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agomez |
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#include "regressionline.h" |
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#include "mathfunc.h" |
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#include "errorhandler.h" |
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#include "gadget.h" |
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#include "global.h" |
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// ********************************************************
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// Functions for Regression
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// ********************************************************
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Regression::Regression() {
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linetype = FREE;
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error = 1;
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useweights = 0;
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sse = a = b = 0.0;
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}
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Regression::Regression(LineType ltype) {
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linetype = ltype;
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error = 0;
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useweights = 0;
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sse = a = b = 0.0;
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}
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void Regression::setWeights(const DoubleVector& weights) {
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if (!useweights) {
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handle.logMessage(LOGWARN, "Warning in regression - unexpected use of weights");
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error = 1;
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return;
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}
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w = weights;
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}
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double Regression::getSSE() {
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if (error)
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return verybig;
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return sse;
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}
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void Regression::calcFit() {
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if (error)
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return;
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switch (linetype) {
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case FREE:
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//need to calculate both the slope and the intercept
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this->calcSlopeIntercept();
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break;
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case FIXEDSLOPE:
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//only need to calculate the intercept
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this->calcIntercept();
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break;
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case FIXEDINTERCEPT:
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//only need to calculate the slope
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this->calcSlope();
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break;
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case FIXED:
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//nothing to be done here
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break;
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default:
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handle.logMessage(LOGWARN, "Warning in regression - unrecognised linetype", linetype);
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break;
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}
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//finally calculate the SSE value
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if (useweights)
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this->calcSSEWeights();
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else
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this->calcSSE();
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}
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void Regression::calcSSE() {
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if (error)
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return;
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int i;
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double tmp;
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sse = 0.0;
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for (i = 0; i < x.Size(); i++) {
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tmp = y[i] - (a + b * x[i]);
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sse += tmp * tmp;
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}
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}
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void Regression::calcSSEWeights() {
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if (error)
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return;
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int i;
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double tmp;
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sse = 0.0;
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for (i = 0; i < x.Size(); i++) {
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tmp = y[i] - (a + b * x[i]);
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sse += w[i] * tmp * tmp;
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}
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}
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void Regression::calcSlope() {
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if (error)
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return;
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int i;
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double sumX, sumY;
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sumX = sumY = 0.0;
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for (i = 0; i < x.Size(); i++) {
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sumX += x[i];
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sumY += y[i];
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}
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if (isZero(sumX))
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b = 0.0;
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else
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b = (sumY - (a * x.Size())) / sumX;
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//JMB - if there is a negative slope for the regression then things are going wrong
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if (b < 0.0) {
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handle.logMessage(LOGWARN, "Warning in regression - negative slope for regression line", b);
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error = 1;
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}
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}
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void Regression::calcIntercept() {
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if (error)
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return;
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int i;
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double sumX, sumY;
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sumX = sumY = 0.0;
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for (i = 0; i < x.Size(); i++) {
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sumX += x[i];
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sumY += y[i];
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}
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a = (sumY - (b * sumX)) / x.Size();
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}
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void Regression::calcSlopeIntercept() {
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if (error)
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return;
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int i;
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double sumX, sumY, nom, denom;
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sumX = sumY = nom = denom = 0.0;
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for (i = 0; i < x.Size(); i++) {
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sumX += x[i];
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sumY += y[i];
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}
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sumX /= x.Size();
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sumY /= y.Size();
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for (i = 0; i < x.Size(); i++) {
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nom += (x[i] - sumX) * (y[i] - sumY);
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denom += (x[i] - sumX) * (x[i] - sumX);
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}
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if (isZero(denom)) {
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b = 0.0;
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a = sumY;
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} else {
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b = nom / denom;
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a = sumY - (b * sumX);
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}
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//JMB - if there is a negative slope for the regression then things are going wrong
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if (b < 0.0) {
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handle.logMessage(LOGWARN, "Warning in regression - negative slope for regression line", b);
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error = 1;
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}
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}
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// ********************************************************
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// Functions for LinearRegression
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// ********************************************************
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LinearRegression::LinearRegression(LineType ltype) : Regression(ltype) {
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}
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void LinearRegression::storeVectors(const DoubleVector& modData, const DoubleVector& obsData) {
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error = 0; //begin by cleaning up error status
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if ((modData.Size() != obsData.Size()) || (modData.Size() < 2)) {
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handle.logMessage(LOGWARN, "Warning in linear regression - invalid vector sizes");
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error = 1;
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return;
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}
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x = modData;
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y = obsData;
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}
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// ********************************************************
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// Functions for LogLinearRegression
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// ********************************************************
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LogLinearRegression::LogLinearRegression(LineType ltype) : Regression(ltype) {
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}
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void LogLinearRegression::storeVectors(const DoubleVector& modData, const DoubleVector& obsData) {
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error = 0; //begin by cleaning up error status
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if ((modData.Size() != obsData.Size()) || (modData.Size() < 2)) {
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handle.logMessage(LOGWARN, "Warning in log linear regression - invalid vector sizes");
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error = 1;
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return;
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}
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x.Reset();
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x.resize(modData.Size(), 0.0);
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y.Reset();
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y.resize(obsData.Size(), 0.0);
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int i, l = 0;
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for (i = 0; i < x.Size(); i++, l++) {
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if (isZero(modData[i]) && isZero(obsData[i])) {
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//omit the point (0.0, 0.0)
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x.Delete(l);
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y.Delete(l);
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l--;
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} else if ((modData[i] < verysmall) || (obsData[i] < verysmall)) {
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handle.logMessage(LOGWARN, "Warning in log linear regession - received invalid values");
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error = 1;
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return;
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} else {
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x[l] = log(modData[i]);
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y[l] = log(obsData[i]);
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}
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}
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}
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// ********************************************************
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// Functions for WeightRegression
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// ********************************************************
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WeightRegression::WeightRegression(LineType ltype) : LinearRegression(ltype) {
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useweights = 1;
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}
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void WeightRegression::storeVectors(const DoubleVector& modData, const DoubleVector& obsData) {
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LinearRegression::storeVectors(modData, obsData);
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if (x.Size() != w.Size()) {
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handle.logMessage(LOGWARN, "Warning in weight regression - invalid vector sizes");
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error = 1;
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}
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}
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// ********************************************************
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// Functions for LogWeightRegression
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// ********************************************************
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LogWeightRegression::LogWeightRegression(LineType ltype) : LogLinearRegression(ltype) {
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useweights = 1;
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}
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void LogWeightRegression::storeVectors(const DoubleVector& modData, const DoubleVector& obsData) {
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LogLinearRegression::storeVectors(modData, obsData);
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if (x.Size() != w.Size()) {
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handle.logMessage(LOGWARN, "Warning in log weight regression - invalid vector sizes");
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error = 1;
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}
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}
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