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a961f8a1 BA |
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 | ) |