| 1 : |
agomez |
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#include "sionstep.h" |
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#include "areatime.h" |
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#include "errorhandler.h" |
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#include "readfunc.h" |
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#include "readword.h" |
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#include "gadget.h" |
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#include "global.h" |
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SIOnStep::~SIOnStep() {
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int i;
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if (LgrpDiv != 0)
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delete LgrpDiv;
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if (LR != 0)
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delete LR;
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for (i = 0; i < areaindex.Size(); i++)
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delete[] areaindex[i];
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for (i = 0; i < colindex.Size(); i++)
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delete[] colindex[i];
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for (i = 0; i < obsIndex.Size(); i++) {
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delete obsIndex[i];
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delete modelIndex[i];
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}
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for (i = 0; i < weightIndex.Size(); i++)
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delete weightIndex[i];
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}
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SIOnStep::SIOnStep(CommentStream& infile, const char* datafilename, const CharPtrVector& aindex,
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const TimeClass* const TimeInfo, const IntMatrix& areas, const CharPtrVector& charindex,
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const char* givenname, int bio, SIType type) : HasName(givenname), Areas(areas), alptr(0) {
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biomass = bio;
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sitype = type;
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useweight = 0;
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timeindex = 0;
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slope = 0.0;
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intercept = 0.0;
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int i;
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for (i = 0; i < aindex.Size(); i++) {
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areaindex.resize(new char[strlen(aindex[i]) + 1]);
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strcpy(areaindex[i], aindex[i]);
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}
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for (i = 0; i < charindex.Size(); i++) {
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colindex.resize(new char[strlen(charindex[i]) + 1]);
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strcpy(colindex[i], charindex[i]);
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}
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//read the fittype
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readSIRegressionData(infile);
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//read the survey indices data from the datafile
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ifstream datafile;
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CommentStream subdata(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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readSIData(subdata, TimeInfo);
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handle.Close();
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datafile.close();
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datafile.clear();
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//resize to store the regression information
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slopes.AddRows(areaindex.Size(), colindex.Size(), slope);
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intercepts.AddRows(areaindex.Size(), colindex.Size(), intercept);
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sse.AddRows(areaindex.Size(), colindex.Size(), 0.0);
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if (useweight)
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tmpWeight.resize(Years.Size(), 0.0);
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tmpModel.resize(Years.Size(), 0.0);
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tmpData.resize(Years.Size(), 0.0);
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likelihoodValues.resize(areaindex.Size(), 0.0);
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}
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void SIOnStep::readSIData(CommentStream& infile, const TimeClass* const TimeInfo) {
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double tmpnumber, tmpweight;
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char tmparea[MaxStrLength], tmplabel[MaxStrLength];
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strncpy(tmparea, "", MaxStrLength);
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strncpy(tmplabel, "", MaxStrLength);
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int keepdata, timeid, colid, areaid;
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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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infile >> ws;
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if ((!useweight) && (countColumns(infile) != 5))
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handle.logFileMessage(LOGFAIL, "wrong number of columns in inputfile - should be 5");
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else if ((useweight) && (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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if (useweight)
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infile >> year >> step >> tmparea >> tmplabel >> tmpnumber >> tmpweight >> ws;
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else
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infile >> year >> step >> tmparea >> tmplabel >> 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 tmplabel is in colindex find colid, else dont keep the data
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colid = -1;
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for (i = 0; i < colindex.Size(); i++)
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if (strcasecmp(colindex[i], tmplabel) == 0)
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colid = i;
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if (colid == -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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| 131 : |
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obsIndex.resize(new DoubleMatrix(areaindex.Size(), colindex.Size(), 0.0));
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modelIndex.resize(new DoubleMatrix(areaindex.Size(), colindex.Size(), 0.0));
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if (useweight)
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weightIndex.resize(new DoubleMatrix(areaindex.Size(), colindex.Size(), 0.0));
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}
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} else
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keepdata = 0;
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if (keepdata == 1) {
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//survey indices data is required, so store it
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count++;
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(*obsIndex[timeid])[areaid][colid] = tmpnumber;
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if (useweight)
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(*weightIndex[timeid])[areaid][colid] = tmpweight;
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} else
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reject++; //count number of rejected data points read from file
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}
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AAT.addActions(Years, Steps, TimeInfo);
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if (count == 0)
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handle.logMessage(LOGWARN, "Warning in surveyindex - found no data in the data file for", this->getName());
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if (Years.Size() < 2)
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handle.logMessage(LOGWARN, "Warning in surveyindex - insufficient data to fit a regression line for", this->getName());
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if ((handle.getLogLevel() >= LOGWARN) && (Steps.Size() > 0) && (this->getSIType() != SIEFFORT)) {
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| 157 : |
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//JMB to be comparable, this should only take place on the same step in each year
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//However the effort data will be on most timesteps so this check isnt required
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step = Steps[0];
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timeid = 0;
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for (i = 1; i < Steps.Size(); i++)
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if (Steps[i] != step)
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timeid++;
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if (timeid != 0)
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handle.logMessage(LOGWARN, "Warning in surveyindex - differing timesteps for", this->getName());
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}
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if (reject != 0)
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handle.logMessage(LOGMESSAGE, "Discarded invalid surveyindex data - number of invalid entries", reject);
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| 171 : |
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handle.logMessage(LOGMESSAGE, "Read surveyindex data file - number of entries", count);
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}
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void SIOnStep::readSIRegressionData(CommentStream& infile) {
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char text[MaxStrLength];
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strncpy(text, "", MaxStrLength);
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infile >> ws >> text;
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if (strcasecmp(text, "linearfit") == 0) {
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fittype = LINEARFIT;
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LR = new LinearRegression(FREE);
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} else if (strcasecmp(text, "loglinearfit") == 0) {
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fittype = LOGLINEARFIT;
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LR = new LogLinearRegression(FREE);
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} else if (strcasecmp(text, "weightlinearfit") == 0) {
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useweight = 1;
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fittype = WEIGHTLINEARFIT;
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LR = new WeightRegression(FREE);
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| 190 : |
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} else if (strcasecmp(text, "logweightlinearfit") == 0) {
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useweight = 1;
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fittype = LOGWEIGHTLINEARFIT;
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| 193 : |
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LR = new LogWeightRegression(FREE);
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| 194 : |
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} else if (strcasecmp(text, "fixedslopelinearfit") == 0) {
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fittype = FIXEDSLOPELINEARFIT;
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| 196 : |
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LR = new LinearRegression(FIXEDSLOPE);
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| 197 : |
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} else if (strcasecmp(text, "fixedslopeloglinearfit") == 0) {
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| 198 : |
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fittype = FIXEDSLOPELOGLINEARFIT;
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| 199 : |
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LR = new LogLinearRegression(FIXEDSLOPE);
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| 200 : |
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} else if (strcasecmp(text, "fixedslopeweightlinearfit") == 0) {
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useweight = 1;
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| 202 : |
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fittype = FIXEDSLOPEWEIGHTLINEARFIT;
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| 203 : |
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LR = new WeightRegression(FIXEDSLOPE);
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| 204 : |
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} else if (strcasecmp(text, "fixedslopelogweightlinearfit") == 0) {
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| 205 : |
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useweight = 1;
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| 206 : |
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fittype = FIXEDSLOPELOGWEIGHTLINEARFIT;
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| 207 : |
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LR = new LogWeightRegression(FIXEDSLOPE);
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| 208 : |
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} else if (strcasecmp(text, "fixedinterceptlinearfit") == 0) {
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| 209 : |
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fittype = FIXEDINTERCEPTLINEARFIT;
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| 210 : |
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LR = new LinearRegression(FIXEDINTERCEPT);
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| 211 : |
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} else if (strcasecmp(text, "fixedinterceptloglinearfit") == 0) {
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fittype = FIXEDINTERCEPTLOGLINEARFIT;
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| 213 : |
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LR = new LogLinearRegression(FIXEDINTERCEPT);
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| 214 : |
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} else if (strcasecmp(text, "fixedinterceptweightlinearfit") == 0) {
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| 215 : |
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useweight = 1;
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| 216 : |
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fittype = FIXEDINTERCEPTWEIGHTLINEARFIT;
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| 217 : |
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LR = new WeightRegression(FIXEDINTERCEPT);
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| 218 : |
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} else if (strcasecmp(text, "fixedinterceptlogweightlinearfit") == 0) {
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| 219 : |
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useweight = 1;
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| 220 : |
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fittype = FIXEDINTERCEPTLOGWEIGHTLINEARFIT;
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| 221 : |
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LR = new LogWeightRegression(FIXEDINTERCEPT);
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| 222 : |
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} else if (strcasecmp(text, "fixedlinearfit") == 0) {
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| 223 : |
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fittype = FIXEDLINEARFIT;
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| 224 : |
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LR = new LinearRegression(FIXED);
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| 225 : |
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} else if (strcasecmp(text, "fixedloglinearfit") == 0) {
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| 226 : |
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fittype = FIXEDLOGLINEARFIT;
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| 227 : |
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LR = new LogLinearRegression(FIXED);
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| 228 : |
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} else if (strcasecmp(text, "fixedweightlinearfit") == 0) {
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| 229 : |
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useweight = 1;
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| 230 : |
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fittype = FIXEDWEIGHTLINEARFIT;
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| 231 : |
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LR = new WeightRegression(FIXED);
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| 232 : |
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} else if (strcasecmp(text, "fixedlogweightlinearfit") == 0) {
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| 233 : |
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useweight = 1;
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| 234 : |
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fittype = FIXEDLOGWEIGHTLINEARFIT;
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| 235 : |
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LR = new LogWeightRegression(FIXED);
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| 236 : |
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} else
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| 237 : |
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handle.logFileMessage(LOGFAIL, "\nError in surveyindex - unrecognised fittype", text);
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| 238 : |
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| 239 : |
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switch (fittype) {
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| 240 : |
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case LINEARFIT:
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| 241 : |
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case LOGLINEARFIT:
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| 242 : |
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case WEIGHTLINEARFIT:
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| 243 : |
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case LOGWEIGHTLINEARFIT:
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| 244 : |
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break;
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| 245 : |
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case FIXEDSLOPELINEARFIT:
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| 246 : |
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case FIXEDSLOPELOGLINEARFIT:
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| 247 : |
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case FIXEDSLOPEWEIGHTLINEARFIT:
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| 248 : |
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case FIXEDSLOPELOGWEIGHTLINEARFIT:
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| 249 : |
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readWordAndVariable(infile, "slope", slope);
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| 250 : |
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LR->setSlope(slope);
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| 251 : |
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break;
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| 252 : |
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case FIXEDINTERCEPTLINEARFIT:
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| 253 : |
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case FIXEDINTERCEPTLOGLINEARFIT:
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| 254 : |
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case FIXEDINTERCEPTWEIGHTLINEARFIT:
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| 255 : |
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case FIXEDINTERCEPTLOGWEIGHTLINEARFIT:
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| 256 : |
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readWordAndVariable(infile, "intercept", intercept);
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| 257 : |
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LR->setIntercept(intercept);
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| 258 : |
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break;
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| 259 : |
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case FIXEDLINEARFIT:
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| 260 : |
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case FIXEDLOGLINEARFIT:
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| 261 : |
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case FIXEDWEIGHTLINEARFIT:
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| 262 : |
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case FIXEDLOGWEIGHTLINEARFIT:
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| 263 : |
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readWordAndVariable(infile, "slope", slope);
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| 264 : |
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readWordAndVariable(infile, "intercept", intercept);
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| 265 : |
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LR->setSlope(slope);
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| 266 : |
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LR->setIntercept(intercept);
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| 267 : |
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break;
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| 268 : |
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default:
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| 269 : |
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handle.logFileMessage(LOGFAIL, "\nError in surveyindex - unrecognised fittype", text);
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| 270 : |
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break;
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| 271 : |
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}
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| 272 : |
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| 273 : |
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//JMB - check that the slope of the regression line is positive
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| 274 : |
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if (slope < 0.0)
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| 275 : |
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handle.logFileMessage(LOGFAIL, "\nError in surveyindex - slope of the regression line must be positive", slope);
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| 276 : |
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}
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| 277 : |
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| 278 : |
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void SIOnStep::printLikelihood(ofstream& outfile, const TimeClass* const TimeInfo) {
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| 279 : |
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| 280 : |
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int a, i;
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| 281 : |
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if (AAT.atCurrentTime(TimeInfo)) {
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| 282 : |
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timeindex = -1;
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| 283 : |
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for (i = 0; i < Years.Size(); i++)
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| 284 : |
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if ((Years[i] == TimeInfo->getYear()) && (Steps[i] == TimeInfo->getStep()))
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| 285 : |
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timeindex = i;
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| 286 : |
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if (timeindex == -1)
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| 287 : |
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handle.logMessage(LOGFAIL, "Error in surveyindex - invalid timestep");
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| 288 : |
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| 289 : |
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for (a = 0; a < areaindex.Size(); a++) {
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| 290 : |
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for (i = 0; i < colindex.Size(); i++) {
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| 291 : |
|
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outfile << setw(lowwidth) << Years[timeindex] << sep << setw(lowwidth)
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| 292 : |
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<< Steps[timeindex] << sep << setw(printwidth) << areaindex[a] << sep
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| 293 : |
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<< setw(printwidth) << colindex[i] << sep << setw(largewidth);
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| 294 : |
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| 295 : |
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//JMB crude filter to remove the 'silly' values from the output
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| 296 : |
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if ((*modelIndex[timeindex])[a][i] < rathersmall)
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| 297 : |
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outfile << 0;
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| 298 : |
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else
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| 299 : |
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outfile << setprecision(largeprecision) << (*modelIndex[timeindex])[a][i];
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| 300 : |
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| 301 : |
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if (useweight)
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| 302 : |
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outfile << sep << setw(printwidth) << (*weightIndex[timeindex])[a][i];
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| 303 : |
|
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outfile << endl;
|
| 304 : |
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}
|
| 305 : |
|
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}
|
| 306 : |
|
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}
|
| 307 : |
|
|
|
| 308 : |
|
|
//JMB - this is nasty hack to output the regression information
|
| 309 : |
|
|
if (TimeInfo->getTime() == TimeInfo->numTotalSteps()) {
|
| 310 : |
|
|
for (a = 0; a < areaindex.Size(); a++) {
|
| 311 : |
|
|
outfile << "; Regression information for area " << areaindex[a] << endl;
|
| 312 : |
|
|
for (i = 0; i < colindex.Size(); i++)
|
| 313 : |
|
|
outfile << "; " << colindex[i] << " intercept " << intercepts[a][i]
|
| 314 : |
|
|
<< " slope " << slopes[a][i] << " sse " << sse[a][i] << endl;
|
| 315 : |
|
|
}
|
| 316 : |
|
|
}
|
| 317 : |
|
|
}
|
| 318 : |
|
|
|
| 319 : |
|
|
double SIOnStep::calcSSE() {
|
| 320 : |
|
|
|
| 321 : |
|
|
if (handle.getLogLevel() >= LOGMESSAGE)
|
| 322 : |
|
|
handle.logMessage(LOGMESSAGE, "Calculating likelihood score for surveyindex component", this->getName());
|
| 323 : |
|
|
|
| 324 : |
|
|
int a, i, j;
|
| 325 : |
|
|
double score = 0.0;
|
| 326 : |
|
|
for (a = 0; a < areaindex.Size(); a++) {
|
| 327 : |
|
|
likelihoodValues[a] = 0.0;
|
| 328 : |
|
|
for (i = 0; i < colindex.Size(); i++) {
|
| 329 : |
|
|
for (j = 0; j < tmpData.Size(); j++) {
|
| 330 : |
|
|
tmpData[j] = (*obsIndex[j])[a][i];
|
| 331 : |
|
|
tmpModel[j] = (*modelIndex[j])[a][i];
|
| 332 : |
|
|
}
|
| 333 : |
|
|
|
| 334 : |
|
|
//if the weights are required then pass them to the regression line
|
| 335 : |
|
|
if (useweight) {
|
| 336 : |
|
|
for (j = 0; j < tmpWeight.Size(); j++)
|
| 337 : |
|
|
tmpWeight[j] = (*weightIndex[j])[a][i];
|
| 338 : |
|
|
LR->setWeights(tmpWeight);
|
| 339 : |
|
|
}
|
| 340 : |
|
|
|
| 341 : |
|
|
//fit the data to the (log) linear regression line
|
| 342 : |
|
|
LR->storeVectors(tmpModel, tmpData);
|
| 343 : |
|
|
LR->calcFit();
|
| 344 : |
|
|
|
| 345 : |
|
|
//and then store the results
|
| 346 : |
|
|
slopes[a][i] = LR->getSlope();
|
| 347 : |
|
|
intercepts[a][i] = LR->getIntercept();
|
| 348 : |
|
|
sse[a][i] = LR->getSSE();
|
| 349 : |
|
|
likelihoodValues[a] += LR->getSSE();
|
| 350 : |
|
|
}
|
| 351 : |
|
|
score += likelihoodValues[a];
|
| 352 : |
|
|
}
|
| 353 : |
|
|
|
| 354 : |
|
|
if (handle.getLogLevel() >= LOGMESSAGE)
|
| 355 : |
|
|
handle.logMessage(LOGMESSAGE, "The likelihood score from the regression line for this component is", score);
|
| 356 : |
|
|
return score;
|
| 357 : |
|
|
}
|
| 358 : |
|
|
|
| 359 : |
|
|
void SIOnStep::printSummary(ofstream& outfile, const double weight) {
|
| 360 : |
|
|
int a;
|
| 361 : |
|
|
for (a = 0; a < areaindex.Size(); a++)
|
| 362 : |
|
|
outfile << "all all " << setw(printwidth) << areaindex[a] << sep
|
| 363 : |
|
|
<< setw(largewidth) << this->getName() << sep << setprecision(smallprecision)
|
| 364 : |
|
|
<< setw(smallwidth) << weight << sep << setprecision(largeprecision)
|
| 365 : |
|
|
<< setw(largewidth) << likelihoodValues[a] << endl;
|
| 366 : |
|
|
outfile.flush();
|
| 367 : |
|
|
}
|
| 368 : |
|
|
|
| 369 : |
|
|
void SIOnStep::Reset() {
|
| 370 : |
|
|
int i;
|
| 371 : |
|
|
for (i = 0; i < modelIndex.Size(); i++)
|
| 372 : |
|
|
(*modelIndex[i]).setToZero();
|
| 373 : |
|
|
}
|
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