├── Chapter 07 ├── Data │ └── Image.jpg ├── 1Self Organizing Map - Visualisations of heatmaps.r └── 2Vector Quantization - Image Clustering.r ├── Chapter 14 ├── Data │ └── DT_4_ind └── Case Study 3 - Forecast of Electricity Consumption.r ├── Chapter 02 ├── 4Poisson Regression.r ├── Data │ ├── brine.txt │ ├── gala.txt │ ├── tobit.csv │ └── hsbdemo.csv ├── 2Multinomial Logistic Regression.r ├── 3Tobit Regression.r └── 1Discriminant Function Analysis script.r ├── Chapter 13 ├── Data │ └── publicdatamay2007.xls └── Case Study 2 - Pricing Reinsurance Contracts.r ├── Chapter 09 ├── 2Hidden Markov Models for Regime Detection.r └── 1HMM - EUR & USD.r ├── Chapter 08 ├── 2Markov Chains - Multi-Channel Attribution Model.r ├── 3Markov Chains - Car Rental Agency Service.r ├── 1Markov Chains - Stocks Regime Switching Model.r ├── Data │ └── StocksRegimeSwitching.csv ├── 4Continuous Time Markov Chains - Vehicle Service at Gas Station.r └── 5Monte Carlo Simulations - Calibrated Hull and White short-rates.r ├── .gitattributes ├── Chapter 03 ├── Data │ ├── foodstuffs.txt │ ├── GSE4051_design.csv │ ├── Europenaprotein.csv │ ├── NASAUnderstory.csv │ ├── WBClust2013.csv │ └── math test.txt ├── 6K-means clustering - Foodstuff.r ├── 5K-means clustering - European Protein Consumption.r ├── 4Binary Clustering - Math test.r ├── 2Hierarchical Clustering - NASA Under story.r ├── 3Hierarchical Clustering - Gene Clustering.r └── 1Hierarchical Clustering - World Bank.r ├── .gitignore ├── LICENSE ├── Chapter 06 ├── 1Decision tree learning - Advance Health Directive for Patients with Chest Pain.r ├── 2Decision tree learning - Income Based Distribution of Real Estate Value.r ├── 6Support Vector Machine - Currency Trading Strategy.r ├── 4Naive Bayes - Predicting the Direction of Stock Movement.r ├── 3Decision tree learning - Predicting the Direction of Stock Movement.r ├── 5Random Forest - Currency Trading Strategy.r └── 7Stochastic Gradient Descent - Adult Income.r ├── Chapter 04 ├── Data │ ├── Calories_Burnt.csv │ └── delta.csv ├── 3Principal Component Analysis - Understanding world cuisine.r ├── 2Dimension Reduction Methods - Delta's aircraft fleet.r └── 1Shrinkage Methods - Calories burnt per day.r ├── Chapter 11 └── 1Recurrent Neural Networks - Predicting periodic signal.r ├── Chapter 10 ├── 1Modelling S&P 500.r ├── 2Measuring Unemployment Rate.r └── Data │ └── FRED-WIUR.csv ├── README.md ├── Chapter 05 ├── 2Smooth Splines.r ├── 1Generalized additive model - Measuring household income of New Zealand.r └── 3Loess - United States Geological Survey.r └── Chapter 12 └── Case Study 1 - World Bank data Analysis.r /Chapter 07/Data/Image.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Practical-Machine-Learning-Cookbook/HEAD/Chapter 07/Data/Image.jpg -------------------------------------------------------------------------------- /Chapter 14/Data/DT_4_ind: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Practical-Machine-Learning-Cookbook/HEAD/Chapter 14/Data/DT_4_ind -------------------------------------------------------------------------------- /Chapter 02/4Poisson Regression.r: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Practical-Machine-Learning-Cookbook/HEAD/Chapter 02/4Poisson Regression.r -------------------------------------------------------------------------------- /Chapter 13/Data/publicdatamay2007.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Practical-Machine-Learning-Cookbook/HEAD/Chapter 13/Data/publicdatamay2007.xls -------------------------------------------------------------------------------- /Chapter 09/2Hidden Markov Models for Regime Detection.r: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Practical-Machine-Learning-Cookbook/HEAD/Chapter 09/2Hidden Markov Models for Regime Detection.r -------------------------------------------------------------------------------- /Chapter 08/2Markov Chains - Multi-Channel Attribution Model.r: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Practical-Machine-Learning-Cookbook/HEAD/Chapter 08/2Markov Chains - Multi-Channel Attribution Model.r -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /Chapter 03/Data/foodstuffs.txt: -------------------------------------------------------------------------------- 1 | Food Energy Protein Fat Calcium Iron 2 | BB 340 20 28 9 2.6 3 | HR 245 21 17 9 2.7 4 | BR 420 15 39 7 2.0 5 | BS 375 19 32 9 2.5 6 | BC 180 22 10 17 3.7 7 | CB 115 20 3 8 1.4 8 | CC 170 25 7 12 1.5 9 | BH 160 26 5 14 5.9 10 | LL 265 20 20 9 2.6 11 | LS 300 18 25 9 2.3 12 | HS 340 20 28 9 2.5 13 | PR 340 19 29 9 2.5 14 | PS 355 19 30 9 2.4 15 | BT 205 18 14 7 2.5 16 | VC 185 23 9 9 2.7 17 | FB 135 22 4 25 0.6 18 | AR 70 11 1 82 6.0 19 | AC 45 7 1 74 5.4 20 | TC 90 14 2 38 0.8 21 | HF 135 16 5 15 0.5 22 | MB 200 19 13 5 1.0 23 | MC 155 16 9 157 1.8 24 | PF 195 16 11 14 1.3 25 | SC 120 17 5 159 0.7 26 | DC 180 22 9 367 2.5 27 | UC 170 25 7 7 1.2 28 | RC 110 23 1 98 2.6 -------------------------------------------------------------------------------- /Chapter 02/Data/brine.txt: -------------------------------------------------------------------------------- 1 | ,HCO3,SO4,Cl,Ca,Mg,Na,GROUP 2 | 1,10.4,30.0,967.1,95.9,53.7,857.7,1 3 | 2,6.2,29.6,1174.9,111.7,43.9,1054.7,1 4 | 3,2.1,11.4,2387.1,348.3,119.3,1932.4,1 5 | 4,8.5,22.5,2186.1,339.6,73.6,1803.4,1 6 | 5,6.7,32.8,2015.5,287.6,75.1,1691.8,1 7 | 6,3.8,18.9,2175.8,340.4,63.8,1793.9,1 8 | 7,1.5,16.5,2367.0,412.0,95.8,1872.5,1 9 | 8,25.6,0,134.7,12.7,7.1,134.7,2 10 | 9,12.0,104.6,3163.8,95.6,90.1,3093.9,2 11 | 10,9.0,104.0,1342.6,104.9,160.2,1190.1,2 12 | 11,13.7,103.3,2151.6,103.7,70.0,2054.6,2 13 | 12,16.6,92.3,905.1,91.5,50.9,871.4,2 14 | 13,14.1,80.1,554.8,118.9,62.3,472.4,2 15 | 14,1.3,10.4,3399.5,532.3,235.6,2642.5,3 16 | 15,3.6,5.2,974.5,147.5,69.0,768.1,3 17 | 16,0.8,9.8,1430.2,295.7,118.4,1027.1,3 18 | 17,1.8,25.6,183.2,35.4,13.5,161.5,3 19 | 18,8.8,3.4,289.9,32.8,22.4,225.2,3 20 | 19,6.3,16.7,360.9,41.9,24.0,318.1,3 -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Windows image file caches 2 | Thumbs.db 3 | ehthumbs.db 4 | 5 | # Folder config file 6 | Desktop.ini 7 | 8 | # Recycle Bin used on file shares 9 | $RECYCLE.BIN/ 10 | 11 | # Windows Installer files 12 | *.cab 13 | *.msi 14 | *.msm 15 | *.msp 16 | 17 | # Windows shortcuts 18 | *.lnk 19 | 20 | # ========================= 21 | # Operating System Files 22 | # ========================= 23 | 24 | # OSX 25 | # ========================= 26 | 27 | .DS_Store 28 | .AppleDouble 29 | .LSOverride 30 | 31 | # Thumbnails 32 | ._* 33 | 34 | # Files that might appear in the root of a volume 35 | .DocumentRevisions-V100 36 | .fseventsd 37 | .Spotlight-V100 38 | .TemporaryItems 39 | .Trashes 40 | .VolumeIcon.icns 41 | 42 | # Directories potentially created on remote AFP share 43 | .AppleDB 44 | .AppleDesktop 45 | Network Trash Folder 46 | Temporary Items 47 | .apdisk 48 | -------------------------------------------------------------------------------- /Chapter 03/Data/GSE4051_design.csv: -------------------------------------------------------------------------------- 1 | sidChar,sidNum,devStage,gType 2 | Sample_20,20,E16,wt 3 | Sample_21,21,E16,wt 4 | Sample_22,22,E16,wt 5 | Sample_23,23,E16,wt 6 | Sample_16,16,E16,NrlKO 7 | Sample_17,17,E16,NrlKO 8 | Sample_6,6,E16,NrlKO 9 | Sample_24,24,P2,wt 10 | Sample_25,25,P2,wt 11 | Sample_26,26,P2,wt 12 | Sample_27,27,P2,wt 13 | Sample_14,14,P2,NrlKO 14 | Sample_3,3,P2,NrlKO 15 | Sample_5,5,P2,NrlKO 16 | Sample_8,8,P2,NrlKO 17 | Sample_28,28,P6,wt 18 | Sample_29,29,P6,wt 19 | Sample_30,30,P6,wt 20 | Sample_31,31,P6,wt 21 | Sample_1,1,P6,NrlKO 22 | Sample_10,10,P6,NrlKO 23 | Sample_4,4,P6,NrlKO 24 | Sample_7,7,P6,NrlKO 25 | Sample_32,32,P10,wt 26 | Sample_33,33,P10,wt 27 | Sample_34,34,P10,wt 28 | Sample_35,35,P10,wt 29 | Sample_13,13,P10,NrlKO 30 | Sample_15,15,P10,NrlKO 31 | Sample_18,18,P10,NrlKO 32 | Sample_19,19,P10,NrlKO 33 | Sample_36,36,4_weeks,wt 34 | Sample_37,37,4_weeks,wt 35 | Sample_38,38,4_weeks,wt 36 | Sample_39,39,4_weeks,wt 37 | Sample_11,11,4_weeks,NrlKO 38 | Sample_12,12,4_weeks,NrlKO 39 | Sample_2,2,4_weeks,NrlKO 40 | Sample_9,9,4_weeks,NrlKO 41 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Packt 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Chapter 02/2Multinomial Logistic Regression.r: -------------------------------------------------------------------------------- 1 | require(foreign) 2 | require(nnet) 3 | require(ggplot2) 4 | require(reshape2) 5 | 6 | ml <- read.table("d:/hsbdemo.csv", header=TRUE, sep=",", row.names="id") 7 | with(ml, table(ses, prog)) 8 | 9 | 10 | with(ml, do.call(rbind, tapply(write, prog, 11 | function(x) c(M = mean(x), SD = sd(x))))) 12 | 13 | ml$prog2 <- relevel(ml$prog, ref = "academic") 14 | test <- multinom(prog2 ~ ses + write, data = ml) 15 | 16 | summary(test) 17 | 18 | z <- summary(test)$coefficients/summary(test)$standard.errors 19 | z 20 | 21 | p <- (1 - pnorm(abs(z), 0, 1))*2 22 | p 23 | 24 | exp(coef(test)) 25 | 26 | head(pp <- fitted(test)) 27 | 28 | dses <- data.frame(ses = c("low", "middle", "high"),write = mean(ml$write)) 29 | 30 | predict(test, newdata = dses, "probs") 31 | 32 | dwrite <- data.frame(ses = rep(c("low", "middle", "high"), each = 41), write = rep(c(30:70), 3)) 33 | 34 | pp.write <- cbind(dwrite, predict(test, newdata = dwrite, type = "probs", se = TRUE)) 35 | 36 | by(pp.write[, 3:5], pp.write$ses, colMeans) 37 | 38 | lpp <- melt(pp.write, id.vars = c("ses", "write"), value.name = "probability") 39 | head(lpp) 40 | 41 | ggplot(lpp, aes(x = write, y = probability, colour = ses)) + 42 | geom_line() + 43 | facet_grid(variable ~ ., scales="free") -------------------------------------------------------------------------------- /Chapter 03/Data/Europenaprotein.csv: -------------------------------------------------------------------------------- 1 | Country,RedMeat,WhiteMeat,Eggs,Milk,Fish,Cereals,Starch,Nuts,Fr&Veg 2 | Albania,10.1,1.4,0.5,8.9,0.2,42.3,0.6,5.5,1.7 3 | Austria,8.9,14,4.3,19.9,2.1,28,3.6,1.3,4.3 4 | Belgium,13.5,9.3,4.1,17.5,4.5,26.6,5.7,2.1,4 5 | Bulgaria,7.8,6,1.6,8.3,1.2,56.7,1.1,3.7,4.2 6 | Czechoslovakia,9.7,11.4,2.8,12.5,2,34.3,5,1.1,4 7 | Denmark,10.6,10.8,3.7,25,9.9,21.9,4.8,0.7,2.4 8 | E Germany,8.4,11.6,3.7,11.1,5.4,24.6,6.5,0.8,3.6 9 | Finland,9.5,4.9,2.7,33.7,5.8,26.3,5.1,1,1.4 10 | France,18,9.9,3.3,19.5,5.7,28.1,4.8,2.4,6.5 11 | Greece,10.2,3,2.8,17.6,5.9,41.7,2.2,7.8,6.5 12 | Hungary,5.3,12.4,2.9,9.7,0.3,40.1,4,5.4,4.2 13 | Ireland,13.9,10,4.7,25.8,2.2,24,6.2,1.6,2.9 14 | Italy,9,5.1,2.9,13.7,3.4,36.8,2.1,4.3,6.7 15 | Netherlands,9.5,13.6,3.6,23.4,2.5,22.4,4.2,1.8,3.7 16 | Norway,9.4,4.7,2.7,23.3,9.7,23,4.6,1.6,2.7 17 | Poland,6.9,10.2,2.7,19.3,3,36.1,5.9,2,6.6 18 | Portugal,6.2,3.7,1.1,4.9,14.2,27,5.9,4.7,7.9 19 | Romania,6.2,6.3,1.5,11.1,1,49.6,3.1,5.3,2.8 20 | Spain,7.1,3.4,3.1,8.6,7,29.2,5.7,5.9,7.2 21 | Sweden,9.9,7.8,3.5,24.7,7.5,19.5,3.7,1.4,2 22 | Switzerland,13.1,10.1,3.1,23.8,2.3,25.6,2.8,2.4,4.9 23 | UK,17.4,5.7,4.7,20.6,4.3,24.3,4.7,3.4,3.3 24 | USSR,9.3,4.6,2.1,16.6,3,43.6,6.4,3.4,2.9 25 | W Germany,11.4,12.5,4.1,18.8,3.4,18.6,5.2,1.5,3.8 26 | Yugoslavia,4.4,5,1.2,9.5,0.6,55.9,3,5.7,3.2 27 | -------------------------------------------------------------------------------- /Chapter 02/Data/gala.txt: -------------------------------------------------------------------------------- 1 | Species Endemics Area Elevation Nearest Scruz Adjacent 2 | Baltra 58 23 25.09 346 0.6 0.6 1.84 3 | Bartolome 31 21 1.24 109 0.6 26.3 572.33 4 | Caldwell 3 3 0.21 114 2.8 58.7 0.78 5 | Champion 25 9 0.1 46 1.9 47.4 0.18 6 | Coamano 2 1 0.05 77 1.9 1.9 903.82 7 | Daphne.Major 18 11 0.34 119 8 8 1.84 8 | Daphne.Minor 24 0 0.08 93 6 12 0.34 9 | Darwin 10 7 2.33 168 34.1 290.2 2.85 10 | Eden 8 4 0.03 71 0.4 0.4 17.95 11 | Enderby 2 2 0.18 112 2.6 50.2 0.1 12 | Espanola 97 26 58.27 198 1.1 88.3 0.57 13 | Fernandina 93 35 634.49 1494 4.3 95.3 4669.32 14 | Gardner1 58 17 0.57 49 1.1 93.1 58.27 15 | Gardner2 5 4 0.78 227 4.6 62.2 0.21 16 | Genovesa 40 19 17.35 76 47.4 92.2 129.49 17 | Isabela 347 89 4669.32 1707 0.7 28.1 634.49 18 | Marchena 51 23 129.49 343 29.1 85.9 59.56 19 | Onslow 2 2 0.01 25 3.3 45.9 0.1 20 | Pinta 104 37 59.56 777 29.1 119.6 129.49 21 | Pinzon 108 33 17.95 458 10.7 10.7 0.03 22 | Las.Plazas 12 9 0.23 94 0.5 0.6 25.09 23 | Rabida 70 30 4.89 367 4.4 24.4 572.33 24 | SanCristobal 280 65 551.62 716 45.2 66.6 0.57 25 | SanSalvador 237 81 572.33 906 0.2 19.8 4.89 26 | SantaCruz 444 95 903.82 864 0.6 0 0.52 27 | SantaFe 62 28 24.08 259 16.5 16.5 0.52 28 | SantaMaria 285 73 170.92 640 2.6 49.2 0.1 29 | Seymour 44 16 1.84 147 0.6 9.6 25.09 30 | Tortuga 16 8 1.24 186 6.8 50.9 17.95 31 | Wolf 21 12 2.85 253 34.1 254.7 2.33 -------------------------------------------------------------------------------- /Chapter 06/1Decision tree learning - Advance Health Directive for Patients with Chest Pain.r: -------------------------------------------------------------------------------- 1 | R version 3.3.0 (2016-05-03) -- "Supposedly Educational" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | install.packages("tree") 19 | install.packages("caret") 20 | install.packages("e1071") 21 | library(tree) 22 | library(caret) 23 | 24 | AHD_data <- read.csv("d:/Heart.csv", header = TRUE) 25 | 26 | str(AHD_data) 27 | 28 | head(AHD_data) 29 | 30 | dim(AHD_data) 31 | 32 | split <- createDataPartition(y=AHD_data$AHD, p = 0.5, list=FALSE) 33 | 34 | split 35 | 36 | train <- AHD_data[split,] 37 | 38 | train 39 | 40 | test <- AHD_data[-split,] 41 | 42 | test 43 | 44 | trees <- tree(AHD ~., train) 45 | 46 | plot(trees) 47 | 48 | cv.trees <- cv.tree(trees, FUN=prune.misclass) 49 | 50 | cv.trees 51 | 52 | plot(cv.trees) 53 | 54 | prune.trees <- prune.misclass(trees, best=4) 55 | 56 | plot(prune.trees) 57 | 58 | text(prune.trees, pretty=0) 59 | 60 | tree.pred <- predict(prune.trees, test, type='class') 61 | 62 | tree.pred 63 | 64 | confusionMatrix(tree.pred, test$AHD) 65 | -------------------------------------------------------------------------------- /Chapter 03/6K-means clustering - Foodstuff.r: -------------------------------------------------------------------------------- 1 | library(cluster) 2 | 3 | food.energycontent <- read.table("d:/foodstuffs.txt", header=T) 4 | head(food.energycontent) 5 | Food Energy Protein Fat Calcium Iron 6 | 1 BB 340 20 28 9 2.6 7 | 2 HR 245 21 17 9 2.7 8 | 3 BR 420 15 39 7 2.0 9 | 4 BS 375 19 32 9 2.5 10 | 5 BC 180 22 10 17 3.7 11 | 6 CB 115 20 3 8 1.4 12 | 13 | str(food.energycontent) 14 | 15 | standard.deviation <- apply(food.energycontent[,-1], 2, sd) 16 | standard.deviation 17 | 18 | foodergycnt.stddev <- sweep(food.energycontent[,-1],2,standard.deviation,FUN="/") 19 | foodergycnt.stddev 20 | 21 | food.5cluster <- kmeans(foodergycnt.stddev, centers=5, iter.max=100, nstart=25) 22 | food.5cluster 23 | 24 | food.4cluster <- kmeans(foodergycnt.stddev, centers=4, iter.max=100, nstart=25) 25 | food.4cluster 26 | 27 | food.4cluster$cluster 28 | 29 | food.4cluster.clust <- lapply(1:4, function(nc) protein[food.4cluster$cluster==nc]) 30 | food.4cluster.clust 31 | 32 | pairs(food.energycontent[,-1], panel=function(x,y) text(x,y,food.4cluster$cluster)) 33 | 34 | food.pc <- princomp(food.energycontent[,-1],cor=T) 35 | my.color.vector <- rep("green", times=nrow(food.energycontent)) 36 | my.color.vector[food.4cluster$cluster==2] <- "blue" 37 | my.color.vector[food.4cluster$cluster==3] <- "red" 38 | my.color.vector[food.4cluster$cluster==4] <- "orange" 39 | par(pty="s") 40 | plot(food.pc$scores[,1], food.pc$scores[,2], ylim=range(food.pc$scores[,1]), 41 | xlab="PC 1", ylab="PC 2", type ='n', lwd=2) 42 | text(food.pc$scores[,1], food.pc$scores[,2], labels=Food, cex=0.7, lwd=2, 43 | col=my.color.vector) -------------------------------------------------------------------------------- /Chapter 03/5K-means clustering - European Protein Consumption.r: -------------------------------------------------------------------------------- 1 | 2 | 3 | R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree" 4 | Copyright (C) 2015 The R Foundation for Statistical Computing 5 | Platform: x86_64-w64-mingw32/x64 (64-bit) 6 | 7 | R is free software and comes with ABSOLUTELY NO WARRANTY. 8 | You are welcome to redistribute it under certain conditions. 9 | Type 'license()' or 'licence()' for distribution details. 10 | 11 | R is a collaborative project with many contributors. 12 | Type 'contributors()' for more information and 13 | 'citation()' on how to cite R or R packages in publications. 14 | 15 | Type 'demo()' for some demos, 'help()' for on-line help, or 16 | 'help.start()' for an HTML browser interface to help. 17 | Type 'q()' to quit R. 18 | 19 | [Workspace loaded from ~/.RData] 20 | 21 | protein = read.csv("d:/Europenaprotein.csv",header=T) 22 | head(protein) 23 | 24 | set.seed(123456789) 25 | groupMeat <- kmeans(protein[,c("WhiteMeat","RedMeat")], centers=3, nstart=10) 26 | 27 | groupMeat 28 | 29 | o=order(groupMeat$cluster) 30 | 31 | data.frame(protein$Country[o],groupMeat$cluster[o]) 32 | 33 | plot(protein$Red, protein$White, type="n", xlim=c(3,19), xlab="Red Meat", ylab="White Meat") 34 | text(x=protein$Red, y=protein$White, labels=protein$Country,col=groupMeat$cluster+1) 35 | 36 | 37 | set.seed(123456789) 38 | groupProtein <- kmeans(protein[,-1], centers=7, nstart=10) 39 | o=order(groupProtein$cluster) 40 | data.frame(protein$Country[o],groupProtein$cluster[o]) 41 | 42 | library(cluster) 43 | clusplot(protein[,-1], groupProtein$cluster, main='2D representation of the Cluster solution', color=TRUE, shade=TRUE, labels=2, lines=0) 44 | foodagg=agnes(protein,diss=FALSE,metric="euclidian") 45 | foodagg 46 | plot(foodagg, main='Dendrogram') 47 | 48 | groups <- cutree(foodagg, k=4) 49 | rect.hclust(foodagg, k=4, border="red") -------------------------------------------------------------------------------- /Chapter 02/3Tobit Regression.r: -------------------------------------------------------------------------------- 1 | require(ggplot2) 2 | require(GGally) 3 | require(VGAM) 4 | 5 | dat <- read.table("d:/tobit.csv", header=TRUE, sep=",", row.names="id") 6 | 7 | summary(dat) 8 | 9 | f <- function(x, var, bw = 15) { 10 | dnorm(x, mean = mean(var), sd(var)) * length(var) * bw 11 | } 12 | 13 | p <- ggplot(dat, aes(x = apt, fill=prog)) 14 | 15 | p + stat_bin(binwidth=15) + 16 | stat_function(fun = f, size = 1, 17 | args = list(var = dat$apt)) 18 | 19 | p + stat_bin(binwidth = 1) + stat_function(fun = f, size = 1, args = list(var = dat$apt, 20 | bw = 1)) 21 | 22 | cor(dat[, c("read", "math", "apt")]) 23 | 24 | ggpairs(dat[, c("read", "math", "apt")]) 25 | 26 | ------------------------------------------------------------------------------------------ 27 | 28 | #Below we run the tobit model, using the vglm function of the VGAM package. 29 | 30 | summary(m <- vglm(apt ~ read + math + prog, tobit(Upper = 800), data = dat)) 31 | 32 | ctable <- coef(summary(m)) 33 | pvals <- 2 * pt(abs(ctable[, "z value"]), df.residual(m), lower.tail = FALSE) 34 | cbind(ctable, pvals) 35 | 36 | m2 <- vglm(apt ~ read + math, tobit(Upper = 800), data = dat) 37 | (p <- pchisq(2 * (logLik(m) - logLik(m2)), df = 2, lower.tail = FALSE)) 38 | 39 | b <- coef(m) 40 | se <- sqrt(diag(vcov(m))) 41 | 42 | 43 | cbind(LL = b - qnorm(0.975) * se, UL = b + qnorm(0.975) * se) 44 | 45 | 46 | dat$yhat <- fitted(m)[,1] 47 | dat$rr <- resid(m, type = "response") 48 | dat$rp <- resid(m, type = "pearson")[,1] 49 | par(mfcol = c(2, 3)) 50 | 51 | with(dat, { 52 | plot(yhat, rr, main = "Fitted vs Residuals") 53 | qqnorm(rr) 54 | plot(yhat, rp, main = "Fitted vs Pearson Residuals") 55 | qqnorm(rp) 56 | plot(apt, rp, main = "Actual vs Pearson Residuals") 57 | plot(apt, yhat, main = "Actual vs Fitted") 58 | }) 59 | 60 | (r <- with(dat, cor(yhat, apt))) 61 | r^2 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | -------------------------------------------------------------------------------- /Chapter 08/3Markov Chains - Car Rental Agency Service.r: -------------------------------------------------------------------------------- 1 | R version 3.2.2 (2015-08-14) -- "Fire Safety" 2 | Copyright (C) 2015 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | [Workspace loaded from ~/.RData] 17 | 18 | install.packages("markovchain") 19 | library(markovchain) 20 | 21 | RentalStates <- c("Downtown", "East", "West") 22 | 23 | RentalStates 24 | 25 | RentalTransitionMatrix <- matrix(c(0.3, 0.3, 0.4, 26 | 0.4, 0.4, 0.2, 27 | 0.5, 0.3, 0.2), 28 | byrow = T, nrow = 3, dimnames = list(RentalStates, RentalStates)) 29 | 30 | 31 | RentalTransitionMatrix 32 | 33 | mcRental <- new("markovchain", states = RentalStates, byrow = T, transitionMatrix = RentalTransitionMatrix, name = "Rental Cars") 34 | 35 | mcRental 36 | 37 | mcRental[2] 38 | 39 | plot(mcRental) 40 | 41 | transitionProbability(mcRental, "East", "West") 42 | 43 | x <- 0.3 * 0.3 44 | y <- 0.3 * 0.4 45 | z <- 0.4 * 0.5 46 | x + y + z 47 | 48 | mcRental ^ 2 49 | 50 | mcRental^20 51 | 52 | mcRental ^ 30 53 | 54 | 70 * steadyStates(mcRental) 55 | 56 | summary(mcRental) 57 | 58 | conditionalDistribution(mcRental, "Downtown") 59 | 60 | conditionalDistribution(mcRental, "West") 61 | 62 | conditionalDistribution(mcRental, "East") 63 | -------------------------------------------------------------------------------- /Chapter 04/Data/Calories_Burnt.csv: -------------------------------------------------------------------------------- 1 | Activities,,,,,,,,, 2 | Date,Calories Burned,Steps,Distance,Floors,Minutes Sedentary,Minutes Lightly Active,Minutes Fairly Active,Minutes Very Active,Activity Calories 3 | 7/07/2016,"2,682","12,541",9.02,13,667,171,18,60,"1,248" 4 | 8/07/2016,"2,423","8,029",5.7,35,760,208,13,6,928 5 | 9/07/2016,"2,875","10,801",7.67,3,496,148,18,46,"1,040" 6 | 10/07/2016,"2,638","11,997",8.52,22,771,248,3,27,"1,285" 7 | 11/07/2016,"2,423","9,039",6.42,12,714,232,10,16,"1,044" 8 | 12/07/2016,"3,102","17,721",12.58,8,519,226,30,107,"1,805" 9 | 13/07/2016,"2,450","10,544",7.49,4,637,191,8,45,"1,064" 10 | 14/07/2016,"2,555","10,047",7.16,68,711,161,10,57,"1,060" 11 | 15/07/2016,"2,245","4,733",3.36,9,783,139,20,6,667 12 | 16/07/2016,"2,936","12,056",8.56,25,604,282,32,36,"1,576" 13 | 17/07/2016,"2,717","11,791",8.37,46,870,235,28,45,"1,340" 14 | 18/07/2016,"2,690","10,721",7.61,7,481,248,10,33,"1,226" 15 | 19/07/2016,"3,147","13,007",9.23,110,448,199,70,80,"1,836" 16 | 20/07/2016,"2,837","13,401",9.51,115,559,201,20,77,"1,450" 17 | 21/07/2016,"2,851","15,281",10.85,143,589,208,18,82,"1,523" 18 | 22/07/2016,"2,611","11,337",8.05,15,620,242,7,31,"1,234" 19 | 23/07/2016,"2,307","7,738",5.49,24,738,133,18,37,806 20 | 24/07/2016,"3,109","11,767",8.4,41,534,333,53,31,"1,840" 21 | 25/07/2016,"3,164","13,324",9.46,41,582,242,40,98,"1,842" 22 | 26/07/2016,"2,593","5,957",4.23,11,694,262,7,4,"1,080" 23 | 27/07/2016,"2,490","10,206",7.25,1,692,204,10,19,"1,060" 24 | 28/07/2016,"2,638","11,557",8.21,2,498,179,46,49,"1,252" 25 | 29/07/2016,"2,769","11,013",7.84,33,478,221,40,40,"1,399" 26 | 30/07/2016,"2,629","10,168",7.22,9,800,245,12,9,"1,164" 27 | 31/07/2016,"2,555","11,686",8.34,28,824,151,14,62,"1,158" 28 | 1/08/2016,"3,010","13,991",9.93,38,632,207,56,62,"1,643" 29 | 2/08/2016,"2,694","13,444",9.59,30,858,161,12,80,"1,273" 30 | 3/08/2016,"2,713","12,398",8.8,53,680,187,10,67,"1,271" 31 | 4/08/2016,"2,640","11,986",8.51,44,651,269,4,12,"1,255" 32 | 5/08/2016,"2,680","12,858",9.15,49,756,169,6,61,"1,220" 33 | -------------------------------------------------------------------------------- /Chapter 03/4Binary Clustering - Math test.r: -------------------------------------------------------------------------------- 1 | 2 | R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree" 3 | Copyright (C) 2015 The R Foundation for Statistical Computing 4 | Platform: x86_64-w64-mingw32/x64 (64-bit) 5 | 6 | R is free software and comes with ABSOLUTELY NO WARRANTY. 7 | You are welcome to redistribute it under certain conditions. 8 | Type 'license()' or 'licence()' for distribution details. 9 | 10 | R is a collaborative project with many contributors. 11 | Type 'contributors()' for more information and 12 | 'citation()' on how to cite R or R packages in publications. 13 | 14 | Type 'demo()' for some demos, 'help()' for on-line help, or 15 | 'help.start()' for an HTML browser interface to help. 16 | Type 'q()' to quit R. 17 | 18 | [Workspace loaded from ~/.RData] 19 | 20 | Mathtest = read.table("d:/math test.txt",header=T) 21 | dist.items <- dist(Mathtest[,-1], method='euclidean')^2 22 | dist.items 23 | 24 | dist.items.2 <- dist(Mathtest[,-1], method='binary') 25 | dist.items.2 26 | 27 | dist.items.3 <- dist(1 - Mathtest[,-1], method='binary') 28 | dist.items.3 29 | 30 | items.complete.link <- hclust(dist.items, method='complete') 31 | items.complete.link 32 | 33 | plot(items.complete.link, labels=Mathtest[,1], ylab="Distance") 34 | 35 | items.sing.link <- hclust(dist.items, method='single') 36 | items.sing.link 37 | 38 | plot(items.sing.link, labels=Mathtest[,1], ylab="Distance") 39 | 40 | library(cluster) 41 | my.k.choices <- 2:8 42 | 43 | avg.sil.width <- rep(0, times=length(my.k.choices)) 44 | for (ii in (1:length(my.k.choices)) ){ 45 | avg.sil.width[ii] <- pam(dist.items, k=my.k.choices[ii])$silinfo$avg.width 46 | } 47 | print( cbind(my.k.choices, avg.sil.width) ) 48 | 49 | items.kmed.2 <- pam(dist.items, k=2, diss=T) 50 | items.kmed.2 51 | 52 | items.2.clust <- lapply(1:2, function(nc) Mathtest[,1][items.kmed.2$clustering==nc]) 53 | items.2.clust 54 | 55 | items.kmed.3 <- pam(dist.items, k=3, diss=T) 56 | items.kmed.3 57 | 58 | items.3.clust <- lapply(1:3, function(nc) Mathtest[,1][items.kmed.3$clustering==nc]) 59 | items.3.clust -------------------------------------------------------------------------------- /Chapter 11/1Recurrent Neural Networks - Predicting periodic signal.r: -------------------------------------------------------------------------------- 1 | 2 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 3 | Copyright (C) 2016 The R Foundation for Statistical Computing 4 | Platform: x86_64-w64-mingw32/x64 (64-bit) 5 | 6 | R is free software and comes with ABSOLUTELY NO WARRANTY. 7 | You are welcome to redistribute it under certain conditions. 8 | Type 'license()' or 'licence()' for distribution details. 9 | 10 | R is a collaborative project with many contributors. 11 | Type 'contributors()' for more information and 12 | 'citation()' on how to cite R or R packages in publications. 13 | 14 | Type 'demo()' for some demos, 'help()' for on-line help, or 15 | 'help.start()' for an HTML browser interface to help. 16 | Type 'q()' to quit R. 17 | install.packages("rnn") 18 | library(rnn) 19 | 20 | set.seed(10) 21 | 22 | f <- 5 23 | 24 | w <- 2*pi*f 25 | 26 | t <- seq(0.005,2,by=0.005) 27 | 28 | x <- sin(t*w) + rnorm(200, 0, 0.25) 29 | 30 | y <- cos(t*w) 31 | 32 | X <- matrix(x, nrow = 40) 33 | 34 | 35 | Y <- matrix(y, nrow = 40) 36 | 37 | 38 | plot(as.vector(X), col='blue', type='l', ylab = "x-matrix, y-matrix", main = "Noisy waves") 39 | 40 | 41 | lines(as.vector(Y), col = "red") 42 | 43 | 44 | X <- (X - min(X)) / (max(X) - min(X)) 45 | 46 | X 47 | 48 | Y <- (Y - min(Y)) / (max(Y) - min(Y)) 49 | 50 | Y 51 | 52 | X <- t(X) 53 | 54 | Y <- t(Y) 55 | 56 | train <- 1:8 57 | 58 | test <- 9:10 59 | 60 | model <- trainr(Y = Y[train,], 61 | X = X[train,], 62 | learningrate = 0.05, 63 | hidden_dim = 16, 64 | numepochs = 1500) 65 | 66 | 67 | 68 | Y_predicted <- predictr(model, X) 69 | 70 | 71 | plot(as.vector(t(Y)), col = 'red', type = 'l', main = "Actual values vs Predicted values", ylab = "Y, Y-predicted") 72 | 73 | 74 | lines(as.vector(t(Y_predicted)), type = 'l', col = 'blue') 75 | 76 | 77 | plot(as.vector(t(Y[test,])), col = 'red', type='l', main = "Actual vs predicted: testing set", ylab = "Y,Y-predicted") 78 | 79 | 80 | 81 | lines(as.vector(t(Y_predicted[test,])), type = 'l', col = 'blue') 82 | -------------------------------------------------------------------------------- /Chapter 06/2Decision tree learning - Income Based Distribution of Real Estate Value.r: -------------------------------------------------------------------------------- 1 | 2 | R version 3.3.0 (2016-05-03) -- "Supposedly Educational" 3 | Copyright (C) 2016 The R Foundation for Statistical Computing 4 | Platform: x86_64-w64-mingw32/x64 (64-bit) 5 | 6 | R is free software and comes with ABSOLUTELY NO WARRANTY. 7 | You are welcome to redistribute it under certain conditions. 8 | Type 'license()' or 'licence()' for distribution details. 9 | 10 | R is a collaborative project with many contributors. 11 | Type 'contributors()' for more information and 12 | 'citation()' on how to cite R or R packages in publications. 13 | 14 | Type 'demo()' for some demos, 'help()' for on-line help, or 15 | 'help.start()' for an HTML browser interface to help. 16 | Type 'q()' to quit R. 17 | 18 | [Workspace loaded from ~/.RData] 19 | 20 | library(tree) 21 | 22 | realEstate <- read.table("d:/RealEstate.txt", header=TRUE) 23 | 24 | dim(realEstate) 25 | 26 | str(realEstate) 27 | 28 | head(realEstate) 29 | 30 | summary(realEstate) 31 | 32 | treeModel <- tree(log(MedianHouseValue) ~ Longitude + Latitude, data=realEstate) 33 | 34 | summary(treeModel) 35 | 36 | plot(treeModel) 37 | 38 | text(treeModel, cex=.75) 39 | 40 | priceDeciles <- quantile(realEstate$MedianHouseValue, 0:10/10) 41 | 42 | priceDeciles 43 | 44 | summary(priceDeciles) 45 | 46 | cutPrices <- cut(realEstate$MedianHouseValue, priceDeciles, include.lowest=TRUE) 47 | 48 | head(cutPrices) 49 | 50 | summary(cutPrices) 51 | 52 | plot(realEstate$Longitude, realEstate$Latitude, col=grey(10:2/11)[cutPrices], pch=20, xlab="Longitude",ylab="Latitude") 53 | 54 | summary(realEstate$Longitude) 55 | 56 | head(realEstate$Longitude) 57 | 58 | summary( realEstate$Latitude) 59 | 60 | head( realEstate$Latitude) 61 | 62 | partition.tree(treeModel, ordvars=c("Longitude","Latitude"), add=TRUE) 63 | 64 | treeModel2 <- tree(log(MedianHouseValue) ~ Longitude + Latitude, data=realEstate, mindev=0.001) 65 | 66 | summary(treeModel2) 67 | 68 | plot(treeModel2) 69 | 70 | text(treeModel2, cex=.65) 71 | 72 | treeModel3 <- tree(log(MedianHouseValue) ~ ., data=realEstate) 73 | 74 | summary(treeModel3) 75 | 76 | plot(treeModel3) 77 | 78 | text(treeModel3, cex=.75) -------------------------------------------------------------------------------- /Chapter 03/2Hierarchical Clustering - NASA Under story.r: -------------------------------------------------------------------------------- 1 | 2 | R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree" 3 | Copyright (C) 2015 The R Foundation for Statistical Computing 4 | Platform: x86_64-w64-mingw32/x64 (64-bit) 5 | 6 | R is free software and comes with ABSOLUTELY NO WARRANTY. 7 | You are welcome to redistribute it under certain conditions. 8 | Type 'license()' or 'licence()' for distribution details. 9 | 10 | R is a collaborative project with many contributors. 11 | Type 'contributors()' for more information and 12 | 'citation()' on how to cite R or R packages in publications. 13 | 14 | Type 'demo()' for some demos, 'help()' for on-line help, or 15 | 'help.start()' for an HTML browser interface to help. 16 | Type 'q()' to quit R. 17 | 18 | [Workspace loaded from ~/.RData] 19 | 20 | NASA = read.csv("d:/NASAUnderstory.csv",header=T) 21 | NASA.lab=NASA$Labels 22 | NASA.lab 23 | NASA=NASA[,-32] 24 | NASA 25 | 26 | NASAscale<-scale(NASA[,3:31]) 27 | NASAscale 28 | 29 | rownames(NASAscale)=as.factor(NASA$Overstory.Species) 30 | rownames(NASAscale) 31 | 32 | dist1<-dist(NASAscale, method="euclidean") 33 | 34 | clust1<-hclust(dist1,method="ward.D") 35 | clust1 36 | 37 | plot(clust1,labels= NASA[,2], cex=0.5, xlab="",ylab="Distance",main="Clustering for NASA Understory Data") 38 | 39 | rect.hclust(clust1,k=2) 40 | 41 | cuts=cutree(clust1,k=2) 42 | 43 | cuts 44 | 45 | 46 | library(vegan) 47 | 48 | dist1<-vegdist(NASA[,3:31], method="jaccard", upper=T) 49 | clust1<-hclust(dist1,method="ward.D") 50 | 51 | clust1 52 | 53 | plot(clust1,labels= NASA[,2], cex=0.5, xlab="",ylab="Distance",main="Clustering for NASA Understory Data") 54 | rect.hclust(clust1,k=2) 55 | cuts=cutree(clust1,k=2) 56 | cuts 57 | 58 | 59 | clusplot(NASA, cuts, color=TRUE, shade=TRUE, labels=2, lines=0, 60 | main="NASA Two Cluster Plot, Ward's Method, First two PC") 61 | 62 | library(fpc) 63 | NASAtrans=t(NASAscale) 64 | 65 | 66 | dist1<-dist(NASAtrans, method="minkowski", p=3) 67 | 68 | 69 | clust1<-hclust(dist1,method="ward.D") 70 | clust1 71 | 72 | plot(clust1,labels= NASA.lab[1:29], cex=1, xlab="",ylab="Distance",main="Clustering for NASA Understory Data") 73 | rect.hclust(clust1,k=3) 74 | 75 | 76 | 77 | -------------------------------------------------------------------------------- /Chapter 08/1Markov Chains - Stocks Regime Switching Model.r: -------------------------------------------------------------------------------- 1 | R version 3.2.2 (2015-08-14) -- "Fire Safety" 2 | Copyright (C) 2015 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | 19 | install.packages("MSwM") 20 | 21 | library(MSwM) 22 | 23 | MarkovSwitchData <- read.csv("d:/StocksRegimeSwitching.csv", header = TRUE) 24 | 25 | attach(MarkovSwitchData) 26 | 27 | head(MarkovSwitchData) 28 | 29 | dim(MarkovSwitchData) 30 | 31 | summary(MarkovSwitchData) 32 | 33 | yLogValueStocks <- cbind(LVS) 34 | 35 | head(yLogValueStocks) 36 | 37 | yLogGrowthStocks <- cbind(LGS) 38 | 39 | head(yLogGrowthStocks) 40 | 41 | x <- cbind(LRY, LRC, INT, LRV) 42 | 43 | olsLogValueStocks <- lm(yLogValueStocks~x) 44 | 45 | summary(olsLogValueStocks) 46 | 47 | olsLogGrowthStocks <- lm(yLogGrowthStocks~x) 48 | 49 | summary(olsLogGrowthStocks) 50 | 51 | MarkovSwtchLogValueStocks <- msmFit(olsLogValueStocks, k = 2, sw = rep(TRUE, 6)) 52 | 53 | summary(MarkovSwtchLogValueStocks) 54 | 55 | MarkoSwtchLogGrowthStocks <- msmFit(olsLogGrowthStocks, k = 2, sw = rep(TRUE, 6)) 56 | 57 | summary(MarkoSwtchLogGrowthStocks) 58 | 59 | par(mar=c(3,3,3,3)) 60 | 61 | plotProb(MarkovSwtchLogValueStocks, which=1) 62 | 63 | plotProb(MarkoSwtchLogValueStocks, which=2) 64 | 65 | plotProb(MarkoSwtchLogGrowthStocks, which=1) 66 | 67 | plotProb(MarkoSwtchLogGrowthStocks, which=2) 68 | 69 | par(mar=c(3,3,3,3)) 70 | 71 | plotDiag(MarkovSwtchLogValueStocks, regime=1, which=1) 72 | 73 | plotDiag(MarkovSwtchLogValueStocks, regime=1, which=2) 74 | 75 | plotDiag(MarkoSwtchLogGrowthStocks, regime=1, which=1) 76 | 77 | plotDiag(MarkoSwtchLogGrowthStocks, regime=1, which=2) 78 | 79 | plotDiag(MarkoSwtchLogGrowthStocks, regime=1, which=3) 80 | 81 | -------------------------------------------------------------------------------- /Chapter 09/1HMM - EUR & USD.r: -------------------------------------------------------------------------------- 1 | R version 3.2.2 (2015-08-14) -- "Fire Safety" 2 | Copyright (C) 2015 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | install.packages("depmixS4") 17 | library(depmixS4) 18 | install.packages("quantmod") 19 | library(quantmod) 20 | install.packages("ggplot2") 21 | library(ggplot2) 22 | 23 | 24 | EuroUSD <- read.csv("d:/EURUSD1d.csv", header = TRUE) 25 | 26 | head(EuroUSD) 27 | 28 | 29 | summary(EuroUSD) 30 | 31 | 32 | str(EuroUSD) 33 | 34 | Date <- as.character(EuroUSD[,1]) 35 | 36 | DateTimeSeries <- as.POSIXlt(Date, format = "%Y.%m.%d %H:%M:%S") 37 | 38 | TimeSeriesData <- data.frame(EuroUSD[,2:5], row.names = DateTimeSeries) 39 | 40 | 41 | head(TimeSeriesData) 42 | 43 | 44 | TimeSeriesData <-as.xts(TimeSeriesData) 45 | 46 | ATRindicator <- ATR(TimeSeriesData[,2:4],n=14) 47 | 48 | head(ATRindicator) 49 | 50 | 51 | TrueRange <- ATRindicator[,2] 52 | 53 | head(TrueRange) 54 | 55 | 56 | LogReturns <- log(EuroUSD$Close) - log(EuroUSD$Open) 57 | 58 | summary(LogReturns) 59 | 60 | HMMModel <- data.frame(LogReturns, TrueRange) 61 | 62 | HMMModel <- HMMModel[-c(1:14),] 63 | 64 | head(HMMModel) 65 | 66 | colnames(HMMModel) <- c("LogReturns","TrueRange") 67 | 68 | 69 | colnames(HMMModel) 70 | 71 | set.seed(1) 72 | 73 | HMM <- depmix(list(LogReturns~1, TrueRange~1), data = HMMModel, nstates=3, family=list(gaussian(), gaussian())) 74 | 75 | HMMfit <- fit(HMM, verbose = FALSE) 76 | 77 | 78 | print(HMMfit) 79 | 80 | summary(HMMfit) 81 | 82 | HMMstate <- posterior(HMMfit) 83 | 84 | head(HMMstate) 85 | 86 | 87 | DFIndicators <- data.frame(DateTimeSeries, LogReturns, TrueRange) 88 | 89 | DFIndicatorsClean <- DFIndicators[-c(1:14), ] 90 | 91 | Plot1Data <- data.frame(DFIndicatorsClean, HMMstate$state) 92 | 93 | LogReturnsPlot <- ggplot(Plot1Data,aes(x=Plot1Data[,1],y=Plot1Data[,2]))+geom_line(color="darkred")+labs(title="Euro USD Daily Log Returns",y="Log Return Values",x="Date") 94 | LogReturnsPlot 95 | -------------------------------------------------------------------------------- /Chapter 04/3Principal Component Analysis - Understanding world cuisine.r: -------------------------------------------------------------------------------- 1 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | install.packages("glmnet") 19 | 20 | library(ggplot2) 21 | library(glmnet) 22 | 23 | datafile <- file.path("d:","epic_recipes.txt") 24 | 25 | recipes_data <- read.table(datafile, fill=TRUE, col.names=1:max(count.fields(datafile)), na.strings=c("", "NA"), stringsAsFactors = FALSE) 26 | 27 | 28 | agg <- aggregate(recipes_data[,-1], by=list(recipes_data[,1]), paste, collapse=",") 29 | 30 | agg$combined <- apply(agg[,2:ncol(agg)], 1, paste, collapse=",") 31 | 32 | agg$combined <- gsub(",NA","",agg$combined) 33 | 34 | 35 | 36 | cuisines <- as.data.frame(table(recipes_data[,1])) 37 | 38 | cuisines 39 | 40 | 41 | ingredients_freq <- lapply(lapply(strsplit(a$combined,","), table), as.data.frame) 42 | 43 | 44 | names(ingredients_freq) <- agg[,1] 45 | 46 | proportion <- lapply(seq_along(ingredients_freq), function(i) { 47 | colnames(ingredients_freq[[i]])[2] <- names(ingredients_freq)[i] 48 | ingredients_freq[[i]][,2] <- ingredients_freq[[i]][,2]/cuisines[i,2] 49 | ingredients_freq[[i]]} 50 | ) 51 | 52 | 53 | names(proportion) <- a[,1] 54 | 55 | 56 | final <- Reduce(function(...) merge(..., all=TRUE, by="Var1"), proportion) 57 | 58 | 59 | row.names(final) <- final[,1] 60 | 61 | final <- final[,-1] 62 | 63 | final[is.na(final)] <- 0 64 | 65 | prop_matrix <- t(final) 66 | 67 | s <- sort(apply(prop_matrix, 2, sd), decreasing=TRUE) 68 | 69 | final_imp<- scale(subset(prop_matrix, select=names(which(s > 0.1)))) 70 | 71 | heatmap.2(final_imp, trace="none", margins = c(6,11), col=topo.colors(7), key=TRUE, key.title=NA, keysize=1.2, density.info="none") 72 | 73 | 74 | pca_computation <- princomp(final_imp) 75 | 76 | 77 | pca_computation 78 | 79 | 80 | biplot(pca_computation, pc.biplot=TRUE, col=c("black","red"), cex=c(0.9,0.8), xlim=c(-2.5,2.5), xlab="PC1, 39.7%", ylab="PC2, 24.5%") -------------------------------------------------------------------------------- /Chapter 06/6Support Vector Machine - Currency Trading Strategy.r: -------------------------------------------------------------------------------- 1 | R version 3.3.0 (2016-05-03) -- "Supposedly Educational" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | install.packages("quantmod") 18 | library(quantmod) 19 | install.packages("e1071") 20 | library(e1071) 21 | install.packages("ggplot2") 22 | install.packages("Hmisc") 23 | library(ggplot2) 24 | 25 | library(Hmisc) 26 | 27 | PoundDollar <- read.csv("d:/PoundDollar.csv") 28 | 29 | head(PoundDollar) 30 | 31 | str(PoundDollar) 32 | 33 | relativeStrengthIndex3 <- RSI(Op(PoundDollar), n= 3) 34 | 35 | summary(relativeStrengthIndex3) 36 | 37 | SeriesMeanAvg50 <- SMA(Op(PoundDollar),n=50) 38 | 39 | summary(SeriesMeanAvg50) 40 | 41 | describe(SeriesMeanAvg50) 42 | 43 | Trend <- Op(PoundDollar) - SeriesMeanAvg50 44 | 45 | summary(Trend) 46 | 47 | PriceDiff <- Cl(PoundDollar) - Op(PoundDollar) 48 | 49 | summary(PriceDiff) 50 | 51 | binaryClassification <- ifelse(PriceDiff>0,"UP","DOWN") 52 | 53 | summary(binaryClassification) 54 | 55 | DataSet <- data.frame(relativeStrengthIndex3, Trend, binaryClassification) 56 | 57 | str(DataSet) 58 | 59 | DataSet<-DataSet[-c(1:49),] 60 | 61 | dim(DataSet) 62 | 63 | TrainingDataSet <- DataSet[1:4528,] 64 | 65 | dim(TrainingDataSet) 66 | 67 | summary(TrainingDataSet) 68 | 69 | TestDataSet <- DataSet[4529:6038,] 70 | 71 | dim(TestDataSet) 72 | 73 | summary(TestDataSet) 74 | 75 | SVM <- svm(binaryClassification~relativeStrengthIndex3+Trend, data=TrainingDataSet, kernel="radial",cost=1,gamma=1/2) 76 | 77 | summary(SVM) 78 | 79 | TrainingPredictions <- predict(SVM,TrainingDataSet, type="class") 80 | 81 | summary(TrainingPredictions) 82 | 83 | describe(TrainingPredictions) 84 | 85 | TrainingData <- data.frame(TrainingDataSet, TrainingPredictions) 86 | 87 | summary(TrainingData) 88 | 89 | ggplot(TrainingData,aes(x=Trend,y=relativeStrengthIndex3))+stat_density2d(geom="contour",aes(color=TrainingPredictions))+labs(title="SVM Relative Strength Index & Trend Predictions",x="Open - SMA50",y="RSI3",color="Training Predictions") -------------------------------------------------------------------------------- /Chapter 07/1Self Organizing Map - Visualisations of heatmaps.r: -------------------------------------------------------------------------------- 1 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R 16 | 17 | library(kohonen) 18 | 19 | training_frame <- data[, c(2,4,5,8)] 20 | 21 | training_matrix <- as.matrix(scale(training_frame)) 22 | 23 | training_matrix 24 | zn chas nox dis 25 | 1 0.28454827 -0.2723291 -0.14407485 0.1400749840 26 | 2 -0.48724019 -0.2723291 -0.73953036 0.5566090496 27 | 3 -0.48724019 -0.2723291 -0.73953036 0.5566090496 28 | 4 -0.48724019 -0.2723291 -0.83445805 1.0766711351 29 | 5 -0.48724019 -0.2723291 -0.83445805 1.0766711351 30 | 6 -0.48724019 -0.2723291 -0.83445805 1.0766711351 31 | 7 0.04872402 -0.2723291 -0.26489191 0.8384142195 32 | 8 0.04872402 -0.2723291 -0.26489191 1.0236248974 33 | 9 0.04872402 -0.2723291 -0.26489191 1.0861216287 34 | 10 0.04872402 -0.2723291 -0.26489191 1.3283202075 35 | 11 0.04872402 -0.2723291 -0.26489191 1.2117799501 36 | 12 0.04872402 -0.2723291 -0.26489191 1.1547920492 37 | 13 0.04872402 -0.2723291 -0.26489191 0.7863652700 38 | 14 -0.48724019 -0.2723291 -0.14407485 0.4333252240 39 | 15 -0.48724019 -0.2723291 -0.14407485 0.3166899868 40 | 16 -0.48724019 -0.2723291 -0.14407485 0.3341187865 41 | 17 -0.48724019 -0.2723291 -0.14407485 0.3341187865 42 | 18 -0.48724019 -0.2723291 -0.14407485 0.2198105553 43 | 19 -0.48724019 -0.2723291 -0.14407485 0.0006920764 44 | 45 | 46 | 47 | som_grid <- somgrid(xdim = 20, ydim=20, topo="hexagonal") 48 | 49 | 50 | som_model <- som(training_matrix, 51 | grid=som_grid, 52 | rlen=1000, 53 | alpha=c(0.05,0.01), 54 | keep.data = TRUE, 55 | n.hood="circular") 56 | 57 | plot(som_model, main ="Training Progress", type="changes", col = "red") 58 | 59 | 60 | 61 | plot(som_model, main ="Node Count", type="count") 62 | 63 | 64 | plot(som_model, main ="Neighbour Distances", type="dist.neighbours") 65 | 66 | 67 | plot(som_model, type="codes") 68 | 69 | plot(som_model, type = "property", property = som_model$codes[,4], main=names(som_model$data)[4]) 70 | -------------------------------------------------------------------------------- /Chapter 10/1Modelling S&P 500.r: -------------------------------------------------------------------------------- 1 | 2 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 3 | Copyright (C) 2016 The R Foundation for Statistical Computing 4 | Platform: x86_64-w64-mingw32/x64 (64-bit) 5 | 6 | R is free software and comes with ABSOLUTELY NO WARRANTY. 7 | You are welcome to redistribute it under certain conditions. 8 | Type 'license()' or 'licence()' for distribution details. 9 | 10 | R is a collaborative project with many contributors. 11 | Type 'contributors()' for more information and 12 | 'citation()' on how to cite R or R packages in publications. 13 | 14 | Type 'demo()' for some demos, 'help()' for on-line help, or 15 | 'help.start()' for an HTML browser interface to help. 16 | Type 'q()' to quit R. 17 | 18 | install.packages("quantmod") 19 | install.packages("neuralnet") 20 | library(quantmod) 21 | library(neuralnet) 22 | 23 | startDate = as.Date("2009-01-01") 24 | 25 | endDate = as.Date("2014-01-01") 26 | 27 | getSymbols("^GSPC",src="yahoo",from=startDate,to=endDate) 28 | 29 | relativeStrengthIndex3 <- RSI(Op(GSPC),n=3) 30 | 31 | summary(relativeStrengthIndex3) 32 | 33 | exponentialMovingAverage5 <- EMA(Op(GSPC),n=5) 34 | 35 | 36 | head(exponentialMovingAverage5) 37 | 38 | summary(exponentialMovingAverage5) 39 | 40 | exponentialMovingAverageDiff <- Op(GSPC) - exponentialMovingAverage5 41 | 42 | head(exponentialMovingAverageDiff) 43 | 44 | summary(exponentialMovingAverageDiff) 45 | 46 | 47 | MACD <- MACD(Op(GSPC),fast = 12, slow = 26, signal = 9) 48 | 49 | tail(MACD) 50 | 51 | 52 | summary(MACD) 53 | 54 | MACDsignal <- MACD[,2] 55 | 56 | BollingerBands <- BBands(Op(GSPC),n=20,sd=2) 57 | 58 | tail(BollingerBands) 59 | 60 | summary(BollingerBands) 61 | 62 | PercentageChngpctB <- BollingerBands[,4] 63 | 64 | 65 | 66 | tail(PercentageChngpctB) 67 | 68 | summary(PercentageChngpctB) 69 | 70 | 71 | Price <- Cl(GSPC)-Op(GSPC) 72 | 73 | tail(Price) 74 | 75 | DataSet<-data.frame(relativeStrengthIndex3, exponentialMovingAverage5, MACDsignal, PercentageChngpctB, Price) 76 | 77 | str(DataSet) 78 | 79 | DataSet<-DataSet[-c(1:33),] 80 | 81 | dim(DataSet) 82 | 83 | colnames(DataSet)<-c("RSI3","EMAcross","MACDsignal","BollingerB","Price") 84 | 85 | str(DataSet) 86 | 87 | Normalized <-function(x) {(x-min(x))/(max(x)-min(x))} 88 | 89 | NormalizedData<-as.data.frame(lapply(DataSet,Normalized)) 90 | 91 | tail(NormalizedData) 92 | 93 | TrainingSet<-NormalizedData[1:816,] 94 | 95 | dim(TrainingSet) 96 | 97 | summary(TrainingSet) 98 | 99 | 100 | TestSet<-NormalizedData[817:1225 ,] 101 | 102 | dim(TestSet) 103 | 104 | summary(TestSet) 105 | 106 | nn1<-neuralnet(Price~RSI3+EMAcross+MACDsignal+BollingerB,data=TrainingSet, hidden=c(3,3), learningrate=.001,algorithm="backprop") 107 | 108 | 109 | plot(nn1) -------------------------------------------------------------------------------- /Chapter 04/2Dimension Reduction Methods - Delta's aircraft fleet.r: -------------------------------------------------------------------------------- 1 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R 16 | 17 | install.packages("rgl") 18 | install.packages("RColorBrewer") 19 | install.packages("scales") 20 | 21 | 22 | library(rgl) 23 | library(RColorBrewer) 24 | library(scales) 25 | 26 | delta <- read.csv(file="d:/delta.csv", header=T, sep=",", row.names=1) 27 | 28 | str(delta) 29 | 30 | plot(delta[,16:22], main = "Aircraft Physical Characteristics", col = "red") 31 | 32 | principal_comp_analysis <- princomp(delta) 33 | 34 | principal_comp_analysis 35 | 36 | plot(principal_comp_analysis, main ="Principal Components Analysis of Raw Data", col ="blue") 37 | 38 | 39 | loadings(principal_comp_analysis) 40 | 41 | mar <- par()$mar 42 | par(mar=mar+c(0,5,0,0)) 43 | 44 | barplot(sapply(delta, var), horiz=T, las=1, cex.names=0.8, main = "Regular Scaling of Variance", col = "Red", xlab = "Variance") 45 | 46 | 47 | barplot(sapply(delta, var), horiz=T, las=1, cex.names=0.8, log='x', main = "Logarithmic Scaling of Variance", col = "Blue", xlab = "Variance") 48 | 49 | 50 | par(mar=mar) 51 | 52 | 53 | delta2 <- data.frame(scale(delta)) 54 | 55 | 56 | plot(sapply(delta2, var), main = "Variances Across Different Variables", ylab = "Variances") 57 | 58 | principal_comp_analysis <- princomp(delta2) 59 | 60 | 61 | 62 | plot(principal_comp_analysis, main ="Principal Components Analysis of Scaled Data", col ="red") 63 | 64 | 65 | 66 | plot(principal_comp_analysis, type='l', main ="Principal Components Analysis of Scaled Data") 67 | 68 | 69 | 70 | summary(principal_comp_analysis) 71 | 72 | 73 | principal_comp_vectors <- prcomp(delta2) 74 | 75 | comp <- data.frame(principal_comp_vectors$x[,1:4]) 76 | 77 | k_means <- kmeans(comp, 4, nstart=25, iter.max=1000) 78 | 79 | 80 | 81 | palette(alpha(brewer.pal(9,'Set1'), 0.5)) 82 | 83 | plot(comp, col=k_means$clust, pch=16) 84 | 85 | 86 | 87 | plot3d(comp$PC1, comp$PC2, comp$PC3, col=k_means$clust) 88 | 89 | 90 | plot3d(comp$PC1, comp$PC3, comp$PC4, col=k_means$clust) 91 | 92 | sort(table(k_means$clust)) 93 | 94 | 95 | clust <- names(sort(table(k_means$clust))) 96 | 97 | row.names(delta[k_means$clust==clust[1],]) 98 | 99 | row.names(delta[k_means$clust==clust[2],]) 100 | 101 | 102 | row.names(delta[k_means$clust==clust[3],]) 103 | 104 | row.names(delta[k_means$clust==clust[4],]) 105 | -------------------------------------------------------------------------------- /Chapter 10/2Measuring Unemployment Rate.r: -------------------------------------------------------------------------------- 1 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | library(forecast) 18 | library(lmtest) 19 | library(caret) 20 | 21 | ud <- read.csv("d:/FRED-WIUR.csv", colClasses=c('Date'='Date')) 22 | 23 | 24 | tail(ud) 25 | 26 | colnames(ud) <- c('date', 'rate') 27 | 28 | ud$date <- as.Date(ud$date) 29 | 30 | summary(ud) 31 | 32 | ud.b <- ud[1:436,] 33 | 34 | 35 | summary(ud.b) 36 | 37 | ud.p <- ud[437:448,] 38 | 39 | summary(ud.p) 40 | 41 | ud.ts <- ts(ud.b$rate, start=c(1976, 1), frequency=12) 42 | 43 | ud.ts 44 | 45 | ud.p.ts<-ts(ud.p$rate, start=c(2012, 5), frequency=12) 46 | 47 | ud.p.ts 48 | 49 | plot.ts(ud.ts) 50 | 51 | 52 | plot.ts(ud.p.ts) 53 | 54 | 55 | mean <- meanf(ud.ts, 12) 56 | 57 | forecast_randomwalk <- rwf(ud.ts, 12) 58 | 59 | forecast_arima <- snaive(ud.ts, 12) 60 | 61 | drift <- rwf(ud.ts, 12, drift=T) 62 | 63 | 64 | m1<-tslm(ud.ts~trend) 65 | 66 | m2<-tslm(ud.ts~trend+season) 67 | 68 | residual_1 <- residuals(m1) 69 | 70 | par(mfrow=c(1,2)) 71 | 72 | plot(residual_1, ylab="Residuals",xlab="Year", title("Residual - Trends"), col = "red") 73 | 74 | acf(residual_1, main="ACF of residuals") 75 | 76 | 77 | residual_2 <- residuals(m2) 78 | 79 | par(mfrow=c(1,2)) 80 | 81 | plot(residual_2, ylab="Residuals",xlab="Year",title("Residual - Trends + Seasonality"), col = "red") 82 | 83 | acf(residual_2, main="ACF of residuals") 84 | 85 | 86 | dwtest(m1, alt="two.sided") 87 | 88 | m3 <- stl(ud.ts, s.window='periodic') 89 | 90 | plot(m3) 91 | 92 | m4<-ets(ud.ts, model='ZZZ') 93 | 94 | plot(m4) 95 | 96 | m5 <- auto.arima(ud.ts) 97 | 98 | plot(forecast(m5, h=12)) 99 | 100 | m6<-nnetar(ud.ts) 101 | 102 | m6 103 | 104 | plot(forecast(m6, h=12)) 105 | 106 | 107 | a1 <- accuracy(mean, ud.p.ts) 108 | 109 | a2 <- accuracy(forecast_randomwalk, ud.p.ts) 110 | 111 | a3<-accuracy(forecast_arima, ud.p.ts) 112 | 113 | a4<-accuracy(drift, ud.p.ts) 114 | 115 | a.table<-rbind(a1, a2, a3, a4) 116 | 117 | a.table 118 | 119 | f1<-forecast(m1, h=12) 120 | 121 | f2<-forecast(m2, h=12) 122 | 123 | f3<-forecast(m3, h=12) 124 | 125 | f4<-forecast(m4, h=12) 126 | 127 | f5<-forecast(m5, h=12) 128 | 129 | f6<-forecast(m6, h=12) 130 | 131 | a5 <- accuracy(f1, ud.p.ts) 132 | 133 | a6 <- accuracy(f2, ud.p.ts) 134 | 135 | a7 <- accuracy(f3, ud.p.ts) 136 | 137 | a8 <- accuracy(f4, ud.p.ts) 138 | 139 | a9 <- accuracy(f5, ud.p.ts) 140 | 141 | a10 <- accuracy(f6, ud.p.ts) 142 | 143 | a.table.1 <- rbind(a5, a6, a7, a8, a9, a10) 144 | 145 | a.table.1 146 | -------------------------------------------------------------------------------- /Chapter 06/4Naive Bayes - Predicting the Direction of Stock Movement.r: -------------------------------------------------------------------------------- 1 | R version 3.3.0 (2016-05-03) -- "Supposedly Educational" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | 19 | install.packages("quantmod") 20 | library(quantmod) 21 | install.packages("lubridate") 22 | library(lubridate) 23 | install.packages("e1071") 24 | library(e1071) 25 | 26 | startDate = as.Date("2012-01-01") 27 | 28 | endDate = as.Date("2014-01-01") 29 | 30 | getSymbols("AAPL", env = .GlobalEnv, src = "yahoo", from = startDate, to = endDate) 31 | 32 | weekDays <- wday(AAPL, label=TRUE) 33 | 34 | head(weekDays) 35 | 36 | changeInPrices <- Cl(AAPL) - Op(AAPL) 37 | 38 | head(changeInPrices) 39 | 40 | summary(changeInPrices) 41 | 42 | dim(changeInPrices) 43 | 44 | binaryClassification <- ifelse(changeInPrices>0,"UP","DOWN") 45 | 46 | binaryClassification 47 | 48 | summary(binaryClassification) 49 | 50 | AAPLDataSet <- data.frame(weekDays,binaryClassification) 51 | 52 | AAPLDataSet 53 | 54 | head(AAPLDataSet) 55 | 56 | dim(AAPLDataSet) 57 | 58 | NaiveBayesclassifier <- naiveBayes(AAPLDataSet[,1], AAPLDataSet[,2]) 59 | 60 | NaiveBayesclassifier 61 | 62 | exponentialMovingAverage5 <- EMA(Op(AAPL),n = 5) 63 | 64 | exponentialMovingAverage5 65 | 66 | summary(exponentialMovingAverage5) 67 | 68 | exponentialMovingAverage10 <-EMA(Op(AAPL),n = 10) 69 | 70 | exponentialMovingAverage10 71 | 72 | summary(exponentialMovingAverage10) 73 | 74 | dim(exponentialMovingAverage10) 75 | 76 | exponentialMovingAverageDiff <- exponentialMovingAverage5 - exponentialMovingAverage10 77 | 78 | exponentialMovingAverageDiff 79 | 80 | summary(exponentialMovingAverageDiff) 81 | 82 | exponentialMovingAverageDiffRound <- round(exponentialMovingAverageDiff, 2) 83 | 84 | summary(exponentialMovingAverageDiffRound) 85 | 86 | AAPLDataSetNew <- data.frame(weekDays,exponentialMovingAverageDiffRound, binaryClassification) 87 | 88 | AAPLDataSetNew 89 | 90 | summary(AAPLDataSetNew) 91 | 92 | AAPLDataSetNew <- AAPLDataSetNew[-c(1:10),] 93 | 94 | AAPLDataSetNew 95 | 96 | summary(AAPLDataSetNew) 97 | 98 | dim(AAPLDataSetNew) 99 | 100 | trainingDataSet <- AAPLDataSetNew[1:328,] 101 | 102 | dim(trainingDataSet) 103 | 104 | summary(trainingDataSet) 105 | 106 | TestDataSet <- AAPLDataSetNew[329:492,] 107 | 108 | dim(TestDataSet) 109 | 110 | summary(TestDataSet) 111 | 112 | exponentialMovingAverageDiffRoundModel <- naiveBayes(trainingDataSet[,1:2],trainingDataSet[,3]) 113 | 114 | exponentialMovingAverageDiffRoundModel 115 | 116 | table(predict(exponentialMovingAverageDiffRoundModel,TestDataSet),TestDataSet[,3],dnn=list('Predicted','Actual')) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | #Practical Machine Learning Cookbook 5 | This is the code repository for [Practical Machine Learning Cookbook](https://www.packtpub.com/big-data-and-business-intelligence/practical-machine-learning-cookbook?utm_source=github&utm_medium=repository&utm_campaign=9781785280511), published by [Packt](https://www.packtpub.com/?utm_source=github). It contains all the supporting project files necessary to work through the book from start to finish. 6 | ## About the Book 7 | Data in today’s world is the new black gold which is growing exponentially. This growth can be attributed to the growth of existing data, and new data in a structured and unstructured format from multiple sources such as social media, Internet, documents and the Internet of Things. The flow of data must be collected, processed, analyzed, and finally presented in real time to ensure that the consumers of the data are able to take informed decisions in today’s fast-changing environment. Machine learning techniques are applied to the data using the context of the problem to be solved to ensure that fast arriving and complex data can be analyzed in a scientific manner using statistical techniques. Using machine learning algorithms that iteratively learn from data, hidden patterns can be discovered. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt and learn to produce reliable decisions from new data sets. 8 | ##Instructions and Navigation 9 | All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02. 10 | 11 | Chapter 1 covers overviews of concepts and does not contain code. 12 | The required support files can be found in the Data folder within the chapter folders. 13 | 14 | The code will look like the following: 15 | ``` 16 | install.packages("ggplot2") 17 | ``` 18 | 19 | This book is focused on building machine learning-based applications in R. We have used R to build various solutions. We focused on how to utilize various R libraries and functions in the best possible way to overcome real-world challenges. We have tried to keep all the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios. 20 | 21 | ## Related Products 22 | * [Python Machine Learning](https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning?utm_source=github&utm_medium=repository&utm_campaign=9781783555130) 23 | 24 | * [Practical Data Analysis - Second Edition](https://www.packtpub.com/big-data-and-business-intelligence/practical-data-analysis-second-edition?utm_source=github&utm_medium=repository&utm_campaign=9781785289712) 25 | 26 | * [Practical Machine Learning](https://www.packtpub.com/big-data-and-business-intelligence/practical-machine-learning?utm_source=github&utm_medium=repository&utm_campaign=9781784399689) 27 | 28 | ### Suggestions and Feedback 29 | [Click here](https://docs.google.com/forms/d/e/1FAIpQLSe5qwunkGf6PUvzPirPDtuy1Du5Rlzew23UBp2S-P3wB-GcwQ/viewform) if you have any feedback or suggestions. 30 | ### Download a free PDF 31 | 32 | If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
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-------------------------------------------------------------------------------- /Chapter 06/3Decision tree learning - Predicting the Direction of Stock Movement.r: -------------------------------------------------------------------------------- 1 | R version 3.3.0 (2016-05-03) -- "Supposedly Educational" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | 19 | install.packages("quantmod") 20 | library("quantmod") 21 | 22 | install.packages("rpart") 23 | library("rpart") 24 | 25 | install.packages("rpart.plot") 26 | library("rpart.plot") 27 | 28 | 29 | startDate = as.Date("2012-01-01") 30 | 31 | endDate = as.Date("2014-01-01") 32 | 33 | getSymbols("BAC", env = .GlobalEnv, src = "yahoo", from = startDate, to = endDate) 34 | 35 | relativeStrengthIndex3<-RSI(Op(BAC), n= 3) 36 | 37 | relativeStrengthIndex3 38 | 39 | exponentialMovingAverage5<-EMA(Op(BAC),n=5) 40 | 41 | exponentialMovingAverage5 42 | 43 | dim(exponentialMovingAverage5) 44 | 45 | str(exponentialMovingAverage5) 46 | 47 | exponentialMovingAverageDiff<- Op(BAC)-exponentialMovingAverage5 48 | 49 | exponentialMovingAverageDiff 50 | 51 | MACD<-MACD(Op(BAC),fast = 12, slow = 26, signal = 9) 52 | 53 | MACD 54 | 55 | head(MACD) 56 | 57 | MACDsignal<-MACD[,2] 58 | 59 | MACDsignal 60 | 61 | stochasticOscillator <- SMI(Op(BAC),n=13,slow=25,fast=2,signal=9) 62 | 63 | 64 | stochasticOscillator 65 | 66 | 67 | stochasticOscillatorSignal <- stochasticOscillator[,1] 68 | 69 | stochasticOscillatorSignal 70 | 71 | 72 | PriceChange <- Cl(BAC) - Op(BAC) 73 | 74 | PriceChange 75 | 76 | binaryClassification <- ifelse(PriceChange>0,"UP","DOWN") 77 | 78 | binaryClassification 79 | 80 | str(binaryClassification) 81 | 82 | 83 | DataSet<-data.frame(relativeStrengthIndex3, exponentialMovingAverageDiff, MACDsignal, stochasticOscillator, binaryClassification) 84 | 85 | DataSet 86 | 87 | 88 | head(DataSet) 89 | 90 | str(DataSet) 91 | 92 | colnames(DataSet) <- c("relativeStrengthIndex3", "exponentialMovingAverageDiff", "MACDsignal", "stochasticOscillator", "binaryClassification") 93 | 94 | 95 | colnames(DataSet) 96 | 97 | DataSet<-DataSet[-c(1:33),] 98 | 99 | 100 | DataSet 101 | 102 | 103 | head(DataSet) 104 | 105 | str(DataSet) 106 | 107 | dim(DataSet) 108 | 109 | TrainingDataSet <- DataSet[1:312,] 110 | TrainingDataSet 111 | 112 | str(TrainingDataSet) 113 | 114 | TestDataSet <- DataSet[313:469,] 115 | 116 | TestDataSet 117 | 118 | str(TestDataSet) 119 | 120 | DecisionTree<-rpart(binaryClassification~relativeStrengthIndex3+exponentialMovingAverageDiff+MACDsignal+stochasticOscillator,data=TrainingDataSet, cp=.001) 121 | 122 | 123 | prp(DecisionTree,type=2) 124 | 125 | printcp(DecisionTree) 126 | 127 | plotcp(DecisionTree,upper="splits") 128 | 129 | PrunedDecisionTree<-prune(DecisionTree,cp=0.041428) 130 | 131 | prp(PrunedDecisionTree, type=4) 132 | 133 | table(predict(PrunedDecisionTree,TestDataSet),TestDataSet[,5],dnn=list('predicted','actual')) 134 | -------------------------------------------------------------------------------- /Chapter 06/5Random Forest - Currency Trading Strategy.r: -------------------------------------------------------------------------------- 1 | R version 3.3.0 (2016-05-03) -- "Supposedly Educational" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | install.packages("quantmod") 18 | library(quantmod) 19 | install.packages("randomForest") 20 | library(randomForest) 21 | install.packages("Hmisc") 22 | library(describe) 23 | 24 | PoundDollar <- read.csv("d:/PoundDollar.csv") 25 | 26 | head(PoundDollar) 27 | 28 | summary(PoundDollar) 29 | 30 | dim(PoundDollar) 31 | 32 | DateAndTime <- as.POSIXlt(PoundDollar[,2],format="%m/%d/%y %H:%M") 33 | 34 | HighLowClose <- PoundDollar[,4:6] 35 | 36 | head(HighLowClose) 37 | 38 | summary(HighLowClose) 39 | 40 | str(HighLowClose) 41 | 42 | HighLowClosets <- data.frame(HighLowClose,row.names=DateAndTime) 43 | 44 | describe(HighLowClosets) 45 | 46 | HighLowClosexts <- as.xts(HighLowClosets) 47 | 48 | BollingerBands <- BBands(HighLowClosexts,n=20,SMA,sd=2) 49 | 50 | describe(BollingerBands) 51 | 52 | Upper <- BollingerBands$up - HighLowClosexts$Close 53 | 54 | summary(Upper) 55 | 56 | Lower <- BollingerBands$dn - HighLowClosexts$Close 57 | 58 | summary(Lower) 59 | 60 | Middle <- BollingerBands$mavg - HighLowClosexts$Close 61 | 62 | summary(Middle) 63 | 64 | PercentageChngpctB <- Delt(BollingerBands$pctB,k=1) 65 | 66 | describe(PercentageChngpctB) 67 | 68 | PercentageChngUp <-Delt(Upper,k=1) 69 | 70 | describe(PercentageChngUp) 71 | 72 | PercentageChngLow <- Delt(Lower,k=1) 73 | 74 | describe(PercentageChngLow) 75 | 76 | PercentageChngMid <- Delt(Middle,k=1) 77 | 78 | describe(PercentageChngMid) 79 | 80 | Returns <- Delt(HighLowClosexts$Close,k=1) 81 | 82 | binaryClassification <- ifelse(Returns>0,"Up","Down") 83 | 84 | 85 | summary(binaryClassification) 86 | 87 | ClassShifted <- binaryClassification[-1] 88 | 89 | FeaturesCombined <- data.frame(Upper, Lower, Middle, BollingerBands$pctB, PercentageChngpctB, PercentageChngUp, PercentageChngLow, PercentageChngMid) 90 | 91 | summary(FeaturesCombined) 92 | 93 | FeaturesShifted <- FeaturesCombined[-5257,] 94 | 95 | FeaturesClassData <- data.frame(FeaturesShifted,ClassShifted) 96 | 97 | summary(FeaturesClassData) 98 | 99 | FinalModelData <- FeaturesClassData[-c(1:20),] 100 | 101 | colnames(FinalModelData) <- c("pctB","LowDiff","UpDiff","MidDiff","PercentageChngpctB","PercentageChngUp","PercentageChngLow","PercentageChngMid","binaryClassification") 102 | 103 | str(FinalModelData) 104 | 105 | set.seed(1) 106 | 107 | FeatureNumber <- tuneRF(FinalModelData[,-9],FinalModelData[,9],ntreeTry=100, stepFactor=1.5,improve=0.01, trace=TRUE, plot=TRUE, dobest=FALSE) 108 | 109 | RandomForest <- randomForest(binaryClassification~.,data=FinalModelData,mtry=2,ntree=2000,keep.forest=TRUE,importance=TRUE) 110 | 111 | varImpPlot(RandomForest, main = 'Random Forest: Measurement of Importance of Each Feature',pch=16,col='blue' ) 112 | 113 | 114 | 115 | 116 | -------------------------------------------------------------------------------- /Chapter 08/Data/StocksRegimeSwitching.csv: -------------------------------------------------------------------------------- 1 | DATE,LRY, LRV ,INT,LRC,LVS,LGS 2 | 1997Q3, 11.49 , 6.74 , 10.73 , 13.35 , 4.95 , 4.90 3 | 1997Q4, 11.51 , 12.97 , 11.09 , 13.33 , 4.63 , 4.43 4 | 1998Q1, 11.44 , 13.41 , 12.45 , 13.30 , 4.36 , 3.99 5 | 1998Q2, 11.42 , 4.26 , 13.51 , 13.31 , 4.55 , 4.20 6 | 1998Q3, 11.39 , 33.71 , 12.55 , 13.28 , 4.05 , 3.83 7 | 1998Q4, 11.39 , 3.54 , 10.00 , 13.31 , 3.90 , 3.36 8 | 1999Q1, 11.43 , 2.64 , 9.59 , 13.31 , 4.49 , 3.83 9 | 1999Q2, 11.47 , 3.53 , 8.73 , 13.29 , 4.40 , 3.70 10 | 1999Q3, 11.47 , 3.93 , 8.10 , 13.28 , 4.91 , 4.15 11 | 1999Q4, 11.50 , 1.34 , 7.82 , 13.26 , 4.63 , 3.93 12 | 2000Q1, 11.54 , 2.04 , 7.77 , 13.23 , 4.78 , 4.19 13 | 2000Q2, 11.55 , 2.25 , 7.73 , 13.23 , 4.95 , 4.30 14 | 2000Q3, 11.56 , 1.49 , 7.63 , 13.22 , 4.75 , 4.06 15 | 2000Q4, 11.57 , 1.34 , 7.54 , 13.23 , 4.58 , 3.89 16 | 2001Q1, 11.55 , 0.67 , 7.36 , 13.26 , 4.55 , 3.90 17 | 2001Q2, 11.55 , 2.52 , 7.25 , 13.28 , 4.52 , 3.89 18 | 2001Q3, 11.55 , 2.22 , 7.10 , 13.30 , 4.52 , 3.67 19 | 2001Q4, 11.57 , 0.67 , 6.80 , 13.32 , 4.60 , 3.74 20 | 2002Q1, 11.58 , 0.61 , 6.64 , 13.32 , 4.69 , 3.82 21 | 2002Q2, 11.60 , 0.63 , 6.61 , 13.30 , 4.84 , 3.87 22 | 2002Q3, 11.62 , 0.73 , 6.63 , 13.30 , 4.83 , 3.83 23 | 2002Q4, 11.64 , 0.54 , 6.55 , 13.31 , 4.68 , 3.71 24 | 2003Q1, 11.64 , 0.58 , 6.53 , 13.31 , 4.65 , 3.66 25 | 2003Q2, 11.66 , 0.41 , 6.34 , 13.34 , 4.63 , 3.70 26 | 2003Q3, 11.67 , 0.41 , 6.19 , 13.36 , 4.75 , 3.81 27 | 2003Q4, 11.70 , 0.62 , 6.13 , 13.35 , 4.80 , 3.81 28 | 2004Q1, 11.72 , 0.66 , 6.12 , 13.33 , 4.83 , 3.87 29 | 2004Q2, 11.73 , 0.71 , 6.05 , 13.34 , 4.93 , 3.99 30 | 2004Q3, 11.73 , 0.31 , 6.03 , 13.33 , 4.81 , 3.89 31 | 2004Q4, 11.75 , 0.32 , 5.99 , 13.33 , 4.83 , 3.90 32 | 2005Q1, 11.81 , 0.24 , 5.94 , 13.35 , 4.89 , 3.96 33 | 2005Q2, 11.81 , 0.32 , 5.94 , 13.34 , 4.85 , 3.88 34 | 2005Q3, 11.82 , 0.23 , 5.90 , 13.33 , 4.83 , 3.86 35 | 2005Q4, 11.84 , 0.11 , 6.02 , 13.35 , 4.87 , 3.89 36 | 2006Q1, 11.87 , 0.16 , 6.24 , 13.36 , 4.84 , 3.85 37 | 2006Q2, 11.86 , 0.28 , 6.51 , 13.36 , 4.87 , 3.90 38 | 2006Q3, 11.87 , 0.18 , 6.65 , 13.36 , 4.83 , 3.88 39 | 2006Q4, 11.89 , 0.45 , 6.58 , 13.39 , 4.90 , 3.94 40 | 2007Q1, 11.92 , 1.58 , 6.55 , 13.40 , 5.02 , 4.09 41 | 2007Q2, 11.92 , 0.47 , 6.46 , 13.39 , 5.13 , 4.20 42 | 2007Q3, 11.94 , 1.54 , 6.33 , 13.41 , 5.20 , 4.26 43 | 2007Q4, 11.96 , 0.59 , 6.29 , 13.36 , 5.16 , 4.20 44 | 2008Q1, 11.99 , 3.35 , 6.25 , 13.37 , 5.19 , 4.23 45 | 2008Q2, 11.99 , 0.61 , 6.13 , 13.36 , 5.03 , 4.02 46 | 2008Q3, 11.99 , 1.15 , 5.99 , 13.38 , 4.91 , 3.90 47 | 2008Q4, 11.96 , 2.14 , 5.95 , 13.46 , 4.88 , 3.79 48 | 2009Q1, 11.93 , 0.88 , 5.47 , 13.51 , 4.85 , 3.66 49 | 2009Q2, 11.95 , 1.03 , 5.06 , 13.53 , 4.90 , 3.70 50 | 2009Q3, 11.98 , 0.43 , 4.92 , 13.54 , 5.05 , 3.87 51 | 2009Q4, 12.00 , 0.19 , 4.88 , 13.53 , 5.13 , 3.92 52 | 2010Q1, 12.02 , 0.32 , 4.89 , 13.54 , 5.17 , 3.96 53 | 2010Q2, 12.04 , 0.40 , 5.00 , 13.57 , 5.22 , 4.02 54 | 2010Q3, 12.04 , 0.23 , 5.20 , 13.58 , 5.17 , 3.98 55 | 2010Q4, 12.05 , 0.20 , 4.92 , 13.59 , 5.25 , 4.10 56 | 2011Q1, 12.07 , 0.38 , 4.95 , 13.58 , 5.27 , 4.13 57 | 2011Q2, 12.08 , 0.18 , 4.95 , 13.62 , 5.28 , 4.14 58 | 2011Q3, 12.10 , 0.77 , 4.89 , 13.64 , 5.31 , 4.16 59 | 2011Q4, 12.10 , 0.65 , 4.87 , 13.67 , 5.17 , 3.97 60 | 2012Q1, 12.12 , 0.20 , 4.83 , 13.67 , 5.24 , 4.09 61 | 2012Q2, 12.14 , 0.23 , 4.85 , 13.72 , 5.29 , 4.14 62 | 2012Q3, 12.15 , 0.17 , 4.73 , 13.76 , 5.31 , 4.12 63 | 2012Q4, 12.17 , 0.13 , 4.73 , 13.79 , 5.31 , 4.15 64 | 2013Q1, 12.16 , 0.26 , 4.70 , 13.80 , 5.30 , 4.17 65 | 2013Q2, 12.18 , 0.49 , 4.68 , 13.83 , 5.34 , 4.19 66 | 2013Q3, 12.20 , 0.33 , 4.52 , 13.84 , 5.40 , 4.23 67 | 2013Q4, 12.21 , 0.12 , 4.55 , 13.85 , 5.37 , 4.19 -------------------------------------------------------------------------------- /Chapter 08/4Continuous Time Markov Chains - Vehicle Service at Gas Station.r: -------------------------------------------------------------------------------- 1 | R version 3.2.2 (2015-08-14) -- "Fire Safety" 2 | Copyright (C) 2015 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | 19 | install.packages("simmer") 20 | library(simmer) 21 | library(ggplot2) 22 | 23 | ArrivalRate <- 3/20 24 | 25 | ArrivalRate 26 | 27 | ServiceRate <- c(1/8, 1/3) 28 | 29 | ServiceRate 30 | 31 | p <- 0.75 32 | 33 | TransitionMatrix <- matrix(c(1, ServiceRate[1], 0, 34 | 1, -ArrivalRate, (1-p)*ArrivalRate, 35 | 1, ServiceRate[2], -ServiceRate[2]), byrow=T, ncol=3) 36 | 37 | TransitionMatrix 38 | 39 | B <- c(1, 0, 0) 40 | 41 | P <- solve(t(A), B) 42 | 43 | P 44 | 45 | Resolution <- sum(P * c(1, 0, 1)) 46 | 47 | Resolution 48 | 49 | set.seed(1234) 50 | 51 | option.1 <- function(t) { 52 | car <- create_trajectory() %>% 53 | seize("pump", amount=1) %>% 54 | timeout(function() rexp(1, ServiceRate[1])) %>% 55 | release("pump", amount=1) 56 | 57 | motorcycle <- create_trajectory() %>% 58 | seize("pump", amount=1) %>% 59 | timeout(function() rexp(1, ServiceRate[2])) %>% 60 | release("pump", amount=1) 61 | 62 | simmer() %>% 63 | add_resource("pump", capacity=1, queue_size=0) %>% 64 | add_generator("car", car, function() rexp(1, p*ArrivalRate)) %>% 65 | add_generator("motorcycle", motorcycle, function() rexp(1, (1-p)*ArrivalRate)) %>% 66 | run(until=t) 67 | } 68 | 69 | 70 | 71 | option.2 <- function(t) { 72 | vehicle <- create_trajectory() %>% 73 | seize("pump", amount=1) %>% 74 | branch(function() sample(c(1, 2), 1, prob=c(p, 1-p)), merge=c(T, T), 75 | create_trajectory("car") %>% 76 | timeout(function() rexp(1, ServiceRate[1])), 77 | create_trajectory("motorcycle") %>% 78 | timeout(function() rexp(1, ServiceRate[2]))) %>% 79 | release("pump", amount=1) 80 | 81 | simmer() %>% 82 | add_resource("pump", capacity=1, queue_size=0) %>% 83 | add_generator("vehicle", vehicle, function() rexp(1, ArrivalRate)) %>% 84 | run(until=t) 85 | } 86 | 87 | option.3 <- function(t) { 88 | vehicle <- create_trajectory() %>% 89 | seize("pump", amount=1) %>% 90 | timeout(function() { 91 | if (runif(1) < p) rexp(1, ServiceRate[1]) 92 | else rexp(1, ServiceRate[2]) 93 | }) %>% 94 | release("pump", amount=1) 95 | 96 | simmer() %>% 97 | add_resource("pump", capacity=1, queue_size=0) %>% 98 | add_generator("vehicle", vehicle, function() rexp(1, ArrivalRate)) %>% 99 | run(until=t) 100 | } 101 | 102 | gas.station <- option.3(5000) 103 | 104 | graph <- plot_resource_usage(gas.station, "pump", items="system") 105 | 106 | graph + geom_hline(yintercept = Resolution) 107 | -------------------------------------------------------------------------------- /Chapter 05/2Smooth Splines.r: -------------------------------------------------------------------------------- 1 | 2 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 3 | Copyright (C) 2016 The R Foundation for Statistical Computing 4 | Platform: x86_64-w64-mingw32/x64 (64-bit) 5 | 6 | R is free software and comes with ABSOLUTELY NO WARRANTY. 7 | You are welcome to redistribute it under certain conditions. 8 | Type 'license()' or 'licence()' for distribution details. 9 | 10 | R is a collaborative project with many contributors. 11 | Type 'contributors()' for more information and 12 | 'citation()' on how to cite R or R packages in publications. 13 | 14 | Type 'demo()' for some demos, 'help()' for on-line help, or 15 | 'help.start()' for an HTML browser interface to help. 16 | Type 'q()' to quit R. 17 | 18 | library(graphics) 19 | library(splines) 20 | 21 | matrx = matrix(cbind(1,.99, .99,1),nrow=2) 22 | 23 | cholsky = t(chol(matrx)) 24 | 25 | nvars = dim(cholsky)[1] 26 | 27 | numobs = 1000 28 | 29 | 30 | set.seed(1) 31 | 32 | random_normal = matrix(rnorm(nvars*numobs,10,1), nrow=nvars, ncol=numobs) 33 | 34 | 35 | X = cholsky %*% random_normal 36 | 37 | 38 | 39 | newX = t(X) 40 | 41 | raw = as.data.frame(newX) 42 | 43 | head(raw) 44 | 45 | raw_original = as.data.frame(t(random_normal)) 46 | 47 | names(raw) = c("response","predictor1") 48 | 49 | raw$predictor1_3 = raw$predictor1^3 50 | 51 | head(raw$predictor1_3) 52 | 53 | raw$predictor1_2 = raw$predictor1^2 54 | 55 | head(raw$predictor1_2) 56 | 57 | 58 | fit = lm(raw$response ~ raw$predictor1_3) 59 | 60 | 61 | fit 62 | 63 | plot(raw$response ~ raw$predictor1_3, pch=16, cex=.4, xlab="Predictor", ylab="Response", col ="red", main="Simulated Data with Slight Curve") 64 | 65 | 66 | abline(fit) 67 | 68 | x_axis <- with(cars, speed) 69 | 70 | y_axis <- with(cars, dist) 71 | 72 | 73 | eval_length = 50 74 | 75 | fit_loess <- loess.smooth(x_axis, y_axis, evaluation = eval_length, family="gaussian", span=.75, degree=1) 76 | 77 | fit_loess 78 | 79 | fit_loess_2 <- loess(y_axis ~ x_axis, family="gaussian", span=.75, degree=1) 80 | 81 | fit_loess_2 82 | 83 | new_x_axis = seq(min(x_axis),max(x_axis), length.out=eval_length) 84 | 85 | new_x_axis 86 | 87 | conf_int = cbind( 88 | predict(fit_loess_2, data.frame(x=new_x_axis)), 89 | predict(fit_loess_2, data.frame(x=new_x_axis))+ 90 | predict(fit_loess_2, data.frame(x=new_x_axis), se=TRUE)$se.fit*qnorm(1-.05/2), 91 | predict(fit_loess_2, data.frame(x=new_x_axis))- 92 | predict(fit_loess_2, data.frame(x=new_x_axis), se=TRUE)$se.fit*qnorm(1-.05/2) 93 | ) 94 | 95 | 96 | 97 | 98 | fit_lm = lm(y_axis ~ x_axis) 99 | 100 | 101 | fit_lm 102 | 103 | fit_poly = lm(y_axis ~ poly(x_axis,3) ) 104 | 105 | fit_poly 106 | 107 | 108 | fit_nat_spline = lm(y_axis ~ ns(x_axis, 3) ) 109 | 110 | fit_nat_spline 111 | 112 | fit_smth_spline = smooth.spline(y_axis ~ x_axis, nknots=15) 113 | 114 | fit_smth_spline 115 | 116 | 117 | plot(x_axis, y_axis, xlim=c(min(x_axis),max(x_axis)), ylim=c(min(y_axis),max(y_axis)), pch=16, cex=.5, ylab = "Stopping Distance (feet)", xlab= "Speed (MPH)", main="Comparison of Models", sub="Splines") 118 | 119 | 120 | matplot(new_x_axis, conf_int, lty = c(1,2,2), col=c(1,2,2), type = "l", add=T) 121 | 122 | 123 | lines(new_x_axis, predict(fit_lm, data.frame(x=new_x_axis)), col="red", lty=3) 124 | 125 | 126 | lines(new_x_axis, predict(fit_poly, data.frame(x=new_x_axis)), col="blue", lty=4) 127 | 128 | lines(new_x_axis, predict(fit_nat_spline, data.frame(x=new_x_axis)), col="green", lty=5) 129 | 130 | lines(fit_smth_spline, col="dark grey", lty=6) 131 | 132 | lines(ksmooth(x_axis, y_axis, "normal", bandwidth = 5), col = 'purple', lty=7) 133 | 134 | 135 | -------------------------------------------------------------------------------- /Chapter 05/1Generalized additive model - Measuring household income of New Zealand.r: -------------------------------------------------------------------------------- 1 | 2 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 3 | Copyright (C) 2016 The R Foundation for Statistical Computing 4 | Platform: x86_64-w64-mingw32/x64 (64-bit) 5 | 6 | R is free software and comes with ABSOLUTELY NO WARRANTY. 7 | You are welcome to redistribute it under certain conditions. 8 | Type 'license()' or 'licence()' for distribution details. 9 | 10 | R is a collaborative project with many contributors. 11 | Type 'contributors()' for more information and 12 | 'citation()' on how to cite R or R packages in publications. 13 | 14 | Type 'demo()' for some demos, 'help()' for on-line help, or 15 | 'help.start()' for an HTML browser interface to help. 16 | Type 'q()' to quit R. 17 | 18 | devtools::install_github("ellisp/nzelect/pkg2") 19 | 20 | library(leaflet) 21 | library(nzcensus) 22 | library(Metrics) 23 | library(ggplot2) 24 | library(scales) 25 | library(boot) 26 | library(dplyr) 27 | library(Hmisc) 28 | library(mgcv) 29 | library(caret) 30 | library(grid) 31 | library(stringr) 32 | library(ggrepel) 33 | library(glmnet) 34 | library(maps) 35 | 36 | tmp <- AreaUnits2013[AreaUnits2013$WGS84Longitude 0 & !is.na(AreaUnits2013$MedianIncome2013), ] 37 | 38 | palette <- colorQuantile("RdBu", NULL, n = 10) 39 | 40 | labels <- paste0(tmp$AU_NAM, " $", format(tmp$MedianIncome2013, big.mark = ",")) 41 | 42 | leaflet() %>% 43 | addProviderTiles("CartoDB.Positron") %>% 44 | addCircles(lng = tmp$WGS84Longitude, lat = tmp$WGS84Latitude, 45 | color = pal(-tmp$MedianIncome2013), 46 | popup = labs, 47 | radius = 500) %>% 48 | addLegend( 49 | pal = pal, 50 | values = -tmp$MedianIncome2013, 51 | title = "Quantile of median% 57 | select(-AU2014, -AU_NAM, -NZTM2000Easting, -NZTM2000Northing) %>% 58 | select(-PropWorked40_49hours2013, -Prop35to39_2013, -PropFemale2013) 59 | 60 | row.names(au) <- AreaUnits2013$AU_NAM 61 | 62 | 63 | names(au) <- gsub("_2013", "", names(au)) 64 | 65 | names(au) <- gsub("2013", "", names(au)) 66 | 67 | names(au) <- gsub("Prop", "", names(au)) 68 | 69 | au <- au[complete.cases(au), ] 70 | 71 | data_use <- au 72 | 73 | dim(data_use) 74 | 75 | data_use <- data_use[the_data$WGS84Longitude > 100, ] 76 | 77 | names(data_use) <- make.names(names(data_use)) 78 | 79 | names(data_use) 80 | 81 | reg_data <- spearman2(MedianIncome ~ ., data = data_use) 82 | reg_data[order(-reg_data[ ,6])[1:15], ] 83 | 84 | 85 | reg_formula <- terms(MedianIncome ~ 86 | s(FullTimeEmployed, k = 6) + 87 | s(InternetHH, k = 6) + 88 | s(NoQualification, k = 5) + 89 | s(UnemploymentBenefit, k = 5) + 90 | s(Smoker, k = 5) + 91 | s(Partnered, k = 5) + 92 | s(Managers, k = 4) + 93 | s(Bachelor, k = 4) + 94 | s(SelfEmployed, k = 4) + 95 | s(NoMotorVehicle, k = 4) + 96 | s(Unemployed, k = 3) + 97 | s(Labourers, k = 3) + 98 | s(Worked50_59hours, k = 3) + 99 | s(Separated, k = 3) + 100 | s(Maori, k = 3) + 101 | s(WGS84Longitude, WGS84Latitude) + 102 | ., 103 | data = data_use) 104 | 105 | 106 | 107 | gam_model <- gam(reg_formula, data = data_use) 108 | 109 | 110 | par(bty = "l", mar = c(5,4, 2, 1)) 111 | 112 | 113 | par(mar = rep(2, 4)) 114 | 115 | plot(gam_model, residuals = TRUE, pages = 1, shade = TRUE, seWithMean = TRUE, ylab = "") 116 | 117 | rmses_gam_boot <- boot(data = data_use, statistic = fit_gam, R = 99) 118 | 119 | rmses_gam_boot 120 | 121 | 122 | gam_rmse <- mean(rmses_gam_boot$t) 123 | 124 | gam_rmse 125 | -------------------------------------------------------------------------------- /Chapter 07/2Vector Quantization - Image Clustering.r: -------------------------------------------------------------------------------- 1 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R 16 | 17 | library(jpeg) 18 | library(ggplot2) 19 | 20 | img <- readJPEG("d:/Image.jpg") 21 | 22 | img_Dim <- dim(img) 23 | 24 | img_Dim 25 | [1] 526 800 3 26 | 27 | 28 | 29 | img_RGB_channels <- data.frame( 30 | x = rep(1:img_Dim[2], each = img_Dim[1]), 31 | y = rep(img_Dim[1]:1, img_Dim[2]), 32 | R = as.vector(img[,,1]), 33 | G = as.vector(img[,,2]), 34 | B = as.vector(img[,,3]) 35 | ) 36 | 37 | 38 | plotTheme <- function() { 39 | theme( 40 | panel.background = element_rect( 41 | size = 3, 42 | colour = "black", 43 | fill = "white"), 44 | axis.ticks = element_line( 45 | size = 2), 46 | panel.grid.major = element_line( 47 | colour = "gray80", 48 | linetype = "dotted"), 49 | panel.grid.minor = element_line( 50 | colour = "gray90", 51 | linetype = "dashed"), 52 | axis.title.x = element_text( 53 | size = rel(1.2), 54 | face = "bold"), 55 | axis.title.y = element_text( 56 | size = rel(1.2), 57 | face = "bold"), 58 | plot.title = element_text( 59 | size = 20, 60 | face = "bold", 61 | vjust = 1.5) 62 | ) 63 | } 64 | 65 | 66 | 67 | ggplot(data = img_RGB_channels, aes(x = x, y = y)) + 68 | geom_point(colour = rgb(img_RGB_channels[c("R", "G", "B")])) + 69 | labs(title = "Original Image: Colorful Bird") + 70 | xlab("x") + 71 | ylab("y") + 72 | plotTheme() 73 | 74 | kClusters <- 3 75 | 76 | kMeans_clst <- kmeans(img_RGB_channels[, c("R", "G", "B")], centers = kClusters) 77 | 78 | kColours <- rgb(kMeans_clst$centers[kMeans_clst$cluster,]) 79 | 80 | ggplot(data = img_RGB_channels, aes(x = x, y = y)) + 81 | geom_point(colour = kColours) + 82 | labs(title = paste("k-Means Clustering of", kClusters, "Colours")) + 83 | xlab("x") + 84 | ylab("y") + 85 | plotTheme() 86 | 87 | kClusters <- 5 88 | 89 | kMeans_clst <- kmeans(img_RGB_channels[, c("R", "G", "B")], centers = kClusters) 90 | 91 | kColours <- rgb(kMeans_clst$centers[kMeans_clst$cluster,]) 92 | 93 | ggplot(data = img_RGB_channels, aes(x = x, y = y)) + 94 | geom_point(colour = kColours) + 95 | labs(title = paste("k-Means Clustering of", kClusters, "Colours")) + 96 | xlab("x") + 97 | ylab("y") + 98 | plotTheme() 99 | 100 | 101 | ggplot(data = img_RGB_channels, aes(x = x, y = y)) + 102 | geom_point(colour = rgb(img_RGB_channels[c("R", "G", "B")])) + 103 | labs(title = "Original Image: Colorful Bird") + 104 | xlab("x") + 105 | ylab("y") + 106 | plotTheme() 107 | 108 | kClusters <- 3 109 | 110 | kMeans_clst <- kmeans(imgRGB[, c("R", "G", "B")], centers = kClusters) 111 | 112 | kColours <- rgb(kMeans_clst$centers[kMeans_clst$cluster,]) 113 | 114 | ggplot(data = imgRGB, aes(x = x, y = y)) + 115 | geom_point(colour = kColours) + 116 | labs(title = paste("k-Means Clustering of", kClusters, "Colours")) + 117 | xlab("x") + 118 | ylab("y") + 119 | plotTheme() 120 | 121 | kClusters <- 5 122 | 123 | kMeans_clst <- kmeans(imgRGB[, c("R", "G", "B")], centers = kClusters) 124 | 125 | kColours <- rgb(kMeans_clst$centers[kMeans_clst$cluster,]) 126 | 127 | ggplot(data = imgRGB, aes(x = x, y = y)) + 128 | geom_point(colour = kColours) + 129 | labs(title = paste("k-Means Clustering of", kClusters, "Colours")) + 130 | xlab("x") + 131 | ylab("y") + 132 | plotTheme() 133 | 134 | -------------------------------------------------------------------------------- /Chapter 13/Case Study 2 - Pricing Reinsurance Contracts.r: -------------------------------------------------------------------------------- 1 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | 19 | install.packages("gdata") 20 | install.packages("evir") 21 | library(gdata) 22 | library(evir) 23 | 24 | StormDamageData <- read.xls("d:/publicdatamay2007.xls", sheet = 1) 25 | 26 | head(StormDamageData) 27 | 28 | tail(StormDamageData) 29 | 30 | dim(StormDamageData) 31 | 32 | ChangeFormat <- function(x){ 33 | x=as.character(x) 34 | for(i in 1:10){x=sub(",","",as.character(x))} 35 | return(as.numeric(x))} 36 | 37 | base <- StormDamageData[,1:4] 38 | 39 | base$Base.Economic.Damage <- Vectorize(ChangeFormat)(StormDamageData$Base.Economic.Damage) 40 | 41 | base$Normalized.PL05 <- Vectorize(ChangeFormat)(StormDamageData$Normalized.PL05) 42 | 43 | base$Normalized.CL05 <- Vectorize(ChangeFormat)(StormDamageData$Normalized.CL05) 44 | 45 | head(base) 46 | 47 | plot(base$Normalized.PL05/1e9,type="h",ylim=c(0,155), main = "207 Hurricanes, Normalized Costs: 1900 - 2005", xlab = "Index of Loss", ylab = "Normalized Costs", col = "red") 48 | 49 | TestBase <- table(base$Year) 50 | 51 | TestBase 52 | 53 | years <- as.numeric(names(TestBase)) 54 | 55 | years 56 | 57 | frequency <- as.numeric(TestBase) 58 | 59 | frequency 60 | 61 | years0frequency <- (1900:2005)[which(!(1900:2005)%in%years)] 62 | 63 | years0frequency 64 | 65 | StormDamageData <- data.frame(years=c(years, years0frequency), frequency=c(frequency, rep(0,length(years0frequency)))) 66 | 67 | head(StormDamageData) 68 | 69 | plot(years, frequency, type="h", main = "Frequency of Hurricanes: 1900 - 2005", xlab = "Time (Years)", ylab = "Annual Frequency", col = "red") 70 | 71 | mean(StormDamageData$frequency) 72 | 73 | LinearTrend <- glm(frequency~years, data = StormDamageData, family=poisson(link="identity"), start=lm(frequency~years, data = StormDamageData)$coefficients) 74 | 75 | LinearTrend 76 | 77 | ExpTrend <- glm(frequency~years, data=StormDamageData, family = poisson(link="log")) 78 | 79 | ExpTrend 80 | 81 | plot(years, frequency, type='h', ylim=c(0,6), main = "No. of Major Hurricanes Predicted for 2014", xlim=c(1900,2020)) 82 | 83 | cpred1 <- predict(ExpTrend, newdata = data.frame(years=1890:2030), type="response") 84 | 85 | cpred1 86 | 87 | lines(1890:2030,cpred1,col="blue") 88 | 89 | cpred0 <- predict(LinearTrend, newdata=data.frame(years=1890:2030), type="response") 90 | 91 | cpred0 92 | 93 | lines(1890:2030, cpred0, col="red") 94 | 95 | abline(h = mean(StormDamageData$frequency), col="black") 96 | 97 | predictions <- cbind(constant = mean(StormDamageData$frequency), linear= cpred0[126], exponential=cpred1[126]) 98 | 99 | predictions 100 | 101 | points(rep((1890:2030)[126],3), predictions, col=c("black","red","blue"), pch=19) 102 | 103 | hill(base$Normalized.PL05) 104 | 105 | threshold <- .5 106 | 107 | gpd.PL <- gpd(base$Normalized.PL05/1e9/20, threshold)$par.ests 108 | 109 | mean(base$Normalized.CL05/1e9/20 >.5) 110 | 111 | ExpectedValue <- function(yinf,ysup,xi,beta){ 112 | as.numeric(integrate(function(x) (x-yinf) * dgpd(x,xi,mu=threshold,beta), 113 | lower=yinf,upper=ysup)$value + 114 | (1-pgpd(ysup,xi,mu=threshold,beta))*(ysup-yinf)) 115 | } 116 | 117 | predictions[1] 118 | 119 | mean(base$Normalized.PL05/1e9/20.5) 120 | 121 | ExpectedValue(2,6,gpd.PL[1],gpd.PL[2])*1e3 122 | 123 | predictions[1] * mean(base$Normalized.PL05/1e9/20 >.5) * ExpectedValue(2, 6, gpd.PL[1], gpd.PL[2]) * 1e3 124 | -------------------------------------------------------------------------------- /Chapter 02/1Discriminant Function Analysis script.r: -------------------------------------------------------------------------------- 1 | 2 | R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree" 3 | Copyright (C) 2015 The R Foundation for Statistical Computing 4 | Platform: x86_64-w64-mingw32/x64 (64-bit) 5 | 6 | R is free software and comes with ABSOLUTELY NO WARRANTY. 7 | You are welcome to redistribute it under certain conditions. 8 | Type 'license()' or 'licence()' for distribution details. 9 | 10 | R is a collaborative project with many contributors. 11 | library(MASS) 12 | 13 | brine <- read.table("d:/brine.txt", header=TRUE, sep=",", row.names=1) 14 | head(brine) 15 | 16 | HCO3 SO4 Cl Ca Mg Na GROUP 17 | 1 10.4 30.0 967.1 95.9 53.7 857.7 1 18 | 2 6.2 29.6 1174.9 111.7 43.9 1054.7 1 19 | 3 2.1 11.4 2387.1 348.3 119.3 1932.4 1 20 | 4 8.5 22.5 2186.1 339.6 73.6 1803.4 1 21 | 5 6.7 32.8 2015.5 287.6 75.1 1691.8 1 22 | 6 3.8 18.9 2175.8 340.4 63.8 1793.9 1 23 | 24 | pairs(brine[ ,1:6]) 25 | 26 | brine.log <- brine 27 | brine.log[ ,1:6] <- log(brine[ ,1:6]+1) 28 | pairs(brine.log[ ,1:6]) 29 | 30 | 31 | brine.log.lda <- lda(GROUP ~ HCO3 + SO4 + Cl + Ca + Mg + Na, data=brine.log) 32 | 33 | brine.log.lda 34 | 35 | Call: 36 | lda(GROUP ~ HCO3 + SO4 + Cl + Ca + Mg + Na, data = brine.log) 37 | 38 | Prior probabilities of groups: 39 | 1 2 3 40 | 0.3684211 0.3157895 0.3157895 41 | 42 | Group means: 43 | HCO3 SO4 Cl Ca Mg Na 44 | 1 1.759502 3.129009 7.496891 5.500942 4.283490 7.320686 45 | 2 2.736481 3.815399 6.829565 4.302573 4.007725 6.765017 46 | 3 1.374438 2.378965 6.510211 4.641049 3.923851 6.289692 47 | 48 | Coefficients of linear discriminants: 49 | LD1 LD2 50 | HCO3 -1.67799521 0.64415802 51 | SO4 0.07983656 0.02903096 52 | Cl 22.27520614 -0.31427770 53 | Ca -1.26859368 2.54458682 54 | Mg -1.88732009 -2.89413332 55 | Na -20.86566883 1.29368129 56 | 57 | Proportion of trace: 58 | LD1 LD2 59 | 0.7435 0.2565 60 | 61 | brine.log.hat <- predict(brine.log.lda) 62 | 63 | brine.log.hat 64 | 65 | $class 66 | [1] 2 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 67 | Levels: 1 2 3 68 | 69 | $posterior 70 | 1 2 3 71 | 1 2.312733e-01 7.627845e-01 5.942270e-03 72 | 2 9.488842e-01 3.257237e-02 1.854347e-02 73 | 3 8.453057e-01 9.482540e-04 1.537461e-01 74 | 4 9.990242e-01 8.794725e-04 9.632578e-05 75 | 5 9.965920e-01 2.849903e-03 5.581176e-04 76 | 6 9.984987e-01 1.845534e-05 1.482872e-03 77 | 7 8.676660e-01 7.666611e-06 1.323263e-01 78 | 8 4.938019e-03 9.949035e-01 1.584755e-04 79 | 9 4.356152e-03 9.956351e-01 8.770078e-06 80 | 10 2.545287e-05 9.999439e-01 3.066264e-05 81 | 11 2.081510e-02 9.791728e-01 1.210748e-05 82 | 12 1.097540e-03 9.989023e-01 1.455693e-07 83 | 13 1.440307e-02 9.854613e-01 1.356671e-04 84 | 14 4.359641e-01 2.367602e-03 5.616683e-01 85 | 15 6.169265e-02 1.540353e-04 9.381533e-01 86 | 16 7.500357e-04 4.706701e-09 9.992500e-01 87 | 17 1.430433e-03 1.095281e-06 9.985685e-01 88 | 18 2.549733e-04 3.225658e-07 9.997447e-01 89 | 19 6.433759e-02 8.576694e-03 9.270857e-01 90 | 91 | $x 92 | LD1 LD2 93 | 1 -1.1576284 -0.1998499 94 | 2 -0.1846803 0.6655823 95 | 3 1.0179998 0.6827867 96 | 4 -0.3939366 2.6798084 97 | 5 -0.3167164 2.0188002 98 | 6 1.0061340 2.6434491 99 | 7 2.0725443 1.5714400 100 | 8 -2.0387449 -0.9731745 101 | 9 -2.6054261 -0.2774844 102 | 10 -2.5191350 -2.8304663 103 | 11 -2.4915044 0.3194247 104 | 12 -3.4448401 0.1869864 105 | 13 -2.0343204 -0.4674925 106 | 14 1.0441237 -0.0991014 107 | 15 1.6987023 -0.6036252 108 | 16 3.9138884 -0.7211078 109 | 17 2.7083649 -1.3896956 110 | 18 2.9310268 -1.9243611 111 | 19 0.7941483 -1.2819190 112 | 113 | 114 | apply(brine.log.hat$posterior, MARGIN=1, FUN=max) 115 | 1 2 3 4 5 6 7 8 116 | 0.7627845 0.9488842 0.8453057 0.9990242 0.9965920 0.9984987 0.8676660 0.9949035 117 | 9 10 11 12 13 14 15 16 118 | 0.9956351 0.9999439 0.9791728 0.9989023 0.9854613 0.5616683 0.9381533 0.9992500 119 | 17 18 19 120 | 0.9985685 0.9997447 0.9270857 121 | 122 | plot(brine.log.lda) 123 | plot(brine.log.lda, dimen=1, type="both") 124 | tab <- table(brine.log$GROUP, brine.log.hat$class) 125 | tab 126 | 127 | 1 2 3 128 | 1 6 1 0 129 | 2 0 6 0 130 | 3 0 0 6 131 | sum(tab[row(tab) == col(tab)]) / sum(tab) 132 | [1] 0.9473684 133 | 134 | brine.log.lda <- lda(GROUP ~ HCO3 + SO4 + Cl + Ca + Mg + Na, data=brine.log, CV=TRUE) 135 | tab <- table(brine.log$GROUP, brine.log.lda$class) 136 | 137 | tab 138 | 139 | 1 2 3 140 | 1 6 1 0 141 | 2 1 4 1 142 | 3 1 0 5 143 | sum(tab[row(tab) == col(tab)]) / sum(tab) 144 | [1] 0.7894737 -------------------------------------------------------------------------------- /Chapter 08/5Monte Carlo Simulations - Calibrated Hull and White short-rates.r: -------------------------------------------------------------------------------- 1 | R version 3.2.2 (2015-08-14) -- "Fire Safety" 2 | Copyright (C) 2015 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | install.packages("RQuantLib", type="binary") 19 | install.packages("ESGtoolkit") 20 | library(RQuantLib) 21 | library(ESGtoolkit) 22 | 23 | 24 | freq <- "monthly" 25 | 26 | delta_t <- 1/12 27 | 28 | delta_t 29 | 30 | params <- list(tradeDate=as.Date('2002-2-15'), 31 | settleDate=as.Date('2002-2-19'), 32 | payFixed=TRUE, 33 | dt=delta_t, 34 | strike=.06, 35 | method="HWAnalytic", 36 | interpWhat="zero", 37 | interpHow= "spline") 38 | 39 | 40 | TermQuotes <- list(d1w =0.0382, 41 | d1m =0.0372, 42 | d3m = 0.0363, 43 | d6m = 0.0353, 44 | d9m = 0.0348, 45 | d1y = 0.0345, 46 | s2y = 0.037125, 47 | s3y =0.0398, 48 | s5y =0.0443, 49 | s10y =0.05165, 50 | s15y =0.055175) 51 | 52 | 53 | SwaptionMaturities <- c(1,2,3,4,5) 54 | 55 | SwaptionMaturities 56 | 57 | SwapTenors <- c(1,2,3,4,5) 58 | 59 | SwapTenors 60 | 61 | VolatilityMatrix <- matrix( 62 | c(0.1490, 0.1340, 0.1228, 0.1189, 0.1148, 63 | 0.1290, 0.1201, 0.1146, 0.1108, 0.1040, 64 | 0.1149, 0.1112, 0.1070, 0.1010, 0.0957, 65 | 0.1047, 0.1021, 0.0980, 0.0951, 0.1270, 66 | 0.1000, 0.0950, 0.0900, 0.1230, 0.1160), 67 | ncol=5, byrow=TRUE) 68 | 69 | 70 | BermudanSwaption <- RQuantLib::BermudanSwaption(params, TermQuotes, SwaptionMaturities, SwapTenors, VolatilityMatrix) 71 | 72 | summary(BermudanSwaption) 73 | 74 | 75 | BermudanSwaption 76 | 77 | 78 | times <- seq(from = delta_t, to = 5, by = delta_t) 79 | 80 | 81 | DiscountCurve <- RQuantLib::DiscountCurve(params, TermQuotes, times) 82 | 83 | 84 | str(DiscountCurve) 85 | 86 | maturities <- DiscountCurve$times 87 | 88 | maturities 89 | 90 | 91 | MarketZeroRates <- DiscountCurve$zerorates 92 | 93 | MarketZeroRates 94 | 95 | MarketPrices <- DiscountCurve$discounts 96 | 97 | MarketPrices 98 | 99 | horizon <- 5 100 | 101 | NoSimulations <- 10000 102 | 103 | a <- BermudanSwaption$a 104 | a 105 | 106 | sigma <- BermudanSwaption$sigma 107 | 108 | sigma 109 | 110 | 111 | GaussianShocks <- ESGtoolkit::simshocks(n = NoSimulations, horizon = horizon, frequency = freq) 112 | 113 | x <- ESGtoolkit::simdiff(n = NoSimulations, horizon = horizon, frequency = freq, model = "OU", x0 = 0, theta1 = 0, theta2 = a, theta3 = sigma, eps = GaussianShocks) 114 | 115 | 116 | ForwardRates <- ts(replicate(nb.sims, DiscountCurve$forwards), start = start(x), deltat = deltat(x)) 117 | 118 | t.out <- seq(from = 0, to = horizon, by = delta_t) 119 | 120 | param.alpha <- ts(replicate(NoSimulations, 0.5*(sigma^2)*(1 - exp(-a*t.out))^2/(a^2)), start = start(x), deltat = deltat(x)) 121 | 122 | 123 | alpha <- ForwardRates + param.alpha 124 | 125 | 126 | ShortRates <- x + alpha 127 | 128 | StochasticDiscount <- ESGtoolkit::esgdiscountfactor(r = ShortRates, X = 1) 129 | 130 | MonteCarloPrices <- rowMeans(StochasticDiscount) 131 | 132 | MonteCarloPrices 133 | 134 | 135 | MonteCarloZeroRates <- -log(MonteCarloPrices)/maturities 136 | 137 | MonteCarloZeroRates 138 | 139 | ConfidenceInterval <- t(apply((StochasticDiscount - MarketPrices)[-1, ], 1, function(x) t.test(x)$conf.int)) 140 | 141 | 142 | head(ConfidenceInterval) 143 | 144 | 145 | par(mfrow = c(2, 2)) 146 | 147 | 148 | 149 | ESGtoolkit::esgplotbands(ShortRates, xlab = "maturities", ylab = "short-rate quantiles", main = "Short Rate Quantiles") 150 | 151 | 152 | plot(maturities, MonteCarloZeroRates, type='l', col = 'blue', lwd = 1, main = "Monte Carlo v/s Market n Zero Rates") 153 | 154 | points(maturities, MonteCarloZeroRates, col = 'red') 155 | 156 | 157 | plot(maturities, MonteCarloPrices, type='l', col = 'blue', lwd = 1, main = "Monte Carlo v/s Market n Zero Rates") 158 | 159 | 160 | 161 | points(maturities, MonteCarloPrices, col = 'red') 162 | 163 | 164 | matplot(maturities[-1], conf.int, type = 'l', main = "Confidence Interval for the price difference") 165 | 166 | 167 | 168 | 169 | -------------------------------------------------------------------------------- /Chapter 03/Data/NASAUnderstory.csv: -------------------------------------------------------------------------------- 1 | PlotID,Overstory Species,SPHA,BLIT,ASMA,MOSS,LEGR,CHCA,GRAS,SEDG,SMTR,PTAQ,COCA,VAAN,GAHI,ARNU,LYOB,PIMA,RUBU,VAOX,ACSP,COCO,ACRU,TRBO,MACA,CLBO,STRO,FUNG,DILO,ERIO,GATR,Labels 2 | 2,Aspen,68,14,0,3,33,5,5,0,14,0,0,4,28,0,0,1,0,6,0,0,0,0,0,0,0,1,0,0,0,Sphagnum Moss 3 | 3,Spruce,0,6,18,2,0,0,5,0,0,2,0,0,0,7,0,0,6,0,2,1,2,2,2,1,1,1,3,0,4,Brown Litter 4 | 12,Aspen,60,1,0,0,5,9,12,1,14,0,0,0,2,0,0,3,0,4,0,0,0,0,0,0,0,4,0,3,0,Big-leaved Aster 5 | 14,Aspen,16,14,0,72,14,1,2,0,13,0,0,5,7,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Mosses (Non-Sphagnum) 6 | 15,Aspen,68,7,0,30,27,8,4,0,12,0,0,5,13,0,0,3,0,3,0,0,0,0,0,0,0,4,0,0,0,Labrador Tea 7 | 16,Spruce,0,14,14,3,0,0,2,0,0,1,0,0,0,2,1,0,1,0,17,6,10,1,0,3,4,1,1,0,2,Leatherleaf 8 | 18,Aspen,62,7,0,3,6,12,4,0,0,0,0,0,0,0,0,13,0,5,0,0,0,0,0,0,0,1,0,11,0,Grasses (Unidentified) 9 | 19,Aspen,62,5,0,0,6,28,1,0,0,0,0,0,0,0,0,15,0,5,0,0,0,0,0,0,0,0,0,10,0,Sedges (Unidentified) 10 | 20,Spruce,0,5,8,14,0,0,4,6,0,14,10,3,0,0,6,0,0,0,1,8,1,1,0,3,0,0,0,0,0,Bog False Solomon Seal 11 | 21,Spruce,0,16,26,5,0,0,1,0,0,18,6,10,0,10,11,0,3,0,1,5,5,4,4,2,0,0,1,0,0,Bracken Fern 12 | 36,Spruce,0,25,38,3,0,0,5,3,0,6,7,0,0,0,0,0,1,0,0,3,6,4,4,6,3,0,1,0,3,Bunchberry 13 | 38,Aspen,82,10,0,10,24,12,8,0,6,0,0,4,2,0,0,2,0,1,0,0,0,0,0,2,0,1,0,0,0,Lowbush Blueberry 14 | 39,Aspen,60,3,0,34,24,0,5,0,9,0,1,5,4,0,1,6,0,4,0,0,0,0,0,0,0,0,0,0,0,Creeping Snowberry 15 | 41,Aspen,72,17,0,4,6,0,3,0,5,0,0,5,6,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Wild Sarsaparilla 16 | 42,Aspen,34,14,0,36,15,1,0,5,6,0,4,9,8,0,1,4,0,2,0,0,0,1,0,0,0,0,0,0,0,Ground Pine 17 | 43,Aspen,32,13,0,34,28,3,0,0,6,0,0,5,5,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Spruce (Black) 18 | 45,Aspen,55,25,0,8,7,4,0,10,4,0,0,5,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,Brier 19 | 47,Aspen,64,8,0,5,9,8,1,32,20,0,0,5,4,0,0,2,0,5,0,0,0,0,0,0,0,3,0,0,0,Small Cranberry 20 | 48,Aspen,64,12,0,13,6,0,10,2,10,0,0,0,1,0,7,1,0,0,0,0,0,0,0,0,0,3,0,0,0,Maple (Mountain) 21 | 49,Aspen,48,5,0,6,1,0,5,14,5,0,1,2,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Hazelnut (Beaked) 22 | 50,Aspen,38,18,0,38,11,0,2,6,4,0,2,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,Maple (Red) 23 | 51,Aspen,74,3,0,2,34,17,1,0,2,0,0,0,0,0,0,6,0,3,0,0,0,0,0,0,0,0,0,8,0,Starflower 24 | 52,Aspen,86,3,0,5,36,9,17,1,0,0,0,0,4,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,Canadian Mayflower 25 | 54,Aspen,62,16,0,0,22,10,5,0,7,1,0,4,3,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,Blue-bead Lily 26 | 55,Aspen,38,30,0,18,4,2,0,2,6,0,0,5,6,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,Twisted Stalk 27 | 56,Aspen,68,13,0,6,34,5,0,5,2,0,0,2,3,0,0,6,0,4,0,0,0,0,0,0,0,4,0,0,0,Fungi 28 | 57,Aspen,72,5,0,4,17,20,0,14,0,0,0,0,4,0,0,3,0,4,0,0,0,0,0,0,0,3,0,0,0,Bush Honeysuckle 29 | 62,Aspen,66,1,0,12,7,20,17,6,0,0,0,0,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,0,0,Cotton Grass 30 | 63,Aspen,56,8,0,1,12,12,6,0,0,0,0,0,0,0,0,8,0,3,0,0,0,0,0,0,0,0,0,24,0,Bedstraw (Narrow Leaves) 31 | 64,Aspen,56,0,0,6,6,24,0,34,0,0,0,0,0,0,0,7,0,4,0,0,0,0,0,0,0,0,0,0,0, 32 | 68,Aspen,62,8,0,9,4,5,1,10,1,0,0,0,5,0,0,1,0,5,0,0,0,0,0,0,0,2,0,0,0, 33 | 69,Spruce,1,15,11,5,0,0,2,0,0,2,17,7,1,1,3,0,0,0,0,2,1,1,3,4,1,0,0,0,0, 34 | 71,Spruce,1,20,16,5,0,0,2,2,0,4,5,3,0,4,2,0,3,0,0,1,2,0,2,5,4,0,2,0,1, 35 | 72,Spruce,0,32,14,4,0,0,1,0,0,0,4,0,0,5,5,0,4,0,3,2,2,5,4,3,5,0,3,0,2, 36 | 73,Spruce,0,34,21,5,0,0,4,0,0,1,3,1,0,6,4,0,2,0,4,2,4,4,4,2,4,0,1,0,4, 37 | 74,Spruce,0,24,4,2,0,0,5,0,0,3,4,1,0,7,3,0,4,0,4,5,2,4,3,2,4,0,2,0,1, 38 | 75,Spruce,0,20,14,6,0,0,6,1,0,0,4,1,0,4,0,0,5,0,7,2,5,5,5,2,4,0,2,0,2, 39 | 77,Spruce,0,46,2,7,0,0,2,1,0,0,4,0,0,4,2,0,3,0,4,0,2,4,3,5,3,0,0,0,2, 40 | 79,Spruce,0,14,14,7,0,0,3,1,0,7,8,2,0,14,2,0,7,0,9,1,3,5,3,3,5,0,8,0,4, 41 | 80,Spruce,0,6,18,2,0,0,5,0,0,2,0,0,0,7,0,0,6,0,2,1,2,2,2,1,1,1,3,0,4, 42 | 81,Spruce,0,32,31,5,0,0,4,0,0,0,0,0,0,6,0,0,0,0,8,0,3,0,4,0,2,0,2,0,3, 43 | 82,Spruce,0,18,48,6,0,0,5,0,0,1,2,0,0,8,0,0,4,0,12,0,1,2,3,1,3,0,3,0,4, 44 | 83,Spruce,0,24,22,6,0,0,5,1,0,2,0,0,0,6,0,0,5,0,3,2,4,1,3,0,4,0,3,0,4, 45 | 84,Spruce,0,22,23,4,0,0,1,1,0,20,1,3,0,8,3,0,0,0,1,1,5,3,2,0,1,1,0,0,0, 46 | 85,Spruce,0,24,30,11,0,0,4,0,0,7,2,5,0,0,4,0,0,0,2,0,4,4,0,1,0,1,1,0,0, 47 | 86,Spruce,2,22,10,4,0,0,5,0,0,4,6,5,0,0,4,0,4,0,0,2,0,0,0,0,0,1,1,0,0, 48 | 87,Spruce,0,30,7,5,0,0,3,0,0,0,0,0,0,0,3,0,1,0,0,3,0,2,0,2,0,3,1,0,0, 49 | 88,Spruce,0,42,12,4,0,0,2,1,0,0,4,2,0,4,6,0,2,0,1,3,2,3,2,4,1,3,1,0,0, 50 | 89,Spruce,0,24,3,17,0,0,1,0,0,0,8,3,0,0,6,0,1,0,1,1,1,0,0,4,0,1,2,0,0, 51 | 90,Spruce,0,24,13,6,0,0,5,1,0,0,4,2,0,0,6,0,1,0,0,3,5,2,0,2,0,2,0,0,0, 52 | 92,Spruce,0,32,10,3,0,0,5,0,0,1,4,0,0,5,8,0,1,0,2,7,1,4,4,6,5,0,1,0,0, 53 | 93,Spruce,0,26,12,1,0,0,4,0,0,3,3,2,0,3,6,0,3,0,4,6,2,4,4,4,5,1,1,0,4, 54 | 94,Spruce,0,10,36,0,1,0,4,0,0,4,4,0,0,6,5,0,3,0,0,5,1,0,0,1,1,0,1,0,1, 55 | 95,Spruce,0,16,26,2,1,0,7,0,0,3,3,6,0,2,0,0,8,0,0,9,0,1,1,0,0,0,1,0,2, 56 | 96,Spruce,0,31,17,3,0,0,4,1,0,6,8,2,0,2,3,0,4,0,1,1,6,5,6,1,1,0,2,0,2, 57 | 97,Spruce,0,11,36,2,0,0,3,2,0,18,4,1,0,2,3,0,3,0,0,1,3,2,5,2,2,0,8,0,1, 58 | 98,Spruce,0,14,38,3,0,0,3,0,0,7,0,2,0,0,6,0,2,0,0,3,0,2,3,0,1,0,1,0,1, 59 | 99,Spruce,0,42,20,4,0,0,5,1,0,9,4,5,0,0,1,0,5,0,0,3,0,0,0,0,1,0,1,0,0, 60 | 100,Aspen,60,17,0,10,0,0,0,20,2,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0, 61 | 101,Spruce,36,35,0,10,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0, 62 | 102,Aspen,50,8,0,10,8,4,6,6,2,0,0,0,4,0,0,3,0,4,0,0,0,0,0,0,0,3,0,0,0, 63 | 103,Aspen,60,2,0,9,10,15,0,26,1,0,0,0,1,0,0,8,0,5,0,0,0,0,0,0,0,3,0,0,0, 64 | 105,Aspen,70,9,0,16,6,0,2,11,3,0,0,0,3,0,0,3,0,5,0,0,0,0,0,0,0,5,0,0,0, 65 | -------------------------------------------------------------------------------- /Chapter 05/3Loess - United States Geological Survey.r: -------------------------------------------------------------------------------- 1 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | library(dataRetrieval) 18 | library(dplyr) 19 | 20 | siteNumber<-c("01538000") 21 | 22 | parameterCd <- "00060" 23 | 24 | 25 | Q_daily <- readNWISdv(siteNumber, parameterCd) 26 | 27 | 28 | tail(Q_daily) 29 | 30 | 31 | str(Q_daily) 32 | 33 | 34 | Q_daily <- renameNWISColumns(Q_daily) 35 | 36 | tail(Q_daily) 37 | 38 | stationInfo <- readNWISsite(siteNumber) 39 | 40 | if(as.numeric(diff(range(Q_daily$Date))) != (nrow(Q_daily)+1)){ 41 | fullDates <- seq(from=min(Q_daily$Date), 42 | to = max(Q_daily$Date), by="1 day") 43 | fullDates <- data.frame(Date = fullDates, 44 | agency_cd = Q_daily$agency_cd[1], 45 | site_no = Q_daily$site_no[1], 46 | stringsAsFactors = FALSE) 47 | Q_daily <- full_join(Q_daily, fullDates, 48 | by=c("Date","agency_cd","site_no")) %>% 49 | arrange(Date) 50 | } 51 | 52 | moving_avg <- function(x,n=30){stats::filter(x,rep(1/n,n), sides=1)} 53 | 54 | 55 | Q_daily <- Q_daily %>% mutate(rollMean = as.numeric(moving_avg(Flow)), day.of.year = as.numeric(strftime(Date, format = "%j"))) 56 | 57 | tail(Q_daily) 58 | 59 | 60 | Q_summary <- Q_daily %>% 61 | group_by(day.of.year) %>% 62 | summarize(p75 = quantile(rollMean, probs = .75, na.rm = TRUE), 63 | p25 = quantile(rollMean, probs = .25, na.rm = TRUE), 64 | p10 = quantile(rollMean, probs = 0.1, na.rm = TRUE), 65 | p05 = quantile(rollMean, probs = 0.05, na.rm = TRUE), 66 | p00 = quantile(rollMean, probs = 0, na.rm = TRUE)) 67 | 68 | current_year <- as.numeric(strftime(Sys.Date(), format = "%Y")) 69 | 70 | 71 | summary.0 <- Q_summary %>% mutate(Date = as.Date(day.of.year - 1, origin = paste0(current_year-2,"-01-01")), day.of.year = day.of.year - 365) 72 | 73 | 74 | summary.1 <- Q_summary %>% mutate(Date = as.Date(day.of.year - 1, origin = paste0(current_year-1,"-01-01"))) 75 | 76 | 77 | summary.2 <- Q_summary %>% mutate(Date = as.Date(day.of.year - 1, origin = paste0(current_year,"-01-01")), day.of.year = day.of.year 365) 78 | 79 | 80 | Q_summary <- bind_rows(summary.0, summary.1, summary.2) 81 | 82 | Q_summary 83 | 84 | smooth.span <- 0.3 85 | 86 | 87 | Q_summary$sm.75 <- predict(loess(p75~day.of.year, data = Q_summary, span = smooth.span)) 88 | 89 | 90 | head(Q_summary$sm.75) 91 | 92 | Q_summary$sm.25 <- predict(loess(p25~day.of.year, data = Q_summary, span = smooth.span)) 93 | 94 | head(summaryQ$sm.25) 95 | 96 | Q_summary$sm.10 <- predict(loess(p10~day.of.year, data = Q_summary, span = smooth.span)) 97 | 98 | head(summaryQ$sm.10) 99 | 100 | Q_summary$sm.05 <- predict(loess(p05~day.of.year, data = Q_summary, span = smooth.span)) 101 | 102 | head(summaryQ$sm.05) 103 | 104 | Q_summary$sm.00 <- predict(loess(p00~day.of.year, data = Q_summary, span = smooth.span)) 105 | 106 | head(summaryQ$sm.00) 107 | 108 | Q_summary <- select(Q_summary, Date, day.of.year, sm.75, sm.25, sm.10, sm.05, sm.00) %>% filter(Date = as.Date(paste0(current_year-1,"-01-01"))) 109 | 110 | Q_summary 111 | 112 | latest.years <- Q_daily %>% filter(Date = as.Date(paste0(current_year-1,"-01-01"))) %>% mutate(day.of.year = 1:nrow(.)) 113 | 114 | title.text <- paste0(stationInfo$station_nm,"\n", "Provisional Data - Subject to change\n", "Record Start = ", min(Q_daily$Date), " Number of years = ", as.integer(as.numeric(difftime(time1 = max(Q_daily$Date), time2 = min(Q_daily$Date), units = "weeks"))/52.25), "\nDate of plot = ",Sys.Date(), " Drainage Area = ",stationInfo$drain_area_va, "mi^2") 115 | 116 | 117 | mid.month.days <- c(15, 45, 74, 105, 135, 166, 196, 227, 258, 288, 319, 349) 118 | 119 | month.letters <- c("J","F","M","A","M","J","J","A","S","O","N","D") 120 | 121 | start.month.days <- c(1, 32, 61, 92, 121, 152, 182, 214, 245, 274, 305, 335) 122 | 123 | label.text <- c("Normal","Drought Watch","Drought Warning","Drought Emergency") 124 | 125 | 126 | year1_summary <- data.frame(Q_summary[2:366,]) 127 | 128 | head(year1_summary) 129 | 130 | year2_summary <- data.frame(Q_summary[367:733,]) 131 | 132 | head(year2_summary) 133 | 134 | simple.plot <- ggplot(data = Q_summary, aes(x = day.of.year)) 135 | geom_ribbon(aes(ymin = sm.25, ymax = sm.75, fill = "Normal")) 136 | geom_ribbon(aes(ymin = sm.10, ymax = sm.25, fill = "Drought Watch")) 137 | geom_ribbon(aes(ymin = sm.05, ymax = sm.10, fill = "Drought Warning")) 138 | geom_ribbon(aes(ymin = sm.00, ymax = sm.05, fill = "Drought Emergency")) 139 | scale_y_log10(limits = c(1,1000)) 140 | geom_line(data = latest.years, aes(x=day.of.year, y=rollMean, color = "30-Day Mean"),size=2) 141 | geom_vline(xintercept = 365) 142 | 143 | simple.plot 144 | 145 | 146 | -------------------------------------------------------------------------------- /Chapter 06/7Stochastic Gradient Descent - Adult Income.r: -------------------------------------------------------------------------------- 1 | R version 3.3.0 (2016-05-03) -- "Supposedly Educational" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | library("klaR") 18 | library("caret") 19 | library ("stringr") 20 | 21 | allData <- read.table('d:/adult.txt', header = FALSE) 22 | 23 | str(allData) 24 | 25 | labels <- as.factor(allData[,15]) 26 | 27 | allFeatures <- allData[,-c(15)] 28 | 29 | head(allFeatures) 30 | 31 | continuousFeatures <- scale(continuousFeatures) 32 | 33 | 34 | head(continuousFeatures) 35 | 36 | labels.n = rep(0,length(labels)) 37 | labels.n[labels==" <=50K"] = -1 38 | labels.n[labels==" 50K"] = 1 39 | labels = labels.n 40 | rm(labels.n) 41 | 42 | 43 | trainingData <- createDataPartition(y=labels, p=.8, list=FALSE) 44 | 45 | dim(trainingData) 46 | 47 | remainingLabels <- labels[-trainingData] 48 | remainingFeatures <- continuousFeatures[-trainingData,] 49 | 50 | testingData <- createDataPartition(y=remainingLabels, p=.5, list=FALSE) 51 | testingLabels <- remainingLabels[testingData] 52 | testingFeatures <- remainingFeatures[testingData,] 53 | 54 | validationLabels <- remainingLabels[-testingData] 55 | validationFeatures <- remainingFeatures[-testingData,] 56 | 57 | 58 | getAccuracy <- function(a,b,features,labels){ 59 | estFxn = features %*% a + b 60 | predictedLabels = rep(0,length(labels)) 61 | predictedLabels [estFxn < 0] = -1 62 | predictedLabels [estFxn = 0] = 1 63 | 64 | return(sum(predictedLabels == labels) / length(labels)) 65 | } 66 | 67 | numEpochs = 100 68 | numStepsPerEpoch = 500 69 | nStepsPerPlot = 30 70 | evalidationSetSize = 50 71 | c1 = 0.01 72 | c2 = 50 73 | 74 | lambda_vals = c(0.001, 0.01, 0.1, 1) 75 | bestAccuracy = 0 76 | 77 | 78 | str(lambda_vals) 79 | 80 | accMat <- matrix(NA, nrow = (numStepsPerEpoch/nStepsPerPlot)*numEpochs+1, ncol = length(lambda_vals)) 81 | accMatv <- matrix(NA, nrow = (numStepsPerEpoch/nStepsPerPlot)*numEpochs+1, ncol = length(lambda_vals)) 82 | 83 | 84 | for(i in 1:4){ 85 | lambda = lambda_vals[i] 86 | accMatRow = 1 87 | accMatCol = i 88 | 89 | a = rep(0,ncol(continuousFeatures)) 90 | b = 0 91 | 92 | stepIndex = 0 93 | 94 | for (e in 1:numEpochs){ 95 | 96 | etrainingData <- createDataPartition(y=trainingLabels, p=(1 - evalidationSetSize/length(trainingLabels)), list=FALSE) 97 | 98 | etrainingFeatures <- trainingFeatures[etrainingData,] 99 | etrainingLabels <- trainingLabels[etrainingData] 100 | 101 | evalidationFeatures <- trainingFeatures[-etrainingData,] 102 | evalidationLabels <- trainingLabels[-etrainingData] 103 | 104 | steplength = 1 / (e*c1 + c2) 105 | 106 | for (step in 1:numStepsPerEpoch){ 107 | stepIndex = stepIndex+1 108 | index = sample.int(nrow(etrainingFeatures),1) 109 | xk = etrainingFeatures[index,] 110 | yk = etrainingLabels[index] 111 | 112 | costfxn = yk * (a %*% xk + b) 113 | 114 | if(costfxn >= 1){ 115 | 116 | a_dir = lambda * a 117 | a = a - steplength * a_dir 118 | 119 | } else { 120 | 121 | a_dir = (lambda * a) - (yk * xk) 122 | a = a - steplength * a_dir 123 | b_dir = -yk 124 | b = b - (steplength * b_dir) 125 | 126 | } 127 | 128 | 129 | if (stepIndex %>% nStepsPerPlot == 1){#30){ 130 | accMat[accMatRow,accMatCol] = getAccuracy(a,b,evalidationFeatures,evalidationLabels) 131 | accMatv[accMatRow,accMatCol] = getAccuracy(a,b,validationFeatures,validationLabels) 132 | accMatRow = accMatRow + 1 133 | } 134 | 135 | } 136 | 137 | } 138 | 139 | tempAccuracy = getAccuracy(a,b,validationFeatures,validationLabels) 140 | print(str_c("tempAcc = ", tempAccuracy," and bestAcc = ", bestAccuracy) ) 141 | if(tempAccuracy > bestAccuracy){ 142 | bestAccuracy = tempAccuracy 143 | best_a = a 144 | best_b = b 145 | best_lambdaIndex = i 146 | } 147 | 148 | } 149 | 150 | getAccuracy(best_a,best_b, testingFeatures, testingLabels) 151 | 152 | colors = c("red","blue","green","black") 153 | 154 | xaxislabel = "Step" 155 | 156 | yaxislabels = c("Accuracy on Randomized Epoch Validation Set","Accuracy on Validation Set") 157 | 158 | title="Accuracy as a Function of Step and Lambda" 159 | 160 | ylims=c(0,1) 161 | 162 | stepValues = seq(1,15000,length=500) 163 | 164 | mats = list(accMat,accMatv) 165 | 166 | for(j in 1:length(mats)){ 167 | 168 | mat = mats[[j]] 169 | 170 | for(i in 1:4){ 171 | 172 | if(i == 1){ 173 | plot(stepValues, mat[1:500,i], type = "l",xlim=c(0, 15000), ylim=ylims, 174 | col=colors[i],xlab=xaxislabel,ylab=yaxislabels[j],main=title) 175 | } else{ 176 | lines(stepValues, mat[1:500,i], type = "l",xlim=c(0, 15000), ylim=ylims, 177 | col=colors[i],xlab=xaxislabel,ylab=yaxislabels[j],main=title) 178 | } 179 | Sys.sleep(1) 180 | } 181 | legend(x=10000,y=.5,legend=c("lambda=.001","lambda=.01","lambda=.1","lambda=1"),fill=colors) 182 | 183 | } 184 | -------------------------------------------------------------------------------- /Chapter 04/Data/delta.csv: -------------------------------------------------------------------------------- 1 | Aircraft,Seat Width (Club),Seat Pitch (Club),Seat (Club),Seat Width (First Class),Seat Pitch (First Class),Seats (First Class),Seat Width (Business),Seat Pitch (Business),Seats (Business),Seat Width (Eco Comfort),Seat Pitch (Eco Comfort),Seats (Eco Comfort),Seat Width (Economy),Seat Pitch (Economy),Seats (Economy),Accommodation,Cruising Speed (mph),Range (miles),Engines,Wingspan (ft),Tail Height (ft),Length (ft),Wifi,Video,Power,Satellite,Flat-bed,Sleeper,Club,First Class,Business,Eco Comfort,Economy 2 | Airbus A319,0,0,0,21,36,12,0,0,0,17.2,34,18,17.2,30.5,96,126,517,2399,2,111.83,38.583,111,1,0,0,0,0,0,0,1,0,1,1 3 | Airbus A319 VIP,19.4,44,12,19.4,40,28,21,59,14,0,0,0,0,0,0,54,517,3119,2,111.83,38.583,111,1,1,0,0,0,0,1,1,1,0,0 4 | Airbus A320,0,0,0,21,36,12,0,0,0,17.2,34,18,17.2,31.5,120,150,517,2420,2,111.83,38.583,123.25,1,0,0,0,0,0,0,1,0,1,1 5 | Airbus A320 32-R,0,0,0,21,36,12,0,0,0,17.2,34,18,17.2,31.5,120,150,517,2420,2,111.83,38.583,123.25,1,0,0,0,0,0,0,1,0,1,1 6 | Airbus A330-200,0,0,0,0,0,0,21,60,32,18,35,30,18,30.5,181,243,531,6536,2,197.83,59.83,188.67,0,1,1,0,1,0,0,0,1,1,1 7 | Airbus A330-200 (3L2),0,0,0,0,0,0,21,80,34,18,35,32,18,30.5,168,243,531,6536,2,197.83,59.83,188.67,0,1,1,0,0,1,0,0,1,1,1 8 | Airbus A330-200 (3L3),0,0,0,0,0,0,21,80,34,18,35,32,18,30.5,227,293,531,5343,2,197.83,56.33,208.83,0,1,1,0,1,0,0,0,1,1,1 9 | Airbus A330-300,0,0,0,0,0,0,20,60,34,18,35,32,18,30.5,232,298,531,5343,2,197.83,56.33,208.83,0,1,1,0,0,1,0,0,1,1,1 10 | Boeing 717,0,0,0,19.6,37,12,0,0,0,18.1,34,15,18.1,31,83,110,504,1510,2,93.33,29.083,120,1,0,1,0,0,0,0,1,0,1,1 11 | Boeing 737-700 (73W),0,0,0,21,37,12,0,0,0,17.2,34,18,17.2,30.5,94,124,517,2925,2,117.416,41.167,110.33,1,1,1,1,0,0,0,1,0,1,1 12 | Boeing 737-800 (738),0,0,0,21,37,16,0,0,0,17.2,34,18,17.2,30.5,126,124,517,2925,2,117.416,41.167,110.33,1,1,1,1,0,0,0,1,0,1,1 13 | Boeing 737-800 (73H),0,0,0,21,38,16,0,0,0,17.2,34,18,17.2,30.5,126,160,517,2850,2,117.416,41.167,129.5,1,1,1,1,0,0,0,1,0,1,1 14 | Boeing 737-900ER (739),0,0,0,21,37,20,0,0,0,17.2,34,21,17.2,30.5,139,180,517,2870,2,117.416,41.167,138.167,1,1,1,0,0,0,0,1,0,1,1 15 | Boeing 747-400 (74S),0,0,0,0,0,0,20.5,82,48,17.2,35,42,17.2,30.5,286,376,564,7365,4,213,62.5416,231.83,0,1,1,0,1,0,0,0,1,1,1 16 | Boeing 757-200 (75A),0,0,0,21,40,24,0,0,0,17.2,35,18,17.2,31,132,174,517,4344,2,134.75,44.5,155.25,0,1,0,0,0,0,0,1,0,1,1 17 | Boeing 757-200 (75E),0,0,0,0,0,0,21,54.5,16,17.2,35,25,17.2,30.5,130,171,517,4705,2,134.75,44.5,155.25,1,1,1,0,0,1,0,0,1,1,1 18 | Boeing 757-200 (75M),0,0,0,21,37,22,0,0,0,17.2,34.5,18,17.2,31,141,181,517,2854,2,134.75,44.5,155.25,1,0,0,0,0,0,0,1,0,1,1 19 | Boeing 757-200 (75N),0,0,0,21,37,22,0,0,0,17.2,34,19,17.2,31.5,141,182,517,2854,2,134.75,44.5,155.25,1,0,0,0,0,0,0,1,0,1,1 20 | Boeing 757-200 (757),0,0,0,21,37.5,24,0,0,0,17.2,34.5,20,17.2,31.5,136,180,517,3393,2,111.83,38.583,123.25,1,1,0,0,0,0,0,1,0,1,1 21 | Boeing 757-200 (75V),0,0,0,21,45,22,0,0,0,17.2,34.5,21,17.2,32,132,175,517,3764,2,134.75,44.5,155.25,1,1,0,0,0,0,0,1,0,1,1 22 | Boeing 757-200 (75X),0,0,0,0,0,0,21,38,26,17.2,34,26,17.2,31.5,132,184,517,3764,2,134.75,44.5,155.25,1,1,1,1,0,0,0,0,1,1,1 23 | Boeing 757-300,0,0,0,21,37,24,0,0,0,17.2,34,23,17.2,31,177,224,517,3228,2,124.83,45.083,177.416,1,1,0,0,0,0,0,1,0,1,1 24 | Boeing 767-300 (76G),0,0,0,0,0,0,18.5,60,30,17.9,35,38,17.9,31.5,140,208,517,6408,2,156.083,52,180.25,0,1,1,0,0,1,0,0,1,1,1 25 | Boeing 767-300 (76L),0,0,0,0,0,0,21,78.95,36,17.9,35,32,17.9,31.5,143,211,517,6408,2,156.083,52,180.25,0,1,1,0,1,0,0,0,1,1,1 26 | Boeing 767-300 (76P),0,0,0,18.5,37.5,30,0,0,0,17.9,34,28,17.9,31.5,203,261,517,3515,2,156.083,52,180.25,1,1,1,1,0,0,0,1,0,1,1 27 | Boeing 767-300 (76Q),0,0,0,18.5,37.5,30,0,0,0,17.9,34,28,17.9,31.5,203,261,517,3515,2,156.083,52,180.25,1,1,1,1,0,0,0,1,0,1,1 28 | Boeing 767-300 (76T),0,0,0,0,0,0,21,78.95,36,17.9,35,29,17.9,31.5,143,208,517,6640,2,156.083,52,180.25,0,1,1,0,1,0,0,0,1,1,1 29 | Boeing 767-300 (76U),0,0,0,18.5,60,36,0,0,0,17.9,35,31,17.9,31.5,143,210,517,6408,2,156.083,52,180.25,0,1,1,0,0,1,0,0,1,1,1 30 | Boeing 767-300 (76Z V.1),0,0,0,0,0,0,21,78.95,26,17.9,35,35,17.9,31.5,171,226,517,6221,2,156.083,52,180.25,0,1,1,0,1,0,0,0,1,1,1 31 | Boeing 767-300 (76Z V.2),0,0,0,0,0,0,21,78.95,26,17.9,35,29,17.9,31.5,171,226,517,6221,2,156.083,52,180.25,0,1,1,0,0,1,0,0,1,1,1 32 | Boeing 767-400 (76D),0,0,0,0,0,0,21,78.95,40,17.9,35,28,17.9,31.5,178,246,517,6336,2,171,54.917,201.33,0,1,1,0,1,0,0,0,1,1,1 33 | Boeing 777-200ER,0,0,0,0,0,0,21,78,45,18.5,36,36,18.5,31.5,188,269,550,8542,2,212.583,61.33,209.083,0,1,1,0,1,0,0,0,1,1,1 34 | Boeing 777-200LR,0,0,0,0,0,0,21,78,45,18.5,35,36,18.5,31.5,188,269,550,10375,2,212.583,61.33,209.083,0,1,1,0,1,0,0,0,1,1,1 35 | CRJ 100/200 Pinnacle/SkyWest,0,0,0,0,0,0,0,0,0,0,0,0,17.3,31,50,50,488,1265,2,69.583,20.416,87.83,0,0,0,0,0,0,0,0,0,0,1 36 | CRJ 100/200 ExpressJet,0,0,0,0,0,0,0,0,0,0,0,0,17.3,31,50,50,488,1265,2,69.583,20.416,87.83,0,0,0,0,0,0,0,0,0,0,1 37 | CRJ 700,0,0,0,19.6,36,9,0,0,0,17.3,34,8,17.3,31,48,65,515,1650,2,76.25,24.83,106.083,1,0,0,0,0,0,0,1,0,1,1 38 | CRJ 900,0,0,0,19.6,37,12,0,0,0,17.3,34,12,17.3,31,52,76,515,1744,2,81.583,24.583,118.916,1,0,0,0,0,0,0,1,0,1,1 39 | E120,0,0,0,0,0,0,0,0,0,0,0,0,18.2,31,28,28,364,920,2,64.916,20.83,65.583,0,0,0,0,0,0,0,0,0,0,1 40 | E170,0,0,0,20,37,9,0,0,0,18.25,34,12,18.25,31,48,69,545,1800,2,85.416,32.33,98.083,1,0,0,0,0,0,0,1,0,1,1 41 | E175,0,0,0,20,37,12,0,0,0,18.25,34,12,18.25,31,52,76,545,1800,2,85.416,31.916,103.916,1,0,0,0,0,0,0,1,0,1,1 42 | ERJ-145,0,0,0,0,0,0,0,0,0,0,0,0,17.3,31,50,50,517,1496,2,65.75,22.167,98,0,0,0,0,0,0,0,0,0,0,1 43 | MD-88,0,0,0,19.6,37,16,0,0,0,18.1,34,15,18.1,32,118,149,498,1510,2,107.83,30.5,147.83,1,0,1,0,0,0,0,1,0,1,1 44 | MD-90,0,0,0,19.6,37,16,0,0,0,18.1,34,15,18.1,31.5,129,160,498,1992,2,107.83,30.583,152.583,1,0,1,0,0,0,0,1,0,1,1 45 | MD-DC9-50,0,0,0,19.6,38,16,0,0,0,18.1,35,14,18.1,31,90,120,504,731,2,93.83,27.5,133.5,1,0,0,0,0,0,0,1,0,1,1 46 | -------------------------------------------------------------------------------- /Chapter 02/Data/tobit.csv: -------------------------------------------------------------------------------- 1 | id,read,math,prog,apt 2 | 1,34,40,"vocational",352 3 | 2,39,33,"vocational",449 4 | 3,63,48,"general",648 5 | 4,44,41,"general",501 6 | 5,47,43,"general",762 7 | 6,47,46,"general",658 8 | 7,57,59,"general",800 9 | 8,39,52,"general",613 10 | 9,48,52,"vocational",531 11 | 10,47,49,"academic",528 12 | 11,34,45,"general",584 13 | 12,37,45,"vocational",610 14 | 13,47,39,"vocational",586 15 | 14,47,54,"general",769 16 | 15,39,44,"vocational",402 17 | 16,47,44,"vocational",521 18 | 17,47,48,"general",478 19 | 18,50,49,"vocational",629 20 | 19,28,43,"academic",603 21 | 20,60,57,"general",633 22 | 21,44,61,"academic",724 23 | 22,42,39,"vocational",515 24 | 23,65,64,"general",748 25 | 24,52,66,"general",634 26 | 25,47,42,"academic",630 27 | 26,60,62,"general",800 28 | 27,53,61,"general",652 29 | 28,39,54,"academic",621 30 | 29,52,49,"academic",683 31 | 30,41,42,"general",531 32 | 31,55,52,"academic",625 33 | 32,50,66,"vocational",605 34 | 33,57,72,"general",698 35 | 34,73,57,"general",679 36 | 35,60,50,"academic",691 37 | 36,44,44,"academic",612 38 | 37,41,40,"vocational",572 39 | 38,45,50,"general",625 40 | 39,66,67,"general",734 41 | 40,42,43,"academic",551 42 | 41,50,45,"general",549 43 | 42,46,55,"vocational",622 44 | 43,47,43,"general",557 45 | 44,47,45,"vocational",678 46 | 45,34,41,"vocational",467 47 | 46,45,44,"general",631 48 | 47,47,49,"general",625 49 | 48,57,52,"general",584 50 | 49,50,39,"vocational",485 51 | 50,50,42,"academic",568 52 | 51,42,42,"academic",593 53 | 52,50,53,"general",590 54 | 53,34,46,"vocational",529 55 | 54,47,46,"academic",661 56 | 55,52,49,"general",579 57 | 56,55,46,"vocational",502 58 | 57,71,72,"general",794 59 | 58,55,40,"vocational",529 60 | 59,65,63,"general",703 61 | 60,57,51,"general",635 62 | 61,76,60,"general",765 63 | 62,65,48,"academic",732 64 | 63,52,60,"academic",537 65 | 64,50,45,"vocational",648 66 | 65,55,66,"general",667 67 | 66,68,56,"vocational",576 68 | 67,37,42,"vocational",476 69 | 68,73,71,"general",797 70 | 69,44,40,"vocational",548 71 | 70,57,41,"academic",599 72 | 71,57,56,"academic",766 73 | 72,42,47,"vocational",596 74 | 73,50,53,"general",716 75 | 74,57,50,"general",661 76 | 75,60,51,"vocational",548 77 | 76,47,51,"general",595 78 | 77,61,49,"general",689 79 | 78,39,54,"general",577 80 | 79,60,49,"general",633 81 | 80,65,68,"general",713 82 | 81,63,59,"general",668 83 | 82,68,65,"general",800 84 | 83,50,41,"vocational",571 85 | 84,63,54,"academic",636 86 | 85,55,57,"academic",691 87 | 86,44,54,"academic",682 88 | 87,50,46,"academic",605 89 | 88,68,64,"general",618 90 | 89,35,40,"vocational",522 91 | 90,42,50,"general",671 92 | 91,50,56,"vocational",666 93 | 92,52,57,"academic",739 94 | 93,73,62,"general",800 95 | 94,55,61,"general",782 96 | 95,73,71,"general",800 97 | 96,65,61,"general",749 98 | 97,60,58,"general",613 99 | 98,57,51,"vocational",648 100 | 99,47,56,"academic",640 101 | 100,63,71,"general",793 102 | 101,60,67,"general",800 103 | 102,52,51,"general",698 104 | 103,76,64,"general",676 105 | 104,54,57,"general",630 106 | 105,50,45,"general",598 107 | 106,36,37,"vocational",404 108 | 107,47,47,"vocational",629 109 | 108,34,41,"academic",637 110 | 109,42,42,"academic",574 111 | 110,52,50,"vocational",620 112 | 111,39,39,"academic",622 113 | 112,52,48,"general",689 114 | 113,44,51,"general",556 115 | 114,68,62,"general",725 116 | 115,42,43,"academic",571 117 | 116,57,54,"general",681 118 | 117,34,39,"vocational",565 119 | 118,55,58,"academic",629 120 | 119,42,45,"academic",584 121 | 120,63,54,"general",589 122 | 121,68,53,"vocational",788 123 | 122,52,58,"general",779 124 | 123,68,56,"academic",605 125 | 124,42,41,"vocational",614 126 | 125,68,58,"general",768 127 | 126,42,57,"academic",715 128 | 127,63,57,"general",770 129 | 128,39,38,"general",508 130 | 129,44,46,"academic",527 131 | 130,43,55,"academic",685 132 | 131,65,57,"general",649 133 | 132,73,73,"general",800 134 | 133,50,40,"vocational",535 135 | 134,44,39,"academic",474 136 | 135,63,65,"general",696 137 | 136,65,70,"general",792 138 | 137,63,65,"general",800 139 | 138,43,40,"vocational",427 140 | 139,68,61,"general",800 141 | 140,44,40,"vocational",399 142 | 141,63,47,"vocational",566 143 | 142,47,52,"vocational",523 144 | 143,63,75,"vocational",800 145 | 144,60,58,"academic",712 146 | 145,42,38,"vocational",458 147 | 146,55,64,"general",688 148 | 147,47,53,"general",619 149 | 148,42,51,"vocational",565 150 | 149,63,49,"academic",727 151 | 150,42,57,"vocational",554 152 | 151,47,52,"vocational",633 153 | 152,55,56,"general",687 154 | 153,39,40,"vocational",665 155 | 154,65,66,"general",796 156 | 155,44,46,"academic",614 157 | 156,50,53,"general",618 158 | 157,68,58,"academic",733 159 | 158,52,55,"academic",657 160 | 159,55,54,"general",592 161 | 160,55,55,"general",746 162 | 161,57,72,"general",800 163 | 162,57,40,"vocational",702 164 | 163,52,64,"general",800 165 | 164,31,46,"vocational",516 166 | 165,36,54,"vocational",604 167 | 166,52,53,"general",669 168 | 167,63,35,"academic",563 169 | 168,52,57,"general",695 170 | 169,55,63,"academic",779 171 | 170,47,61,"general",712 172 | 171,60,60,"general",678 173 | 172,47,57,"general",618 174 | 173,50,61,"academic",650 175 | 174,68,71,"general",750 176 | 175,36,42,"academic",454 177 | 176,47,41,"general",586 178 | 177,55,62,"general",688 179 | 178,47,57,"vocational",640 180 | 179,47,60,"general",609 181 | 180,71,69,"general",800 182 | 181,50,45,"general",662 183 | 182,44,43,"general",462 184 | 183,63,49,"general",591 185 | 184,50,53,"vocational",496 186 | 185,63,55,"general",647 187 | 186,57,63,"general",681 188 | 187,57,57,"academic",800 189 | 188,63,56,"general",796 190 | 189,47,63,"general",669 191 | 190,47,54,"general",661 192 | 191,47,43,"general",567 193 | 192,65,63,"general",800 194 | 193,44,48,"general",666 195 | 194,63,69,"general",800 196 | 195,57,60,"academic",727 197 | 196,44,49,"general",539 198 | 197,50,50,"general",594 199 | 198,47,51,"general",616 200 | 199,52,50,"general",558 201 | 200,68,75,"general",800 202 | -------------------------------------------------------------------------------- /Chapter 12/Case Study 1 - World Bank data Analysis.r: -------------------------------------------------------------------------------- 1 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | 19 | install.packages("wbstats") 20 | install.packages("data.table") 21 | install.packages("googleVis") 22 | 23 | library(wbstats) 24 | library(data.table) 25 | library(googleVis) 26 | 27 | 28 | Pop_LifeExp_FertRt <- data.table(wb(indicator = c("SP.POP.TOTL", "SP.DYN.LE00.IN", "SP.DYN.TFRT.IN"), startdate = 1960, enddate = 2016)) 29 | 30 | Pop_GDPUSD_HeadCnt <- data.table(wb(indicator = c("SP.POP.TOTL", "NY.GDP.MKTP.CD", "SI.POV.2DAY"), startdate = 1960, enddate = 2016)) 31 | 32 | Pop_GDPUSD_Sanitation <- data.table(wb(indicator = c("SP.POP.TOTL", "NY.GDP.MKTP.CD", "SH.STA.ACSN"), startdate = 1960, enddate = 2016)) 33 | 34 | GDPUSD_Electricity_CO2 <- data.table(wb(indicator = c("NY.GDP.MKTP.CD", "EG.ELC.ACCS.ZS", "EN.ATM.CO2E.KT"), startdate = 1960, enddate = 2016)) 35 | 36 | dim(Pop_LifeExp_FertRt) 37 | 38 | dim(Pop_GDPUSD_HeadCnt) 39 | 40 | dim(Pop_GDPUSD_Sanitation) 41 | 42 | dim(GDPUSD_Electricity_CO2) 43 | 44 | str(Pop_LifeExp_FertRt) 45 | 46 | str(Pop_GDPUSD_HeadCnt) 47 | 48 | str(Pop_GDPUSD_Sanitation) 49 | 50 | str(GDPUSD_Electricity_CO2) 51 | 52 | head(Pop_LifeExp_FertRt) 53 | 54 | head(Pop_GDPUSD_HeadCnt) 55 | 56 | 57 | head(Pop_GDPUSD_Sanitation) 58 | 59 | 60 | 61 | head(GDPUSD_Electricity_CO2) 62 | 63 | dim(wb(indicator = "SP.POP.TOTL")) 64 | 65 | dim(wb(indicator = "SP.DYN.LE00.IN")) 66 | 67 | dim(wb(indicator = "SP.DYN.TFRT.IN")) 68 | 69 | dim(wb(indicator = "NY.GDP.MKTP.CD")) 70 | 71 | dim(wb(indicator = "SI.POV.2DAY")) 72 | 73 | dim(wb(indicator = "SH.STA.ACSN")) 74 | 75 | dim(wb(indicator = "EG.ELC.ACCS.ZS")) 76 | 77 | dim(wb(indicator = "EN.ATM.CO2E.KT")) 78 | 79 | Countries <- data.table(wbcountries()) 80 | 81 | head(Countries) 82 | 83 | setkey(Pop_LifeExp_FertRt, iso2c) 84 | 85 | setkey(Pop_GDPUSD_HeadCnt, iso2c) 86 | 87 | setkey(Pop_GDPUSD_Sanitation, iso2c) 88 | 89 | setkey(GDPUSD_Electricity_CO2, iso2c) 90 | 91 | 92 | setkey(Countries, iso2c) 93 | 94 | head(setkey(Countries, iso2c)) 95 | 96 | Pop_LifeExp_FertRt <- Countries[Pop_LifeExp_FertRt][ ! region %in% "Aggregates"] 97 | 98 | 99 | head(Pop_LifeExp_FertRt) 100 | 101 | Pop_GDPUSD_HeadCnt <- Countries[Pop_GDPUSD_HeadCnt][ ! region %in% "Aggregates"] 102 | 103 | Pop_GDPUSD_Sanitation <- Countries[Pop_GDPUSD_Sanitation][ ! region %in% "Aggregates"] 104 | 105 | GDPUSD_Electricity_CO2 <- Countries[GDPUSD_Electricity_CO2][ ! region %in% "Aggregates"] 106 | 107 | 108 | wPop_LifeExp_FertRt <- reshape(Pop_LifeExp_FertRt[, list(country, region, date, value, indicator)], v.names = "value", idvar=c("date", "country", "region"), timevar="indicator", direction = "wide") 109 | 110 | wPop_GDPUSD_HeadCnt <- reshape(Pop_GDPUSD_HeadCnt[, list(country, region, date, value, indicator)], v.names = "value", idvar=c("date", "country", "region"), timevar="indicator", direction = "wide") 111 | 112 | wPop_GDPUSD_Sanitation <- reshape(Pop_GDPUSD_Sanitation[, list(country, region, date, value, indicator)], v.names = "value", idvar=c("date", "country", "region"), timevar="indicator", direction = "wide") 113 | 114 | wGDPUSD_Electricity_CO2 <- reshape(GDPUSD_Electricity_CO2[, list(country, region, date, value, indicator)], v.names = "value", idvar=c("date", "country", "region"), timevar="indicator", direction = "wide") 115 | 116 | wPop_LifeExp_FertRt 117 | 118 | wGDPUSD_Electricity_CO2 119 | 120 | wPop_LifeExp_FertRt[, date := as.integer(date)] 121 | 122 | wPop_GDPUSD_HeadCnt[, date := as.integer(date)] 123 | 124 | wPop_GDPUSD_Sanitation[, date := as.integer(date)] 125 | 126 | wGDPUSD_Electricity_CO2[, date := as.integer(date)] 127 | 128 | 129 | setnames(wPop_LifeExp_FertRt, names(wPop_LifeExp_FertRt), c("Country", "Region", "Year", "Population", "Fertility", "LifeExpectancy")) 130 | 131 | setnames(wPop_GDPUSD_HeadCnt, names(wPop_GDPUSD_HeadCnt), c("Country", "Region", "Year", "Population", "GDPUSD", "PovertyHead")) 132 | 133 | setnames(wPop_GDPUSD_Sanitation, names(wPop_GDPUSD_Sanitation), c("Country", "Region", "Year", "Population", "GDPUSD", "SanitationAccess")) 134 | 135 | setnames(wGDPUSD_Electricity_CO2, names(wGDPUSD_Electricity_CO2), c("Country", "Region", "Year", "GDPUSD", "ElectricityConsumption", "CO2Emissions")) 136 | 137 | pltPop_LifeExp_FertRt <- gvisMotionChart(wPop_LifeExp_FertRt, idvar = "Country", timevar = "Year", xvar = "LifeExpectancy", yvar = "Fertility", sizevar = "Population", colorvar = "Region") 138 | 139 | plot(pltPop_LifeExp_FertRt) 140 | 141 | pltPop_GDPUSD_HeadCnt <- gvisMotionChart(wPop_GDPUSD_HeadCnt, idvar = "Country", timevar = "Year", xvar = "GDPUSD", yvar = "PovertyHead", sizevar = "Population", colorvar = "Region") 142 | 143 | plot(pltPop_GDPUSD_HeadCnt) 144 | 145 | pltPop_GDPUSD_Sanitation <- gvisMotionChart(wPop_GDPUSD_Sanitation, idvar = "Country", timevar = "Year", xvar = "GDPUSD", yvar = "SanitationAccess", sizevar = "Population", colorvar = "Region") 146 | 147 | plot(pltPop_GDPUSD_Sanitation) 148 | 149 | pltGDPUSD_Electricity_CO2 <- gvisMotionChart(wGDPUSD_Electricity_CO2, idvar = "Country", timevar = "Year", xvar = "GDPUSD", yvar = "ElectricityAccess", sizevar = "CO2Emissions", colorvar = "Region") 150 | 151 | plot(pltGDPUSD_Electricity_CO2) 152 | -------------------------------------------------------------------------------- /Chapter 03/3Hierarchical Clustering - Gene Clustering.r: -------------------------------------------------------------------------------- 1 | install.packages(c("RColorBrewer", "cluster", "pvclust", "xtable", "plyr")) 2 | library(RColorBrewer) 3 | library(cluster) 4 | library(pvclust) 5 | library(xtable) 6 | library(plyr) 7 | 8 | 9 | GSE4051_data <- read.csv("d:/GSE4051_data.csv", header = TRUE) 10 | str(GSE4051_data, max.level = 0) 11 | 'data.frame': 29949 obs. of 39 variables: 12 | 13 | 14 | GSE4051_design <- read.csv("d:/GSE4051_design.csv", header = TRUE) 15 | str(GSE4051_design) 16 | 'data.frame': 39 obs. of 4 variables: 17 | $ sidChar : Factor w/ 39 levels "Sample_1","Sample_10",..: 13 14 15 16 8 9 36 17 18 19 ... 18 | $ sidNum : int 20 21 22 23 16 17 6 24 25 26 ... 19 | $ devStage: Factor w/ 5 levels "4_weeks","E16",..: 2 2 2 2 2 2 2 4 4 4 ... 20 | $ gType : Factor w/ 2 levels "NrlKO","wt": 2 2 2 2 1 1 1 2 2 2 ... 21 | 22 | 23 | trans_GSE4051_data <- t(scale(t(GSE4051_data))) 24 | str(trans_GSE4051_data, max.level = 0, give.attr = FALSE) 25 | num [1:29949, 1:39] 0.0838 0.1758 0.7797 -0.3196 0.8358 ... 26 | 27 | 28 | round(data.frame(avgBefore = rowMeans(head(GSE4051_data)), 29 | avgAfter = rowMeans(head(trans_GSE4051_data)), 30 | varBefore = apply(head(GSE4051_data), 1, var), 31 | varAfter = apply(head(trans_GSE4051_data), 1, var)), 2) 32 | avgBefore avgAfter varBefore varAfter 33 | 1 7.22 0 0.02 1 34 | 2 9.37 0 0.35 1 35 | 3 9.70 0 0.15 1 36 | 4 8.42 0 0.03 1 37 | 5 8.47 0 0.02 1 38 | 6 9.67 0 0.03 1 39 | 40 | 41 | pair_dist_GSE4051_data <- dist(t(trans_GSE4051_data), method = 'euclidean') 42 | 43 | 44 | GSE4051_design$group <- with(GSE4051_design, interaction(gType, devStage)) 45 | summary(GSE4051_design$group) 46 | NrlKO.4_weeks wt.4_weeks NrlKO.E16 wt.E16 NrlKO.P10 wt.P10 NrlKO.P2 wt.P2 NrlKO.P6 47 | 4 4 3 4 4 4 4 4 4 48 | wt.P6 49 | 4 50 | 51 | 52 | pr.hc.single <- hclust(pair_dist_GSE4051_data, method = 'single') 53 | pr.hc.single 54 | Call: 55 | hclust(d = pr.dis, method = "single") 56 | 57 | Cluster method : single 58 | Distance : euclidean 59 | Number of objects: 39 60 | 61 | 62 | pr.hc.complete <- hclust(pair_dist_GSE4051_data, method = 'complete') 63 | pr.hc.complete 64 | 65 | Call: 66 | hclust(d = pr.dis, method = "complete") 67 | 68 | Cluster method : complete 69 | 70 | 71 | pr.hc.average <- hclust(pair_dist_GSE4051_data, method = 'average') 72 | pr.hc.average 73 | 74 | Call: 75 | hclust(d = pr.dis, method = "average") 76 | 77 | Cluster method : average 78 | Distance : euclidean 79 | Number of objects: 39 80 | 81 | 82 | pr.hc.ward <- hclust(pair_dist_GSE4051_data, method = 'ward.D2') 83 | pr.hc.ward 84 | 85 | Call: 86 | hclust(d = pr.dis, method = "ward.D2") 87 | 88 | Cluster method : ward.D2 89 | Distance : euclidean 90 | Number of objects: 39 91 | 92 | 93 | Distance : euclidean 94 | Number of objects: 39 95 | 96 | 97 | op <- par(mar = c(0,4,4,2), mfrow = c(2,2)) 98 | 99 | 100 | plot(pr.hc.single, labels = FALSE, main = "Single Linkage Representation", xlab = "") 101 | 102 | plot(pr.hc.complete, labels = FALSE, main = "Complete Linkage Representation", xlab = "") 103 | 104 | plot(pr.hc.average, labels = FALSE, main = "Average Linkage Representation", xlab = "") 105 | 106 | plot(pr.hc.ward, labels = FALSE, main = "Ward Linkage Representation", xlab = "") 107 | 108 | par(op) 109 | 110 | op <- par(mar = c(1,4,4,1)) 111 | 112 | plot(pr.hc.single, labels = GSE4051_design$group, cex = 0.6, main = "Single Hierarchical Cluster - 10 clusters") 113 | rect.hclust(pr.hc.single, k = 10) 114 | par(op) 115 | jGraysFun <- colorRampPalette(brewer.pal(n = 9, "Blues")) 116 | gTypeCols <- brewer.pal(9, "Spectral")[c(4,7)] 117 | heatmap(as.matrix(trans_GSE4051_data), Rowv = NA, col = jGraysFun(256), hclustfun = function(x) hclust(x, method = 'single'), 118 | scale = "none", labCol = GSE4051_design $group, labRow = NA, margins = c(8,1), 119 | ColSideColor = gTypeCols[unclass(GSE4051_design$gType)]) 120 | legend("topright", legend = levels(GSE4051_design$gType), col = gTypeCols, lty = 1, lwd = 5, cex = 0.5) 121 | 122 | 123 | plot(pr.hc.complete, labels = GSE4051_design$group, cex = 0.6, main = "Complete Hierarchical Cluster - 10 clusters") 124 | rect.hclust(pr.hc.complete, k = 10) 125 | par(op) 126 | jGraysFun <- colorRampPalette(brewer.pal(n = 9, "Greens")) 127 | gTypeCols <- brewer.pal(11, "PRGn")[c(4,7)] 128 | heatmap(as.matrix(trans_GSE4051_data), Rowv = NA, col = jGraysFun(256), hclustfun = function(x) hclust(x, method = 'complete'), 129 | scale = "none", labCol = GSE4051_design$group, labRow = NA, margins = c(8,1), 130 | ColSideColor = gTypeCols[unclass(GSE4051_design$gType)]) 131 | legend("topright", legend = levels(GSE4051_design$gType), col = gTypeCols, lty = 1, lwd = 5, cex = 0.5) 132 | 133 | 134 | plot(pr.hc.average, labels = GSE4051_design$group, cex = 0.6, main = "Average Hierarchical Cluster - 10 clusters") 135 | rect.hclust(pr.hc.average, k = 10) 136 | jGraysFun <- colorRampPalette(brewer.pal(n = 9, "Oranges")) 137 | gTypeCols <- brewer.pal(9, "Oranges")[c(4,7)] 138 | heatmap(as.matrix(trans_GSE4051_data), Rowv = NA, col = jGraysFun(256), hclustfun = function(x) hclust(x, method = 'average'), 139 | scale = "none", labCol = GSE4051_design$group, labRow = NA, margins = c(8,1), 140 | ColSideColor = gTypeCols[unclass(GSE4051_design$gType)]) 141 | legend("topright", legend = levels(GSE4051_design$gType), col = gTypeCols, lty = 1, lwd = 5, cex = 0.5) 142 | 143 | 144 | plot(pr.hc.ward, labels = GSE4051_design$group, cex = 0.6, main = "Ward Hierarchical Cluster - 10 clusters") 145 | rect.hclust(pr.hc.ward, k = 10) 146 | jGraysFun <- colorRampPalette(brewer.pal(n = 9, "Reds")) 147 | gTypeCols <- brewer.pal(9, "Reds")[c(4,7)] 148 | heatmap(as.matrix(trans_GSE4051_data), Rowv = NA, col = jGraysFun(256), hclustfun = function(x) hclust(x, method = 'ward.D2'), 149 | scale = "none", labCol = GSE4051_design$group, labRow = NA, margins = c(8,1), 150 | ColSideColor = gTypeCols[unclass(GSE4051_design$gType)]) 151 | legend("topright", legend = levels(GSE4051_design$gType), col = gTypeCols, lty = 1, lwd = 5, cex = 0.5) 152 | 153 | 154 | 155 | -------------------------------------------------------------------------------- /Chapter 03/1Hierarchical Clustering - World Bank.r: -------------------------------------------------------------------------------- 1 | R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree" 2 | Copyright (C) 2015 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | 19 | 20 | wbclust=read.csv("d:/WBClust2013.csv",header=T) 21 | head(wbclust) 22 | 23 | wbnorm<-scale(wbclust[,2:13]) 24 | wbnorm 25 | 26 | rownames(wbnorm)=wbclust[,1] 27 | rownames(wbnorm) 28 | 29 | dist1<-dist(wbnorm, method="euclidean") 30 | 31 | clust1<-hclust(dist1,method="ward.D") 32 | clust1 33 | 34 | plot(clust1,labels= wbclust$Country, cex=0.7, xlab="",ylab="Distance",main="Clustering for 80 Most Populous Countries") 35 | 36 | 37 | rect.hclust(clust1,k=5) 38 | 39 | cuts=cutree(clust1,k=5) 40 | 41 | cuts 42 | China India United States Indonesia 43 | 1 2 1 2 44 | Brazil Pakistan Nigeria Bangladesh 45 | 2 3 4 3 46 | Russian Federation Japan Mexico Philippines 47 | 1 1 2 2 48 | Ethiopia Vietnam Egypt, Arab Rep. Germany 49 | 4 5 2 5 50 | Turkey Thailand France United Kingdom 51 | 1 5 1 1 52 | Italy South Africa Korea, Rep. Tanzania 53 | 1 1 5 4 54 | Colombia Spain Ukraine Kenya 55 | 2 1 5 4 56 | Algeria Sudan Canada Iraq 57 | 2 3 1 2 58 | Morocco Peru Uzbekistan Malaysia 59 | 2 2 2 5 60 | Saudi Arabia Nepal Ghana Mozambique 61 | 1 3 3 4 62 | Australia Cameroon Angola Sri Lanka 63 | 1 4 4 3 64 | Cote d'Ivoire Chile Kazakhstan Netherlands 65 | 4 1 1 5 66 | Ecuador Guatemala Cambodia Zambia 67 | 2 2 3 4 68 | Zimbabwe Senegal Belgium Greece 69 | 3 3 5 1 70 | Tunisia Bolivia Czech Republic Portugal 71 | 5 2 5 1 72 | Dominican Republic Benin Haiti Hungary 73 | 2 3 3 5 74 | Sweden Belarus Azerbaijan United Arab Emirates 75 | 5 5 2 5 76 | Austria Tajikistan Honduras Switzerland 77 | 5 3 3 5 78 | Israel Bulgaria Serbia Togo 79 | 1 5 1 4 80 | Paraguay Jordan El Salvador Nicaragua 81 | 3 5 2 3 82 | 83 | for (i in 1:5){ 84 | print(paste("Countries in Cluster ",i)) 85 | print(wbclust$Country[cuts==i]) 86 | print (" ") 87 | } 88 | [1] "Countries in Cluster 1" 89 | [1] China United States Russian Federation Japan 90 | [5] Turkey France United Kingdom Italy 91 | [9] South Africa Spain Canada Saudi Arabia 92 | [13] Australia Chile Kazakhstan Greece 93 | [17] Portugal Israel Serbia 94 | 80 Levels: Algeria Angola Australia Austria Azerbaijan Bangladesh Belarus ... Zimbabwe 95 | [1] " " 96 | [1] "Countries in Cluster 2" 97 | [1] India Indonesia Brazil Mexico 98 | [5] Philippines Egypt, Arab Rep. Colombia Algeria 99 | [9] Iraq Morocco Peru Uzbekistan 100 | [13] Ecuador Guatemala Bolivia Dominican Republic 101 | [17] Azerbaijan El Salvador 102 | 80 Levels: Algeria Angola Australia Austria Azerbaijan Bangladesh Belarus ... Zimbabwe 103 | [1] " " 104 | [1] "Countries in Cluster 3" 105 | [1] Pakistan Bangladesh Sudan Nepal Ghana Sri Lanka Cambodia 106 | [8] Zimbabwe Senegal Benin Haiti Tajikistan Honduras Paraguay 107 | [15] Nicaragua 108 | 80 Levels: Algeria Angola Australia Austria Azerbaijan Bangladesh Belarus ... Zimbabwe 109 | [1] " " 110 | [1] "Countries in Cluster 4" 111 | [1] Nigeria Ethiopia Tanzania Kenya Mozambique 112 | [6] Cameroon Angola Cote d'Ivoire Zambia Togo 113 | 80 Levels: Algeria Angola Australia Austria Azerbaijan Bangladesh Belarus ... Zimbabwe 114 | [1] " " 115 | [1] "Countries in Cluster 5" 116 | [1] Vietnam Germany Thailand 117 | [4] Korea, Rep. Ukraine Malaysia 118 | [7] Netherlands Belgium Tunisia 119 | [10] Czech Republic Hungary Sweden 120 | [13] Belarus United Arab Emirates Austria 121 | [16] Switzerland Bulgaria Jordan 122 | 80 Levels: Algeria Angola Australia Austria Azerbaijan Bangladesh Belarus ... Zimbabwe 123 | [1] " " -------------------------------------------------------------------------------- /Chapter 04/1Shrinkage Methods - Calories burnt per day.r: -------------------------------------------------------------------------------- 1 | R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree" 2 | Copyright (C) 2015 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | install.packages("glmnet") 19 | install.packages("dplyr") 20 | install.packages("tidyr") 21 | install.packages("ggplot2") 22 | install.packages("caret") 23 | install.packages("glmnet") 24 | install.packages("boot") 25 | install.packages("RColorBrewer") 26 | install.packages("glmnet") 27 | install.packages("Metrics") 28 | 29 | library(dplyr) 30 | library(tidyr) 31 | library(ggplot2) 32 | library(caret) 33 | library(glmnet) 34 | library(boot) 35 | library(RColorBrewer) 36 | library(glmnet) 37 | library(Metrics) 38 | 39 | fitbit_details <- read.csv("https://raw.githubusercontent.com/ellisp/ellisp.github.io/source/data/fitbit_export_20160806.csv", 40 | skip = 1, stringsAsFactors = FALSE) %>% 41 | mutate( 42 | Calories.Burned = as.numeric(gsub(",", "", Calories.Burned)), 43 | Steps = as.numeric(gsub(",", "", Steps)), 44 | Activity.Calories = as.numeric(gsub(",", "", Activity.Calories)), 45 | Date = as.Date(Date, format = "%d/%m/%Y") 46 | ) 47 | 48 | fitbit <- fitbit_details 49 | 50 | head(fitbit) 51 | 52 | 53 | 54 | fitbit$Activity.Calories <- NULL 55 | 56 | fitbit$Date <- NULL 57 | 58 | fitbit$Steps <- fitbit$Steps / 1000 59 | 60 | fitbit$Steps 61 | 62 | panel_correlations <- function(x, y, digits = 2, prefix = "", cex.cor, ...){ 63 | usr <- par("usr"); on.exit(par(usr)) 64 | par(usr = c(0, 1, 0, 1)) 65 | r <- abs(cor(x, y)) 66 | txt <- format(c(r, 0.123456789), digits = digits)[1] 67 | txt <- paste0(prefix, txt) 68 | if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt) 69 | text(0.5, 0.5, txt, cex = cex.cor * r) 70 | } 71 | 72 | pairs(fitbit[ , -1], lower.panel = panel_correlations, main = "Pairwise Relationship - Fitbit's Measured Activities") 73 | 74 | ggplot(fitbit, aes(x = Distance / Steps)) + geom_rug() + geom_density() +ggtitle("Stride Length Reverse- Engineered from Fitbit Data", subtitle = "Not all strides identical, due to rounding or other jitter") 75 | 76 | moderate <- lm(Calories.Burned ~ Steps, data = fitbit) 77 | 78 | moderate 79 | 80 | 81 | round(coef(moderate)) 82 | 83 | plot(moderate, which = 1, bty = "l", main ="Predicted Calories compared with Residuals") 84 | 85 | pacf(resid(moderate), main = "Partial Autocorrelation of residuals from single variable regression") 86 | grid() 87 | 88 | X <- as.matrix(fitbit[ , -1]) 89 | 90 | head(X) 91 | 92 | Y <- fitbit$Calories.Burned 93 | 94 | Y 95 | 96 | 97 | set.seed(123) 98 | 99 | 100 | alphas <- seq(from = 0, to = 1, length.out = 10) 101 | 102 | 103 | res <- matrix(0, nrow = length(alphas), ncol = 6) 104 | 105 | 106 | for(i in 1:length(alphas)){ 107 | for(j in 2:6){ 108 | cvmod <- cv.glmnet(X, Y, alpha = alphas[i]) 109 | res[i, c(1, j)] <- c(alphas[i], sqrt(min(cvmod$cvm))) 110 | } 111 | } 112 | 113 | res <- data.frame(res) 114 | 115 | res 116 | 117 | res$average_rmse <- apply(res[ , 2:6], 1, mean) 118 | 119 | res$average_rmse 120 | [1] 109.2894 108.6945 108.4060 109.9822 111.0122 108.6028 106.0425 108.7103 109.5337 109.7035 121 | 122 | 123 | 124 | res <- res[order(res$average_rmse), ] 125 | 126 | res 127 | 128 | 129 | names(res)[1] <- "alpha" 130 | 131 | res %>% 132 | select(-average_rmse) %>% 133 | gather(trial, rmse, -alpha) %>% 134 | ggplot(aes(x = alpha, y = rmse)) + 135 | geom_point() + 136 | geom_smooth(se = FALSE) + 137 | labs(y = "Root Mean Square Error") + 138 | ggtitle("Cross Validation best RMSE for differing values of alpha") 139 | 140 | 141 | 142 | bestalpha <- res[1, 1] 143 | 144 | bestalpha 145 | 146 | crossvalidated <- cv.glmnet(X, Y, alpha = bestalpha) 147 | 148 | 149 | moderate1 <- glmnet(X, Y, alpha = bestalpha) 150 | 151 | 152 | OLSmodel <- lm(Calories.Burned ~ ., data = fitbit) 153 | 154 | OLSmodel 155 | 156 | 157 | coeffs <- data.frame(original = coef(OLSmodel), 158 | shrunk = as.vector(coef(moderate1, s = crossvalidated$lambda.min)), 159 | very.shrunk = as.vector(coef(moderate1, s = crossvalidated$lambda.1se))) 160 | 161 | coeffs 162 | 163 | 164 | round(coeffs, 3) 165 | 166 | moderate2 <- glmnet(X, Y, lambda = 0) 167 | 168 | 169 | moderate2 170 | 171 | 172 | round(data.frame("elastic, lambda = 0" = as.vector(coef(moderate2)), "lm" = coef(OLSmodel), check.names = FALSE), 3) 173 | 174 | 175 | moderate3 <- glmnet(X[ , -2], Y, lambda = 0) 176 | 177 | moderate3 178 | 179 | 180 | moderate4 <- lm(Y ~ X[ , -2]) 181 | 182 | moderate4 183 | 184 | 185 | round(data.frame("elastic, lambda = 0" = as.vector(coef(moderate3)), "lm" = coef(moderate4), check.names = FALSE), 3) 186 | 187 | 188 | > modellingfucn1 <- function(data, i){ 189 | X <- as.matrix(data[i , -1]) 190 | Y <- data[i , 1] 191 | # k-fold cross-validation for glmnet 192 | crossvalidated <- cv.glmnet(X, Y, alpha = 1, nfolds = 30) 193 | # Fitting a generalized linear model via penalized maximum likelihood 194 | moderate1 <- glmnet(X, Y, alpha = 1) 195 | # Computing the root mean squared error 196 | rmse(predict(moderate1, newx = as.matrix(data[ , -1]), s = crossvalidated$lambda.min), data[ , 1]) 197 | } 198 | 199 | 200 | 201 | elastic_boot <- boot(fitbit, statistic = modellingfucn1, R = 99) 202 | 203 | 204 | elastic_boot 205 | 206 | 207 | 208 | modellingfucn2 <- function(data, i){ 209 | OLSmodel <- lm(Calories.Burned ~ ., data = data[i, ]) 210 | rmse(predict(OLSmodel, newdata = data), data[ , 1]) 211 | } 212 | 213 | lm_boot <- boot(fitbit, statistic = modellingfucn2, R = 99) 214 | 215 | lm_boot 216 | 217 | 218 | 219 | modellingOLS <- function(data, i){ 220 | mod0 <- lm(Calories.Burned ~ Steps, data = data[i, ]) 221 | rmse(predict(moderate, newdata = data), data[ , 1]) 222 | } 223 | 224 | 225 | lmOLS_boot <- boot(fitbit, statistic = modellingOLS, R = 99) 226 | 227 | lmOLS_boot 228 | 229 | 230 | 231 | round(c("elastic modelling" = mean(elastic_boot$t), 232 | "OLS modelling" = mean(lm_boot$t), 233 | "OLS modelling, only one explanatory variable" = mean(lmOLS_boot$t)), 1) 234 | elastic modelling OLS modelling 235 | 95.8 99.7 236 | OLS modelling, only one explanatory variable 237 | 159.7 238 | 239 | 240 | 241 | 242 | ordering <- c(7,5,6,2,1,3,4) 243 | par(mar = c(5.1, 4.1, 6.5, 1), bg = "grey90") 244 | 245 | model_scaled <- glmnet(scale(X), Y, alpha = bestalpha) 246 | 247 | the_palette <- brewer.pal(7, "Set1") 248 | 249 | 250 | plot(model_scaled, xvar = "dev", label = TRUE, col = the_palette, lwd = 2, main = "Increasing contribution of different explanatory variables\nas penalty for including them is relaxed") 251 | 252 | legend("topleft", legend = colnames(X)[ordering], text.col = the_palette[ordering], lwd = 2, bty = "n", col = the_palette[ordering]) 253 | 254 | -------------------------------------------------------------------------------- /Chapter 10/Data/FRED-WIUR.csv: -------------------------------------------------------------------------------- 1 | DATE,VALUE 2 | 2013-04-01,6.8 3 | 2013-03-01,6.9 4 | 2013-02-01,6.9 5 | 2013-01-01,6.9 6 | 2012-12-01,6.9 7 | 2012-11-01,6.9 8 | 2012-10-01,6.9 9 | 2012-09-01,7.0 10 | 2012-08-01,7.0 11 | 2012-07-01,7.1 12 | 2012-06-01,7.1 13 | 2012-05-01,7.1 14 | 2012-04-01,7.1 15 | 2012-03-01,7.1 16 | 2012-02-01,7.1 17 | 2012-01-01,7.1 18 | 2011-12-01,7.2 19 | 2011-11-01,7.4 20 | 2011-10-01,7.5 21 | 2011-09-01,7.6 22 | 2011-08-01,7.7 23 | 2011-07-01,7.8 24 | 2011-06-01,7.8 25 | 2011-05-01,7.8 26 | 2011-04-01,7.9 27 | 2011-03-01,7.9 28 | 2011-02-01,7.9 29 | 2011-01-01,8.0 30 | 2010-12-01,8.1 31 | 2010-11-01,8.2 32 | 2010-10-01,8.3 33 | 2010-09-01,8.3 34 | 2010-08-01,8.4 35 | 2010-07-01,8.4 36 | 2010-06-01,8.5 37 | 2010-05-01,8.7 38 | 2010-04-01,8.9 39 | 2010-03-01,9.0 40 | 2010-02-01,9.1 41 | 2010-01-01,9.2 42 | 2009-12-01,9.2 43 | 2009-11-01,9.1 44 | 2009-10-01,9.1 45 | 2009-09-01,9.0 46 | 2009-08-01,9.0 47 | 2009-07-01,9.0 48 | 2009-06-01,8.9 49 | 2009-05-01,8.8 50 | 2009-04-01,8.6 51 | 2009-03-01,8.2 52 | 2009-02-01,7.7 53 | 2009-01-01,7.2 54 | 2008-12-01,6.6 55 | 2008-11-01,6.0 56 | 2008-10-01,5.6 57 | 2008-09-01,5.2 58 | 2008-08-01,5.0 59 | 2008-07-01,4.8 60 | 2008-06-01,4.6 61 | 2008-05-01,4.5 62 | 2008-04-01,4.4 63 | 2008-03-01,4.4 64 | 2008-02-01,4.5 65 | 2008-01-01,4.6 66 | 2007-12-01,4.8 67 | 2007-11-01,4.9 68 | 2007-10-01,5.0 69 | 2007-09-01,5.0 70 | 2007-08-01,5.1 71 | 2007-07-01,5.0 72 | 2007-06-01,5.0 73 | 2007-05-01,4.9 74 | 2007-04-01,4.9 75 | 2007-03-01,4.9 76 | 2007-02-01,4.9 77 | 2007-01-01,4.9 78 | 2006-12-01,4.9 79 | 2006-11-01,4.9 80 | 2006-10-01,4.8 81 | 2006-09-01,4.8 82 | 2006-08-01,4.8 83 | 2006-07-01,4.7 84 | 2006-06-01,4.7 85 | 2006-05-01,4.7 86 | 2006-04-01,4.7 87 | 2006-03-01,4.7 88 | 2006-02-01,4.7 89 | 2006-01-01,4.7 90 | 2005-12-01,4.8 91 | 2005-11-01,4.8 92 | 2005-10-01,4.8 93 | 2005-09-01,4.8 94 | 2005-08-01,4.7 95 | 2005-07-01,4.7 96 | 2005-06-01,4.6 97 | 2005-05-01,4.6 98 | 2005-04-01,4.6 99 | 2005-03-01,4.6 100 | 2005-02-01,4.7 101 | 2005-01-01,4.7 102 | 2004-12-01,4.8 103 | 2004-11-01,4.8 104 | 2004-10-01,4.8 105 | 2004-09-01,4.9 106 | 2004-08-01,4.9 107 | 2004-07-01,5.0 108 | 2004-06-01,5.0 109 | 2004-05-01,5.1 110 | 2004-04-01,5.1 111 | 2004-03-01,5.2 112 | 2004-02-01,5.3 113 | 2004-01-01,5.3 114 | 2003-12-01,5.4 115 | 2003-11-01,5.5 116 | 2003-10-01,5.6 117 | 2003-09-01,5.7 118 | 2003-08-01,5.8 119 | 2003-07-01,5.8 120 | 2003-06-01,5.8 121 | 2003-05-01,5.8 122 | 2003-04-01,5.8 123 | 2003-03-01,5.8 124 | 2003-02-01,5.7 125 | 2003-01-01,5.6 126 | 2002-12-01,5.5 127 | 2002-11-01,5.4 128 | 2002-10-01,5.4 129 | 2002-09-01,5.3 130 | 2002-08-01,5.3 131 | 2002-07-01,5.3 132 | 2002-06-01,5.3 133 | 2002-05-01,5.4 134 | 2002-04-01,5.4 135 | 2002-03-01,5.5 136 | 2002-02-01,5.5 137 | 2002-01-01,5.4 138 | 2001-12-01,5.3 139 | 2001-11-01,5.1 140 | 2001-10-01,4.9 141 | 2001-09-01,4.8 142 | 2001-08-01,4.6 143 | 2001-07-01,4.5 144 | 2001-06-01,4.5 145 | 2001-05-01,4.4 146 | 2001-04-01,4.3 147 | 2001-03-01,4.2 148 | 2001-02-01,4.1 149 | 2001-01-01,3.9 150 | 2000-12-01,3.8 151 | 2000-11-01,3.7 152 | 2000-10-01,3.7 153 | 2000-09-01,3.7 154 | 2000-08-01,3.7 155 | 2000-07-01,3.7 156 | 2000-06-01,3.6 157 | 2000-05-01,3.5 158 | 2000-04-01,3.4 159 | 2000-03-01,3.3 160 | 2000-02-01,3.2 161 | 2000-01-01,3.2 162 | 1999-12-01,3.2 163 | 1999-11-01,3.2 164 | 1999-10-01,3.1 165 | 1999-09-01,3.1 166 | 1999-08-01,3.1 167 | 1999-07-01,3.0 168 | 1999-06-01,3.0 169 | 1999-05-01,3.0 170 | 1999-04-01,3.1 171 | 1999-03-01,3.1 172 | 1999-02-01,3.2 173 | 1999-01-01,3.4 174 | 1998-12-01,3.5 175 | 1998-11-01,3.6 176 | 1998-10-01,3.6 177 | 1998-09-01,3.6 178 | 1998-08-01,3.5 179 | 1998-07-01,3.4 180 | 1998-06-01,3.3 181 | 1998-05-01,3.2 182 | 1998-04-01,3.1 183 | 1998-03-01,3.1 184 | 1998-02-01,3.2 185 | 1998-01-01,3.2 186 | 1997-12-01,3.3 187 | 1997-11-01,3.4 188 | 1997-10-01,3.4 189 | 1997-09-01,3.5 190 | 1997-08-01,3.5 191 | 1997-07-01,3.6 192 | 1997-06-01,3.6 193 | 1997-05-01,3.6 194 | 1997-04-01,3.7 195 | 1997-03-01,3.7 196 | 1997-02-01,3.7 197 | 1997-01-01,3.6 198 | 1996-12-01,3.6 199 | 1996-11-01,3.5 200 | 1996-10-01,3.5 201 | 1996-09-01,3.5 202 | 1996-08-01,3.5 203 | 1996-07-01,3.5 204 | 1996-06-01,3.6 205 | 1996-05-01,3.7 206 | 1996-04-01,3.7 207 | 1996-03-01,3.8 208 | 1996-02-01,3.8 209 | 1996-01-01,3.8 210 | 1995-12-01,3.8 211 | 1995-11-01,3.8 212 | 1995-10-01,3.8 213 | 1995-09-01,3.7 214 | 1995-08-01,3.7 215 | 1995-07-01,3.7 216 | 1995-06-01,3.7 217 | 1995-05-01,3.7 218 | 1995-04-01,3.8 219 | 1995-03-01,3.8 220 | 1995-02-01,3.8 221 | 1995-01-01,3.8 222 | 1994-12-01,3.9 223 | 1994-11-01,4.1 224 | 1994-10-01,4.2 225 | 1994-09-01,4.3 226 | 1994-08-01,4.4 227 | 1994-07-01,4.5 228 | 1994-06-01,4.5 229 | 1994-05-01,4.6 230 | 1994-04-01,4.7 231 | 1994-03-01,4.8 232 | 1994-02-01,4.9 233 | 1994-01-01,4.9 234 | 1993-12-01,4.9 235 | 1993-11-01,4.8 236 | 1993-10-01,4.7 237 | 1993-09-01,4.7 238 | 1993-08-01,4.8 239 | 1993-07-01,4.8 240 | 1993-06-01,4.9 241 | 1993-05-01,4.9 242 | 1993-04-01,4.8 243 | 1993-03-01,4.7 244 | 1993-02-01,4.7 245 | 1993-01-01,4.6 246 | 1992-12-01,4.7 247 | 1992-11-01,4.8 248 | 1992-10-01,5.0 249 | 1992-09-01,5.2 250 | 1992-08-01,5.3 251 | 1992-07-01,5.3 252 | 1992-06-01,5.3 253 | 1992-05-01,5.2 254 | 1992-04-01,5.1 255 | 1992-03-01,5.1 256 | 1992-02-01,5.1 257 | 1992-01-01,5.2 258 | 1991-12-01,5.3 259 | 1991-11-01,5.3 260 | 1991-10-01,5.4 261 | 1991-09-01,5.4 262 | 1991-08-01,5.4 263 | 1991-07-01,5.4 264 | 1991-06-01,5.5 265 | 1991-05-01,5.6 266 | 1991-04-01,5.7 267 | 1991-03-01,5.7 268 | 1991-02-01,5.6 269 | 1991-01-01,5.4 270 | 1990-12-01,5.1 271 | 1990-11-01,4.9 272 | 1990-10-01,4.7 273 | 1990-09-01,4.5 274 | 1990-08-01,4.4 275 | 1990-07-01,4.2 276 | 1990-06-01,4.1 277 | 1990-05-01,4.1 278 | 1990-04-01,4.1 279 | 1990-03-01,4.1 280 | 1990-02-01,4.2 281 | 1990-01-01,4.2 282 | 1989-12-01,4.3 283 | 1989-11-01,4.3 284 | 1989-10-01,4.3 285 | 1989-09-01,4.4 286 | 1989-08-01,4.4 287 | 1989-07-01,4.4 288 | 1989-06-01,4.4 289 | 1989-05-01,4.4 290 | 1989-04-01,4.4 291 | 1989-03-01,4.3 292 | 1989-02-01,4.2 293 | 1989-01-01,4.2 294 | 1988-12-01,4.2 295 | 1988-11-01,4.2 296 | 1988-10-01,4.2 297 | 1988-09-01,4.2 298 | 1988-08-01,4.2 299 | 1988-07-01,4.3 300 | 1988-06-01,4.3 301 | 1988-05-01,4.4 302 | 1988-04-01,4.5 303 | 1988-03-01,4.6 304 | 1988-02-01,4.8 305 | 1988-01-01,5.0 306 | 1987-12-01,5.2 307 | 1987-11-01,5.4 308 | 1987-10-01,5.6 309 | 1987-09-01,5.7 310 | 1987-08-01,5.7 311 | 1987-07-01,5.8 312 | 1987-06-01,5.9 313 | 1987-05-01,6.0 314 | 1987-04-01,6.1 315 | 1987-03-01,6.3 316 | 1987-02-01,6.4 317 | 1987-01-01,6.5 318 | 1986-12-01,6.6 319 | 1986-11-01,6.7 320 | 1986-10-01,6.8 321 | 1986-09-01,6.8 322 | 1986-08-01,6.9 323 | 1986-07-01,7.0 324 | 1986-06-01,7.1 325 | 1986-05-01,7.1 326 | 1986-04-01,7.2 327 | 1986-03-01,7.2 328 | 1986-02-01,7.1 329 | 1986-01-01,7.1 330 | 1985-12-01,7.2 331 | 1985-11-01,7.2 332 | 1985-10-01,7.2 333 | 1985-09-01,7.2 334 | 1985-08-01,7.2 335 | 1985-07-01,7.2 336 | 1985-06-01,7.2 337 | 1985-05-01,7.2 338 | 1985-04-01,7.2 339 | 1985-03-01,7.1 340 | 1985-02-01,7.1 341 | 1985-01-01,7.0 342 | 1984-12-01,6.9 343 | 1984-11-01,6.9 344 | 1984-10-01,7.0 345 | 1984-09-01,7.0 346 | 1984-08-01,7.2 347 | 1984-07-01,7.3 348 | 1984-06-01,7.4 349 | 1984-05-01,7.5 350 | 1984-04-01,7.6 351 | 1984-03-01,7.7 352 | 1984-02-01,7.9 353 | 1984-01-01,8.2 354 | 1983-12-01,8.5 355 | 1983-11-01,8.8 356 | 1983-10-01,9.2 357 | 1983-09-01,9.5 358 | 1983-08-01,9.8 359 | 1983-07-01,10.0 360 | 1983-06-01,10.3 361 | 1983-05-01,10.6 362 | 1983-04-01,11.0 363 | 1983-03-01,11.4 364 | 1983-02-01,11.7 365 | 1983-01-01,11.9 366 | 1982-12-01,11.8 367 | 1982-11-01,11.6 368 | 1982-10-01,11.4 369 | 1982-09-01,11.1 370 | 1982-08-01,10.9 371 | 1982-07-01,10.7 372 | 1982-06-01,10.5 373 | 1982-05-01,10.3 374 | 1982-04-01,10.0 375 | 1982-03-01,9.7 376 | 1982-02-01,9.4 377 | 1982-01-01,9.1 378 | 1981-12-01,8.8 379 | 1981-11-01,8.5 380 | 1981-10-01,8.3 381 | 1981-09-01,8.0 382 | 1981-08-01,7.8 383 | 1981-07-01,7.7 384 | 1981-06-01,7.6 385 | 1981-05-01,7.6 386 | 1981-04-01,7.7 387 | 1981-03-01,7.9 388 | 1981-02-01,8.0 389 | 1981-01-01,8.1 390 | 1980-12-01,8.3 391 | 1980-11-01,8.4 392 | 1980-10-01,8.6 393 | 1980-09-01,8.7 394 | 1980-08-01,8.5 395 | 1980-07-01,7.1 396 | 1980-06-01,7.1 397 | 1980-05-01,6.8 398 | 1980-04-01,6.5 399 | 1980-03-01,6.0 400 | 1980-02-01,5.5 401 | 1980-01-01,5.2 402 | 1979-12-01,4.9 403 | 1979-11-01,4.7 404 | 1979-10-01,4.6 405 | 1979-09-01,4.5 406 | 1979-08-01,4.5 407 | 1979-07-01,4.4 408 | 1979-06-01,4.4 409 | 1979-05-01,4.4 410 | 1979-04-01,4.5 411 | 1979-03-01,4.6 412 | 1979-02-01,4.7 413 | 1979-01-01,4.9 414 | 1978-12-01,5.0 415 | 1978-11-01,5.1 416 | 1978-10-01,5.1 417 | 1978-09-01,5.1 418 | 1978-08-01,5.0 419 | 1978-07-01,4.9 420 | 1978-06-01,4.9 421 | 1978-05-01,4.9 422 | 1978-04-01,4.9 423 | 1978-03-01,4.9 424 | 1978-02-01,4.9 425 | 1978-01-01,4.9 426 | 1977-12-01,5.0 427 | 1977-11-01,5.0 428 | 1977-10-01,5.0 429 | 1977-09-01,5.0 430 | 1977-08-01,5.0 431 | 1977-07-01,5.0 432 | 1977-06-01,5.0 433 | 1977-05-01,5.1 434 | 1977-04-01,5.2 435 | 1977-03-01,5.3 436 | 1977-02-01,5.4 437 | 1977-01-01,5.4 438 | 1976-12-01,5.5 439 | 1976-11-01,5.4 440 | 1976-10-01,5.4 441 | 1976-09-01,5.4 442 | 1976-08-01,5.4 443 | 1976-07-01,5.4 444 | 1976-06-01,5.4 445 | 1976-05-01,5.5 446 | 1976-04-01,5.6 447 | 1976-03-01,5.7 448 | 1976-02-01,5.9 449 | 1976-01-01,6.0 450 | -------------------------------------------------------------------------------- /Chapter 14/Case Study 3 - Forecast of Electricity Consumption.r: -------------------------------------------------------------------------------- 1 | R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" 2 | Copyright (C) 2016 The R Foundation for Statistical Computing 3 | Platform: x86_64-w64-mingw32/x64 (64-bit) 4 | 5 | R is free software and comes with ABSOLUTELY NO WARRANTY. 6 | You are welcome to redistribute it under certain conditions. 7 | Type 'license()' or 'licence()' for distribution details. 8 | 9 | R is a collaborative project with many contributors. 10 | Type 'contributors()' for more information and 11 | 'citation()' on how to cite R or R packages in publications. 12 | 13 | Type 'demo()' for some demos, 'help()' for on-line help, or 14 | 'help.start()' for an HTML browser interface to help. 15 | Type 'q()' to quit R. 16 | 17 | [Workspace loaded from ~/.RData] 18 | 19 | install.packages("feather") 20 | install.packages("data.table") 21 | install.packages("ggplot2") 22 | install.packages("plotly") 23 | install.packages("animation") 24 | 25 | library(feather) 26 | library(data.table) 27 | library(ggplot2) 28 | library(plotly) 29 | library(animation) 30 | 31 | AggData <- as.data.table(read_feather("d:/DT_4_ind")) 32 | 33 | 34 | str(AggData) 35 | 36 | 37 | 38 | head(AggData) 39 | 40 | 41 | 42 | ggplot(data = AggData, aes(x = date, y = value)) + 43 | geom_line() + 44 | facet_grid(type ~ ., scales = "free_y") + 45 | theme(panel.border = element_blank(), 46 | panel.background = element_blank(), 47 | panel.grid.minor = element_line(colour = "grey90"), 48 | panel.grid.major = element_line(colour = "green"), 49 | panel.grid.major.x = element_line(colour = "red"), 50 | axis.text = element_text(size = 10), 51 | axis.title = element_text(size = 12, face = "bold"), 52 | strip.text = element_text(size = 9, face = "bold")) + 53 | labs(title = "Electricity Consumption - Industry", x = "Date", y = "Load (kW)") 54 | 55 | 56 | 57 | AggData 58 | 59 | 60 | AggData[, week_num := as.integer(as.factor(AggData[, week]))] 61 | 62 | 63 | AggData 64 | 65 | 66 | n_type <- unique(AggData[, type]) 67 | 68 | n_type 69 | 70 | 71 | n_date <- unique(AggData[, date]) 72 | 73 | 74 | n_weekdays <- unique(AggData[, week]) 75 | 76 | period <- 48 77 | 78 | data_reg <- AggData[(type == n_type[2] & date %in% n_date[57:70])] 79 | 80 | data_reg 81 | 82 | 83 | ggplot(data_reg, aes(date_time, value)) + 84 | geom_line() + 85 | theme(panel.border = element_blank(), 86 | panel.background = element_blank(), 87 | panel.grid.minor = element_line(colour = "grey90"), 88 | panel.grid.major = element_line(colour = "green"), 89 | panel.grid.major.x = element_line(colour = "red"), 90 | axis.text = element_text(size = 10), 91 | axis.title = element_text(size = 12, face = "bold")) + 92 | labs(title = "Regression Analysis - Education Buildings", x = "Date", y = "Load (kW)") 93 | 94 | 95 | N <- nrow(data_reg) 96 | 97 | trainset_window <- N / period 98 | 99 | matrix_train <- data.table(Load = data_reg[, value], Daily = as.factor(rep(1:period, trainset_window)), Weekly = as.factor(data_reg[, week_num])) 100 | 101 | matrix_train 102 | 103 | linear_model_1 <- lm(Load ~ 0 + ., data = matrix_train) 104 | 105 | linear_model_1 106 | 107 | summary_1 <- summary(linear_model_1) 108 | 109 | summary_1 110 | 111 | paste("R-squared: ", round(summary_1$r.squared, 3), ", p-value of F test: ", 1-pf(summary_1$fstatistic[1], summary_1$fstatistic[2], summary_1$fstatistic[3])) 112 | 113 | 114 | 115 | 116 | datas <- rbindlist(list(data_reg[, .(value, date_time)], data.table(value = linear_model_1$fitted.values, data_time = data_reg[, date_time]))) 117 | 118 | datas 119 | 120 | 121 | datas[, type := rep(c("Real", "Fitted"), each = nrow(data_reg))] 122 | 123 | datas 124 | 125 | 126 | ggplot(data = datas, aes(date_time, value, group = type, colour = type)) + geom_line(size = 0.8) + theme_bw() + 127 | labs(x = "Time", y = "Load (kW)", title = "Fit from Multiple Linear Regression") 128 | 129 | ggplot(data = data.table(Fitted_values = linear_model_1$fitted.values, 130 | Residuals = linear_model_1$residuals), 131 | aes(Fitted_values, Residuals)) + 132 | geom_point(size = 1.7) + 133 | geom_smooth() + 134 | geom_hline(yintercept = 0, color = "red", size = 1) + 135 | labs(title = "Fitted values vs Residuals") 136 | 137 | 138 | ggQQ <- function(lm){ 139 | d <- data.frame(std.resid = rstandard(lm)) 140 | y <- quantile(d$std.resid[!is.na(d$std.resid)], c(0.25, 0.75)) 141 | x <- qnorm(c(0.25, 0.75)) 142 | slope <- diff(y)/diff(x) 143 | int <- y[1L] - slope * x[1L] 144 | 145 | p <- ggplot(data = d, aes(sample = std.resid)) + 146 | stat_qq(shape = 1, size = 3) + 147 | labs(title = "Normal Q-Q", 148 | x = "Theoretical Quantiles", 149 | y = "Standardized Residuals") + 150 | geom_abline(slope = slope, intercept = int, linetype = "dashed", 151 | size = 1, col = "firebrick1") 152 | return(p) 153 | } 154 | 155 | 156 | ggQQ(linear_model_1) 157 | 158 | linear_model_2 <- lm(Load ~ 0 + Daily + Weekly + Daily:Weekly, data = matrix_train) 159 | 160 | linear_model_2 161 | 162 | 163 | c(Previous = summary(linear_model_1)$r.squared, New = summary(linear_model_2)$r.squared) 164 | 165 | 166 | ggplot(data.table(Residuals = c(linear_model_1$residuals, linear_model_2$residuals), Type = c(rep("Multiple Linear Reg - simple", nrow(data_reg)), rep("Multiple Linear Reg with interactions", nrow(data_reg)))), aes(Type, Residuals, fill = Type)) + geom_boxplot() 167 | 168 | 169 | ggplotly() 170 | 171 | datas <- rbindlist(list(data_reg[, .(value, date_time)], data.table(value = linear_model_2$fitted.values, data_time = data_reg[, date_time]))) 172 | 173 | datas 174 | 175 | 176 | datas[, type := rep(c("Real", "Fitted"), each = nrow(data_reg))] 177 | 178 | datas 179 | 180 | ggplot(data = datas, aes(date_time, value, group = type, colour = type)) + geom_line(size = 0.8) + theme_bw() + labs(x = "Time", y = "Load (kW)", title = "Fit from Multiple Linear Reg") 181 | 182 | 183 | ggplot(data = data.table(Fitted_values = linear_model_2$fitted.values, Residuals = linear_model_2$residuals), aes(Fitted_values, Residuals)) + geom_point(size = 1.7) + geom_hline(yintercept = 0, color = "red", size = 1) + 184 | labs(title = "Fitted values vs Residuals") 185 | 186 | ggQQ(linear_model_2) 187 | 188 | 189 | predWeekReg <- function(data, set_of_date){ 190 | data_train <- data[date %in% set_of_date] 191 | 192 | N <- nrow(data_train) 193 | window <- N / period # number of days in the train set 194 | matrix_train <- data.table(Load = data_train[, value], 195 | Daily = as.factor(rep(1:period, window)), 196 | Weekly = as.factor(data_train[, week_num])) 197 | lm_m <- lm(Load ~ 0 + Daily + Weekly + Daily:Weekly, data = matrix_train) 198 | 199 | pred_week <- predict(lm_m, matrix_train[1:(7*period), -1, with = FALSE]) 200 | 201 | return(as.vector(pred_week)) 202 | } 203 | 204 | 205 | mape <- function(real, pred){ 206 | return(100 * mean(abs((real - pred)/real))) 207 | } 208 | 209 | 210 | 211 | n_weeks <- floor(length(n_date)/7) - 2 212 | 213 | n_weeks 214 | 215 | 216 | 217 | lm_pred_weeks_1 <- sapply(0:(n_weeks-1), function(i) 218 | predWeekReg(AggData[type == n_type[1]], n_date[((i*7)+1):((i*7)+7*2)])) 219 | 220 | 221 | lm_pred_weeks_2 <- sapply(0:(n_weeks-1), function(i) 222 | predWeekReg(AggData[type == n_type[2]], n_date[((i*7)+1):((i*7)+7*2)])) 223 | 224 | 225 | lm_pred_weeks_3 <- sapply(0:(n_weeks-1), function(i) 226 | predWeekReg(AggData[type == n_type[3]], n_date[((i*7)+1):((i*7)+7*2)])) 227 | 228 | 229 | lm_pred_weeks_4 <- sapply(0:(n_weeks-1), function(i) 230 | predWeekReg(AggData[type == n_type[4]], n_date[((i*7)+1):((i*7)+7*2)])) 231 | 232 | 233 | lm_err_mape_1 <- sapply(0:(n_weeks-1), function(i) 234 | mape(AggData[(type == n_type[1] & date %in% n_date[(15+(i*7)):(21+(i*7))]), value], 235 | lm_pred_weeks_1[, i+1])) 236 | 237 | 238 | lm_err_mape_1 239 | 240 | lm_err_mape_2 <- sapply(0:(n_weeks-1), function(i) 241 | mape(AggData[(type == n_type[2] & date %in% n_date[(15+(i*7)):(21+(i*7))]), value], 242 | lm_pred_weeks_2[, i+1])) 243 | 244 | lm_err_mape_2 245 | 246 | 247 | lm_err_mape_3 <- sapply(0:(n_weeks-1), function(i) 248 | mape(AggData[(type == n_type[3] & date %in% n_date[(15+(i*7)):(21+(i*7))]), value], 249 | lm_pred_weeks_3[, i+1])) 250 | 251 | lm_err_mape_3 252 | 253 | lm_err_mape_4 <- sapply(0:(n_weeks-1), function(i) 254 | mape(AggData[(type == n_type[4] & date %in% n_date[(15+(i*7)):(21+(i*7))]), value], 255 | lm_pred_weeks_4[, i+1])) 256 | 257 | lm_err_mape_4 258 | 259 | 260 | datas <- data.table(value = c(as.vector(lm_pred_weeks_1), 261 | AggData[(type == n_type[1]) & (date %in% n_date[-c(1:14,365)]), value]), 262 | date_time = c(rep(AggData[-c(1:(14*48), (17473:nrow(AggData))), date_time], 2)), 263 | type = c(rep("MLR", nrow(lm_pred_weeks_1)*ncol(lm_pred_weeks_1)), 264 | rep("Real", nrow(lm_pred_weeks_1)*ncol(lm_pred_weeks_1))), 265 | week = c(rep(1:50, each = 336), rep(1:50, each = 336))) 266 | 267 | 268 | 269 | 270 | saveGIF({ 271 | oopt = ani.options(interval = 0.9, nmax = 50) 272 | for(i in 1:ani.options("nmax")){ 273 | print(ggplot(data = datas[week == i], aes(date_time, value, group = type, colour = type)) + 274 | geom_line(size = 0.8) + 275 | scale_y_continuous(limits = c(min(datas[, value]), max(datas[, value]))) + 276 | theme(panel.border = element_blank(), panel.background = element_blank(), 277 | panel.grid.minor = element_line(colour = "grey90"), 278 | panel.grid.major = element_line(colour = "grey90"), 279 | panel.grid.major.x = element_line(colour = "grey90"), 280 | title = element_text(size = 15), 281 | axis.text = element_text(size = 10), 282 | axis.title = element_text(size = 12, face = "bold")) + 283 | labs(x = "Time", y = "Load (kW)", 284 | title = paste("Forecast of MLR (", n_type[1], "); ", "week: ", i, "; MAPE: ", 285 | round(lm_err_mape_1[i], 2), "%", sep = ""))) 286 | ani.pause() 287 | }}, movie.name = "industry_1.gif", ani.height = 450, ani.width = 750) -------------------------------------------------------------------------------- /Chapter 03/Data/WBClust2013.csv: -------------------------------------------------------------------------------- 1 | Country,new.forest,Rural,log.CO2,log.GNI,log.Energy.2011,LifeExp,Fertility,InfMort,log.Exports,log.Imports,CellPhone,RuralWater,Pop 2 | China,-5.929374602,46.832,1.839733042,8.651724084,7.615477213,75.1995122,1.663,10.9,3.350966008,3.259900191,88.70833462,84.9,1357380000 3 | India,-2.735634368,68.006,0.568835582,7.34601021,6.419536925,66.21085366,2.505,41.4,3.17248544,3.408213415,70.78318167,90.7,1252139596 4 | United States,-1.688899022,18.723,2.871537728,10.86570722,8.858292522,78.74146341,1.8805,5.9,2.604612908,2.84419291,95.52954726,98,316128839 5 | Indonesia,4.636429459,47.748,0.6435402,8.13739583,6.753775337,70.6072439,2.37,24.5,3.271910744,3.216864756,121.5434067,76.4,249865631 6 | Brazil,3.222813308,14.829,0.81104934,9.362202721,7.223404787,73.61787805,1.811,12.3,2.475627706,2.535484629,135.3050481,85.3,200361925 7 | Pakistan,6.053448784,62.14,0.031613484,7.13089883,6.177147466,66.43587805,3.264,69,2.636212664,2.942568239,70.13037598,89,182142594 8 | Nigeria,7.231399689,53.906,-0.520722812,7.807916629,6.580144764,52.10902439,6.002,74.3,3.444569888,3.066391075,73.29196161,49.1,173615345 9 | Bangladesh,1.95669311,67.247,-0.751700659,6.721425701,5.321644762,70.29485366,2.208,33.2,3.131174391,3.453487433,67.08453366,84.4,156594962 10 | Russian Federation,-0.179277519,26.149,2.511694234,9.452501929,8.539587423,70.46097561,1.59,8.6,3.410135198,3.078918034,152.8353524,92.2,143499861 11 | Japan,-0.432173052,7.509,2.228470291,10.77247701,8.191565931,83.09609756,1.41,2.1,2.716517908,2.774735635,115.1885934,100,127338621 12 | Mexico,2.872378525,21.309,1.351592205,9.181940897,7.352266494,77.13507317,2.216,12.5,3.441960374,3.481173501,85.83640136,90.8,122332399 13 | Philippines,-4.281921929,55.367,-0.027218803,7.989560449,6.053432903,68.55373171,3.076,23.5,3.464773594,3.572818237,104.5023215,91.2,98393574 14 | Ethiopia,5.311115459,81.41,-1.745458287,6.01615716,5.942948958,62.96595122,4.642,44.4,2.833157626,3.469950184,27.25468539,42.1,94100756 15 | Vietnam,-7.101588109,67.691,0.603287261,7.34601021,6.546547749,75.60668293,1.768,19,4.374383052,4.425129792,130.8904335,93.6,89708900 16 | "Egypt, Arab Rep.",-7.862453931,56.975,1.001657573,7.999678579,6.88554715,70.9072439,2.807,18.6,3.023708641,3.206325662,121.507893,98.8,82056378 17 | Germany,-1.766038005,25.11,2.220815392,10.71818843,8.245770767,80.89268293,1.38,3.2,3.924572763,3.816259065,119.0306009,100,80621788 18 | Turkey,-4.420556788,27.63,1.442445644,9.288226911,7.339078748,74.86243902,2.06,16.5,3.177080078,3.485767145,92.96497103,98.8,74932641 19 | Thailand,1.673362851,52.057,1.514435908,8.565983356,7.489767958,74.18797561,1.412,11.3,4.343071632,4.28230261,137.9828493,95.3,67010502 20 | France,-3.226131523,20.945,1.73272852,10.642086,8.260800196,82.56585366,2.01,3.5,3.291951557,3.396475544,98.49547248,100,66028467 21 | United Kingdom,-3.300360344,17.908,2.074751655,10.55320518,7.997354088,81.5,1.9,3.9,3.467897284,3.514009229,123.7698841,100,64097085 22 | Italy,-4.753474563,31.314,1.939365189,10.45765998,7.944286271,82.93658537,1.4,3,3.3615716,3.408910595,158.8835332,100,59831093 23 | South Africa,0,36.212,2.21271853,8.917310693,7.916023279,56.09831707,2.412,32.8,3.421193457,3.406965992,147.4645166,88.3,52981991 24 | "Korea, Rep.",1.590789818,17.751,2.449866985,10.11212642,8.562524185,81.36829268,1.297,3.2,4.020832108,3.993650513,110.9984678,87.9,50219669 25 | Tanzania,4.62441641,69.804,-1.377414,6.345636361,6.103834615,60.84643902,5.287,36.4,3.43583762,3.916313924,55.71793149,44,49253126 26 | Colombia,1.885237266,24.117,0.547804309,8.85509298,6.509500831,73.77707317,2.316,14.5,2.930452491,2.992364745,104.0845439,73.6,48321405 27 | Spain,-5.836325814,20.871,1.773236362,10.28670705,7.895964563,82.37804878,1.32,3.6,3.428904749,3.462392333,106.9052178,100,46647421 28 | Ukraine,-2.282128106,30.726,1.908781792,8.199738961,7.92527176,70.94414634,1.531,8.6,3.995830396,4.104093527,138.0646549,97.7,45489600 29 | Kenya,2.663225936,75.22,-0.906880984,6.768493212,6.174061246,61.08317073,4.459,47.5,3.350727323,3.808683742,70.58942851,55.1,44353691 30 | Algeria,3.399071824,30.49,1.233000936,8.511175119,7.010566012,70.88217073,2.82,21.6,3.656764718,3.358265995,102.0107812,79.5,39208194 31 | Sudan,10,66.54,-0.889506147,7.286191715,5.872339646,61.86395122,4.491,51.2,2.866595849,2.74376606,72.85231291,50.2,37964306 32 | Canada,0,18.528,2.693155429,10.83269451,8.900178041,81.23804878,1.61,4.6,3.424879915,3.464530426,78.39781723,99,35158304 33 | Iraq,-1.616149838,30.738,1.335903996,8.720950029,7.14391003,69.24192683,4.086,28,3.76483461,3.29457351,96.10477428,68.5,33417476 34 | Morocco,-1.421338109,40.8,0.530265511,7.97590836,6.289868653,70.64312195,2.706,26.1,3.571652109,3.885385772,128.5252097,63.6,33008150 35 | Peru,1.874079206,22.046,0.726416441,8.681011277,6.543904875,74.51553659,2.448,12.9,3.389643637,3.242063257,98.08298127,71.6,30375603 36 | Uzbekistan,-2.704372182,63.783,1.323535003,7.43838353,7.394906704,68.104,2.53,36.7,3.498969714,3.44183483,74.30678167,80.9,30241100 37 | Malaysia,3.058832023,26.716,2.049944156,9.192176401,7.878318976,74.84229268,1.975,7.2,4.5175056,4.319877314,144.6850309,98.5,29716965 38 | Saudi Arabia,0,17.281,2.841409863,10.11293778,8.815580129,75.49690244,2.702,13.4,4.028814483,3.386780262,176.4978024,97,28828870 39 | Nepal,4.951498202,82.123,-1.427649513,6.551080335,5.947085623,67.98270732,2.389,32.2,2.186504031,3.494208685,71.46285,87.6,27797457 40 | Ghana,6.06401582,47.265,-0.753135077,7.365180126,6.052172677,60.94712195,3.92,52.3,3.786865769,3.905954439,108.1911482,81.3,25904598 41 | Mozambique,3.319133077,68.331,-1.512954434,6.234410726,6.028359065,49.83626829,5.263,61.5,3.427117769,4.102937082,48.00421557,35,25833752 42 | Australia,2.135839951,10.847,2.835195569,10.9985937,8.612647033,82.09512195,1.927,3.4,3.05100135,3.002864811,106.8434974,100,23130900 43 | Cameroon,4.461455992,46.75,-0.796734565,7.081708586,5.760866249,54.5875122,4.859,60.8,3.38393443,3.473099603,70.3904685,51.9,22253959 44 | Angola,2.121969647,57.51,0.5043843,8.414052432,6.511357758,51.464,5.979,101.6,4.174032974,3.768313569,61.87329711,34.3,21471618 45 | Sri Lanka,4.700384774,81.7,-0.334915767,7.97590836,6.21328392,74.06804878,2.346,8.2,3.137885442,3.627225265,95.49632054,92.9,20483000 46 | Cote d'Ivoire,-1.327729042,47.234,-0.901658487,7.106606138,6.361823919,50.40173171,4.894,71.3,4.054932466,3.634853836,95.44605196,67.8,20316086 47 | Chile,-2.614350651,10.825,1.461661878,9.567315271,7.57029898,79.57265854,1.832,7.1,3.638230558,3.551478589,134.2782809,91.3,17619708 48 | Kazakhstan,1.90511324,46.639,2.73041606,9.188094763,8.458975784,69.61,2.59,14.6,3.901466767,3.323198525,180.5020819,86,17037508 49 | Netherlands,-2.407717062,10.729,2.403187149,10.78124533,8.441988283,81.10487805,1.72,3.3,4.429212809,4.321228728,113.7299037,100,16804224 50 | Ecuador,5.609161336,36.702,0.822242809,8.550627968,6.743854336,76.19256098,2.59,19.1,3.451303806,3.538436942,111.4620027,75.2,15737878 51 | Guatemala,5.034422369,49.339,-0.133242755,8.048788284,6.538262585,71.66385366,3.844,25.8,3.281866431,3.620567904,140.393535,88.6,15468203 52 | Cambodia,4.897591548,79.681,-0.939014768,6.779921907,5.90028732,71.40882927,2.894,32.5,3.990478409,4.085994592,133.8902393,65.6,15135169 53 | Zambia,2.634704284,59.973,-1.259946258,7.251344983,6.43078853,57.02258537,5.731,55.8,3.829700204,3.611910261,71.50463179,49.2,14538640 54 | Zimbabwe,5.697195807,67.346,-0.197292168,6.684611728,6.5468933,58.04597561,3.564,55,3.757931516,4.353266577,96.3498668,68.7,14149648 55 | Senegal,3.196562198,56.921,-0.438395115,6.937314081,5.574629633,63.20217073,4.981,43.9,3.225526038,3.730177095,92.92798275,60.3,14133280 56 | Belgium,-0.65223171,2.224,2.310210309,10.71018661,8.584660157,80.38536585,1.79,3.5,4.442465493,4.432656894,110.9031799,100,11195138 57 | Greece,-4.487698318,22.657,2.063683065,10.0719637,7.784234934,80.63414634,1.34,3.7,3.221070705,3.500906635,116.8206478,99.4,11032328 58 | Tunisia,-7.845712839,33.544,0.937309116,8.352318548,6.791647597,75.1,2.17,13.1,3.899300587,4.033103188,115.6035799,90.5,10886500 59 | Bolivia,3.14579256,32.304,0.483542083,7.705262475,6.614935656,66.92663415,3.264,31.2,3.786876556,3.648367932,97.69945273,71.9,10671200 60 | Czech Republic,-1.103264632,26.94,2.376674692,9.805323304,8.32788531,78.07560976,1.45,2.9,4.288957918,4.230372041,131.2534239,99.6,10521468 61 | Portugal,-2.026277627,37.662,1.619842272,9.934016758,7.690060059,80.37317073,1.28,3.1,3.574632428,3.690155767,113.035602,99.9,10459806 62 | Dominican Republic,0,22.918,0.785228718,8.599694413,6.589548632,73.23297561,2.517,23.6,3.22071992,3.565713716,88.43350015,77.2,10403761 63 | Benin,4.750318602,56.914,-0.437532943,6.620073207,5.952102492,59.1194878,4.927,56.2,2.706843759,3.294401447,93.25782193,69.1,10323474 64 | Haiti,3.782901641,43.832,-1.157816532,6.633318433,5.768448855,62.70265854,3.212,54.7,2.597975432,3.992060125,69.39885695,47.5,10317461 65 | Hungary,-3.698822539,29.694,1.640597019,9.429475902,7.82542454,75.06341463,1.34,5.2,4.543275192,4.460179918,116.4278724,100,9897247 66 | Sweden,-1.838380132,14.486,1.740421303,10.93524753,8.554554297,81.70487805,1.91,2.4,3.909128179,3.790550341,124.3952501,100,9592552 67 | Belarus,-3.452952241,24.123,1.895601277,8.764053269,8.0437322,72.06341463,1.62,3.7,4.396002026,4.419677139,118.786176,99,9466000 68 | Azerbaijan,0,45.9,1.639142113,8.74671635,7.222071643,70.62495122,2,29.9,4.032947422,3.181425825,107.6134072,70.7,9416598 69 | United Arab Emirates,-5.511398208,15.019,2.993419487,10.56152556,8.910182683,76.95787805,1.82,7,4.503306079,4.28053198,171.8737993,100,9346129 70 | Austria,-1.790097526,34.116,2.088605428,10.77812261,8.275864605,80.93658537,1.44,3.2,4.048318047,3.994698209,156.2304116,100,8473786 71 | Tajikistan,-0.700140042,73.379,-0.744436956,6.779921907,5.725201254,67.25717073,3.819,40.9,2.882112659,4.207266166,91.8281242,64,8207834 72 | Honduras,6.255774127,46.463,0.151724357,7.668561108,6.412618747,73.49343902,3.05,18.9,3.936864994,4.262104691,95.91916853,81.5,8097688 73 | Switzerland,-2.920909371,26.213,1.619975771,11.30158696,8.073079874,82.69756098,1.52,3.6,3.936759386,3.71046252,133.79826,100,8081482 74 | Israel,-4.045199175,7.988,2.237302864,10.37442824,8.004515131,81.70487805,3.04,3.2,3.56958338,3.583483237,122.8478352,100,8059400 75 | Bulgaria,-4.620884102,26.7,1.815029748,8.832003931,7.869032869,74.31463415,1.5,10.1,4.19715621,4.197481553,145.1876887,99,7265115 76 | Serbia,-4.625162855,44.627,1.856857806,8.58485184,7.713059061,75.23414634,1.32,5.8,3.599265979,3.969914259,119.3917104,98.9,7163976 77 | Togo,7.809782374,61.021,-1.066435013,6.194405391,6.056903482,56.15034146,4.695,55.8,3.674645665,4.031228887,62.53490181,40.3,6816982 78 | Paraguay,4.311126401,40.826,-0.121425016,8.107720062,6.605306221,72.19373171,2.897,18.7,3.963456083,3.913950214,103.6893284,83.4,6802295 79 | Jordan,0,16.789,1.26520012,8.446770727,7.041326875,73.74739024,3.314,16,3.86398806,4.302160013,141.7962745,90.5,6459000 80 | El Salvador,5.119267189,34.225,0.099740838,8.188689124,6.537127246,72.10453659,2.209,13.5,3.331491174,3.842956045,136.1874402,81,6340454 81 | Nicaragua,5.840897474,41.854,-0.12671037,7.432483808,6.243199302,74.46539024,2.541,20,3.690407785,4.021385091,111.9801766,67.8,6080478 82 | -------------------------------------------------------------------------------- /Chapter 03/Data/math test.txt: -------------------------------------------------------------------------------- 1 | 1 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 2 | 2 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 3 | 3 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 4 | 4 1 1 1 1 0 1 1 0 0 1 1 1 1 1 0 1 0 1 1 1 1 1 0 0 1 0 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 5 | 5 1 0 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 0 0 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 6 | 6 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 7 | 7 0 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 8 | 8 0 0 1 1 0 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 9 | 9 0 0 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 0 1 1 0 0 0 1 1 0 0 1 1 1 1 0 1 1 1 10 | 10 0 0 0 1 0 1 1 0 0 1 1 0 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 11 | 11 1 0 1 1 0 1 1 1 0 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 0 0 1 1 12 | 12 1 1 1 1 0 1 1 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 13 | 13 0 0 1 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1 1 0 0 0 1 0 0 1 0 1 1 0 1 0 1 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 1 14 | 14 1 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 1 15 | 15 0 1 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 16 | 16 0 0 1 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 0 1 0 1 0 0 1 0 1 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 17 | 17 0 1 1 0 1 1 1 1 0 1 1 1 1 0 0 0 1 1 0 1 1 1 0 0 0 1 1 1 1 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 18 | 18 0 1 0 1 0 1 1 0 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0 1 1 1 19 | 19 0 1 1 1 0 1 1 0 1 1 1 1 0 1 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 1 20 | 20 1 0 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 1 1 0 0 1 0 0 0 0 1 0 21 | 21 1 0 1 1 0 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 1 1 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 1 1 1 22 | 22 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 1 0 1 0 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 1 23 | 23 0 1 1 0 0 1 1 0 1 1 1 0 1 1 0 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 1 0 0 1 1 0 0 1 1 0 0 1 1 1 0 1 0 1 1 1 24 | 24 0 1 0 1 1 1 1 0 0 1 1 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 0 0 1 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