From c197101d0e76384ab561c6c92e6cffbcd483cf93 Mon Sep 17 00:00:00 2001 From: edxu96 Date: Wed, 22 May 2019 16:13:30 +0200 Subject: [PATCH] Update --- FuncCrossVali.R | 6 +- Output/7/listVecKernalValue_7.csv | 25 + Output/7/vecCoef_7.csv | 360 +++++++++++++++ Output/7/vecKernalValuePred_7.csv | 25 + Output/7/vecPowerPred_7.csv | 744 ++++++++++++++++++++++++++++++ PreSeasonAdap.R | 3 +- main.R | 2 +- 7 files changed, 1160 insertions(+), 5 deletions(-) create mode 100644 Output/7/listVecKernalValue_7.csv create mode 100644 Output/7/vecCoef_7.csv create mode 100644 Output/7/vecKernalValuePred_7.csv create mode 100644 Output/7/vecPowerPred_7.csv diff --git a/FuncCrossVali.R b/FuncCrossVali.R index 7a03307..8734647 100644 --- a/FuncCrossVali.R +++ b/FuncCrossVali.R @@ -14,9 +14,9 @@ calPredictionRMSE <- function(vecPred, vecValid){ crossValid <- function(vecKernal, listVecKernalValue, datf = datfTrain, numFold = 10){ vecRootMeanSquaredError <- rep(NA, numFold) for (i in 1:numFold) { - datForTest <- datf[(datf$index == i),] - vecPowerPred <- predLocalReg(datForTest$speed.center, vecKernal, listVecKernalValue[[i]]) - vecRootMeanSquaredError[i] <- calPredictionRMSE(vecPowerPred, datForTest$power) + datForVali <- datf[(datf$index == i),] + vecPowerPred <- predLocalReg(datForVali$speed.center, vecKernal, listVecKernalValue[[i]]) + vecRootMeanSquaredError[i] <- calPredictionRMSE(vecPowerPred, datForVali$power) } aveRootMeanSquaredError <- sum(vecRootMeanSquaredError) / numFold # [mean of mse] return(aveRootMeanSquaredError) diff --git a/Output/7/listVecKernalValue_7.csv b/Output/7/listVecKernalValue_7.csv new file mode 100644 index 0000000..1d88da6 --- /dev/null +++ b/Output/7/listVecKernalValue_7.csv @@ -0,0 +1,25 @@ +0.0204960556709308,0.0393978441851328,0.0272624117213049,0.0167277666169511,0.0396942264719224,0.00873874574319529,0.0115791052808685,0.0309098671172761,0.034064557123678,0.0107608029285483 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+0.327559139955485 +0.414117412032516 +0.388283115779407 +0.485640858305396 +0.51821817811135 +0.494897303611554 +0.528915629863657 +0.561464203383342 +0.542550365724317 +0.58178587764657 +0.632298307765562 +0.654291645520733 +0.595107941650202 +0.612696932950783 +0.630917199032563 +0.616303569617949 +0.640555937314111 +0.565333226714144 +0.569547539219864 +0.574490728877281 +0.523698262154659 +0.4274548931433 +0.35027908950825 +0.311353928562561 +0.280769851060701 +0.286213491433974 +0.271838945444272 +0.259505497168233 +0.287781972564979 +0.33371463583581 +0.243962936454589 +0.214801373915698 +0.162614795372386 +0.146268000037098 diff --git a/PreSeasonAdap.R b/PreSeasonAdap.R index 99fe4fa..bd4fc5f 100644 --- a/PreSeasonAdap.R +++ b/PreSeasonAdap.R @@ -11,7 +11,8 @@ for (i in 1:numFold) { maxSeriesVali <- max(datfTrain[(datfTrain$index == i),]$series) # [end of the validation period] vecKernalSeason[i] <- (minSeriesVali + maxSeriesVali) / 2 # Set the center time of validation period as the kernal listMatWeightSeason[[i]] <- calMatWeightSeasonGaussian(matWeight, datfTrain$series, vecKernalSeason[i]) - listMatWeightSeason[[i]][minSeriesVali:maxSeriesVali,] <- 0 + listMatWeightSeason[[i]][minSeriesVali:maxSeriesVali,] <- 0 # Set the weight for the data in validation period to + # 0, the effect of those data cann't be considered in the cross-validation. } cat("vecKernalSeason = [", paste(vecKernalSeason, collapse = ", "), "]\n", sep = "") cat("--------------------------------------------------------------------------------\n") diff --git a/main.R b/main.R index 54c64d1..cd8a6c6 100644 --- a/main.R +++ b/main.R @@ -11,7 +11,7 @@ cat("#### 0, Control Parameters, Data and Functions ########################### ## 0.1, Control Parameters 1 numFold <- 10 # [number of folds for cross validation] numIte <- 1 # [number of further iterations] if = 1, there is no further iteration to optimize the coeffcients. -outputSeries <- 7 # [series number of the output file] +outputSeries <- 8 # [series number of the output file] wheOutput <- T # [whether to output the results] wheVali <- F # [whether to validate the result] numConCoef <- 360 # [number of concentration coefficients]