Predict a linear-fuel fit with linear fuel biomass - #16
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Removes plotSpreadProbByFuelType() (no caller), the if (FALSE) pre-June-2025 prediction branch, and commented-out code. The live else-branch is unwrapped and dedented; no behaviour change. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
…scheduling 74 expectations (was 13). Predicted spreadProb values are hand-computed through the logistic from toy covariates and a toy fitted model, including rescaling with the fit's covMinMax. All pass on development and on this branch. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Removed parameters coefToUse, mutuallyExclusiveCols and .saveInterval, and input fireSense_SpreadFitted: nothing reads them. The save event called spreadPredictSave(), which does not exist; it now only emits a message. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013B4sgRg9EwyHaAQdDzUaQW
fireSense_SpreadFit now fits fuel biomass on the linear scale, divided by 1e4, and records that as covMinMax_spread = c(0, 1e4) for each fuel column. Fuel still reaches this module logged (fireSenseUtils::logMinB()), so for those columns the run event undoes the log with fireSenseUtils::fuelLogToLinear(), the function the fit used, before rescaling. A fit made earlier, on the log scale, has the log range in covMinMax_spread and is predicted exactly as before. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013B4sgRg9EwyHaAQdDzUaQW
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Merge #15 first; this branch contains it. Needs fireSenseUtils >= 0.2.3.9029 (its
feat/linear-fuel-covariatesPR).fireSense_SpreadFit now fits fuel biomass on the linear scale, divided by 1e4, and stores
c(0, 1e4)ascovMinMax_spreadfor each fuel column. Fuel still reaches this module logged, so for those columns the run event callsfireSenseUtils::fuelLogToLinear(), the function the fit used, before rescaling. A fit made earlier on the log scale has the log range there and is predicted exactly as before.Two new tests with hand-computed values, one for each kind of fit. Removing the transform, or applying it to a log fit, each fails them.
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https://claude.ai/code/session_013B4sgRg9EwyHaAQdDzUaQW