| 1 | #' @include b_Algorithm.R |
| 2 | |
| 3 | #' @title Linear Algorithm |
| 4 | #' |
| 5 | #' @description Generic class to represent a linear algorithm. |
| 6 | #' TODO: not needed in production environment; weights growing infinitely. |
| 7 | #' Inherits \code{\link{Algorithm}} |
| 8 | #' |
| 9 | #' @field weights The matrix of weights (in rows) associated to each expert (in columns) |
| 10 | #' |
| 11 | LinearAlgorithm = setRefClass( |
| 12 | Class = "LinearAlgorithm", |
| 13 | |
| 14 | fields = c( |
| 15 | weights = "matrix" |
| 16 | ), |
| 17 | |
| 18 | contains = "Algorithm", |
| 19 | |
| 20 | methods = list( |
| 21 | initialize = function(...) |
| 22 | { |
| 23 | callSuper(...) |
| 24 | weights <<- matrix(nrow=0, ncol=ncol(data)-3) |
| 25 | }, |
| 26 | |
| 27 | appendWeight = function(weight) |
| 28 | { |
| 29 | "Append the last computed weights to the weights matrix, for further plotting" |
| 30 | |
| 31 | n = nrow(data) |
| 32 | nx = n - nrow(subset(data, subset = (Date == data[n,"Date"]))) |
| 33 | x = data[(nx+1):n, !names(data) %in% c("Date","Measure","Station")] |
| 34 | iy = getNoNAindices(x, 2) |
| 35 | |
| 36 | completedWeight = rep(NA, ncol(x)) |
| 37 | completedWeight[iy] = weight |
| 38 | weights <<- rbind(weights, completedWeight) |
| 39 | }, |
| 40 | |
| 41 | plotWeights = function(station=1, start=1, ...) |
| 42 | { |
| 43 | "Plot the weights of each expert over time" |
| 44 | |
| 45 | if (is.character(station)) |
| 46 | station = match(station, stations) |
| 47 | |
| 48 | #keep only full weights (1 to K) |
| 49 | weights_ = weights[getNoNAindices(weights),] |
| 50 | weights_ = weights_[start:nrow(weights_),] |
| 51 | |
| 52 | yRange = range(weights_, na.rm=TRUE) |
| 53 | K = ncol(weights_) |
| 54 | cols = rainbow(K) |
| 55 | par(mar=c(5,4.5,1,1), cex=1.5) |
| 56 | for (i in 1:K) |
| 57 | { |
| 58 | plot(weights_[,i], type="l", xaxt="n", ylim=yRange, col=cols[i], xlab="", ylab="",cex.axis=1.5, ...) |
| 59 | par(new=TRUE) |
| 60 | } |
| 61 | axis(side=1, at=seq(from=1,to=nrow(weights_),by=30), labels=seq(from=0,to=nrow(weights_),by=30) + start, cex.axis=1.5) |
| 62 | title(xlab="Time",ylab="Weight", cex.lab=1.6) |
| 63 | }, |
| 64 | |
| 65 | plotWeights_rn = function(station=1, start=1, ...) |
| 66 | { |
| 67 | "Weights plotting tailored for AirNormand reports" |
| 68 | |
| 69 | if (is.character(station)) |
| 70 | station = match(station, stations) |
| 71 | |
| 72 | cols = c( |
| 73 | colors()[258], #CLM (vert) |
| 74 | colors()[258], #GAM |
| 75 | colors()[258], #CLM1 |
| 76 | colors()[258], #CLM2 |
| 77 | colors()[53], #S_AIRPARIF (orange) |
| 78 | colors()[53], #S_INERIS |
| 79 | colors()[28], #D_ESMERALDA (bleu) |
| 80 | colors()[28], #D_PREVAIR |
| 81 | colors()[28], #D_PREVAIR2 |
| 82 | 1 #PERSIST (noir) |
| 83 | ) |
| 84 | #l : ligne, b : cercles, o : cercles+ligne |
| 85 | plotTypes = rep("b", length(cols)) |
| 86 | ltypes = c(1,3,1,3,1,3,1,3,1,1) |
| 87 | pchtypes = c(21,22,24,25,21,22,21,22,24,21) |
| 88 | |
| 89 | #keep only full weights (1 to K) |
| 90 | weights_ = weights[getNoNAindices(weights),] |
| 91 | weights_ = weights_[start:nrow(weights_),] |
| 92 | |
| 93 | #TODO: HACK for plotting for presentation... |
| 94 | #should be: yRange = range(weights_, na.rm = TRUE) |
| 95 | yRange = quantile(weights_, probs=c(0.02, 0.98)) |
| 96 | |
| 97 | par(mar=c(5,4.5,1,1), cex=1.5) |
| 98 | for (j in 1:ncol(weights_)) |
| 99 | { |
| 100 | plot(weights_[,j],xaxt="n",ylim=yRange,type=plotTypes[j],col=cols[j],bg=cols[j],lty=ltypes[j],pch=pchtypes[j],xlab="",ylab="",cex.axis=1.5, ...) |
| 101 | par(new=TRUE) |
| 102 | } |
| 103 | axis(side=1, at=seq(from=1,to=nrow(weights_),by=30), labels=seq(from=0,to=nrow(weights_),by=30) + start, cex.axis=1.5) |
| 104 | title(xlab="Time",ylab="Weight", cex.lab=1.6) |
| 105 | }, |
| 106 | |
| 107 | plotWeightsByFamily_rn = function(station=1, type="Absolute", start=1, legend=TRUE, ...) |
| 108 | { |
| 109 | "Weights plotting for AirNormand reports, by family of experts. type == 'Relative' or 'Absolute'" |
| 110 | |
| 111 | if (is.character(station)) |
| 112 | station = match(station, stations) |
| 113 | |
| 114 | #keep only full weights (1 to K) |
| 115 | weights_ = weights[getNoNAindices(weights),] |
| 116 | weights_ = weights_[start:nrow(weights_),] |
| 117 | |
| 118 | summary = matrix(nrow=nrow(weights_), ncol=4) |
| 119 | if (type == "Relative") |
| 120 | { |
| 121 | summary[,1] = weights_[,1] + weights_[,2] + weights_[,3] + weights_[,4] |
| 122 | summary[,2] = weights_[,5] + weights_[,6] |
| 123 | summary[,3] = weights_[,7] + weights_[,8] + weights_[,9] |
| 124 | summary[,4] = weights_[,10] |
| 125 | } |
| 126 | else |
| 127 | { |
| 128 | summary[,1] = abs(weights_[,1]) + abs(weights_[,2]) + abs(weights_[,3]) + abs(weights_[,4]) |
| 129 | summary[,2] = abs(weights_[,5]) + abs(weights_[,6]) |
| 130 | summary[,3] = abs(weights_[,7]) + abs(weights_[,8]) + abs(weights_[,9]) |
| 131 | summary[,4] = abs(weights_[,10]) |
| 132 | } |
| 133 | |
| 134 | cols = c( |
| 135 | colors()[258], #GAM,CLM,1,2 (vert) |
| 136 | colors()[53], #S_AIRPARIF,S_INERIS (orange) |
| 137 | colors()[28], #D_ESMERALDA,D_PREVAIR,D_PREVAIR2 (bleu) |
| 138 | 1 #PERSIST |
| 139 | ) |
| 140 | #l : ligne, b : cercles, o : cercles+ligne |
| 141 | plotTypes = c("l", "l", "l", "l") |
| 142 | |
| 143 | yRange = range(summary) |
| 144 | par(mar=c(5,4.5,3,1), cex=1.5) |
| 145 | for (j in 1:4) |
| 146 | { |
| 147 | plot(summary[,j],xaxt="n",ylim=yRange,type=plotTypes[j],col=cols[j], xlab="", ylab="", cex.axis=1.5, lwd=2, ...) |
| 148 | par(new=TRUE) |
| 149 | } |
| 150 | axis(side=1, at=seq(from=1,to=nrow(weights_),by=30), labels=seq(from=0,to=nrow(weights_),by=30) + start, cex.axis=1.5) |
| 151 | title(xlab="Time",ylab=paste(type, "sum of weights"), cex.lab=1.6, main=paste(type, "sum of weights by family")) |
| 152 | if (legend) |
| 153 | { |
| 154 | legend("topright", #title="Somme des poids par famille", |
| 155 | col=c(colors()[258], colors()[53], colors()[28], 1), lwd=2, cex=1.1, |
| 156 | lty=rep("solid",4),legend=c("Stat. Air Normand","Stat. others","Deterministic", "Persistence")) |
| 157 | } |
| 158 | } |
| 159 | ) |
| 160 | ) |