
|
| 503 : |
|
|
case 2:
|
| 504 : |
|
|
l = calcLikMultinomial();
|
| 505 : |
|
|
break;
|
| 506 : |
|
|
case 3:
|
| 507 : |
|
|
l = calcLikGamma();
|
| 508 : |
|
|
break;
|
| 509 : |
|
|
case 4:
|
| 510 : |
|
|
l = calcLikLog();
|
| 511 : |
|
|
break;
|
| 512 : |
|
|
default:
|
| 513 : |
|
|
handle.logMessage(LOGWARN, "Warning in surveydistribution - unrecognised likelihoodtype", liketype);
|
| 514 : |
|
|
break;
|
| 515 : |
|
|
}
|
| 516 : |
|
|
|
| 517 : |
|
|
if (handle.getLogLevel() >= LOGMESSAGE)
|
| 518 : |
|
|
handle.logMessage(LOGMESSAGE, "The likelihood score for this component on this timestep is", l);
|
| 519 : |
|
|
likelihood += l;
|
| 520 : |
|
|
}
|
| 521 : |
|
|
|
| 522 : |
|
|
double SurveyDistribution::calcLikMultinomial() {
|
| 523 : |
|
|
//written by kgf 30/10 98
|
| 524 : |
|
|
//Multinomial formula from H J Skaug
|
| 525 : |
|
|
|
| 526 : |
|
|
double temp, total, obstotal, modtotal;
|
| 527 : |
|
|
int area, age, len;
|
| 528 : |
|
|
|
| 529 : |
|
|
total = 0.0;
|
| 530 : |
|
|
for (area = 0; area < areas.Nrow(); area++) {
|
| 531 : |
|
|
temp = 0.0;
|
| 532 : |
|
|
obstotal = 0.0;
|
| 533 : |
|
|
modtotal = 0.0;
|
| 534 : |
|
|
for (age = 0; age < (*obsDistribution[timeindex][area]).Nrow(); age++) {
|
| 535 : |
|
|
for (len = 0; len < (*obsDistribution[timeindex][area]).Ncol(age); len++) {
|
| 536 : |
|
|
temp -= (*obsDistribution[timeindex][area])[age][len] *
|
| 537 : |
|
|
log(((*modelDistribution[timeindex][area])[age][len]) + epsilon);
|
| 538 : |
|
|
obstotal += (*obsDistribution[timeindex][area])[age][len];
|
| 539 : |
|
|
modtotal += ((*modelDistribution[timeindex][area])[age][len] + epsilon);
|
| 540 : |
|
|
}
|
| 541 : |
|
|
}
|
| 542 : |
|
|
|
| 543 : |
|
|
if ((modtotal < verysmall) && (!(isZero(obstotal)))) {
|
| 544 : |
|
|
likelihoodValues[timeindex][area] = 0.0;
|
| 545 : |
|
|
} else {
|
| 546 : |
|
|
temp /= obstotal;
|
| 547 : |
|
|
temp += log(modtotal);
|
| 548 : |
|
|
likelihoodValues[timeindex][area] = temp;
|
| 549 : |
|
|
}
|
| 550 : |
|
|
total += likelihoodValues[timeindex][area];
|
| 551 : |
|
|
}
|
| 552 : |
|
|
return total;
|
| 553 : |
|
|
}
|
| 554 : |
|
|
|
| 555 : |
|
|
double SurveyDistribution::calcLikPearson() {
|
| 556 : |
|
|
//written by kgf 13/10 98
|
| 557 : |
|
|
|
| 558 : |
|
|
double temp, total, diff;
|
| 559 : |
|
|
int area, age, len;
|
| 560 : |
|
|
|
| 561 : |
|
|
total = 0.0;
|
| 562 : |
|
|
for (area = 0; area < areas.Nrow(); area++) {
|
| 563 : |
|
|
temp = 0.0;
|
| 564 : |
|
|
for (age = 0; age < (*obsDistribution[timeindex][area]).Nrow(); age++) {
|
| 565 : |
|
|
for (len = 0; len < (*obsDistribution[timeindex][area]).Ncol(age); len++) {
|
| 566 : |
|
|
diff = ((*modelDistribution[timeindex][area])[age][len] - (*obsDistribution[timeindex][area])[age][len]);
|
| 567 : |
|
|
diff *= diff;
|
| 568 : |
|
|
diff /= ((*modelDistribution[timeindex][area])[age][len] + epsilon);
|
| 569 : |
|
|
temp += diff;
|
| 570 : |
|
|
}
|
| 571 : |
|
|
}
|
| 572 : |
|
|
likelihoodValues[timeindex][area] = temp;
|
| 573 : |
|
|
total += likelihoodValues[timeindex][area];
|
| 574 : |
|
|
}
|
| 575 : |
|
|
return total;
|
| 576 : |
|
|
}
|
| 577 : |
|
|
|
| 578 : |
|
|
double SurveyDistribution::calcLikGamma() {
|
| 579 : |
|
|
//written by kgf 24/5 00
|
| 580 : |
|
|
//The gamma likelihood function as described by
|
| 581 : |
|
|
//Hans J Skaug 15/3 00, at present without internal weighting.
|
| 582 : |
|
|
|
| 583 : |
|
|
double total, temp;
|
| 584 : |
|
|
int area, age, len;
|
| 585 : |
|
|
|
| 586 : |
|
|
total = 0.0;
|
| 587 : |
|
|
for (area = 0; area < areas.Nrow(); area++) {
|
| 588 : |
|
|
temp = 0.0;
|
| 589 : |
|
|
for (age = 0; age < (*obsDistribution[timeindex][area]).Nrow(); age++)
|
| 590 : |
|
|
for (len = 0; len < (*obsDistribution[timeindex][area]).Ncol(age); len++)
|
| 591 : |
|
|
temp += (((*obsDistribution[timeindex][area])[age][len] /
|
| 592 : |
|
|
((*modelDistribution[timeindex][area])[age][len] + epsilon)) +
|
| 593 : |
|
|
log((*modelDistribution[timeindex][area])[age][len] + epsilon));
|
| 594 : |
|
|
|
| 595 : |
|
|
likelihoodValues[timeindex][area] = temp;
|
| 596 : |
|
|
total += likelihoodValues[timeindex][area];
|
| 597 : |
|
|
}
|
| 598 : |
|
|
return total;
|
| 599 : |
|
|
}
|
| 600 : |
|
|
|
| 601 : |
|
|
double SurveyDistribution::calcLikLog() {
|
| 602 : |
|
|
//corrected by kgf 27/11 98 to sum first and then take the logarithm
|
| 603 : |
|
|
double total, obstotal, modtotal;
|
| 604 : |
|
|
int area, age, len;
|
| 605 : |
|
|
|
| 606 : |
|
|
total = 0.0;
|
| 607 : |
|
|
for (area = 0; area < areas.Nrow(); area++) {
|
| 608 : |
|
|
obstotal = 0.0;
|
| 609 : |
|
|
modtotal = 0.0;
|
| 610 : |
|
|
for (age = 0; age < (*obsDistribution[timeindex][area]).Nrow(); age++) {
|
| 611 : |
|
|
for (len = 0; len < (*obsDistribution[timeindex][area]).Ncol(age); len++) {
|
| 612 : |
|
|
modtotal += (*modelDistribution[timeindex][area])[age][len];
|
| 613 : |
|
|
obstotal += (*obsDistribution[timeindex][area])[age][len];
|
| 614 : |
|
|
}
|
| 615 : |
|
|
}
|
| 616 : |
|
|
|
| 617 : |
|
|
if (!(isZero(modtotal)))
|
| 618 : |
|
|
likelihoodValues[timeindex][area] = (log(obstotal / modtotal) * log(obstotal / modtotal));
|
| 619 : |
|
|
else
|
| 620 : |
|
|
likelihoodValues[timeindex][area] = verybig;
|
| 621 : |
|
|
|
| 622 : |
|
|
total += likelihoodValues[timeindex][area];
|
| 623 : |
|
|
}
|
| 624 : |
|
|
return total;
|
| 625 : |
|
|
}
|
| 626 : |
|
|
|
| 627 : |
|
|
void SurveyDistribution::printSummary(ofstream& outfile) {
|
| 628 : |
|
|
int year, area;
|
| 629 : |
|
|
|
| 630 : |
|
|
for (year = 0; year < likelihoodValues.Nrow(); year++) {
|
| 631 : |
|
|
for (area = 0; area < likelihoodValues.Ncol(year); area++) {
|
| 632 : |
|
|
outfile << setw(lowwidth) << Years[year] << sep << setw(lowwidth)
|
| 633 : |
|
|
<< Steps[year] << sep << setw(printwidth) << areaindex[area] << sep
|
| 634 : |
|
|
<< setw(largewidth) << this->getName() << sep << setprecision(smallprecision)
|
| 635 : |
|
|
<< setw(smallwidth) << weight << sep << setprecision(largeprecision)
|
| 636 : |
|
|
<< setw(largewidth) << likelihoodValues[year][area] << endl;
|
| 637 : |
|
|
}
|
| 638 : |
|
|
}
|
| 639 : |
|
|
outfile.flush();
|
| 640 : |
|
|
}
|
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