├── .gitignore ├── LICENSE ├── README.md ├── RRegrs-package-tutorial.pdf ├── RRegrs ├── CITATION ├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── R │ └── RRegrs_Functions.R ├── inst │ ├── CITATION │ ├── doc │ │ └── tutorial.pdf │ ├── extdata │ │ ├── chemClassic.tsv │ │ ├── ds.House.csv │ │ └── ds.gajewicz.csv │ └── models │ │ ├── glmnetModel.RData │ │ └── model.svmRadialReg.RData ├── man │ ├── AppendList2CSv.Rd │ ├── AppendList2txt.Rd │ ├── DsSplit.Rd │ ├── ENETreg.Rd │ ├── GLMreg.Rd │ ├── LASSOreg.Rd │ ├── LMreg.Rd │ ├── NNreg.Rd │ ├── PLSreg.Rd │ ├── RFRFEreg.Rd │ ├── RFreg.Rd │ ├── RRegrs.Rd │ ├── RemCorrs.Rd │ ├── RemNear0VarCols.Rd │ ├── SVMRFEreg.Rd │ └── ScalingDS.Rd └── tests │ ├── testthat.R │ └── testthat │ ├── test-helperMethods.R │ └── test-simpleCall.R ├── TEST ├── NOTES.txt ├── TestRRegrs.R ├── ds │ ├── ds.CASP.csv │ ├── ds.House.csv │ ├── ds.MachineCPU.csv │ ├── ds.ParkinsonMotor.csv │ ├── ds.abalone.csv │ ├── ds.automobile.csv │ ├── ds.gajewicz.csv │ ├── ds.pcorona.csv │ ├── ds.toxicity.csv │ └── ds.winequality-red.csv ├── glmnetModel.RData └── model.svmRadialReg.RData └── install.deps.R /.gitignore: -------------------------------------------------------------------------------- 1 | RRegrs_*.tar.gz 2 | RRegrs.Rcheck/ 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright (c) 2014, muntisa 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without 5 | modification, are permitted provided that the following conditions are met: 6 | 7 | * Redistributions of source code must retain the above copyright notice, this 8 | list of conditions and the following disclaimer. 9 | 10 | * Redistributions in binary form must reproduce the above copyright notice, 11 | this list of conditions and the following disclaimer in the documentation 12 | and/or other materials provided with the distribution. 13 | 14 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 15 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 16 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 17 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 18 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 19 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 20 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 21 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 22 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 23 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 24 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | RRegrs 2 | ====== 3 | 4 | eNanoMapper Developers | [eNanoMapper Project] (http://www.enanomapper.net/) 5 | 6 | 7 | The current tool is a collection of regression tools from R that could be used to search the best regression models for any dataset. The initial use of the script is aimed at finding QSAR models for chemoinformatics / nanotoxicology. 8 | 9 | The full R script will contain: Loading dataset, Filter dataset, Scaling dataset, Feature selection, Regression models, Summary with top models, Statistics of the best model, etc. 10 | 11 | The script will be modular in order to create flexible APIs. 12 | 13 | The main authors are from the National Technical University of Athens (NTUA, Greece), Maastricht University (UM, Netherlands) and University of A Coruna (Spain). 14 | 15 | Outputs: 16 | - CSV files for statistics 17 | - PDF files for plots 18 | 19 | Releases 20 | -------- 21 | * Version 0.0.5 [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.32580.svg)](http://dx.doi.org/10.5281/zenodo.32580) 22 | * Bug in function ScalingDS: normalization 23 | * Incomplete option in function ScalingDS: standarization 24 | * Bug in plot 4, function SVMRFEreg 25 | * Bug in function RFRFEreg, removed abs() from resids calculation 26 | * Bug in function LASSOreg, removed abs() from resids calculation 27 | * Version 0.0.4 [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.21946.svg)](http://dx.doi.org/10.5281/zenodo.21946) 28 | * adds testing using the testthat package 29 | * removed the dependency on doMC so that it should install on Windows now (where doMC is not available) 30 | * added the tutorial PDF so that it is part of the distribution now 31 | * allows absolute paths now to point to the data 32 | * adds additional parameter value checking 33 | * Version 0.0.3 [![DOI](https://zenodo.org/badge/6059/egonw/RRegrs.svg)](http://dx.doi.org/10.5281/zenodo.16446) 34 | * Completed the full author list 35 | * Version 0.0.2: initial R package 36 | 37 | Install 38 | ------- 39 | 40 | > install.packages(c("caret", "corrplot", "data.table")) # dependencies 41 | > install.packages("testthat") # if you want to test the package 42 | > install.packages("devtools") # to install from GitHub 43 | > library(devtools) 44 | > install_github("enanomapper/RRegrs", subdir="RRegrs") 45 | 46 | If you have errors you should try two extra parameters for *install.packages* such as *dependencies=TRUE,repos='http://cran.us.r-project.org'*. 47 | 48 | Compile from source 49 | ------------------- 50 | 51 | $ R CMD build RRegrs 52 | $ R CMD check --as-cran RRegrs_0.0.4.tar.gz 53 | 54 | Notes for Windows 55 | ------------------- 56 | 57 | Before installing RRegrs: 58 | * Update R to 3.2.2 59 | * Install Rtools with the default configuration options from [https://cran.r-project.org/bin/windows/Rtools/] (https://cran.r-project.org/bin/windows/Rtools/). 60 | 61 | 62 | Notes for Linux 63 | ------------------- 64 | 65 | Before calling your RRegrs() you should load doMC library: library(doMC). 66 | 67 | 68 | Caret version for RRegrs 69 | ---------------------------- 70 | 71 | The new version of caret is introducing some modifications that generates problems with some RRegrs methods. 72 | Therefore, RRegrs is working well with caret version 6.0.52: 73 | 74 | * please check caret version and report to this list 75 | 76 | ```> packageVersion('caret')``` 77 | 78 | * remove this version and the bug 79 | 80 | ```> remove.packages(c('caret'))``` 81 | 82 | * install Caret Package version 6.0.52, without the bug 83 | 84 | ```> library(devtools)``` 85 | 86 | ```> install_version(package='caret',version='6.0-52')``` 87 | 88 | 89 | Help 90 | ------------------- 91 | 92 | For questions, suggestions, problems or praise please use the Issues tab here or subscribe to [rregrs] (https://groups.google.com/forum/#!forum/rregrs) Google group. 93 | -------------------------------------------------------------------------------- /RRegrs-package-tutorial.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/enanomapper/RRegrs/e5194f812e949be7694023ede0e62e27264a13b1/RRegrs-package-tutorial.pdf -------------------------------------------------------------------------------- /RRegrs/CITATION: -------------------------------------------------------------------------------- 1 | bibentry(bibtype = "Article", 2 | title = "RRegrs: an R package for computer-aided model selection with multiple regression models", 3 | author = c(person("Georgia", "Tsiliki"), 4 | person("Cristian", "Munteanu"), 5 | person("Jose", "Fernandez-Lozano"), 6 | person("Carlos", "Bates"), 7 | person("Haralambos", "Sarimveis"), 8 | person("Egon", "Willighagen")) 9 | ), 10 | year = 2015, 11 | journal = "Journal of Cheminformatics", 12 | url = "https://jcheminf.springeropen.com/articles/10.1186/s13321-015-0094-2") 13 | -------------------------------------------------------------------------------- /RRegrs/DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: RRegrs 2 | Type: Package 3 | Title: RRegrs - regression model builder 4 | Version: 0.0.5 5 | Date: 2015-10-23 6 | Author: Cristian R Munteanu , Georgia Tsiliki , 7 | Jose A. Seoane , Carlos Fernandez-Lozano , 8 | Haralambos Sarimveis , 9 | Egon Willighagen 10 | Maintainer: Egon Willighagen 11 | Depends: R (>= 3.1.0), caret, corrplot, data.table, kernlab, pls, randomForest, RSNNS, 12 | doSNOW, parallel, elasticnet, glmnet 13 | Suggests: testthat 14 | Description: Use this package to make regression models using a wide 15 | variety of modeling and validation methods. 16 | License: BSD_2_clause + file LICENSE | MIT + file LICENSE 17 | BugReports: https://github.com/enanomapper/RRegrs/issues 18 | MailingList: https://groups.google.com/forum/#!forum/rregrs 19 | LazyLoad: yes 20 | -------------------------------------------------------------------------------- /RRegrs/LICENSE: -------------------------------------------------------------------------------- 1 | YEAR: 2014-2016 2 | COPYRIGHT HOLDER: Cristian R Munteanu, Georgia Tsiliki, et al. 3 | -------------------------------------------------------------------------------- /RRegrs/NAMESPACE: -------------------------------------------------------------------------------- 1 | import(caret) 2 | import(parallel) 3 | import(pls) 4 | import(corrplot) 5 | import(data.table) 6 | import(kernlab) 7 | import(pls) 8 | import(randomForest) 9 | import(RSNNS) 10 | import(doSNOW) 11 | import(parallel) 12 | import(elasticnet) 13 | import(glmnet) 14 | import(data.table) 15 | 16 | export( 17 | RRegrs, 18 | AppendList2CSv, 19 | AppendList2txt, 20 | DsSplit, 21 | ENETreg, 22 | GLMreg, 23 | LASSOreg, 24 | LMreg, 25 | NNreg, 26 | PLSreg, 27 | RemCorrs, 28 | RemNear0VarCols, 29 | RFreg, 30 | RFRFEreg, 31 | ScalingDS, 32 | SVMRFEreg 33 | ) 34 | -------------------------------------------------------------------------------- /RRegrs/inst/CITATION: -------------------------------------------------------------------------------- 1 | bibentry( 2 | bibtype = "Article", 3 | title = "{RRegrs}: an {R} package for computer-aided model selection with multiple regression models", 4 | author = c( 5 | person("Georgia", "Tsiliki"), 6 | person("Cristian", "Munteanu"), 7 | person("Jose", "Seoane"), 8 | person("Carlos", "Fernandez-Lozano"), 9 | person("Haralambos", "Sarimveis"), 10 | person("Egon", "Willighagen") 11 | ), 12 | volume = 7, 13 | day = 15, 14 | month = "sep", 15 | year = 2015, 16 | journal = "Journal of Cheminformatics", 17 | number = 1, 18 | pages = "46", 19 | url = "http://dx.doi.org/10.1186/s13321-015-0094-2" 20 | ) 21 | -------------------------------------------------------------------------------- /RRegrs/inst/doc/tutorial.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/enanomapper/RRegrs/e5194f812e949be7694023ede0e62e27264a13b1/RRegrs/inst/doc/tutorial.pdf -------------------------------------------------------------------------------- /RRegrs/inst/extdata/chemClassic.tsv: -------------------------------------------------------------------------------- 1 | name boiling point w0 p0 SMILES 2 | n-Butane -0.5 10 1 CCCC 3 | n-Pentane 36.1 20 2 CCCCC 4 | n-Hexane 68.7 35 3 CCCCCC 5 | n-Heptane 98.4 56 4 CCCCCCC 6 | n-Octane 125.7 84 5 CCCCCCCC 7 | n-Nonane 150.8 120 6 CCCCCCCCC 8 | n-Decane 174.0 165 7 CCCCCCCCCC 9 | n-Undecane 195.8 220 8 CCCCCCCCCCC 10 | n-Dodecane 216.2 286 9 CCCCCCCCCCCC 11 | -------------------------------------------------------------------------------- /RRegrs/inst/extdata/ds.House.csv: -------------------------------------------------------------------------------- 1 | MEDV,CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT 2 | 24,0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98 3 | 21.6,0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14 4 | 34.7,0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03 5 | 33.4,0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94 6 | 36.2,0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33 7 | 28.7,0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21 8 | 22.9,0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43 9 | 27.1,0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15 10 | 16.5,0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93 11 | 18.9,0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1 12 | 15,0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45 13 | 18.9,0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27 14 | 21.7,0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71 15 | 20.4,0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26 16 | 18.2,0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26 17 | 19.9,0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47 18 | 23.1,1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58 19 | 17.5,0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67 20 | 20.2,0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69 21 | 18.2,0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28 22 | 13.6,1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02 23 | 19.6,0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83 24 | 15.2,1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72 25 | 14.5,0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,394.54,19.88 26 | 15.6,0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3 27 | 13.9,0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51 28 | 16.6,0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,376.88,14.81 29 | 14.8,0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28 30 | 18.4,0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8 31 | 21,1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,380.23,11.98 32 | 12.7,1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,360.17,22.6 33 | 14.5,1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,376.73,13.04 34 | 13.2,1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,232.6,27.71 35 | 13.1,1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,358.77,18.35 36 | 13.5,1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34 37 | 18.9,0.06417,0,5.96,0,0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68 38 | 20,0.09744,0,5.96,0,0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41 39 | 21,0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77 40 | 24.7,0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13 41 | 30.8,0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32 42 | 34.9,0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98 43 | 26.6,0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84 44 | 25.3,0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81 45 | 24.7,0.15936,0,6.91,0,0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44 46 | 21.2,0.12269,0,6.91,0,0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55 47 | 19.3,0.17142,0,6.91,0,0.448,5.682,33.8,5.1004,3,233,17.9,396.9,10.21 48 | 20,0.18836,0,6.91,0,0.448,5.786,33.3,5.1004,3,233,17.9,396.9,14.15 49 | 16.6,0.22927,0,6.91,0,0.448,6.03,85.5,5.6894,3,233,17.9,392.74,18.8 50 | 14.4,0.25387,0,6.91,0,0.448,5.399,95.3,5.87,3,233,17.9,396.9,30.81 51 | 19.4,0.21977,0,6.91,0,0.448,5.602,62,6.0877,3,233,17.9,396.9,16.2 52 | 19.7,0.08873,21,5.64,0,0.439,5.963,45.7,6.8147,4,243,16.8,395.56,13.45 53 | 20.5,0.04337,21,5.64,0,0.439,6.115,63,6.8147,4,243,16.8,393.97,9.43 54 | 25,0.0536,21,5.64,0,0.439,6.511,21.1,6.8147,4,243,16.8,396.9,5.28 55 | 23.4,0.04981,21,5.64,0,0.439,5.998,21.4,6.8147,4,243,16.8,396.9,8.43 56 | 18.9,0.0136,75,4,0,0.41,5.888,47.6,7.3197,3,469,21.1,396.9,14.8 57 | 35.4,0.01311,90,1.22,0,0.403,7.249,21.9,8.6966,5,226,17.9,395.93,4.81 58 | 24.7,0.02055,85,0.74,0,0.41,6.383,35.7,9.1876,2,313,17.3,396.9,5.77 59 | 31.6,0.01432,100,1.32,0,0.411,6.816,40.5,8.3248,5,256,15.1,392.9,3.95 60 | 23.3,0.15445,25,5.13,0,0.453,6.145,29.2,7.8148,8,284,19.7,390.68,6.86 61 | 19.6,0.10328,25,5.13,0,0.453,5.927,47.2,6.932,8,284,19.7,396.9,9.22 62 | 18.7,0.14932,25,5.13,0,0.453,5.741,66.2,7.2254,8,284,19.7,395.11,13.15 63 | 16,0.17171,25,5.13,0,0.453,5.966,93.4,6.8185,8,284,19.7,378.08,14.44 64 | 22.2,0.11027,25,5.13,0,0.453,6.456,67.8,7.2255,8,284,19.7,396.9,6.73 65 | 25,0.1265,25,5.13,0,0.453,6.762,43.4,7.9809,8,284,19.7,395.58,9.5 66 | 33,0.01951,17.5,1.38,0,0.4161,7.104,59.5,9.2229,3,216,18.6,393.24,8.05 67 | 23.5,0.03584,80,3.37,0,0.398,6.29,17.8,6.6115,4,337,16.1,396.9,4.67 68 | 19.4,0.04379,80,3.37,0,0.398,5.787,31.1,6.6115,4,337,16.1,396.9,10.24 69 | 22,0.05789,12.5,6.07,0,0.409,5.878,21.4,6.498,4,345,18.9,396.21,8.1 70 | 17.4,0.13554,12.5,6.07,0,0.409,5.594,36.8,6.498,4,345,18.9,396.9,13.09 71 | 20.9,0.12816,12.5,6.07,0,0.409,5.885,33,6.498,4,345,18.9,396.9,8.79 72 | 24.2,0.08826,0,10.81,0,0.413,6.417,6.6,5.2873,4,305,19.2,383.73,6.72 73 | 21.7,0.15876,0,10.81,0,0.413,5.961,17.5,5.2873,4,305,19.2,376.94,9.88 74 | 22.8,0.09164,0,10.81,0,0.413,6.065,7.8,5.2873,4,305,19.2,390.91,5.52 75 | 23.4,0.19539,0,10.81,0,0.413,6.245,6.2,5.2873,4,305,19.2,377.17,7.54 76 | 24.1,0.07896,0,12.83,0,0.437,6.273,6,4.2515,5,398,18.7,394.92,6.78 77 | 21.4,0.09512,0,12.83,0,0.437,6.286,45,4.5026,5,398,18.7,383.23,8.94 78 | 20,0.10153,0,12.83,0,0.437,6.279,74.5,4.0522,5,398,18.7,373.66,11.97 79 | 20.8,0.08707,0,12.83,0,0.437,6.14,45.8,4.0905,5,398,18.7,386.96,10.27 80 | 21.2,0.05646,0,12.83,0,0.437,6.232,53.7,5.0141,5,398,18.7,386.4,12.34 81 | 20.3,0.08387,0,12.83,0,0.437,5.874,36.6,4.5026,5,398,18.7,396.06,9.1 82 | 28,0.04113,25,4.86,0,0.426,6.727,33.5,5.4007,4,281,19,396.9,5.29 83 | 23.9,0.04462,25,4.86,0,0.426,6.619,70.4,5.4007,4,281,19,395.63,7.22 84 | 24.8,0.03659,25,4.86,0,0.426,6.302,32.2,5.4007,4,281,19,396.9,6.72 85 | 22.9,0.03551,25,4.86,0,0.426,6.167,46.7,5.4007,4,281,19,390.64,7.51 86 | 23.9,0.05059,0,4.49,0,0.449,6.389,48,4.7794,3,247,18.5,396.9,9.62 87 | 26.6,0.05735,0,4.49,0,0.449,6.63,56.1,4.4377,3,247,18.5,392.3,6.53 88 | 22.5,0.05188,0,4.49,0,0.449,6.015,45.1,4.4272,3,247,18.5,395.99,12.86 89 | 22.2,0.07151,0,4.49,0,0.449,6.121,56.8,3.7476,3,247,18.5,395.15,8.44 90 | 23.6,0.0566,0,3.41,0,0.489,7.007,86.3,3.4217,2,270,17.8,396.9,5.5 91 | 28.7,0.05302,0,3.41,0,0.489,7.079,63.1,3.4145,2,270,17.8,396.06,5.7 92 | 22.6,0.04684,0,3.41,0,0.489,6.417,66.1,3.0923,2,270,17.8,392.18,8.81 93 | 22,0.03932,0,3.41,0,0.489,6.405,73.9,3.0921,2,270,17.8,393.55,8.2 94 | 22.9,0.04203,28,15.04,0,0.464,6.442,53.6,3.6659,4,270,18.2,395.01,8.16 95 | 25,0.02875,28,15.04,0,0.464,6.211,28.9,3.6659,4,270,18.2,396.33,6.21 96 | 20.6,0.04294,28,15.04,0,0.464,6.249,77.3,3.615,4,270,18.2,396.9,10.59 97 | 28.4,0.12204,0,2.89,0,0.445,6.625,57.8,3.4952,2,276,18,357.98,6.65 98 | 21.4,0.11504,0,2.89,0,0.445,6.163,69.6,3.4952,2,276,18,391.83,11.34 99 | 38.7,0.12083,0,2.89,0,0.445,8.069,76,3.4952,2,276,18,396.9,4.21 100 | 43.8,0.08187,0,2.89,0,0.445,7.82,36.9,3.4952,2,276,18,393.53,3.57 101 | 33.2,0.0686,0,2.89,0,0.445,7.416,62.5,3.4952,2,276,18,396.9,6.19 102 | 27.5,0.14866,0,8.56,0,0.52,6.727,79.9,2.7778,5,384,20.9,394.76,9.42 103 | 26.5,0.11432,0,8.56,0,0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67 104 | 18.6,0.22876,0,8.56,0,0.52,6.405,85.4,2.7147,5,384,20.9,70.8,10.63 105 | 19.3,0.21161,0,8.56,0,0.52,6.137,87.4,2.7147,5,384,20.9,394.47,13.44 106 | 20.1,0.1396,0,8.56,0,0.52,6.167,90,2.421,5,384,20.9,392.69,12.33 107 | 19.5,0.13262,0,8.56,0,0.52,5.851,96.7,2.1069,5,384,20.9,394.05,16.47 108 | 19.5,0.1712,0,8.56,0,0.52,5.836,91.9,2.211,5,384,20.9,395.67,18.66 109 | 20.4,0.13117,0,8.56,0,0.52,6.127,85.2,2.1224,5,384,20.9,387.69,14.09 110 | 19.8,0.12802,0,8.56,0,0.52,6.474,97.1,2.4329,5,384,20.9,395.24,12.27 111 | 19.4,0.26363,0,8.56,0,0.52,6.229,91.2,2.5451,5,384,20.9,391.23,15.55 112 | 21.7,0.10793,0,8.56,0,0.52,6.195,54.4,2.7778,5,384,20.9,393.49,13 113 | 22.8,0.10084,0,10.01,0,0.547,6.715,81.6,2.6775,6,432,17.8,395.59,10.16 114 | 18.8,0.12329,0,10.01,0,0.547,5.913,92.9,2.3534,6,432,17.8,394.95,16.21 115 | 18.7,0.22212,0,10.01,0,0.547,6.092,95.4,2.548,6,432,17.8,396.9,17.09 116 | 18.5,0.14231,0,10.01,0,0.547,6.254,84.2,2.2565,6,432,17.8,388.74,10.45 117 | 18.3,0.17134,0,10.01,0,0.547,5.928,88.2,2.4631,6,432,17.8,344.91,15.76 118 | 21.2,0.13158,0,10.01,0,0.547,6.176,72.5,2.7301,6,432,17.8,393.3,12.04 119 | 19.2,0.15098,0,10.01,0,0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3 120 | 20.4,0.13058,0,10.01,0,0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37 121 | 19.3,0.14476,0,10.01,0,0.547,5.731,65.2,2.7592,6,432,17.8,391.5,13.61 122 | 22,0.06899,0,25.65,0,0.581,5.87,69.7,2.2577,2,188,19.1,389.15,14.37 123 | 20.3,0.07165,0,25.65,0,0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27 124 | 20.5,0.09299,0,25.65,0,0.581,5.961,92.9,2.0869,2,188,19.1,378.09,17.93 125 | 17.3,0.15038,0,25.65,0,0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41 126 | 18.8,0.09849,0,25.65,0,0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58 127 | 21.4,0.16902,0,25.65,0,0.581,5.986,88.4,1.9929,2,188,19.1,385.02,14.81 128 | 15.7,0.38735,0,25.65,0,0.581,5.613,95.6,1.7572,2,188,19.1,359.29,27.26 129 | 16.2,0.25915,0,21.89,0,0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19 130 | 18,0.32543,0,21.89,0,0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39 131 | 14.3,0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34 132 | 19.2,0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6 133 | 19.6,1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26 134 | 23,0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12 135 | 18.4,0.32982,0,21.89,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03 136 | 15.6,0.97617,0,21.89,0,0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31 137 | 18.1,0.55778,0,21.89,0,0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96 138 | 17.4,0.32264,0,21.89,0,0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9 139 | 17.1,0.35233,0,21.89,0,0.624,6.454,98.4,1.8498,4,437,21.2,394.08,14.59 140 | 13.3,0.2498,0,21.89,0,0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32 141 | 17.8,0.54452,0,21.89,0,0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46 142 | 14,0.2909,0,21.89,0,0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16 143 | 14.4,1.62864,0,21.89,0,0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41 144 | 13.4,3.32105,0,19.58,1,0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82 145 | 15.6,4.0974,0,19.58,0,0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42 146 | 11.8,2.77974,0,19.58,0,0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29 147 | 13.8,2.37934,0,19.58,0,0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8 148 | 15.6,2.15505,0,19.58,0,0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65 149 | 14.6,2.36862,0,19.58,0,0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53 150 | 17.8,2.33099,0,19.58,0,0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32 151 | 15.4,2.73397,0,19.58,0,0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45 152 | 21.5,1.6566,0,19.58,0,0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1 153 | 19.6,1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28 154 | 15.3,1.12658,0,19.58,1,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12 155 | 19.4,2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79 156 | 17,1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12 157 | 15.6,3.53501,0,19.58,1,0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02 158 | 13.1,2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14 159 | 41.3,1.22358,0,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59 160 | 24.3,1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43 161 | 23.3,1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39 162 | 27,1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5 163 | 50,1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73 164 | 50,1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92 165 | 50,1.51902,0,19.58,1,0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32 166 | 22.7,2.24236,0,19.58,0,0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64 167 | 25,2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81 168 | 50,2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7 169 | 23.8,1.80028,0,19.58,0,0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14 170 | 23.8,2.3004,0,19.58,0,0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1 171 | 22.3,2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32 172 | 17.4,1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43 173 | 19.1,2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03 174 | 23.1,0.13914,0,4.05,0,0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69 175 | 23.6,0.09178,0,4.05,0,0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04 176 | 22.6,0.08447,0,4.05,0,0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64 177 | 29.4,0.06664,0,4.05,0,0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33 178 | 23.2,0.07022,0,4.05,0,0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11 179 | 24.6,0.05425,0,4.05,0,0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29 180 | 29.9,0.06642,0,4.05,0,0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92 181 | 37.2,0.0578,0,2.46,0,0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04 182 | 39.8,0.06588,0,2.46,0,0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56 183 | 36.2,0.06888,0,2.46,0,0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45 184 | 37.9,0.09103,0,2.46,0,0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82 185 | 32.5,0.10008,0,2.46,0,0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68 186 | 26.4,0.08308,0,2.46,0,0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98 187 | 29.6,0.06047,0,2.46,0,0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15 188 | 50,0.05602,0,2.46,0,0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45 189 | 32,0.07875,45,3.44,0,0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68 190 | 29.8,0.12579,45,3.44,0,0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56 191 | 34.9,0.0837,45,3.44,0,0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39 192 | 37,0.09068,45,3.44,0,0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1 193 | 30.5,0.06911,45,3.44,0,0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69 194 | 36.4,0.08664,45,3.44,0,0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87 195 | 31.1,0.02187,60,2.93,0,0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03 196 | 29.1,0.01439,60,2.93,0,0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38 197 | 50,0.01381,80,0.46,0,0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97 198 | 33.3,0.04011,80,1.52,0,0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08 199 | 30.3,0.04666,80,1.52,0,0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61 200 | 34.6,0.03768,80,1.52,0,0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62 201 | 34.9,0.0315,95,1.47,0,0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56 202 | 32.9,0.01778,95,1.47,0,0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45 203 | 24.1,0.03445,82.5,2.03,0,0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43 204 | 42.3,0.02177,82.5,2.03,0,0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11 205 | 48.5,0.0351,95,2.68,0,0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81 206 | 50,0.02009,95,2.68,0,0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88 207 | 22.6,0.13642,0,10.59,0,0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87 208 | 24.4,0.22969,0,10.59,0,0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97 209 | 22.5,0.25199,0,10.59,0,0.489,5.783,72.7,4.3549,4,277,18.6,389.43,18.06 210 | 24.4,0.13587,0,10.59,1,0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66 211 | 20,0.43571,0,10.59,1,0.489,5.344,100,3.875,4,277,18.6,396.9,23.09 212 | 21.7,0.17446,0,10.59,1,0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27 213 | 19.3,0.37578,0,10.59,1,0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98 214 | 22.4,0.21719,0,10.59,1,0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03 215 | 28.1,0.14052,0,10.59,0,0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38 216 | 23.7,0.28955,0,10.59,0,0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55 217 | 25,0.19802,0,10.59,0,0.489,6.182,42.4,3.9454,4,277,18.6,393.63,9.47 218 | 23.3,0.0456,0,13.89,1,0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51 219 | 28.7,0.07013,0,13.89,0,0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69 220 | 21.5,0.11069,0,13.89,1,0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92 221 | 23,0.11425,0,13.89,1,0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5 222 | 26.7,0.35809,0,6.2,1,0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71 223 | 21.7,0.40771,0,6.2,1,0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46 224 | 27.5,0.62356,0,6.2,1,0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93 225 | 30.1,0.6147,0,6.2,0,0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6 226 | 44.8,0.31533,0,6.2,0,0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14 227 | 50,0.52693,0,6.2,0,0.504,8.725,83,2.8944,8,307,17.4,382,4.63 228 | 37.6,0.38214,0,6.2,0,0.504,8.04,86.5,3.2157,8,307,17.4,387.38,3.13 229 | 31.6,0.41238,0,6.2,0,0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36 230 | 46.7,0.29819,0,6.2,0,0.504,7.686,17,3.3751,8,307,17.4,377.51,3.92 231 | 31.5,0.44178,0,6.2,0,0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76 232 | 24.3,0.537,0,6.2,0,0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65 233 | 31.7,0.46296,0,6.2,0,0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25 234 | 41.7,0.57529,0,6.2,0,0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47 235 | 48.3,0.33147,0,6.2,0,0.507,8.247,70.4,3.6519,8,307,17.4,378.95,3.95 236 | 29,0.44791,0,6.2,1,0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05 237 | 24,0.33045,0,6.2,0,0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88 238 | 25.1,0.52058,0,6.2,1,0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54 239 | 31.5,0.51183,0,6.2,0,0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73 240 | 23.7,0.08244,30,4.93,0,0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36 241 | 23.3,0.09252,30,4.93,0,0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37 242 | 22,0.11329,30,4.93,0,0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38 243 | 20.1,0.10612,30,4.93,0,0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4 244 | 22.2,0.1029,30,4.93,0,0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22 245 | 23.7,0.12757,30,4.93,0,0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19 246 | 17.6,0.20608,22,5.86,0,0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5 247 | 18.5,0.19133,22,5.86,0,0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46 248 | 24.3,0.33983,22,5.86,0,0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16 249 | 20.5,0.19657,22,5.86,0,0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15 250 | 24.5,0.16439,22,5.86,0,0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52 251 | 26.2,0.19073,22,5.86,0,0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56 252 | 24.4,0.1403,22,5.86,0,0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9 253 | 24.8,0.21409,22,5.86,0,0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59 254 | 29.6,0.08221,22,5.86,0,0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53 255 | 42.8,0.36894,22,5.86,0,0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54 256 | 21.9,0.04819,80,3.64,0,0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57 257 | 20.9,0.03548,80,3.64,0,0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25 258 | 44,0.01538,90,3.75,0,0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11 259 | 50,0.61154,20,3.97,0,0.647,8.704,86.9,1.801,5,264,13,389.7,5.12 260 | 36,0.66351,20,3.97,0,0.647,7.333,100,1.8946,5,264,13,383.29,7.79 261 | 30.1,0.65665,20,3.97,0,0.647,6.842,100,2.0107,5,264,13,391.93,6.9 262 | 33.8,0.54011,20,3.97,0,0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59 263 | 43.1,0.53412,20,3.97,0,0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26 264 | 48.8,0.52014,20,3.97,0,0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91 265 | 31,0.82526,20,3.97,0,0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25 266 | 36.5,0.55007,20,3.97,0,0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1 267 | 22.8,0.76162,20,3.97,0,0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45 268 | 30.7,0.7857,20,3.97,0,0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79 269 | 50,0.57834,20,3.97,0,0.575,8.297,67,2.4216,5,264,13,384.54,7.44 270 | 43.5,0.5405,20,3.97,0,0.575,7.47,52.6,2.872,5,264,13,390.3,3.16 271 | 20.7,0.09065,20,6.96,1,0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65 272 | 21.1,0.29916,20,6.96,0,0.464,5.856,42.1,4.429,3,223,18.6,388.65,13 273 | 25.2,0.16211,20,6.96,0,0.464,6.24,16.3,4.429,3,223,18.6,396.9,6.59 274 | 24.4,0.1146,20,6.96,0,0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73 275 | 35.2,0.22188,20,6.96,1,0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58 276 | 32.4,0.05644,40,6.41,1,0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53 277 | 32,0.09604,40,6.41,0,0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98 278 | 33.2,0.10469,40,6.41,1,0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05 279 | 33.1,0.06127,40,6.41,1,0.447,6.826,27.6,4.8628,4,254,17.6,393.45,4.16 280 | 29.1,0.07978,40,6.41,0,0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19 281 | 35.1,0.21038,20,3.33,0,0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85 282 | 45.4,0.03578,20,3.33,0,0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76 283 | 35.4,0.03705,20,3.33,0,0.4429,6.968,37.2,5.2447,5,216,14.9,392.23,4.59 284 | 46,0.06129,20,3.33,1,0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01 285 | 50,0.01501,90,1.21,1,0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16 286 | 32.2,0.00906,90,2.97,0,0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85 287 | 22,0.01096,55,2.25,0,0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23 288 | 20.1,0.01965,80,1.76,0,0.385,6.23,31.5,9.0892,1,241,18.2,341.6,12.93 289 | 23.2,0.03871,52.5,5.32,0,0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14 290 | 22.3,0.0459,52.5,5.32,0,0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6 291 | 24.8,0.04297,52.5,5.32,0,0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51 292 | 28.5,0.03502,80,4.95,0,0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33 293 | 37.3,0.07886,80,4.95,0,0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56 294 | 27.9,0.03615,80,4.95,0,0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7 295 | 23.9,0.08265,0,13.92,0,0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58 296 | 21.7,0.08199,0,13.92,0,0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4 297 | 28.6,0.12932,0,13.92,0,0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27 298 | 27.1,0.05372,0,13.92,0,0.437,6.549,51,5.9604,4,289,16,392.85,7.39 299 | 20.3,0.14103,0,13.92,0,0.437,5.79,58,6.32,4,289,16,396.9,15.84 300 | 22.5,0.06466,70,2.24,0,0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97 301 | 29,0.05561,70,2.24,0,0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74 302 | 24.8,0.04417,70,2.24,0,0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07 303 | 22,0.03537,34,6.09,0,0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5 304 | 26.4,0.09266,34,6.09,0,0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67 305 | 33.1,0.1,34,6.09,0,0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86 306 | 36.1,0.05515,33,2.18,0,0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93 307 | 28.4,0.05479,33,2.18,0,0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93 308 | 33.4,0.07503,33,2.18,0,0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47 309 | 28.2,0.04932,33,2.18,0,0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53 310 | 22.8,0.49298,0,9.9,0,0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54 311 | 20.3,0.3494,0,9.9,0,0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97 312 | 16.1,2.63548,0,9.9,0,0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64 313 | 22.1,0.79041,0,9.9,0,0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98 314 | 19.4,0.26169,0,9.9,0,0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72 315 | 21.6,0.26938,0,9.9,0,0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9 316 | 23.8,0.3692,0,9.9,0,0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28 317 | 16.2,0.25356,0,9.9,0,0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5 318 | 17.8,0.31827,0,9.9,0,0.544,5.914,83.2,3.9986,4,304,18.4,390.7,18.33 319 | 19.8,0.24522,0,9.9,0,0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94 320 | 23.1,0.40202,0,9.9,0,0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36 321 | 21,0.47547,0,9.9,0,0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73 322 | 23.8,0.1676,0,7.38,0,0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2 323 | 23.1,0.18159,0,7.38,0,0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87 324 | 20.4,0.35114,0,7.38,0,0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7 325 | 18.5,0.28392,0,7.38,0,0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74 326 | 25,0.34109,0,7.38,0,0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12 327 | 24.6,0.19186,0,7.38,0,0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08 328 | 23,0.30347,0,7.38,0,0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15 329 | 22.2,0.24103,0,7.38,0,0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79 330 | 19.3,0.06617,0,3.24,0,0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97 331 | 22.6,0.06724,0,3.24,0,0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34 332 | 19.8,0.04544,0,3.24,0,0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09 333 | 17.1,0.05023,35,6.06,0,0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43 334 | 19.4,0.03466,35,6.06,0,0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83 335 | 22.2,0.05083,0,5.19,0,0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68 336 | 20.7,0.03738,0,5.19,0,0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75 337 | 21.1,0.03961,0,5.19,0,0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01 338 | 19.5,0.03427,0,5.19,0,0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8 339 | 18.5,0.03041,0,5.19,0,0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56 340 | 20.6,0.03306,0,5.19,0,0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51 341 | 19,0.05497,0,5.19,0,0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74 342 | 18.7,0.06151,0,5.19,0,0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29 343 | 32.7,0.01301,35,1.52,0,0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49 344 | 16.5,0.02498,0,1.89,0,0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65 345 | 23.9,0.02543,55,3.78,0,0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18 346 | 31.2,0.03049,55,3.78,0,0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61 347 | 17.5,0.03113,0,4.39,0,0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53 348 | 17.2,0.06162,0,4.39,0,0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67 349 | 23.1,0.0187,85,4.15,0,0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36 350 | 24.5,0.01501,80,2.01,0,0.435,6.635,29.7,8.344,4,280,17,390.94,5.99 351 | 26.6,0.02899,40,1.25,0,0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89 352 | 22.9,0.06211,40,1.25,0,0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98 353 | 24.1,0.0795,60,1.69,0,0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49 354 | 18.6,0.07244,60,1.69,0,0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79 355 | 30.1,0.01709,90,2.02,0,0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5 356 | 18.2,0.04301,80,1.91,0,0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05 357 | 20.6,0.10659,80,1.91,0,0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57 358 | 17.8,8.98296,0,18.1,1,0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6 359 | 21.7,3.8497,0,18.1,1,0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27 360 | 22.7,5.20177,0,18.1,1,0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48 361 | 22.6,4.26131,0,18.1,0,0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67 362 | 25,4.54192,0,18.1,0,0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79 363 | 19.9,3.83684,0,18.1,0,0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19 364 | 20.8,3.67822,0,18.1,0,0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19 365 | 16.8,4.22239,0,18.1,1,0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64 366 | 21.9,3.47428,0,18.1,1,0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29 367 | 27.5,4.55587,0,18.1,0,0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12 368 | 21.9,3.69695,0,18.1,0,0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14 369 | 23.1,13.5222,0,18.1,0,0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33 370 | 50,4.89822,0,18.1,0,0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26 371 | 50,5.66998,0,18.1,1,0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73 372 | 50,6.53876,0,18.1,1,0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96 373 | 50,9.2323,0,18.1,0,0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53 374 | 50,8.26725,0,18.1,1,0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88 375 | 13.8,11.1081,0,18.1,0,0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77 376 | 13.8,18.4982,0,18.1,0,0.668,4.138,100,1.137,24,666,20.2,396.9,37.97 377 | 15,19.6091,0,18.1,0,0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44 378 | 13.9,15.288,0,18.1,0,0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24 379 | 13.3,9.82349,0,18.1,0,0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24 380 | 13.1,23.6482,0,18.1,0,0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69 381 | 10.2,17.8667,0,18.1,0,0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78 382 | 10.4,88.9762,0,18.1,0,0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21 383 | 10.9,15.8744,0,18.1,0,0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08 384 | 11.3,9.18702,0,18.1,0,0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6 385 | 12.3,7.99248,0,18.1,0,0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56 386 | 8.8,20.0849,0,18.1,0,0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63 387 | 7.2,16.8118,0,18.1,0,0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81 388 | 10.5,24.3938,0,18.1,0,0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28 389 | 7.4,22.5971,0,18.1,0,0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99 390 | 10.2,14.3337,0,18.1,0,0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62 391 | 11.5,8.15174,0,18.1,0,0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85 392 | 15.1,6.96215,0,18.1,0,0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11 393 | 23.2,5.29305,0,18.1,0,0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76 394 | 9.7,11.5779,0,18.1,0,0.7,5.036,97,1.77,24,666,20.2,396.9,25.68 395 | 13.8,8.64476,0,18.1,0,0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17 396 | 12.7,13.3598,0,18.1,0,0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35 397 | 13.1,8.71675,0,18.1,0,0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12 398 | 12.5,5.87205,0,18.1,0,0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37 399 | 8.5,7.67202,0,18.1,0,0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92 400 | 5,38.3518,0,18.1,0,0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59 401 | 6.3,9.91655,0,18.1,0,0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97 402 | 5.6,25.0461,0,18.1,0,0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77 403 | 7.2,14.2362,0,18.1,0,0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32 404 | 12.1,9.59571,0,18.1,0,0.693,6.404,100,1.639,24,666,20.2,376.11,20.31 405 | 8.3,24.8017,0,18.1,0,0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77 406 | 8.5,41.5292,0,18.1,0,0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38 407 | 5,67.9208,0,18.1,0,0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98 408 | 11.9,20.7162,0,18.1,0,0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34 409 | 27.9,11.9511,0,18.1,0,0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13 410 | 17.2,7.40389,0,18.1,0,0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4 411 | 27.5,14.4383,0,18.1,0,0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78 412 | 15,51.1358,0,18.1,0,0.597,5.757,100,1.413,24,666,20.2,2.6,10.11 413 | 17.2,14.0507,0,18.1,0,0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22 414 | 17.9,18.811,0,18.1,0,0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37 415 | 16.3,28.6558,0,18.1,0,0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08 416 | 7,45.7461,0,18.1,0,0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98 417 | 7.2,18.0846,0,18.1,0,0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05 418 | 7.5,10.8342,0,18.1,0,0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79 419 | 10.4,25.9406,0,18.1,0,0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64 420 | 8.8,73.5341,0,18.1,0,0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62 421 | 8.4,11.8123,0,18.1,0,0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74 422 | 16.7,11.0874,0,18.1,0,0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02 423 | 14.2,7.02259,0,18.1,0,0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7 424 | 20.8,12.0482,0,18.1,0,0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1 425 | 13.4,7.05042,0,18.1,0,0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29 426 | 11.7,8.79212,0,18.1,0,0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16 427 | 8.3,15.8603,0,18.1,0,0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39 428 | 10.2,12.2472,0,18.1,0,0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69 429 | 10.9,37.6619,0,18.1,0,0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52 430 | 11,7.36711,0,18.1,0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52 431 | 9.5,9.33889,0,18.1,0,0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08 432 | 14.5,8.49213,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64 433 | 14.1,10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69 434 | 16.1,6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03 435 | 14.3,5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22 436 | 11.7,13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17 437 | 13.4,11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27 438 | 9.6,14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05 439 | 8.7,15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45 440 | 8.4,13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02 441 | 12.8,9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88 442 | 10.5,22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11 443 | 17.1,9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52 444 | 18.4,5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59 445 | 15.4,9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85 446 | 10.8,12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79 447 | 11.8,10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98 448 | 14.9,6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79 449 | 12.6,9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44 450 | 14.1,9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13 451 | 13,7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31 452 | 13.4,6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44 453 | 15.2,5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73 454 | 16.1,5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27 455 | 17.8,8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74 456 | 14.9,9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71 457 | 14.1,4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13 458 | 12.7,4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01 459 | 13.5,8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94 460 | 14.9,7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23 461 | 20,6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7 462 | 16.4,4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42 463 | 17.7,3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65 464 | 19.5,6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99 465 | 20.2,5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29 466 | 21.4,7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22 467 | 19.9,3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13 468 | 19,3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15 469 | 19.1,4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32 470 | 19.1,15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13 471 | 20.1,13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76 472 | 19.9,4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29 473 | 19.6,4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87 474 | 23.2,3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36 475 | 29.8,4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66 476 | 13.8,8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14 477 | 13.3,6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1 478 | 16.7,4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68 479 | 12,15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91 480 | 14.6,10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03 481 | 21.4,14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11 482 | 23,5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74 483 | 23.7,5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74 484 | 25,5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01 485 | 21.8,2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42 486 | 20.6,2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34 487 | 21.2,3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58 488 | 19.1,5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98 489 | 20.6,4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45 490 | 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22.4,0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67 504 | 20.6,0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08 505 | 23.9,0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64 506 | 22,0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48 507 | 11.9,0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88 508 | -------------------------------------------------------------------------------- /RRegrs/inst/extdata/ds.gajewicz.csv: -------------------------------------------------------------------------------- 1 | net.c,DHcf,TotalEnergy,ElectronicEnergy,CoreCoreRepulsionEnergyCore,SolventAccessibleSurface,HOMO.HOMO,LUMO.LUMO,ChemicalHardness,TotalSoftness,HOMO.LUMO.EnergyGap,ElectronicChemicalPotential,ValanceBand,ConductionBand,MullikensElectronegativity,ParrPopleAbsoluteHardness,SchuurmannMO.shift.alpha,PolarizabilityAhof,Polarizability derived from the dipole moment 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2.67,-266.6,-3511,-17713,14202,359.32,-6.97,-2.18,-2.4,-0.21,-4.79,-4.57,-2.18,-6.97,4.57,2.4,-4.57,34.22,34.14,9.30E+08,1.68E+06,1.72E+04,148,1.09E-02,0.408,0.078,-6.23E+04,-4.28E+05,7.37E-05,4.10E-03,46.1,264.9,-10.5 14 | 1.76,-1492,-2782.9,-12685.1,9902.2,271.58,-7.08,-2.73,-2.18,-0.23,-4.36,-4.91,-2.73,-7.08,4.91,2.14,-4.33,49.54,24.94,9.20E+08,1.77E+06,1.71E+04,150,1.16E-02,0.408,0.078,9.79E+03,-3.98E+05,7.70E-05,3.70E-03,42.3,1307,-9.6 15 | 2.24,-139.5,-2168.1,-7623.9,5455.8,206.12,-5.81,-0.66,-2.58,-0.19,-5.15,-3.24,-0.66,-5.81,3.24,2.58,-3.24,26.36,26.22,1.30E+09,2.20E+06,2.03E+04,161,1.02E-02,0.569,0.059,-5.81E+03,-2.20E+05,6.29E-05,3.40E-03,,433.9,-22.8 16 | 2.56,-715.4,-4310.9,-21750.8,17439.9,302.37,-6.84,-6.61,-0.11,-4.41,-0.23,-6.73,-6.61,-6.84,6.73,0.11,-6.73,40.16,39.79,9.80E+08,1.72E+06,1.76E+04,149,1.06E-02,0.409,0.067,-1.67E+05,-4.30E+05,7.11E-05,4.10E-03,,179.6,-9.1 17 | 2.21,-135.3,-2179.8,-9171.1,6991.3,636.97,-5.19,-1.51,-1.84,-0.27,-3.68,-3.35,-1.51,-5.19,3.35,1.84,-3.35,107,106.98,1.28E+09,2.26E+06,2.02E+04,163,1.06E-02,0.633,0.058,1.02E+04,-1.89E+05,6.51E-05,3.10E-03,38,1222.9,-10.7 18 | 3.32,-449.4,-1320.2,-3221.7,1901.5,153.42,-11.36,-5.3,-3.03,-0.16,-6.07,-8.33,-5.3,-11.36,8.33,3.03,-8.33,9.09,9.07,1.11E+09,1.95E+06,1.88E+04,155,1.05E-02,0.535,0.071,1.83E+03,-3.04E+05,6.80E-05,3.70E-03,71,188.9,-10.8 19 | 2.02,-638.1,-1331.3,-3510.7,2179.4,178.99,-10.92,1.03,-5.97,-0.08,-11.95,-4.95,1.03,-10.92,4.95,5.97,-4.95,10.74,10.71,1.20E+09,2.22E+06,1.95E+04,162,1.11E-02,0.508,0.059,9.68E+03,-2.19E+05,6.86E-05,3.10E-03,46.7,661.4,-8.5 20 | -------------------------------------------------------------------------------- /RRegrs/inst/models/glmnetModel.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/enanomapper/RRegrs/e5194f812e949be7694023ede0e62e27264a13b1/RRegrs/inst/models/glmnetModel.RData -------------------------------------------------------------------------------- /RRegrs/inst/models/model.svmRadialReg.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/enanomapper/RRegrs/e5194f812e949be7694023ede0e62e27264a13b1/RRegrs/inst/models/model.svmRadialReg.RData -------------------------------------------------------------------------------- /RRegrs/man/AppendList2CSv.Rd: -------------------------------------------------------------------------------- 1 | \name{AppendList2CSv} 2 | \alias{AppendList2CSv} 3 | \title{ 4 | Append list items to a CSV file. 5 | } 6 | \description{ 7 | AppendList2CSv appends the items of a list to a CSV file using write.table function. 8 | } 9 | \usage{ 10 | AppendList2CSv(l,csvFile) 11 | } 12 | \arguments{ 13 | \item{l}{the list object to be writen into a CSV file.} 14 | \item{csvFile}{a string to define the CSV file name whre the items of a list will be appended.} 15 | } 16 | \details{ 17 | AppendList2CSv appends data using comma as separation (CSV format), without quotes, column names or row names. 18 | } 19 | \value{ 20 | New info into CSV files. 21 | } 22 | \examples{ 23 | \dontrun{ 24 | AppendList2CSv(iris,"Results.csv") 25 | } 26 | } 27 | \author{ 28 | Cristian R. Munteanu 29 | } 30 | -------------------------------------------------------------------------------- /RRegrs/man/AppendList2txt.Rd: -------------------------------------------------------------------------------- 1 | \name{AppendList2txt} 2 | \alias{AppendList2txt} 3 | \title{ 4 | Append list items to a CSV file. 5 | } 6 | \description{ 7 | AppendList2txt appends the items of a list to a text file using write.table function. 8 | } 9 | \usage{ 10 | AppendList2txt(l,csvFile) 11 | } 12 | \arguments{ 13 | \item{l}{the list object to be writen into a text file.} 14 | \item{csvFile}{a string to define the text file name whre the items of a list will be appended.} 15 | } 16 | \details{ 17 | AppendList2txt appends data using space separation, without quotes, column names or row names. 18 | } 19 | \value{ 20 | New info into TXT files. 21 | } 22 | \examples{ 23 | \dontrun{ 24 | AppendList2txt(iris,"Results.csv") 25 | } 26 | } 27 | \author{ 28 | Cristian R. Munteanu 29 | } 30 | -------------------------------------------------------------------------------- /RRegrs/man/DsSplit.Rd: -------------------------------------------------------------------------------- 1 | \name{DsSplit} 2 | \alias{DsSplit} 3 | \title{ 4 | Produce a list with training and test data frames for a dataset. 5 | } 6 | \description{ 7 | DsSplit split one data frame (a dataset) into training and test data frames using createDataPartition function by caret package. 8 | } 9 | \usage{ 10 | DsSplit(ds,trainFrac=3/4,fDet=FALSE,PathDataSet="",iSeed) 11 | } 12 | \arguments{ 13 | \item{ds}{the object to be filtered out, a numeric vecror, matrix or data frame. The first column corresponds to the dependent variable, and the remaining to independent variables.} 14 | \item{trainFrac}{the fraction of the training set from the entire dataset; the default values is 3/4: 75\% of the cases are using for training and 25\% for the test.} 15 | \item{fDet}{a logical value (TRUE or FALSE). If TRUE the generated training and test data frames are included at the details folder.} 16 | \item{PathDataSet}{a character string naming the path for the working/results folder. } 17 | \item{iSeed}{the value of the seed function used to split dataset.} 18 | } 19 | \details{ 20 | If fDet is TRUE, this functions generates two CSV files for training and test dataset: ds.Train.csv and ds.Test.csv. 21 | } 22 | \value{ 23 | A list of two data frames for training and test sets based on the createDataPartition function by caret package. 24 | } 25 | \examples{ 26 | \dontrun{ 27 | DsSplit(iris,3/4,TRUE,"DataResults",1) 28 | } 29 | } 30 | \author{ 31 | Georgia Tsiliki, Cristian R. Munteanu 32 | } 33 | -------------------------------------------------------------------------------- /RRegrs/man/ENETreg.Rd: -------------------------------------------------------------------------------- 1 | \name{ENETreg} 2 | \alias{ENETreg} 3 | \title{ 4 | Fitting ElasticNet Models. 5 | } 6 | \description{ 7 | ENETreg fits elastic net regression models and returns resampling based performance measure using the train function by caret package. 8 | } 9 | \usage{ 10 | ENETreg(my.datf.train,my.datf.test,sCV,iSplit=1, 11 | fDet=F,outFile="")} 12 | \arguments{ 13 | \item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.} 14 | \item{my.datf.test}{the test data set.} 15 | \item{sCV}{A string or a character vector specifying which resampling method to use. See details below.} 16 | \item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.} 17 | \item{fDet}{A logical value for saving model statistics; the default value is FALSE. See below for details.} 18 | \item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).} 19 | } 20 | \details{ 21 | RMSE is the summary metric used to select the optimal model. 22 | 23 | Elastic net from glmnet package have mainly two parameters, alpha and lambda. Instead of using the standard caret sCV parameterization, the proper alpha value is chosen by sCV (alpha =1 lasso, alpha =0 ridge), and lambda is chosen using the glmnet package. Lambda is excluded from the cross-validation search and is extracted from an internal cross-validation in the fit function. This is done to reflect that alpha and lambda parameters are dependent, thus elastic net should select lambda with better accuracy for each alpha. 24 | To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation). 25 | 26 | sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". 27 | 28 | If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook`s distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook`s Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook). 29 | 30 | } 31 | \value{ 32 | A list is returned containing: 33 | \item{stat.values}{model`s statistics} 34 | \item{model}{the full enet model, i.e. a list of class train} 35 | } 36 | \examples{ 37 | \dontrun{ 38 | fDet <- FALSE 39 | iSeed <- i 40 | 41 | # the fraction of training set from the entire dataset; 42 | trainFrac <- 0.75 43 | 44 | # dataset folder for input and output files 45 | PathDataSet <- 'DataResults' 46 | 47 | # upload data set 48 | ds <- read.csv(ds.Housing,header=T) 49 | 50 | # split the data into training and test sets 51 | dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed) 52 | ds.train<- dsList$train 53 | ds.test <- dsList$test 54 | 55 | # types of cross-validation methods 56 | CVtypes <- c('repeatedcv','LOOCV') 57 | 58 | outENET<- 'ENEToutput.csv' 59 | 60 | ENET.fit <- ENETreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outENET) 61 | } 62 | } 63 | \author{ 64 | Jose A. Seoane, Carlos Fernandez-Lozano 65 | } 66 | -------------------------------------------------------------------------------- /RRegrs/man/GLMreg.Rd: -------------------------------------------------------------------------------- 1 | \name{GLMreg} 2 | \alias{GLMreg} 3 | \title{ 4 | Fitting Generalized Linear Predictive Models. 5 | } 6 | \description{ 7 | GLMreg performs stepwise generalized linear model selction by AIC using stepAIC function of the MASS package. Resampling based performance measures are defined using the train function by caret package. 8 | } 9 | \usage{ 10 | GLMreg(my.datf.train,my.datf.test,sCV,iSplit=1, 11 | fDet=F,outFile="")} 12 | \arguments{ 13 | \item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.} 14 | \item{my.datf.test}{the test data set.} 15 | \item{sCV}{A string or a charcater vector specifying which resampling method to use. See details below.} 16 | \item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.} 17 | \item{fDet}{A logical value for saving model' statistics; the defualt value is FALSE. See below for details.} 18 | \item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).} 19 | } 20 | \details{ 21 | RMSE is the summary metric used to select the optimal model. 22 | 23 | To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation). 24 | 25 | sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". 26 | 27 | If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features'importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook's distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook's Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook). 28 | 29 | } 30 | \value{ 31 | A list is returned containing: 32 | \item{stat.values}{model's statistics} 33 | \item{model}{the full stepwise-selected model, i.e. a list of class train} 34 | } 35 | \examples{ 36 | \dontrun{ 37 | fDet <- FALSE 38 | iSeed <- i 39 | 40 | # the fraction of training set from the entire dataset; 41 | trainFrac <- 0.75 42 | 43 | # dataset folder for input and output files 44 | PathDataSet <- 'DataResults' 45 | 46 | # upload data set 47 | ds <- read.csv(ds.Housing,header=T) 48 | 49 | # split the data into training and test sets 50 | dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed) 51 | ds.train<- dsList$train 52 | ds.test <- dsList$test 53 | 54 | # types of cross-validation methods 55 | CVtypes <- c('repeatedcv','LOOCV') 56 | 57 | outGLM<- 'GLMoutput.csv' 58 | 59 | GLM.fit <- GLMreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outGLM) 60 | } 61 | } 62 | \author{ 63 | Georgia Tsiliki, Cristian R. Munteanu 64 | } 65 | -------------------------------------------------------------------------------- /RRegrs/man/LASSOreg.Rd: -------------------------------------------------------------------------------- 1 | \name{LASSOreg} 2 | \alias{LASSOreg} 3 | \title{ 4 | Fitting Lasso Elastic Net Predictive Models over Different Tuning Parameters. 5 | } 6 | \description{ 7 | LASSOreg fits Lasso elastic net regression models employing the enet function from the elasticnet package, and returns a resampling based performance measure using the train function by caret package. 8 | } 9 | \usage{ 10 | LASSOreg(my.datf.train,my.datf.test,sCV,iSplit=1, 11 | fDet=F,outFile="")} 12 | \arguments{ 13 | \item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.} 14 | \item{my.datf.test}{the test data set.} 15 | \item{sCV}{A string or a charcater vector specifying which resampling method to use. See details below.} 16 | \item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.} 17 | \item{fDet}{A logical value for saving model' statistics; the defualt value is FALSE. See below for details.} 18 | \item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).} 19 | } 20 | \details{ 21 | RMSE is the summary metric used to select the optimal model. 22 | 23 | The tunning parameter of the model is the fraction, which refers to the ratio of the L1 norm of the coefficient vector, relative to the norm at the full LS solution. Fraction is set to vary in a sequence of 10 values between zero and one. 24 | 25 | To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation). 26 | 27 | sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". 28 | 29 | If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features'importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook's distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook's Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook). 30 | 31 | } 32 | \value{ 33 | A list is returned containing: 34 | \item{stat.values}{model's statistics} 35 | \item{model}{the full lasso model, i.e. a list of class train} 36 | } 37 | \examples{ 38 | \dontrun{ 39 | fDet <- FALSE 40 | iSeed <- i 41 | 42 | # the fraction of training set from the entire dataset; 43 | trainFrac <- 0.75 44 | 45 | # dataset folder for input and output files 46 | PathDataSet <- 'DataResults' 47 | 48 | # upload data set 49 | ds <- read.csv(ds.Housing,header=T) 50 | 51 | # split the data into training and test sets 52 | dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed) 53 | ds.train<- dsList$train 54 | ds.test <- dsList$test 55 | 56 | # types of cross-validation methods 57 | CVtypes <- c('repeatedcv','LOOCV') 58 | 59 | outLASSO<- 'LASSOoutput.csv' 60 | 61 | LASSO.fit <- LASSOreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outLASSO) 62 | } 63 | } 64 | \author{ 65 | Georgia Tsiliki, Cristian R. Munteanu 66 | } 67 | -------------------------------------------------------------------------------- /RRegrs/man/LMreg.Rd: -------------------------------------------------------------------------------- 1 | \name{LMreg} 2 | \alias{LMreg} 3 | \title{ 4 | Fitting Linear Predictive Models. 5 | } 6 | \description{ 7 | LMreg fits linear regression models and returns resampling based performance measure using the train function by caret package. 8 | } 9 | \usage{ 10 | LMreg(my.datf.train,my.datf.test,sCV,iSplit=1, 11 | fDet=F,outFile="")} 12 | \arguments{ 13 | \item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.} 14 | \item{my.datf.test}{the test data set.} 15 | \item{sCV}{A string or a charcater vector specifying which resampling method to use. See details below.} 16 | \item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.} 17 | \item{fDet}{A logical value for saving model' statistics; the defualt value is FALSE. See below for details.} 18 | \item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).} 19 | } 20 | \details{ 21 | RMSE is the summary metric used to select the optimal model. 22 | 23 | To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation). 24 | 25 | sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". 26 | 27 | If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features'importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook's distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook's Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook). 28 | 29 | } 30 | \value{ 31 | A list is returned containing: 32 | \item{stat.values}{model's statistics} 33 | \item{model}{the full lm model, i.e. a list of class train} 34 | } 35 | \examples{ 36 | \dontrun{ 37 | fDet <- FALSE 38 | iSeed <- i 39 | 40 | # the fraction of training set from the entire dataset; 41 | trainFrac <- 0.75 42 | 43 | # dataset folder for input and output files 44 | PathDataSet <- 'DataResults' 45 | 46 | # upload data set 47 | ds <- read.csv(ds.Housing,header=T) 48 | 49 | # split the data into training and test sets 50 | dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed) 51 | ds.train<- dsList$train 52 | ds.test <- dsList$test 53 | 54 | # types of cross-validation methods 55 | CVtypes <- c('repeatedcv','LOOCV') 56 | 57 | outLM<- 'LMoutput.csv' 58 | 59 | LM.fit <- LMreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outLM) 60 | } 61 | } 62 | \author{ 63 | Georgia Tsiliki, Cristian R. Munteanu 64 | } 65 | -------------------------------------------------------------------------------- /RRegrs/man/NNreg.Rd: -------------------------------------------------------------------------------- 1 | \name{NNreg} 2 | \alias{NNreg} 3 | \title{ 4 | Fitting Neural Network Predictive Models over Different Tuning Parameters. 5 | } 6 | \description{ 7 | NNreg fits single-hidden-layer neural network models based on nnet function by nnet package, and returns a resampling based performance measure using the train function by caret package. 8 | } 9 | \usage{ 10 | NNreg(my.datf.train,my.datf.test,sCV,iSplit=1, 11 | fDet=F,outFile="")} 12 | \arguments{ 13 | \item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.} 14 | \item{my.datf.test}{the test data set.} 15 | \item{sCV}{A string or a charcater vector specifying which resampling method to use. See details below.} 16 | \item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.} 17 | \item{fDet}{A logical value for saving model' statistics; the defualt value is FALSE. See below for details.} 18 | \item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).} 19 | } 20 | \details{ 21 | RMSE is the summary metric used to select the optimal model. 22 | 23 | The tuning parameters examined are the size and the decay, where size refers to the number of units in the hidden layer and decay to the weight decay. Size is set to vary in $c(1,5,10,15)$ and decay within a sequence of values in $[0,0.001]$. 24 | 25 | To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation). 26 | 27 | sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". 28 | 29 | If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features'importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook's distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook's Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook). 30 | 31 | } 32 | \value{ 33 | A list is returned containing: 34 | \item{stat.values}{model's statistics} 35 | \item{model}{the full nnet model, i.e. a list of class train} 36 | } 37 | \examples{ 38 | \dontrun{ 39 | fDet <- FALSE 40 | iSeed <- i 41 | 42 | # the fraction of training set from the entire dataset; 43 | trainFrac <- 0.75 44 | 45 | # dataset folder for input and output files 46 | PathDataSet <- 'DataResults' 47 | 48 | # upload data set 49 | ds <- read.csv(ds.Housing,header=T) 50 | 51 | # split the data into training and test sets 52 | dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed) 53 | ds.train<- dsList$train 54 | ds.test <- dsList$test 55 | 56 | # types of cross-validation methods 57 | CVtypes <- c('repeatedcv','LOOCV') 58 | 59 | outNN<- 'NNoutput.csv' 60 | 61 | NN.fit <- NNreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outNN) 62 | } 63 | } 64 | \author{ 65 | Georgia Tsiliki, Cristian R. Munteanu 66 | } 67 | -------------------------------------------------------------------------------- /RRegrs/man/PLSreg.Rd: -------------------------------------------------------------------------------- 1 | \name{PLSreg} 2 | \alias{PLSreg} 3 | \title{ 4 | Fitting Partial Least Squares Predictive Models over Different Tuning Parameters. 5 | } 6 | \description{ 7 | PLSreg fits partial least squares regression models based on mvr function by pls package, and returns a resampling based performance measure using the train function by caret package. 8 | } 9 | \usage{ 10 | PLSreg(my.datf.train,my.datf.test,sCV,iSplit=1,fDet=F,outFile="")} 11 | \arguments{ 12 | \item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.} 13 | \item{my.datf.test}{the test data set.} 14 | \item{sCV}{A string or a charcater vector specifying which resampling method to use. See details below.} 15 | \item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.} 16 | \item{fDet}{A logical value for saving model' statistics; the defualt value is FALSE. See below for details.} 17 | \item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).} 18 | } 19 | \details{ 20 | RMSE is the summary metric used to select the optimal model. 21 | 22 | The tuning parameter examined is the model's number of components, which is set to a sequence of integers from 1 to one fifth of the number of features in the training data set. (If the later is smaller than 1, tuning parameter is set to 1.) 23 | 24 | To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation). 25 | 26 | sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". 27 | 28 | If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features'importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook's distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook's Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook). 29 | 30 | } 31 | \value{ 32 | A list is returned containing: 33 | \item{stat.values}{model's statistics} 34 | \item{model}{the full pls model, i.e. a list of class train} 35 | } 36 | \examples{ 37 | \dontrun{ 38 | fDet <- FALSE 39 | iSeed <- i 40 | 41 | # the fraction of training set from the entire dataset; 42 | trainFrac <- 0.75 43 | 44 | # dataset folder for input and output files 45 | PathDataSet <- 'DataResults' 46 | 47 | # upload data set 48 | ds <- read.csv(ds.Housing,header=T) 49 | 50 | # split the data into training and test sets 51 | dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed) 52 | ds.train<- dsList$train 53 | ds.test <- dsList$test 54 | 55 | # types of cross-validation methods 56 | CVtypes <- c('repeatedcv','LOOCV') 57 | 58 | outPLS<- 'PLSoutput.csv' 59 | 60 | PLS.fit <- PLSreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outPLS) 61 | } 62 | } 63 | \author{ 64 | Georgia Tsiliki, Cristian R. Munteanu 65 | } 66 | -------------------------------------------------------------------------------- /RRegrs/man/RFRFEreg.Rd: -------------------------------------------------------------------------------- 1 | \name{RFRFEreg} 2 | \alias{RFRFEreg} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Fitting Recursive Feature Elimination - Random Forest Models. 6 | } 7 | \description{ 8 | RFRFEreg fits RFE RF regression models and returns resampling based performance measure using the train function by caret package. 9 | } 10 | \usage{ 11 | RFRFEreg(my.datf.train,my.datf.test,sCV,iSplit=1, 12 | fDet=F,outFile="") 13 | } 14 | %- maybe also 'usage' for other objects documented here. 15 | \arguments{ 16 | \item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.} 17 | \item{my.datf.test}{the test data set.} 18 | \item{sCV}{A string or a character vector specifying which resampling method to use. See details below.} 19 | \item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.} 20 | \item{fDet}{A logical value for saving model statistics; the default value is FALSE. See below for details.} 21 | \item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).} 22 | } 23 | \details{ 24 | RMSE is the summary metric used to select the optimal model. 25 | 26 | RFE Random Forest uses the RFE function of caret and combined with the rffunctions and randomForest modeling to obtaing the best model with the best feature set. 27 | To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation). 28 | 29 | sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". 30 | 31 | If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook`s distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook`s Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook). 32 | 33 | } 34 | \value{ 35 | A list is returned containing: 36 | \item{stat.values}{model`s statistics} 37 | \item{model}{the full rferf model, i.e. a list of class train} 38 | } 39 | \examples{ 40 | \dontrun{ 41 | fDet <- FALSE 42 | iSeed <- i 43 | 44 | # the fraction of training set from the entire dataset; 45 | trainFrac <- 0.75 46 | 47 | # dataset folder for input and output files 48 | PathDataSet <- 'DataResults' 49 | 50 | # upload data set 51 | ds <- read.csv(ds.Housing,header=T) 52 | 53 | # split the data into training and test sets 54 | dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed) 55 | ds.train<- dsList$train 56 | ds.test <- dsList$test 57 | 58 | # types of cross-validation methods 59 | CVtypes <- c('repeatedcv','LOOCV') 60 | 61 | outLM<- 'RFRFEoutput.csv' 62 | 63 | RFRFE.fit <- RFRFEreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outLM) 64 | } 65 | } 66 | \author{ 67 | Jose A. Seoane, Carlos Fernandez-Lozano 68 | } -------------------------------------------------------------------------------- /RRegrs/man/RFreg.Rd: -------------------------------------------------------------------------------- 1 | \name{RFreg} 2 | \alias{RFreg} 3 | \title{ 4 | Fitting Random Forest Models. 5 | } 6 | \description{ 7 | RFreg fits random forest regression models and returns resampling based performance measure using the train function by caret package. 8 | } 9 | \usage{ 10 | RFreg(my.datf.train,my.datf.test,sCV,iSplit, 11 | fDet=F,outFile="")} 12 | \arguments{ 13 | \item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.} 14 | \item{my.datf.test}{the test data set.} 15 | \item{sCV}{A string or a character vector specifying which resampling method to use. See details below.} 16 | \item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.} 17 | \item{fDet}{A logical value for saving model statistics; the default value is FALSE. See below for details.} 18 | \item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).} 19 | } 20 | \details{ 21 | Each random forest grows 1500 trees. 22 | RMSE is the summary metric used to select the optimal model. 23 | 24 | To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation). 25 | During the model selection process, the sCV method try to find the best number of features automaticaly chosen in each tree of the RF. The possible values are: numberFeatures/3 (default in randomForest Package), numberFeatures and numberFeatures/2. 26 | sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". 27 | 28 | If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook`s distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook`s Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook). 29 | 30 | } 31 | \value{ 32 | A list is returned containing: 33 | \item{stat.values}{model`s statistics} 34 | \item{model}{the full rf model, i.e. a list of class train} 35 | } 36 | \examples{ 37 | \dontrun{ 38 | fDet <- FALSE 39 | iSeed <- i 40 | 41 | # the fraction of training set from the entire dataset; 42 | trainFrac <- 0.75 43 | 44 | # dataset folder for input and output files 45 | PathDataSet <- 'DataResults' 46 | 47 | # upload data set 48 | ds <- read.csv(ds.Housing,header=T) 49 | 50 | # split the data into training and test sets 51 | dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed) 52 | ds.train<- dsList$train 53 | ds.test <- dsList$test 54 | 55 | # types of cross-validation methods 56 | CVtypes <- c('repeatedcv','LOOCV') 57 | 58 | outRF<- 'RFoutput.csv' 59 | 60 | RF.fit <- RFreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outRF) 61 | } 62 | } 63 | \author{ 64 | Jose A. Seoane, Carlos Fernandez-Lozano 65 | } 66 | -------------------------------------------------------------------------------- /RRegrs/man/RRegrs.Rd: -------------------------------------------------------------------------------- 1 | \name{RRegrs} 2 | \alias{RRegrs} 3 | \title{ 4 | Fitting a set of ten fully utilized Predictive Models. 5 | } 6 | \description{ 7 | The current tool is a collection of regression tools from R that could be used to search the best regression models for any dataset. Filtering, Cross-Validation, Y-randomization are applied. 8 | } 9 | \usage{ 10 | RRegrs(DataFileName="ds.House.csv",DataFileSep=",", 11 | PathDataSet="DataResults", noCores=1, ResAvgs="RRegsResAvgs.csv", ResBySplits="RRegrsResAllSplits.csv", ResBest="RRegrsResBest.csv", fDet="T", fFilters="F", fScaling="T", fRemNear0Var="T", fRemCorr="T",fLM="T", fGLM="T", fPLS="T", fLASSO="T", fSVRM="T", fNN="T", fRF="T", fRFRFE="T", fSVMRFE="T", fENET="T", RFE_SVM_C="1;5;10;15;20", RFE_SVM_epsilon="0.01;0.1;0.3", cutoff=0.9, iScaling=1, iScalCol=1, trainFrac=0.75, iSplitTimes=10, noYrand=100, CVtypes="repeatedcv;LOOCV", NoNAValFile="ds.NoNA.csv", No0NearVarFile="ds.No0Var.csv", ScaledFile="ds.scaled.csv", NoCorrFile="ds.scaled.NoCorrs.csv", 12 | lmFile="LM.details.csv", glmFile="GLM.details.csv", plsFile="PLS.details.csv",lassoFile="Lasso.details.csv", svrmFile="SVMRadial.details.csv", nnFile="NN.details.csv", rfFile="RF.details.csv", rfrfeFile="RFRFE.details.csv", svmrfeFile="SVMRFE.details.csv",enetFile="ENET.details.csv",fR2rule="T") 13 | } 14 | \arguments{ 15 | \item{DataFileName}{ a character string naming the data set file; the data set should be an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample. Default value is the "ds.House.csv" data.} 16 | \item{DataFileSep}{ the column separator character in the data file. Default value is a comma (CSV file).} 17 | \item{PathDataSet}{ a character string naming the directory where the data set file is stored. Default value is the "DataResults" directory.} 18 | \item{noCores}{The number of cores used for calculations. Default value is 1.} 19 | \item{ResAvgs}{a character string naming the file where average values of all models are stored. Default value is the "RRegsResAvgs.csv" file.} 20 | \item{ResBySplits}{a character string naming the file where statistics for all splits and all models are stored. Default value is the "RRegrsResAllSplits.csv" file.} 21 | \item{ResBest}{a character string naming the file where statistics for the best model are stored. Default value is the "RRegrsResBest.csv" file.} 22 | \item{fDet}{logical. If TRUE all details for all splits and models are stored.} 23 | \item{fFilters}{If run custom flters. Not implemented yet.} 24 | \item{fScaling}{logical. If scaling will be performed. Default value is TRUE.} 25 | \item{fRemNear0Var}{logical. If run Removal of near zero variance columns. Default value is TRUE.} 26 | \item{fRemCorr}{logical. If correlated features will be removed based on 'cutoff' value. Default value is TRUE.} 27 | \item{fLM}{logical. If Linear model will be applied to the data. Default value is TRUE.} 28 | \item{fGLM}{logical. If Generalized Linear model will be applied to the data. Default value is TRUE.} 29 | \item{fPLS}{logical. If Partial Leasts Square regression model will be applied to the data. Default value is TRUE.} 30 | \item{fLASSO}{logical. If Lasso regression model will be applied to the data. Default value is TRUE.} 31 | \item{fSVRM}{logical. If Support vector machine regression model (radial basis function kernel) will be applied to the data. Default value is TRUE.} 32 | \item{fNN}{logical. If Neural network model will be applied to the data. Default value is TRUE.} 33 | \item{fRF}{logical. If Random Forest model will be applied to the data. Default value is TRUE.} 34 | \item{fRFRFE}{logical. If random Forest with Random Feature Elimination model will be applied to the data. Default value is TRUE.} 35 | \item{fSVMRFE}{logical. If Support vector machines with Random Feature elimination model (radial basis function kernel) will be applied to the data. Default value is TRUE.} 36 | \item{fENET}{logical. If Elastic net model will be applied to the data. Default value is TRUE.} 37 | \item{RFE_SVM_C}{Support vector machines cost parameter. Default values are c(1,5,10,15,20).} 38 | \item{RFE_SVM_epsilon}{Support vector machines epsilon parameter. Default values are c(0.01,0.1,0.3).} 39 | \item{cutoff}{=0.9}{Cutoff for correlated features (default = 0.9).} 40 | \item{iScaling}{Type of scaling: 1 = normalization; 2 = standardization; 3 = other; any other: no scaling} 41 | \item{iScalCol}{Scaling columns: 1 = including dependent variable; 2: only all the features} 42 | \item{trainFrac}{ Fraction of training set from the entire dataset (default = 0.75); the rest of dataset is the test set.} 43 | \item{iSplitTimes}{Number of splittings the dataset into train and test (default = 10)} 44 | \item{noYrand}{Number of Y-Randomization (default = 100)} 45 | \item{CVtypes}{a character vector indicating which cross validation method will be used. Default value is c("repeatedcv","LOOCV")} 46 | \item{NoNAValFile}{a character string naming the file where the data set without NA values will be stored. Default value is the "ds.NoNA.csv" file.} 47 | \item{No0NearVarFile}{a character string naming the file where the data set without without near zero variance values will be stored. Default value is the "ds.No0Var.csv" file.} 48 | \item{ScaledFile}{a character string naming the file where the scaled data set will be stored. Default value is the "ds.scaled.csv" file.} 49 | \item{NoCorrFile}{a character string naming the file where the data set without correlated independent variables will be stored. Default value is the "ds.scaled.NoCorrs.csv" file.} 50 | \item{lmFile}{a character string naming the file where LM model details will be stored. Default value is the "LM.details.csv" file.} 51 | \item{glmFile}{a character string naming the file where GLM model details will be stored. Default value is the "GLM.details.csv" file.} 52 | \item{plsFile}{a character string naming the file where PLS model details will be stored. Default value is the "PLS.details.csv" file.} 53 | \item{lassoFile}{a character string naming the file where LASSO model details will be stored. Default value is the "Lasso.details.csv" file.} 54 | \item{svrmFile}{a character string naming the file where SVMR model details will be stored. Default value is the "SVMRadial.details.csv" file.} 55 | \item{nnFile}{a character string naming the file where NN model details will be stored. Default value is the "NN.details.csv" file.} 56 | \item{rfFile}{a character string naming the file where RF model details will be stored. Default value is the "RF.details.csv" file.} 57 | \item{rfrfeFile}{a character string naming the file where RFRFE model details will be stored. Default value is the "RFRFE.details.csv" file.} 58 | \item{svmrfeFile}{a character string naming the file where SVMRRFE model details will be stored. Default value is the "SVMRFE.details.csv" file.} 59 | \item{enetFile}{a character string naming the file where ENET model details will be stored. Default value is the "ENET.details.csv" file.} 60 | \item{fR2rule}{logical. If R2 rule will be used to find the best regression model. Default value is the TRUE (R2 rule).} 61 | } 62 | \details{ RRegrs() function contains several sections: loading parameters and dataset, remove near zero variance features, scaling dataset, remove correlated features, dataset splitting, run the 10 regression methods, summary of statistics for all methods and splittings, averages for each method and cross-validation type for all splittings, automatic best model statistics, best model Y-randomization. Assessment of Applicability Domain was included in each method. 63 | 64 | In order to use RRegrs function, it is needed to specify a minimum set of parameters (the others will get default values). All the parameters will be written into a CSV file (ex: Parameters.csv), in the same working folder where it should be present the input dataset file and the outputs files. The dataset needs to have CSV format, and the first column should be the dependent variable. The input and output files will be placed into a working folder. The output files are CSV statistics files, PDF and PNG plots. 65 | 66 | If details are needed (fDet=T), several output files are generated. A CSV file with detailed statistics about the regression model is returned (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, Full statistics, Feature importance, Residuals of the fitted model, Assessment of applicability domain / leverage analysis). If the determinant is not zero then the following are reported: mean of hat values, hat values with warnings (X3 and X2 for values 3 and 2 times than hat mean), leverage threshold, list of points with leverage greater than threshold, Cook's distance cutoff, Cook's distances, points influence. Also, 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method are returned. Along with the above, plots for Training Yobs versus Ypred are generated, plots for Test Yobs versus Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook's Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook. 67 | 68 | The outputs can differ depending on the regression method used. 69 | 70 | After filtering the dataset for correlated variables, near-zero variance features and splitting the dataset into training and test sets, the user's selected regression methods will be executed for each splitting and cross-validation type. 71 | 72 | Averaged statistics across all splittings, for each regression method and cross-validation type are calculated and used to chose the best regression model. Particularly, this is chosen based on the following criteria: from the best test R-squared (+/- 0.05), the model with minimum RMSE is the final one. If fR2rule is FALSE, adjusted R2 values will be used to choose the best model. For the best model, an additional CSV file is generated containing detailed statistics as well as PDF plots for important statistics. Finally, the best model (last data split) is tested with Y-randomization runs (100 by default). 73 | 74 | Please note that the Cross-validation types available are the resampling methods available from trainControl() and rfeControl() caret. 75 | } 76 | \value{ 77 | RRegrs() returns a list with the following 3 items: 78 | \item{BestMethod}{the name of the best method} 79 | \item{BestStats}{the statistics for the best model} 80 | \item{Models}{the list with all the fitted models based on caret functions (including the best model).} 81 | } 82 | \examples{ 83 | \dontrun{ 84 | library(data.table) 85 | library(corrplot) 86 | source("../RRegrs/R/RRegrs_Functions.R") 87 | 88 | #Run RRegrs with all default parameters 89 | # (use default dataset file and working folder, 90 | # run all regression methods, without wrappers, 91 | # 10 splitings, 100 times Y-randomization, 92 | # no parallel calculation = 1 CPU core) 93 | 94 | #Run RRegrs for a specific dataset file and the rest default #parameters 95 | RRegrsResults = RRegrs(DataFileName="MyDataSet.csv") 96 | } 97 | } 98 | \author{ 99 | Georgia Tsiliki, Cristian R. Munteanu, Jose A. Seoane, Carlos Fernandez-Lozano 100 | } 101 | -------------------------------------------------------------------------------- /RRegrs/man/RemCorrs.Rd: -------------------------------------------------------------------------------- 1 | \name{RemCorrs} 2 | \alias{RemCorrs} 3 | \title{ 4 | Produce a data frame reduced by data predictors that are correlated. 5 | } 6 | \description{ 7 | RemCorrs filters out data predictors that are correlated based on the cor function by corrplot package. 8 | } 9 | \usage{ 10 | RemCorrs(ds,fDet,cutoff,outFile) 11 | } 12 | \arguments{ 13 | \item{ds}{the object to be filtered out, a numeric vecror, matrix or data frame. The first column corresponds to the dependent variable, and the remaining to independent variables.} 14 | \item{fDet}{a logical value (TRUE or FALSE). If TRUE the corrected data frame is included at the details folder.} 15 | \item{cutoff}{the correlation cut off (ex: 0.9).} 16 | \item{outFile}{a character string naming an output file (could include path). } 17 | } 18 | \details{ 19 | This functions generates several output plots if fDet = TRUE: [outFile].corrMAT.csv and [outFile].corrs.png as correlation matrix and plot before removal the corralated features, [outFile].corrMAT4Selected.csv and [outFile].afterRemCorr.png as correlation matrix and plot after removing the correlated features by using cutoff value and outfile as the filtered dataset. 20 | } 21 | \value{ 22 | A data frame containing the filtered data based on the cor function by corrplot package. 23 | } 24 | \examples{ 25 | \dontrun{ 26 | RemCorrs(iris,TRUE,0.9,'filtered_iris.csv') 27 | } 28 | } 29 | \author{ 30 | Cristian R. Munteanu 31 | } 32 | -------------------------------------------------------------------------------- /RRegrs/man/RemNear0VarCols.Rd: -------------------------------------------------------------------------------- 1 | \name{RemNear0VarCols} 2 | \alias{RemNear0VarCols} 3 | \title{ 4 | Produce a data frame reduced by data predictors that have unique values or have very few unique values. 5 | } 6 | \description{ 7 | RemNear0VarCols filters out data predictors that have unique values or have very few unique values based on the nearZeroVar function by caret package. 8 | } 9 | \usage{ 10 | RemNear0VarCols(ds, fDet=FALSE, outFile="ds3.No0Var.csv") 11 | } 12 | \arguments{ 13 | \item{ds}{the object to be filtered out, a numeric vecror, matrix or data frame. The first column corresponds to the dependent variable, and the remaining to independent variables.} 14 | \item{fDet}{a logical value (TRUE or FALSE). If TRUE the corrected data frame is included at the details folder.} 15 | \item{outFile}{a character sring naming a file (could include path). The default the a csv file "ds3.No0Var.csv".} 16 | } 17 | \value{ 18 | A data frame containing the filtered data based on the nearZeroVar function by caret package. 19 | } 20 | \examples{ 21 | \dontrun{ 22 | RemNear0VarCols(iris,'filtered_iris.csv') 23 | } 24 | } 25 | \author{ 26 | Georgia Tsiliki, Cristian R. Munteanu 27 | } 28 | -------------------------------------------------------------------------------- /RRegrs/man/SVMRFEreg.Rd: -------------------------------------------------------------------------------- 1 | \name{SVMRFEreg} 2 | \alias{SVMRFEreg} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Fitting Recursive Feature Elimination - Support Vector Machines Regression Models. 6 | } 7 | \description{ 8 | SVMRFEreg fits RFE SVM regression models and returns resampling based performance measure using the train function by caret package. 9 | } 10 | \usage{ 11 | SVMRFEreg(my.datf.train,my.datf.test,sCV,iSplit=1, 12 | fDet=F,outFile="",cs=c(1,5,10,15,20),eps=c(0.01,0.1,0.3)) 13 | } 14 | %- maybe also 'usage' for other objects documented here. 15 | \arguments{ 16 | \item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.} 17 | \item{my.datf.test}{the test data set.} 18 | \item{sCV}{A string or a character vector specifying which resampling method to use. See details below.} 19 | \item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.} 20 | \item{fDet}{A logical value for saving model statistics; the default value is FALSE. See below for details.} 21 | \item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).} 22 | \item{cs}{A numeric vector specifying the possible values of the cost function for the SVM mimization.} 23 | \item{eps}{A numeric vector specifying the possible values epsilon function eps-svr insensitive-loss function (more details in kernlab package)} 24 | } 25 | \details{ 26 | RMSE is the summary metric used to select the optimal model. 27 | 28 | RFE SVM uses the RFE function of caret and the eps-svr function in ksvm from the kernlab package modeling to obtaing the best svm model with the best feature set. 29 | To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation). All three parameters are included in the grid, i.e. sigma, C, epsilon. 30 | 31 | sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV". 32 | 33 | If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook`s distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook`s Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook). 34 | 35 | } 36 | \value{ 37 | A list is returned containing: 38 | \item{stat.values}{model`s statistics} 39 | \item{model}{the full svmrfe model, i.e. a list of class train} 40 | } 41 | \examples{ 42 | \dontrun{ 43 | fDet <- FALSE 44 | iSeed <- i 45 | 46 | # the fraction of training set from the entire dataset; 47 | trainFrac <- 0.75 48 | 49 | # dataset folder for input and output files 50 | PathDataSet <- 'DataResults' 51 | 52 | # upload data set 53 | ds <- read.csv(ds.Housing,header=T) 54 | 55 | # split the data into training and test sets 56 | dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed) 57 | ds.train<- dsList$train 58 | ds.test <- dsList$test 59 | 60 | # types of cross-validation methods 61 | CVtypes <- c('repeatedcv','LOOCV') 62 | 63 | outSVMRFE<- 'SVMRFEoutput.csv' 64 | 65 | SVMRFE.fit <- SVMRFEreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outSVMRFE) 66 | } 67 | } 68 | \author{ 69 | Jose A. Seoane, Carlos Fernandez-Lozano 70 | } -------------------------------------------------------------------------------- /RRegrs/man/ScalingDS.Rd: -------------------------------------------------------------------------------- 1 | \name{ScalingDS} 2 | \alias{ScalingDS} 3 | \title{ 4 | Produce a scaled or centered data frame. 5 | } 6 | \description{ 7 | ScalingDS produces a transformed data frame (centering, scaling, etc.). 8 | } 9 | \usage{ 10 | ScalingDS(ds,s=1,c=1,fDet=FALSE,outFileName="ds4.scaled.csv") 11 | } 12 | \arguments{ 13 | \item{ds}{a matrix or data frame. The first column corresponds to the dependent variable, and the remaining to independent variables.} 14 | \item{s}{type of scaling, default value is 1(normalization). Please see details.} 15 | \item{c}{indicator variable to define whether the first column of the data should be considered for scaling (c=1) or not (c=2).} 16 | \item{fDet}{a logical value (TRUE or FALSE). If TRUE the corrected data frame is included at the details folder.} 17 | \item{outFileName}{a character sring naming a file (could include path). The default the a csv file "ds4.scaled.csv".} 18 | } 19 | \details{ 20 | ScalingDS offers normilization options (s=1), standardization (s=2), the user can define tailor-made scaling by setting s=3. 21 | } 22 | \value{ 23 | A data frame containing the sacled data based on the scale function by caret package. 24 | } 25 | \examples{ 26 | \dontrun{ 27 | ScalingDS(iris,'scaled_iris.csv') 28 | } 29 | } 30 | \author{ 31 | Georgia Tsiliki, Cristian R. Munteanu 32 | } 33 | -------------------------------------------------------------------------------- /RRegrs/tests/testthat.R: -------------------------------------------------------------------------------- 1 | library(testthat) 2 | library(RRegrs) 3 | 4 | test_check("RRegrs") 5 | -------------------------------------------------------------------------------- /RRegrs/tests/testthat/test-helperMethods.R: -------------------------------------------------------------------------------- 1 | library(RRegrs) 2 | context("Dummy testing") 3 | 4 | test_that("dummy", { 5 | expect_equal(4-3, 1) 6 | }) 7 | -------------------------------------------------------------------------------- /RRegrs/tests/testthat/test-simpleCall.R: -------------------------------------------------------------------------------- 1 | library(RRegrs) 2 | context("simple") 3 | 4 | test_that("it fails when only one modeling method is selected", { 5 | dsData = system.file("extdata", "ds.House.csv", package = "RRegrs") 6 | expect_error( 7 | RRegrs( 8 | DataFileName=dsData, 9 | fLM="T",fGLM="F",fPLS="F",fLASSO="F",fSVRM="F",fNN="F",fRF="F", 10 | fRFRFE="F",fSVMRFE="F",fENET="F", 11 | trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="LOOCV" 12 | ), 13 | "You must select at least two modelling methods to compare." 14 | ) 15 | }) 16 | 17 | test_that("the chemical classics data can be loaded", { 18 | chemClassicsData = system.file("extdata", "chemClassic.tsv", package = "RRegrs") 19 | data = read.table(chemClassicsData, sep="\t", header=TRUE) 20 | expect_equal(9, nrow(data)) 21 | }) 22 | 23 | test_that("the method doesn't fail for just MLR and PLS", { 24 | dsData = system.file("extdata", "ds.House.csv", package = "RRegrs") 25 | results = RRegrs( 26 | DataFileName=dsData, 27 | fLM="T",fGLM="F",fPLS="T",fLASSO="F",fSVRM="F",fNN="F",fRF="F", 28 | fRFRFE="F",fSVMRFE="F",fENET="F", 29 | trainFrac=0.75,iSplitTimes=3,noYrand=5,CVtypes="repeatedcv" 30 | ) 31 | }) 32 | 33 | test_that("the method works without Y-randomization", { 34 | dsData = system.file("extdata", "ds.House.csv", package = "RRegrs") 35 | results = RRegrs( 36 | DataFileName=dsData, 37 | fLM="T",fGLM="F",fPLS="T",fLASSO="F",fSVRM="F",fNN="F",fRF="F", 38 | fRFRFE="F",fSVMRFE="F",fENET="F", 39 | trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv" 40 | ) 41 | }) 42 | 43 | test_that("the method works for ds.gajewicz.csv", { 44 | dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 45 | results = RRegrs( 46 | DataFileName=dsData, 47 | fLM="T",fGLM="F",fPLS="T",fLASSO="F",fSVRM="F",fNN="F",fRF="F", 48 | fRFRFE="F",fSVMRFE="F",fENET="F", 49 | trainFrac=0.75,iSplitTimes=3,noYrand=5,CVtypes="repeatedcv" 50 | ) 51 | }) 52 | 53 | test_that("the method works with both repeatedcv and LOOCV", { 54 | dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 55 | results = RRegrs( 56 | DataFileName=dsData, 57 | fLM="T",fGLM="F",fPLS="T",fLASSO="F",fSVRM="F",fNN="F",fRF="F", 58 | fRFRFE="F",fSVMRFE="F",fENET="F", 59 | trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv;LOOCV" 60 | ) 61 | }) 62 | 63 | #test_that("the method works with GLM", { 64 | # dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 65 | # results = RRegrs( 66 | # DataFileName=dsData, 67 | # fLM="T",fGLM="T",fPLS="F",fLASSO="F",fSVRM="F",fNN="F",fRF="F", 68 | # fRFRFE="F",fSVMRFE="F",fENET="F", 69 | # trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv;LOOCV" 70 | # ) 71 | #}) 72 | 73 | test_that("the method works with LASSO", { 74 | dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 75 | results = RRegrs( 76 | DataFileName=dsData, 77 | fLM="T",fGLM="F",fPLS="F",fLASSO="T",fSVRM="F",fNN="F",fRF="F", 78 | fRFRFE="F",fSVMRFE="F",fENET="F", 79 | trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv;LOOCV" 80 | ) 81 | }) 82 | 83 | 84 | test_that("the method works with SVRM", { 85 | dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 86 | results = RRegrs( 87 | DataFileName=dsData, 88 | fLM="T",fGLM="F",fPLS="F",fLASSO="F",fSVRM="T",fNN="F",fRF="F", 89 | fRFRFE="F",fSVMRFE="F",fENET="F", 90 | trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv;LOOCV" 91 | ) 92 | }) 93 | 94 | test_that("the method works with NN", { 95 | dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 96 | results = RRegrs( 97 | DataFileName=dsData, 98 | fLM="T",fGLM="F",fPLS="F",fLASSO="F",fSVRM="F",fNN="T",fRF="F", 99 | fRFRFE="F",fSVMRFE="F",fENET="F", 100 | trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv;LOOCV" 101 | ) 102 | }) 103 | 104 | test_that("the method works with RF", { 105 | dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 106 | results = RRegrs( 107 | DataFileName=dsData, 108 | fLM="T",fGLM="F",fPLS="F",fLASSO="F",fSVRM="F",fNN="F",fRF="T", 109 | fRFRFE="F",fSVMRFE="F",fENET="F", 110 | trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv;LOOCV" 111 | ) 112 | }) 113 | 114 | test_that("the method works with RFRFE", { 115 | dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 116 | results = RRegrs( 117 | DataFileName=dsData, 118 | fLM="T",fGLM="F",fPLS="F",fLASSO="F",fSVRM="F",fNN="F",fRF="F", 119 | fRFRFE="T",fSVMRFE="F",fENET="F", 120 | trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv;LOOCV" 121 | ) 122 | }) 123 | 124 | #test_that("the method works with SVRMRFE", { 125 | # dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 126 | # results = RRegrs( 127 | # DataFileName=dsData, 128 | # fLM="T",fGLM="F",fPLS="F",fLASSO="F",fSVRM="F",fNN="F",fRF="F", 129 | # fRFRFE="F",fSVMRFE="T",fENET="F", 130 | # trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv;LOOCV" 131 | # ) 132 | #}) 133 | 134 | test_that("the method works with ENET", { 135 | dsData = system.file("extdata", "ds.gajewicz.csv", package = "RRegrs") 136 | results = RRegrs( 137 | DataFileName=dsData, 138 | fLM="T",fGLM="F",fPLS="F",fLASSO="F",fSVRM="F",fNN="F",fRF="F", 139 | fRFRFE="F",fSVMRFE="F",fENET="T", 140 | trainFrac=0.75,iSplitTimes=3,noYrand=0,CVtypes="repeatedcv;LOOCV" 141 | ) 142 | }) 143 | -------------------------------------------------------------------------------- /TEST/NOTES.txt: -------------------------------------------------------------------------------- 1 | 2 | General: 3 | - check the appends in order to create a new output file for each new calculation 4 | - Add a parallel package depending on the operating system 5 | - catch the errors in each step 6 | - check the null results, etc. 7 | 8 | Particular: 9 | - step 1 = reading ds: the names of features are changed! 10 | 11 | 12 | Errors: 13 | - with ds.House2.csv, the training and test files contain only one column! It seamns that there are errors into the splitting 14 | 15 | - Lasso error 16 | 17 | Loading required package: pROC 18 | Error in plot.window(...) : need finite 'xlim' values 19 | In addition: There were 50 or more warnings (use warnings() to see the first 50) 20 | 21 | - error if you restart a calculation 22 | 23 | -> [5] Removing correlated features ... 24 | Error in plot.xy(xy, type, ...) : invalid plot type 'f' 25 | 26 | - -------------------------------------------------------------------------------- /TEST/TestRRegrs.R: -------------------------------------------------------------------------------- 1 | # ----------------------------------- 2 | # Example of RRegrs use 3 | # ----------------------------------- 4 | 5 | # Libraries and external custom functions 6 | library(RRegrs) # load the RRegrs functions 7 | 8 | # if you downloaded a source distribution, you can also use the version in the package: 9 | #library(data.table) 10 | #library(caret) 11 | #library(corrplot) 12 | #source("../RRegrs/R/RRegrs_Functions.R") 13 | 14 | # ------------------------------ 15 | # Default parameters of RRegrs 16 | # ------------------------------ 17 | # DataFileName="ds.House.csv",PathDataSet="DataResults",noCores=1, 18 | # ResAvgs="RRegsResAvgs.csv",ResBySplits="RRegrsResAllSplits.csv",ResBest="RRegrsResBest.csv", 19 | # fDet="T",fFilters="F",fScaling="T",fRemNear0Var="T",fRemCorr="T", 20 | # fLM="T",fGLM="T",fPLS="T",fLASSO="T",fRBFdda="T",fSVRM="T",fNN="T",fRF="T",fRFRFE="T",fSVMRFE="T",fENET="T", 21 | # RFE_SVM_C="1;5;10;15;20",RFE_SVM_epsilon="0.01;0.1;0.3", 22 | # cutoff=0.9,iScaling=1,iScalCol=1,trainFrac=0.75,iSplitTimes=10,noYrand=100, 23 | # CVtypes="repeatedcv;LOOCV",NoNAValFile="ds.NoNA.csv", 24 | # No0NearVarFile="ds.No0Var.csv",ScaledFile="ds.scaled.csv",NoCorrFile="ds.scaled.NoCorrs.csv", 25 | # lmFile="LM.details.csv",glmFile="GLM.details.csv",plsFile="PLS.details.csv", 26 | # lassoFile="Lasso.details.csv",rbfDDAFile="RBF_DDA.details.csv",negThrStep=0.5,svrmFile="SVMRadial.details.csv", 27 | # nnFile="NN.details.csv",rfFile="RF.details.csv",rfrfeFile="RFRFE.details.csv",svmrfeFile="SVMRFE.details.csv", 28 | # enetFile="ENET.details.csv,fR2rule="T"" 29 | 30 | # noCores = CPU cores: 0 = all CPU cores, 1 = no parallel, >1 = specific cores 31 | 32 | RRegrsResults = RRegrs(DataFileName="ds.House.csv",noCores=0,iSplitTimes=2,noYrand=2) 33 | -------------------------------------------------------------------------------- /TEST/ds/ds.House.csv: -------------------------------------------------------------------------------- 1 | MEDV,CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT 2 | 24,0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98 3 | 21.6,0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14 4 | 34.7,0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03 5 | 33.4,0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94 6 | 36.2,0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33 7 | 28.7,0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21 8 | 22.9,0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43 9 | 27.1,0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15 10 | 16.5,0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93 11 | 18.9,0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1 12 | 15,0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45 13 | 18.9,0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27 14 | 21.7,0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71 15 | 20.4,0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26 16 | 18.2,0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26 17 | 19.9,0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47 18 | 23.1,1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58 19 | 17.5,0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67 20 | 20.2,0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69 21 | 18.2,0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28 22 | 13.6,1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02 23 | 19.6,0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83 24 | 15.2,1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72 25 | 14.5,0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,394.54,19.88 26 | 15.6,0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3 27 | 13.9,0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51 28 | 16.6,0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,376.88,14.81 29 | 14.8,0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28 30 | 18.4,0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8 31 | 21,1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,380.23,11.98 32 | 12.7,1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,360.17,22.6 33 | 14.5,1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,376.73,13.04 34 | 13.2,1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,232.6,27.71 35 | 13.1,1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,358.77,18.35 36 | 13.5,1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34 37 | 18.9,0.06417,0,5.96,0,0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68 38 | 20,0.09744,0,5.96,0,0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41 39 | 21,0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77 40 | 24.7,0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13 41 | 30.8,0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32 42 | 34.9,0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98 43 | 26.6,0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84 44 | 25.3,0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81 45 | 24.7,0.15936,0,6.91,0,0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44 46 | 21.2,0.12269,0,6.91,0,0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55 47 | 19.3,0.17142,0,6.91,0,0.448,5.682,33.8,5.1004,3,233,17.9,396.9,10.21 48 | 20,0.18836,0,6.91,0,0.448,5.786,33.3,5.1004,3,233,17.9,396.9,14.15 49 | 16.6,0.22927,0,6.91,0,0.448,6.03,85.5,5.6894,3,233,17.9,392.74,18.8 50 | 14.4,0.25387,0,6.91,0,0.448,5.399,95.3,5.87,3,233,17.9,396.9,30.81 51 | 19.4,0.21977,0,6.91,0,0.448,5.602,62,6.0877,3,233,17.9,396.9,16.2 52 | 19.7,0.08873,21,5.64,0,0.439,5.963,45.7,6.8147,4,243,16.8,395.56,13.45 53 | 20.5,0.04337,21,5.64,0,0.439,6.115,63,6.8147,4,243,16.8,393.97,9.43 54 | 25,0.0536,21,5.64,0,0.439,6.511,21.1,6.8147,4,243,16.8,396.9,5.28 55 | 23.4,0.04981,21,5.64,0,0.439,5.998,21.4,6.8147,4,243,16.8,396.9,8.43 56 | 18.9,0.0136,75,4,0,0.41,5.888,47.6,7.3197,3,469,21.1,396.9,14.8 57 | 35.4,0.01311,90,1.22,0,0.403,7.249,21.9,8.6966,5,226,17.9,395.93,4.81 58 | 24.7,0.02055,85,0.74,0,0.41,6.383,35.7,9.1876,2,313,17.3,396.9,5.77 59 | 31.6,0.01432,100,1.32,0,0.411,6.816,40.5,8.3248,5,256,15.1,392.9,3.95 60 | 23.3,0.15445,25,5.13,0,0.453,6.145,29.2,7.8148,8,284,19.7,390.68,6.86 61 | 19.6,0.10328,25,5.13,0,0.453,5.927,47.2,6.932,8,284,19.7,396.9,9.22 62 | 18.7,0.14932,25,5.13,0,0.453,5.741,66.2,7.2254,8,284,19.7,395.11,13.15 63 | 16,0.17171,25,5.13,0,0.453,5.966,93.4,6.8185,8,284,19.7,378.08,14.44 64 | 22.2,0.11027,25,5.13,0,0.453,6.456,67.8,7.2255,8,284,19.7,396.9,6.73 65 | 25,0.1265,25,5.13,0,0.453,6.762,43.4,7.9809,8,284,19.7,395.58,9.5 66 | 33,0.01951,17.5,1.38,0,0.4161,7.104,59.5,9.2229,3,216,18.6,393.24,8.05 67 | 23.5,0.03584,80,3.37,0,0.398,6.29,17.8,6.6115,4,337,16.1,396.9,4.67 68 | 19.4,0.04379,80,3.37,0,0.398,5.787,31.1,6.6115,4,337,16.1,396.9,10.24 69 | 22,0.05789,12.5,6.07,0,0.409,5.878,21.4,6.498,4,345,18.9,396.21,8.1 70 | 17.4,0.13554,12.5,6.07,0,0.409,5.594,36.8,6.498,4,345,18.9,396.9,13.09 71 | 20.9,0.12816,12.5,6.07,0,0.409,5.885,33,6.498,4,345,18.9,396.9,8.79 72 | 24.2,0.08826,0,10.81,0,0.413,6.417,6.6,5.2873,4,305,19.2,383.73,6.72 73 | 21.7,0.15876,0,10.81,0,0.413,5.961,17.5,5.2873,4,305,19.2,376.94,9.88 74 | 22.8,0.09164,0,10.81,0,0.413,6.065,7.8,5.2873,4,305,19.2,390.91,5.52 75 | 23.4,0.19539,0,10.81,0,0.413,6.245,6.2,5.2873,4,305,19.2,377.17,7.54 76 | 24.1,0.07896,0,12.83,0,0.437,6.273,6,4.2515,5,398,18.7,394.92,6.78 77 | 21.4,0.09512,0,12.83,0,0.437,6.286,45,4.5026,5,398,18.7,383.23,8.94 78 | 20,0.10153,0,12.83,0,0.437,6.279,74.5,4.0522,5,398,18.7,373.66,11.97 79 | 20.8,0.08707,0,12.83,0,0.437,6.14,45.8,4.0905,5,398,18.7,386.96,10.27 80 | 21.2,0.05646,0,12.83,0,0.437,6.232,53.7,5.0141,5,398,18.7,386.4,12.34 81 | 20.3,0.08387,0,12.83,0,0.437,5.874,36.6,4.5026,5,398,18.7,396.06,9.1 82 | 28,0.04113,25,4.86,0,0.426,6.727,33.5,5.4007,4,281,19,396.9,5.29 83 | 23.9,0.04462,25,4.86,0,0.426,6.619,70.4,5.4007,4,281,19,395.63,7.22 84 | 24.8,0.03659,25,4.86,0,0.426,6.302,32.2,5.4007,4,281,19,396.9,6.72 85 | 22.9,0.03551,25,4.86,0,0.426,6.167,46.7,5.4007,4,281,19,390.64,7.51 86 | 23.9,0.05059,0,4.49,0,0.449,6.389,48,4.7794,3,247,18.5,396.9,9.62 87 | 26.6,0.05735,0,4.49,0,0.449,6.63,56.1,4.4377,3,247,18.5,392.3,6.53 88 | 22.5,0.05188,0,4.49,0,0.449,6.015,45.1,4.4272,3,247,18.5,395.99,12.86 89 | 22.2,0.07151,0,4.49,0,0.449,6.121,56.8,3.7476,3,247,18.5,395.15,8.44 90 | 23.6,0.0566,0,3.41,0,0.489,7.007,86.3,3.4217,2,270,17.8,396.9,5.5 91 | 28.7,0.05302,0,3.41,0,0.489,7.079,63.1,3.4145,2,270,17.8,396.06,5.7 92 | 22.6,0.04684,0,3.41,0,0.489,6.417,66.1,3.0923,2,270,17.8,392.18,8.81 93 | 22,0.03932,0,3.41,0,0.489,6.405,73.9,3.0921,2,270,17.8,393.55,8.2 94 | 22.9,0.04203,28,15.04,0,0.464,6.442,53.6,3.6659,4,270,18.2,395.01,8.16 95 | 25,0.02875,28,15.04,0,0.464,6.211,28.9,3.6659,4,270,18.2,396.33,6.21 96 | 20.6,0.04294,28,15.04,0,0.464,6.249,77.3,3.615,4,270,18.2,396.9,10.59 97 | 28.4,0.12204,0,2.89,0,0.445,6.625,57.8,3.4952,2,276,18,357.98,6.65 98 | 21.4,0.11504,0,2.89,0,0.445,6.163,69.6,3.4952,2,276,18,391.83,11.34 99 | 38.7,0.12083,0,2.89,0,0.445,8.069,76,3.4952,2,276,18,396.9,4.21 100 | 43.8,0.08187,0,2.89,0,0.445,7.82,36.9,3.4952,2,276,18,393.53,3.57 101 | 33.2,0.0686,0,2.89,0,0.445,7.416,62.5,3.4952,2,276,18,396.9,6.19 102 | 27.5,0.14866,0,8.56,0,0.52,6.727,79.9,2.7778,5,384,20.9,394.76,9.42 103 | 26.5,0.11432,0,8.56,0,0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67 104 | 18.6,0.22876,0,8.56,0,0.52,6.405,85.4,2.7147,5,384,20.9,70.8,10.63 105 | 19.3,0.21161,0,8.56,0,0.52,6.137,87.4,2.7147,5,384,20.9,394.47,13.44 106 | 20.1,0.1396,0,8.56,0,0.52,6.167,90,2.421,5,384,20.9,392.69,12.33 107 | 19.5,0.13262,0,8.56,0,0.52,5.851,96.7,2.1069,5,384,20.9,394.05,16.47 108 | 19.5,0.1712,0,8.56,0,0.52,5.836,91.9,2.211,5,384,20.9,395.67,18.66 109 | 20.4,0.13117,0,8.56,0,0.52,6.127,85.2,2.1224,5,384,20.9,387.69,14.09 110 | 19.8,0.12802,0,8.56,0,0.52,6.474,97.1,2.4329,5,384,20.9,395.24,12.27 111 | 19.4,0.26363,0,8.56,0,0.52,6.229,91.2,2.5451,5,384,20.9,391.23,15.55 112 | 21.7,0.10793,0,8.56,0,0.52,6.195,54.4,2.7778,5,384,20.9,393.49,13 113 | 22.8,0.10084,0,10.01,0,0.547,6.715,81.6,2.6775,6,432,17.8,395.59,10.16 114 | 18.8,0.12329,0,10.01,0,0.547,5.913,92.9,2.3534,6,432,17.8,394.95,16.21 115 | 18.7,0.22212,0,10.01,0,0.547,6.092,95.4,2.548,6,432,17.8,396.9,17.09 116 | 18.5,0.14231,0,10.01,0,0.547,6.254,84.2,2.2565,6,432,17.8,388.74,10.45 117 | 18.3,0.17134,0,10.01,0,0.547,5.928,88.2,2.4631,6,432,17.8,344.91,15.76 118 | 21.2,0.13158,0,10.01,0,0.547,6.176,72.5,2.7301,6,432,17.8,393.3,12.04 119 | 19.2,0.15098,0,10.01,0,0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3 120 | 20.4,0.13058,0,10.01,0,0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37 121 | 19.3,0.14476,0,10.01,0,0.547,5.731,65.2,2.7592,6,432,17.8,391.5,13.61 122 | 22,0.06899,0,25.65,0,0.581,5.87,69.7,2.2577,2,188,19.1,389.15,14.37 123 | 20.3,0.07165,0,25.65,0,0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27 124 | 20.5,0.09299,0,25.65,0,0.581,5.961,92.9,2.0869,2,188,19.1,378.09,17.93 125 | 17.3,0.15038,0,25.65,0,0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41 126 | 18.8,0.09849,0,25.65,0,0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58 127 | 21.4,0.16902,0,25.65,0,0.581,5.986,88.4,1.9929,2,188,19.1,385.02,14.81 128 | 15.7,0.38735,0,25.65,0,0.581,5.613,95.6,1.7572,2,188,19.1,359.29,27.26 129 | 16.2,0.25915,0,21.89,0,0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19 130 | 18,0.32543,0,21.89,0,0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39 131 | 14.3,0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34 132 | 19.2,0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6 133 | 19.6,1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26 134 | 23,0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12 135 | 18.4,0.32982,0,21.89,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03 136 | 15.6,0.97617,0,21.89,0,0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31 137 | 18.1,0.55778,0,21.89,0,0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96 138 | 17.4,0.32264,0,21.89,0,0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9 139 | 17.1,0.35233,0,21.89,0,0.624,6.454,98.4,1.8498,4,437,21.2,394.08,14.59 140 | 13.3,0.2498,0,21.89,0,0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32 141 | 17.8,0.54452,0,21.89,0,0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46 142 | 14,0.2909,0,21.89,0,0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16 143 | 14.4,1.62864,0,21.89,0,0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41 144 | 13.4,3.32105,0,19.58,1,0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82 145 | 15.6,4.0974,0,19.58,0,0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42 146 | 11.8,2.77974,0,19.58,0,0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29 147 | 13.8,2.37934,0,19.58,0,0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8 148 | 15.6,2.15505,0,19.58,0,0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65 149 | 14.6,2.36862,0,19.58,0,0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53 150 | 17.8,2.33099,0,19.58,0,0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32 151 | 15.4,2.73397,0,19.58,0,0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45 152 | 21.5,1.6566,0,19.58,0,0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1 153 | 19.6,1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28 154 | 15.3,1.12658,0,19.58,1,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12 155 | 19.4,2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79 156 | 17,1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12 157 | 15.6,3.53501,0,19.58,1,0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02 158 | 13.1,2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14 159 | 41.3,1.22358,0,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59 160 | 24.3,1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43 161 | 23.3,1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39 162 | 27,1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5 163 | 50,1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73 164 | 50,1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92 165 | 50,1.51902,0,19.58,1,0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32 166 | 22.7,2.24236,0,19.58,0,0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64 167 | 25,2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81 168 | 50,2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7 169 | 23.8,1.80028,0,19.58,0,0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14 170 | 23.8,2.3004,0,19.58,0,0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1 171 | 22.3,2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32 172 | 17.4,1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43 173 | 19.1,2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03 174 | 23.1,0.13914,0,4.05,0,0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69 175 | 23.6,0.09178,0,4.05,0,0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04 176 | 22.6,0.08447,0,4.05,0,0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64 177 | 29.4,0.06664,0,4.05,0,0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33 178 | 23.2,0.07022,0,4.05,0,0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11 179 | 24.6,0.05425,0,4.05,0,0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29 180 | 29.9,0.06642,0,4.05,0,0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92 181 | 37.2,0.0578,0,2.46,0,0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04 182 | 39.8,0.06588,0,2.46,0,0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56 183 | 36.2,0.06888,0,2.46,0,0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45 184 | 37.9,0.09103,0,2.46,0,0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82 185 | 32.5,0.10008,0,2.46,0,0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68 186 | 26.4,0.08308,0,2.46,0,0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98 187 | 29.6,0.06047,0,2.46,0,0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15 188 | 50,0.05602,0,2.46,0,0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45 189 | 32,0.07875,45,3.44,0,0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68 190 | 29.8,0.12579,45,3.44,0,0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56 191 | 34.9,0.0837,45,3.44,0,0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39 192 | 37,0.09068,45,3.44,0,0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1 193 | 30.5,0.06911,45,3.44,0,0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69 194 | 36.4,0.08664,45,3.44,0,0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87 195 | 31.1,0.02187,60,2.93,0,0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03 196 | 29.1,0.01439,60,2.93,0,0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38 197 | 50,0.01381,80,0.46,0,0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97 198 | 33.3,0.04011,80,1.52,0,0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08 199 | 30.3,0.04666,80,1.52,0,0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61 200 | 34.6,0.03768,80,1.52,0,0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62 201 | 34.9,0.0315,95,1.47,0,0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56 202 | 32.9,0.01778,95,1.47,0,0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45 203 | 24.1,0.03445,82.5,2.03,0,0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43 204 | 42.3,0.02177,82.5,2.03,0,0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11 205 | 48.5,0.0351,95,2.68,0,0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81 206 | 50,0.02009,95,2.68,0,0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88 207 | 22.6,0.13642,0,10.59,0,0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87 208 | 24.4,0.22969,0,10.59,0,0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97 209 | 22.5,0.25199,0,10.59,0,0.489,5.783,72.7,4.3549,4,277,18.6,389.43,18.06 210 | 24.4,0.13587,0,10.59,1,0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66 211 | 20,0.43571,0,10.59,1,0.489,5.344,100,3.875,4,277,18.6,396.9,23.09 212 | 21.7,0.17446,0,10.59,1,0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27 213 | 19.3,0.37578,0,10.59,1,0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98 214 | 22.4,0.21719,0,10.59,1,0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03 215 | 28.1,0.14052,0,10.59,0,0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38 216 | 23.7,0.28955,0,10.59,0,0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55 217 | 25,0.19802,0,10.59,0,0.489,6.182,42.4,3.9454,4,277,18.6,393.63,9.47 218 | 23.3,0.0456,0,13.89,1,0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51 219 | 28.7,0.07013,0,13.89,0,0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69 220 | 21.5,0.11069,0,13.89,1,0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92 221 | 23,0.11425,0,13.89,1,0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5 222 | 26.7,0.35809,0,6.2,1,0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71 223 | 21.7,0.40771,0,6.2,1,0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46 224 | 27.5,0.62356,0,6.2,1,0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93 225 | 30.1,0.6147,0,6.2,0,0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6 226 | 44.8,0.31533,0,6.2,0,0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14 227 | 50,0.52693,0,6.2,0,0.504,8.725,83,2.8944,8,307,17.4,382,4.63 228 | 37.6,0.38214,0,6.2,0,0.504,8.04,86.5,3.2157,8,307,17.4,387.38,3.13 229 | 31.6,0.41238,0,6.2,0,0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36 230 | 46.7,0.29819,0,6.2,0,0.504,7.686,17,3.3751,8,307,17.4,377.51,3.92 231 | 31.5,0.44178,0,6.2,0,0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76 232 | 24.3,0.537,0,6.2,0,0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65 233 | 31.7,0.46296,0,6.2,0,0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25 234 | 41.7,0.57529,0,6.2,0,0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47 235 | 48.3,0.33147,0,6.2,0,0.507,8.247,70.4,3.6519,8,307,17.4,378.95,3.95 236 | 29,0.44791,0,6.2,1,0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05 237 | 24,0.33045,0,6.2,0,0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88 238 | 25.1,0.52058,0,6.2,1,0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54 239 | 31.5,0.51183,0,6.2,0,0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73 240 | 23.7,0.08244,30,4.93,0,0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36 241 | 23.3,0.09252,30,4.93,0,0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37 242 | 22,0.11329,30,4.93,0,0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38 243 | 20.1,0.10612,30,4.93,0,0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4 244 | 22.2,0.1029,30,4.93,0,0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22 245 | 23.7,0.12757,30,4.93,0,0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19 246 | 17.6,0.20608,22,5.86,0,0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5 247 | 18.5,0.19133,22,5.86,0,0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46 248 | 24.3,0.33983,22,5.86,0,0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16 249 | 20.5,0.19657,22,5.86,0,0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15 250 | 24.5,0.16439,22,5.86,0,0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52 251 | 26.2,0.19073,22,5.86,0,0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56 252 | 24.4,0.1403,22,5.86,0,0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9 253 | 24.8,0.21409,22,5.86,0,0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59 254 | 29.6,0.08221,22,5.86,0,0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53 255 | 42.8,0.36894,22,5.86,0,0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54 256 | 21.9,0.04819,80,3.64,0,0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57 257 | 20.9,0.03548,80,3.64,0,0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25 258 | 44,0.01538,90,3.75,0,0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11 259 | 50,0.61154,20,3.97,0,0.647,8.704,86.9,1.801,5,264,13,389.7,5.12 260 | 36,0.66351,20,3.97,0,0.647,7.333,100,1.8946,5,264,13,383.29,7.79 261 | 30.1,0.65665,20,3.97,0,0.647,6.842,100,2.0107,5,264,13,391.93,6.9 262 | 33.8,0.54011,20,3.97,0,0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59 263 | 43.1,0.53412,20,3.97,0,0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26 264 | 48.8,0.52014,20,3.97,0,0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91 265 | 31,0.82526,20,3.97,0,0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25 266 | 36.5,0.55007,20,3.97,0,0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1 267 | 22.8,0.76162,20,3.97,0,0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45 268 | 30.7,0.7857,20,3.97,0,0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79 269 | 50,0.57834,20,3.97,0,0.575,8.297,67,2.4216,5,264,13,384.54,7.44 270 | 43.5,0.5405,20,3.97,0,0.575,7.47,52.6,2.872,5,264,13,390.3,3.16 271 | 20.7,0.09065,20,6.96,1,0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65 272 | 21.1,0.29916,20,6.96,0,0.464,5.856,42.1,4.429,3,223,18.6,388.65,13 273 | 25.2,0.16211,20,6.96,0,0.464,6.24,16.3,4.429,3,223,18.6,396.9,6.59 274 | 24.4,0.1146,20,6.96,0,0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73 275 | 35.2,0.22188,20,6.96,1,0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58 276 | 32.4,0.05644,40,6.41,1,0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53 277 | 32,0.09604,40,6.41,0,0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98 278 | 33.2,0.10469,40,6.41,1,0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05 279 | 33.1,0.06127,40,6.41,1,0.447,6.826,27.6,4.8628,4,254,17.6,393.45,4.16 280 | 29.1,0.07978,40,6.41,0,0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19 281 | 35.1,0.21038,20,3.33,0,0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85 282 | 45.4,0.03578,20,3.33,0,0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76 283 | 35.4,0.03705,20,3.33,0,0.4429,6.968,37.2,5.2447,5,216,14.9,392.23,4.59 284 | 46,0.06129,20,3.33,1,0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01 285 | 50,0.01501,90,1.21,1,0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16 286 | 32.2,0.00906,90,2.97,0,0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85 287 | 22,0.01096,55,2.25,0,0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23 288 | 20.1,0.01965,80,1.76,0,0.385,6.23,31.5,9.0892,1,241,18.2,341.6,12.93 289 | 23.2,0.03871,52.5,5.32,0,0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14 290 | 22.3,0.0459,52.5,5.32,0,0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6 291 | 24.8,0.04297,52.5,5.32,0,0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51 292 | 28.5,0.03502,80,4.95,0,0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33 293 | 37.3,0.07886,80,4.95,0,0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56 294 | 27.9,0.03615,80,4.95,0,0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7 295 | 23.9,0.08265,0,13.92,0,0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58 296 | 21.7,0.08199,0,13.92,0,0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4 297 | 28.6,0.12932,0,13.92,0,0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27 298 | 27.1,0.05372,0,13.92,0,0.437,6.549,51,5.9604,4,289,16,392.85,7.39 299 | 20.3,0.14103,0,13.92,0,0.437,5.79,58,6.32,4,289,16,396.9,15.84 300 | 22.5,0.06466,70,2.24,0,0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97 301 | 29,0.05561,70,2.24,0,0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74 302 | 24.8,0.04417,70,2.24,0,0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07 303 | 22,0.03537,34,6.09,0,0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5 304 | 26.4,0.09266,34,6.09,0,0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67 305 | 33.1,0.1,34,6.09,0,0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86 306 | 36.1,0.05515,33,2.18,0,0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93 307 | 28.4,0.05479,33,2.18,0,0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93 308 | 33.4,0.07503,33,2.18,0,0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47 309 | 28.2,0.04932,33,2.18,0,0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53 310 | 22.8,0.49298,0,9.9,0,0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54 311 | 20.3,0.3494,0,9.9,0,0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97 312 | 16.1,2.63548,0,9.9,0,0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64 313 | 22.1,0.79041,0,9.9,0,0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98 314 | 19.4,0.26169,0,9.9,0,0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72 315 | 21.6,0.26938,0,9.9,0,0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9 316 | 23.8,0.3692,0,9.9,0,0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28 317 | 16.2,0.25356,0,9.9,0,0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5 318 | 17.8,0.31827,0,9.9,0,0.544,5.914,83.2,3.9986,4,304,18.4,390.7,18.33 319 | 19.8,0.24522,0,9.9,0,0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94 320 | 23.1,0.40202,0,9.9,0,0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36 321 | 21,0.47547,0,9.9,0,0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73 322 | 23.8,0.1676,0,7.38,0,0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2 323 | 23.1,0.18159,0,7.38,0,0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87 324 | 20.4,0.35114,0,7.38,0,0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7 325 | 18.5,0.28392,0,7.38,0,0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74 326 | 25,0.34109,0,7.38,0,0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12 327 | 24.6,0.19186,0,7.38,0,0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08 328 | 23,0.30347,0,7.38,0,0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15 329 | 22.2,0.24103,0,7.38,0,0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79 330 | 19.3,0.06617,0,3.24,0,0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97 331 | 22.6,0.06724,0,3.24,0,0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34 332 | 19.8,0.04544,0,3.24,0,0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09 333 | 17.1,0.05023,35,6.06,0,0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43 334 | 19.4,0.03466,35,6.06,0,0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83 335 | 22.2,0.05083,0,5.19,0,0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68 336 | 20.7,0.03738,0,5.19,0,0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75 337 | 21.1,0.03961,0,5.19,0,0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01 338 | 19.5,0.03427,0,5.19,0,0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8 339 | 18.5,0.03041,0,5.19,0,0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56 340 | 20.6,0.03306,0,5.19,0,0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51 341 | 19,0.05497,0,5.19,0,0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74 342 | 18.7,0.06151,0,5.19,0,0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29 343 | 32.7,0.01301,35,1.52,0,0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49 344 | 16.5,0.02498,0,1.89,0,0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65 345 | 23.9,0.02543,55,3.78,0,0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18 346 | 31.2,0.03049,55,3.78,0,0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61 347 | 17.5,0.03113,0,4.39,0,0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53 348 | 17.2,0.06162,0,4.39,0,0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67 349 | 23.1,0.0187,85,4.15,0,0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36 350 | 24.5,0.01501,80,2.01,0,0.435,6.635,29.7,8.344,4,280,17,390.94,5.99 351 | 26.6,0.02899,40,1.25,0,0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89 352 | 22.9,0.06211,40,1.25,0,0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98 353 | 24.1,0.0795,60,1.69,0,0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49 354 | 18.6,0.07244,60,1.69,0,0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79 355 | 30.1,0.01709,90,2.02,0,0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5 356 | 18.2,0.04301,80,1.91,0,0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05 357 | 20.6,0.10659,80,1.91,0,0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57 358 | 17.8,8.98296,0,18.1,1,0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6 359 | 21.7,3.8497,0,18.1,1,0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27 360 | 22.7,5.20177,0,18.1,1,0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48 361 | 22.6,4.26131,0,18.1,0,0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67 362 | 25,4.54192,0,18.1,0,0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79 363 | 19.9,3.83684,0,18.1,0,0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19 364 | 20.8,3.67822,0,18.1,0,0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19 365 | 16.8,4.22239,0,18.1,1,0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64 366 | 21.9,3.47428,0,18.1,1,0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29 367 | 27.5,4.55587,0,18.1,0,0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12 368 | 21.9,3.69695,0,18.1,0,0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14 369 | 23.1,13.5222,0,18.1,0,0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33 370 | 50,4.89822,0,18.1,0,0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26 371 | 50,5.66998,0,18.1,1,0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73 372 | 50,6.53876,0,18.1,1,0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96 373 | 50,9.2323,0,18.1,0,0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53 374 | 50,8.26725,0,18.1,1,0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88 375 | 13.8,11.1081,0,18.1,0,0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77 376 | 13.8,18.4982,0,18.1,0,0.668,4.138,100,1.137,24,666,20.2,396.9,37.97 377 | 15,19.6091,0,18.1,0,0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44 378 | 13.9,15.288,0,18.1,0,0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24 379 | 13.3,9.82349,0,18.1,0,0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24 380 | 13.1,23.6482,0,18.1,0,0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69 381 | 10.2,17.8667,0,18.1,0,0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78 382 | 10.4,88.9762,0,18.1,0,0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21 383 | 10.9,15.8744,0,18.1,0,0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08 384 | 11.3,9.18702,0,18.1,0,0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6 385 | 12.3,7.99248,0,18.1,0,0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56 386 | 8.8,20.0849,0,18.1,0,0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63 387 | 7.2,16.8118,0,18.1,0,0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81 388 | 10.5,24.3938,0,18.1,0,0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28 389 | 7.4,22.5971,0,18.1,0,0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99 390 | 10.2,14.3337,0,18.1,0,0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62 391 | 11.5,8.15174,0,18.1,0,0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85 392 | 15.1,6.96215,0,18.1,0,0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11 393 | 23.2,5.29305,0,18.1,0,0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76 394 | 9.7,11.5779,0,18.1,0,0.7,5.036,97,1.77,24,666,20.2,396.9,25.68 395 | 13.8,8.64476,0,18.1,0,0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17 396 | 12.7,13.3598,0,18.1,0,0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35 397 | 13.1,8.71675,0,18.1,0,0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12 398 | 12.5,5.87205,0,18.1,0,0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37 399 | 8.5,7.67202,0,18.1,0,0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92 400 | 5,38.3518,0,18.1,0,0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59 401 | 6.3,9.91655,0,18.1,0,0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97 402 | 5.6,25.0461,0,18.1,0,0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77 403 | 7.2,14.2362,0,18.1,0,0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32 404 | 12.1,9.59571,0,18.1,0,0.693,6.404,100,1.639,24,666,20.2,376.11,20.31 405 | 8.3,24.8017,0,18.1,0,0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77 406 | 8.5,41.5292,0,18.1,0,0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38 407 | 5,67.9208,0,18.1,0,0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98 408 | 11.9,20.7162,0,18.1,0,0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34 409 | 27.9,11.9511,0,18.1,0,0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13 410 | 17.2,7.40389,0,18.1,0,0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4 411 | 27.5,14.4383,0,18.1,0,0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78 412 | 15,51.1358,0,18.1,0,0.597,5.757,100,1.413,24,666,20.2,2.6,10.11 413 | 17.2,14.0507,0,18.1,0,0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22 414 | 17.9,18.811,0,18.1,0,0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37 415 | 16.3,28.6558,0,18.1,0,0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08 416 | 7,45.7461,0,18.1,0,0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98 417 | 7.2,18.0846,0,18.1,0,0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05 418 | 7.5,10.8342,0,18.1,0,0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79 419 | 10.4,25.9406,0,18.1,0,0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64 420 | 8.8,73.5341,0,18.1,0,0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62 421 | 8.4,11.8123,0,18.1,0,0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74 422 | 16.7,11.0874,0,18.1,0,0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02 423 | 14.2,7.02259,0,18.1,0,0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7 424 | 20.8,12.0482,0,18.1,0,0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1 425 | 13.4,7.05042,0,18.1,0,0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29 426 | 11.7,8.79212,0,18.1,0,0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16 427 | 8.3,15.8603,0,18.1,0,0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39 428 | 10.2,12.2472,0,18.1,0,0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69 429 | 10.9,37.6619,0,18.1,0,0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52 430 | 11,7.36711,0,18.1,0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52 431 | 9.5,9.33889,0,18.1,0,0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08 432 | 14.5,8.49213,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64 433 | 14.1,10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69 434 | 16.1,6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03 435 | 14.3,5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22 436 | 11.7,13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17 437 | 13.4,11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27 438 | 9.6,14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05 439 | 8.7,15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45 440 | 8.4,13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02 441 | 12.8,9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88 442 | 10.5,22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11 443 | 17.1,9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52 444 | 18.4,5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59 445 | 15.4,9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85 446 | 10.8,12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79 447 | 11.8,10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98 448 | 14.9,6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79 449 | 12.6,9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44 450 | 14.1,9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13 451 | 13,7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31 452 | 13.4,6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44 453 | 15.2,5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73 454 | 16.1,5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27 455 | 17.8,8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74 456 | 14.9,9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71 457 | 14.1,4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13 458 | 12.7,4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01 459 | 13.5,8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94 460 | 14.9,7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23 461 | 20,6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7 462 | 16.4,4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42 463 | 17.7,3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65 464 | 19.5,6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99 465 | 20.2,5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29 466 | 21.4,7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22 467 | 19.9,3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13 468 | 19,3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15 469 | 19.1,4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32 470 | 19.1,15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13 471 | 20.1,13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76 472 | 19.9,4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29 473 | 19.6,4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87 474 | 23.2,3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36 475 | 29.8,4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66 476 | 13.8,8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14 477 | 13.3,6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1 478 | 16.7,4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68 479 | 12,15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91 480 | 14.6,10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03 481 | 21.4,14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11 482 | 23,5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74 483 | 23.7,5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74 484 | 25,5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01 485 | 21.8,2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42 486 | 20.6,2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34 487 | 21.2,3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58 488 | 19.1,5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98 489 | 20.6,4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45 490 | 15.2,0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06 491 | 7,0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97 492 | 8.1,0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68 493 | 13.6,0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07 494 | 20.1,0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35 495 | 21.8,0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01 496 | 24.5,0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59 497 | 23.1,0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6 498 | 19.7,0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14 499 | 18.3,0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1 500 | 21.2,0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92 501 | 17.5,0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1 502 | 16.8,0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33 503 | 22.4,0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67 504 | 20.6,0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08 505 | 23.9,0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64 506 | 22,0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48 507 | 11.9,0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88 508 | -------------------------------------------------------------------------------- /TEST/ds/ds.MachineCPU.csv: -------------------------------------------------------------------------------- 1 | PRP,MYCT,MMIN,MMAX,CACH,CHMIN,CHMAX 2 | 198,125,256,6000,256,16,128 3 | 269,29,8000,32000,32,8,32 4 | 220,29,8000,32000,32,8,32 5 | 172,29,8000,32000,32,8,32 6 | 132,29,8000,16000,32,8,16 7 | 318,26,8000,32000,64,8,32 8 | 367,23,16000,32000,64,16,32 9 | 489,23,16000,32000,64,16,32 10 | 636,23,16000,64000,64,16,32 11 | 1144,23,32000,64000,128,32,64 12 | 38,400,1000,3000,0,1,2 13 | 40,400,512,3500,4,1,6 14 | 92,60,2000,8000,65,1,8 15 | 138,50,4000,16000,65,1,8 16 | 10,350,64,64,0,1,4 17 | 35,200,512,16000,0,4,32 18 | 19,167,524,2000,8,4,15 19 | 28,143,512,5000,0,7,32 20 | 31,143,1000,2000,0,5,16 21 | 120,110,5000,5000,142,8,64 22 | 30,143,1500,6300,0,5,32 23 | 33,143,3100,6200,0,5,20 24 | 61,143,2300,6200,0,6,64 25 | 76,110,3100,6200,0,6,64 26 | 23,320,128,6000,0,1,12 27 | 69,320,512,2000,4,1,3 28 | 33,320,256,6000,0,1,6 29 | 27,320,256,3000,4,1,3 30 | 77,320,512,5000,4,1,5 31 | 27,320,256,5000,4,1,6 32 | 274,25,1310,2620,131,12,24 33 | 368,25,1310,2620,131,12,24 34 | 32,50,2620,10480,30,12,24 35 | 63,50,2620,10480,30,12,24 36 | 106,56,5240,20970,30,12,24 37 | 208,64,5240,20970,30,12,24 38 | 20,50,500,2000,8,1,4 39 | 29,50,1000,4000,8,1,5 40 | 71,50,2000,8000,8,1,5 41 | 26,50,1000,4000,8,3,5 42 | 36,50,1000,8000,8,3,5 43 | 40,50,2000,16000,8,3,5 44 | 52,50,2000,16000,8,3,6 45 | 60,50,2000,16000,8,3,6 46 | 72,133,1000,12000,9,3,12 47 | 72,133,1000,8000,9,3,12 48 | 18,810,512,512,8,1,1 49 | 20,810,1000,5000,0,1,1 50 | 40,320,512,8000,4,1,5 51 | 62,200,512,8000,8,1,8 52 | 24,700,384,8000,0,1,1 53 | 24,700,256,2000,0,1,1 54 | 138,140,1000,16000,16,1,3 55 | 36,200,1000,8000,0,1,2 56 | 26,110,1000,4000,16,1,2 57 | 60,110,1000,12000,16,1,2 58 | 71,220,1000,8000,16,1,2 59 | 12,800,256,8000,0,1,4 60 | 14,800,256,8000,0,1,4 61 | 20,800,256,8000,0,1,4 62 | 16,800,256,8000,0,1,4 63 | 22,800,256,8000,0,1,4 64 | 36,125,512,1000,0,8,20 65 | 144,75,2000,8000,64,1,38 66 | 144,75,2000,16000,64,1,38 67 | 259,75,2000,16000,128,1,38 68 | 17,90,256,1000,0,3,10 69 | 26,105,256,2000,0,3,10 70 | 32,105,1000,4000,0,3,24 71 | 32,105,2000,4000,8,3,19 72 | 62,75,2000,8000,8,3,24 73 | 64,75,3000,8000,8,3,48 74 | 22,175,256,2000,0,3,24 75 | 36,300,768,3000,0,6,24 76 | 44,300,768,3000,6,6,24 77 | 50,300,768,12000,6,6,24 78 | 45,300,768,4500,0,1,24 79 | 53,300,384,12000,6,1,24 80 | 36,300,192,768,6,6,24 81 | 84,180,768,12000,6,1,31 82 | 16,330,1000,3000,0,2,4 83 | 38,300,1000,4000,8,3,64 84 | 38,300,1000,16000,8,2,112 85 | 16,330,1000,2000,0,1,2 86 | 22,330,1000,4000,0,3,6 87 | 29,140,2000,4000,0,3,6 88 | 40,140,2000,4000,0,4,8 89 | 35,140,2000,4000,8,1,20 90 | 134,140,2000,32000,32,1,20 91 | 66,140,2000,8000,32,1,54 92 | 141,140,2000,32000,32,1,54 93 | 189,140,2000,32000,32,1,54 94 | 22,140,2000,4000,8,1,20 95 | 132,57,4000,16000,1,6,12 96 | 237,57,4000,24000,64,12,16 97 | 465,26,16000,32000,64,16,24 98 | 465,26,16000,32000,64,8,24 99 | 277,26,8000,32000,0,8,24 100 | 185,26,8000,16000,0,8,16 101 | 6,480,96,512,0,1,1 102 | 24,203,1000,2000,0,1,5 103 | 45,115,512,6000,16,1,6 104 | 7,1100,512,1500,0,1,1 105 | 13,1100,768,2000,0,1,1 106 | 16,600,768,2000,0,1,1 107 | 32,400,2000,4000,0,1,1 108 | 32,400,4000,8000,0,1,1 109 | 11,900,1000,1000,0,1,2 110 | 11,900,512,1000,0,1,2 111 | 18,900,1000,4000,4,1,2 112 | 22,900,1000,4000,8,1,2 113 | 37,900,2000,4000,0,3,6 114 | 40,225,2000,4000,8,3,6 115 | 34,225,2000,4000,8,3,6 116 | 50,180,2000,8000,8,1,6 117 | 76,185,2000,16000,16,1,6 118 | 66,180,2000,16000,16,1,6 119 | 24,225,1000,4000,2,3,6 120 | 49,25,2000,12000,8,1,4 121 | 66,25,2000,12000,16,3,5 122 | 100,17,4000,16000,8,6,12 123 | 133,17,4000,16000,32,6,12 124 | 12,1500,768,1000,0,0,0 125 | 18,1500,768,2000,0,0,0 126 | 20,800,768,2000,0,0,0 127 | 27,50,2000,4000,0,3,6 128 | 45,50,2000,8000,8,3,6 129 | 56,50,2000,8000,8,1,6 130 | 70,50,2000,16000,24,1,6 131 | 80,50,2000,16000,24,1,6 132 | 136,50,8000,16000,48,1,10 133 | 16,100,1000,8000,0,2,6 134 | 26,100,1000,8000,24,2,6 135 | 32,100,1000,8000,24,3,6 136 | 45,50,2000,16000,12,3,16 137 | 54,50,2000,16000,24,6,16 138 | 65,50,2000,16000,24,6,16 139 | 30,150,512,4000,0,8,128 140 | 50,115,2000,8000,16,1,3 141 | 40,115,2000,4000,2,1,5 142 | 62,92,2000,8000,32,1,6 143 | 60,92,2000,8000,32,1,6 144 | 50,92,2000,8000,4,1,6 145 | 66,75,4000,16000,16,1,6 146 | 86,60,4000,16000,32,1,6 147 | 74,60,2000,16000,64,5,8 148 | 93,60,4000,16000,64,5,8 149 | 111,50,4000,16000,64,5,10 150 | 143,72,4000,16000,64,8,16 151 | 105,72,2000,8000,16,6,8 152 | 214,40,8000,16000,32,8,16 153 | 277,40,8000,32000,64,8,24 154 | 370,35,8000,32000,64,8,24 155 | 510,38,16000,32000,128,16,32 156 | 214,48,4000,24000,32,8,24 157 | 326,38,8000,32000,64,8,24 158 | 510,30,16000,32000,256,16,24 159 | 8,112,1000,1000,0,1,4 160 | 12,84,1000,2000,0,1,6 161 | 17,56,1000,4000,0,1,6 162 | 21,56,2000,6000,0,1,8 163 | 24,56,2000,8000,0,1,8 164 | 34,56,4000,8000,0,1,8 165 | 42,56,4000,12000,0,1,8 166 | 46,56,4000,16000,0,1,8 167 | 51,38,4000,8000,32,16,32 168 | 116,38,4000,8000,32,16,32 169 | 100,38,8000,16000,64,4,8 170 | 140,38,8000,24000,160,4,8 171 | 212,38,4000,16000,128,16,32 172 | 25,200,1000,2000,0,1,2 173 | 30,200,1000,4000,0,1,4 174 | 41,200,2000,8000,64,1,5 175 | 25,250,512,4000,0,1,7 176 | 50,250,512,4000,0,4,7 177 | 50,250,1000,16000,1,1,8 178 | 30,160,512,4000,2,1,5 179 | 32,160,512,2000,2,3,8 180 | 38,160,1000,4000,8,1,14 181 | 60,160,1000,8000,16,1,14 182 | 109,160,2000,8000,32,1,13 183 | 6,240,512,1000,8,1,3 184 | 11,240,512,2000,8,1,5 185 | 22,105,2000,4000,8,3,8 186 | 33,105,2000,6000,16,6,16 187 | 58,105,2000,8000,16,4,14 188 | 130,52,4000,16000,32,4,12 189 | 75,70,4000,12000,8,6,8 190 | 113,59,4000,12000,32,6,12 191 | 188,59,8000,16000,64,12,24 192 | 173,26,8000,24000,32,8,16 193 | 248,26,8000,32000,64,12,16 194 | 405,26,8000,32000,128,24,32 195 | 70,116,2000,8000,32,5,28 196 | 114,50,2000,32000,24,6,26 197 | 208,50,2000,32000,48,26,52 198 | 307,50,2000,32000,112,52,104 199 | 397,50,4000,32000,112,52,104 200 | 915,30,8000,64000,96,12,176 201 | 1150,30,8000,64000,128,12,176 202 | 12,180,262,4000,0,1,3 203 | 14,180,512,4000,0,1,3 204 | 18,180,262,4000,0,1,3 205 | 21,180,512,4000,0,1,3 206 | 42,124,1000,8000,0,1,8 207 | 46,98,1000,8000,32,2,8 208 | 52,125,2000,8000,0,2,14 209 | 67,480,512,8000,32,0,0 210 | 45,480,1000,4000,0,0,0 211 | -------------------------------------------------------------------------------- /TEST/ds/ds.automobile.csv: -------------------------------------------------------------------------------- 1 | price,symboling,wheelBase,length,width,height,curbWeight,engineSize,bore,stroke,compressionRatio,horsepower,peakRpm,cityMpg,highwayMpg 2 | 6488,1,95.7,158.7,63.6,54.5,2015,92,3.05,3.03,9,62,4800,31,38 3 | 17669,2,98.4,176.2,65.6,53,2975,146,3.62,3.5,9.3,116,4800,24,30 4 | 6669,2,93.7,157.3,64.4,50.8,2004,92,2.97,3.23,9.4,68,5500,31,38 5 | 13950,2,99.8,176.6,66.2,54.3,2337,109,3.19,3.4,10,102,5500,24,30 6 | 6785,0,94.3,170.7,61.8,53.5,2337,111,3.31,3.23,8.5,78,4800,24,29 7 | 7775,0,97.2,172,65.4,52.5,2190,108,3.62,2.64,9.5,82,4400,28,33 8 | 17075,0,114.2,198.9,68.4,58.7,3485,152,3.7,3.52,21,95,4150,25,25 9 | 40960,0,120.9,208.1,71.7,56.7,3900,308,3.8,3.35,8,184,4500,14,16 10 | 18150,3,99.1,186.6,66.5,56.1,2808,121,3.54,3.07,9,160,5500,19,26 11 | 31600,-1,115.6,202.6,71.7,56.3,3770,183,3.58,3.64,21.5,123,4350,22,25 12 | 12940,-2,104.3,188.8,67.2,56.2,2912,141,3.78,3.15,9.5,114,5400,23,28 13 | 7898,0,95.7,166.3,64.4,53,2275,110,3.27,3.35,22.5,56,4500,34,36 14 | 6849,1,94.5,165.3,63.8,54.5,1938,97,3.15,3.29,9.4,69,5200,31,37 15 | 7395,1,93.1,166.8,64.2,54.1,1950,91,3.08,3.15,9,68,5000,31,38 16 | 32528,3,89.5,168.9,65,51.6,2756,194,3.74,2.9,9.5,207,5900,17,25 17 | 16500,3,88.6,168.8,64.1,48.8,2548,130,3.47,2.68,9,111,5000,21,27 18 | 7198,0,95.7,166.3,64.4,52.8,2109,98,3.19,3.03,9,70,4800,30,37 19 | 15250,2,99.8,177.3,66.3,53.1,2507,136,3.19,3.4,8.5,110,5500,19,25 20 | 9988,-1,102.4,175.6,66.5,53.9,2414,122,3.31,3.54,8.7,92,4200,27,32 21 | 12945,0,96.5,175.4,65.2,54.1,2465,110,3.15,3.58,9,101,5800,24,28 22 | 32250,0,113,199.6,69.6,52.8,4066,258,3.63,4.17,8.1,176,4750,15,19 23 | 11900,0,107.9,186.7,68.4,56.7,3020,120,3.46,3.19,8.4,97,5000,19,24 24 | 6529,1,93.7,150,64,52.6,1940,92,2.91,3.41,9.2,76,6000,30,34 25 | 10198,0,97,173.5,65.4,53,2455,108,3.62,2.64,9,94,5200,25,31 26 | 6918,0,95.7,169.7,63.6,59.1,2280,92,3.05,3.03,9,62,4800,31,37 27 | 18920,1,105.8,192.7,71.4,55.7,2954,136,3.19,3.4,8.5,110,5500,19,25 28 | 7609,1,93.7,167.3,63.8,50.8,2191,98,2.97,3.23,9.4,68,5500,31,38 29 | 8189,1,96.3,172.4,65.4,51.6,2405,122,3.35,3.46,8.5,88,5000,25,32 30 | 8921,-1,103.3,174.6,64.6,59.8,2535,122,3.34,3.46,8.5,88,5000,24,30 31 | 7299,1,94.5,165.3,63.8,54.5,1951,97,3.15,3.29,9.4,69,5200,31,37 32 | 6692,1,93.7,157.3,63.8,50.6,1989,90,2.97,3.23,9.4,68,5500,31,38 33 | 6695,1,93.1,166.8,64.2,54.1,1945,91,3.03,3.15,9,68,5000,31,38 34 | 9233,0,97,172,65.4,54.3,2385,108,3.62,2.64,9,82,4800,24,25 35 | 16503,1,102.7,178.4,68,54.8,2910,140,3.78,3.12,8,175,5000,19,24 36 | 8358,0,95.7,166.3,64.4,52.8,2122,98,3.19,3.03,9,70,4800,28,34 37 | 12440,0,114.2,198.9,68.4,58.7,3230,120,3.46,3.19,8.4,97,5000,19,24 38 | 35056,3,96.6,180.3,70.5,50.8,3685,234,3.46,3.1,8.3,155,4750,16,18 39 | 16695,0,114.2,198.9,68.4,56.7,3285,120,3.46,2.19,8.4,95,5000,19,24 40 | 7957,1,93.7,157.3,63.8,50.8,2128,98,3.03,3.39,7.6,102,5500,24,30 41 | 15580,0,107.9,186.7,68.4,56.7,3075,120,3.46,2.19,8.4,95,5000,19,24 42 | 9279,1,96.3,172.4,65.4,51.6,2403,110,3.17,3.46,7.5,116,5500,23,30 43 | 34184,-1,115.6,202.6,71.7,56.5,3740,234,3.46,3.1,8.3,155,4750,16,18 44 | 7995,2,97.3,171.7,65.5,55.7,2264,97,3.01,3.4,23,52,4800,37,46 45 | 6338,1,95.7,158.7,63.6,54.5,2040,92,3.05,3.03,9,62,4800,31,38 46 | 10245,0,98.8,177.8,66.5,55.5,2410,122,3.39,3.39,8.6,84,4800,26,32 47 | 30760,0,103.5,189,66.9,55.7,3230,209,3.62,3.39,8,182,5400,16,22 48 | 12964,3,95.9,173.2,66.3,50.2,2811,156,3.6,3.9,7,145,5000,19,24 49 | 21485,-1,109.1,188.8,68.9,55.5,3012,173,3.58,2.87,8.8,134,5500,18,23 50 | 11245,0,98.8,177.8,66.5,55.5,2425,122,3.39,3.39,8.6,84,4800,26,32 51 | 9639,2,98.4,176.2,65.6,52,2536,146,3.62,3.5,9.3,116,4800,24,30 52 | 14869,3,95.9,173.2,66.3,50.2,2921,156,3.59,3.86,7,145,5000,19,24 53 | 6649,1,94.5,165.3,63.8,54.5,1918,97,3.15,3.29,9.4,69,5200,31,37 54 | 6855,2,86.6,144.6,63.9,50.8,1819,92,2.91,3.41,9.2,76,6000,31,38 55 | 23875,1,105.8,192.7,71.4,55.9,3086,131,3.13,3.4,8.3,140,5500,17,20 56 | 7126,0,97.2,172,65.4,52.5,2145,108,3.62,2.64,9.5,82,4800,32,37 57 | 11549,2,98.4,176.2,65.6,52,2714,146,3.62,3.5,9.3,116,4800,24,30 58 | 11199,2,98.4,176.2,65.6,52,2679,146,3.62,3.5,9.3,116,4800,24,30 59 | 24565,1,103.5,189,66.9,55.7,3055,164,3.31,3.19,9,121,4250,20,25 60 | 16430,2,101.2,176.8,64.8,54.3,2395,108,3.5,2.8,8.8,101,5800,23,29 61 | 8495,2,97.3,171.7,65.5,55.7,2275,109,3.19,3.4,9,85,5250,27,34 62 | 8495,0,98.8,177.8,66.5,55.5,2410,122,3.39,3.39,8.6,84,4800,26,32 63 | 8845,1,98.8,177.8,66.5,53.7,2385,122,3.39,3.39,8.6,84,4800,26,32 64 | 22625,-1,109.1,188.8,68.9,55.5,3062,141,3.78,3.15,9.5,114,5400,19,25 65 | 36880,0,110,197,70.9,56.3,3505,209,3.62,3.39,8,182,5400,15,20 66 | 6938,0,95.7,166.3,64.4,53,2081,98,3.19,3.03,9,70,4800,30,37 67 | 10295,0,96.5,175.4,62.5,54.1,2372,110,3.15,3.58,9,86,5800,27,33 68 | 19699,3,91.3,170.7,67.9,49.7,3139,181,3.43,3.27,7.8,200,5200,17,23 69 | 21105,0,101.2,176.8,64.8,54.3,2765,164,3.31,3.19,9,121,4250,21,28 70 | 6189,2,93.7,157.3,64.4,50.8,1944,92,2.97,3.23,9.4,68,5500,31,38 71 | 7609,1,93.7,157.3,63.8,50.6,1989,90,2.97,3.23,9.4,68,5500,31,38 72 | 14399,0,100.4,184.6,66.5,56.1,3296,181,3.43,3.27,9,152,5200,17,22 73 | 15690,-1,104.5,187.8,66.5,54.1,3131,171,3.27,3.35,9.2,156,5200,20,24 74 | 7898,0,95.7,169.7,63.6,59.1,2290,92,3.05,3.03,9,62,4800,27,32 75 | 28176,0,106.7,187.5,70.3,54.9,3495,183,3.58,3.64,21.5,123,4350,22,25 76 | 10795,0,98.8,177.8,66.5,55.5,2443,122,3.39,3.39,22.7,64,4650,36,42 77 | 41315,0,103.5,193.8,67.9,53.7,3380,209,3.62,3.39,8,182,5400,16,22 78 | 12170,2,99.1,186.6,66.5,56.1,2695,121,3.54,3.07,9.3,110,5250,21,28 79 | 28248,-1,110,190.9,70.3,58.7,3750,183,3.58,3.64,21.5,123,4350,22,25 80 | 7295,0,96.5,157.1,63.9,58.3,2024,92,2.92,3.41,9.2,76,6000,30,34 81 | 7689,1,93,157.3,63.8,50.8,2145,98,3.03,3.39,7.6,102,5500,24,30 82 | 6692,1,93.7,167.3,63.8,50.8,1989,90,2.97,3.23,9.4,68,5500,31,38 83 | 7799,1,94.5,165.6,63.8,53.3,2028,97,3.15,3.29,9.4,69,5200,31,37 84 | 8249,2,95.1,162.4,63.8,53.3,2008,97,3.15,3.29,9.4,69,5200,31,37 85 | 5348,1,95.7,158.7,63.6,54.5,1985,92,3.05,3.03,9,62,4800,35,39 86 | 18950,-1,104.3,188.8,67.2,57.5,3157,130,3.62,3.15,7.5,162,5100,17,22 87 | 7975,2,97.3,171.7,65.5,55.7,2209,109,3.19,3.4,9,85,5250,27,34 88 | 7999,1,94.5,170.2,63.8,53.5,2037,97,3.15,3.29,9.4,69,5200,31,37 89 | 17450,2,99.4,176.6,66.4,54.3,2824,136,3.19,3.4,8,115,5500,18,22 90 | 18280,0,104.9,175,66.1,54.4,2670,140,3.76,3.16,8,120,5000,19,27 91 | 5572,1,93.7,157.3,63.8,50.8,1876,90,2.97,3.23,9.41,68,5500,37,41 92 | 12290,0,100.4,183.1,66.9,55.1,2563,109,3.19,3.4,9,88,5500,25,31 93 | 11248,-1,102.4,175.6,66.5,53.9,2458,122,3.31,3.54,8.7,92,4200,27,32 94 | 7738,0,95.7,166.3,64.4,53,2094,98,3.19,3.03,9,70,4800,38,47 95 | 11595,3,94.5,159.3,64.2,55.6,2254,109,3.19,3.4,8.5,90,5500,24,29 96 | 9258,0,95.7,166.3,64.4,52.8,2140,98,3.19,3.03,9,70,4800,28,34 97 | 8013,0,96.9,173.6,65.4,54.9,2420,108,3.62,2.64,9,82,4800,23,29 98 | 6295,1,94.5,155.9,63.6,52,1874,90,3.03,3.11,9.6,70,5400,38,43 99 | 9279,-1,96.3,172.4,65.4,51.6,2403,110,3.17,3.46,7.5,116,5500,23,30 100 | 15750,-1,104.5,187.8,66.5,54.1,3151,161,3.27,3.35,9.2,156,5200,19,24 101 | 10595,1,98.8,177.8,66.5,53.7,2385,122,3.39,3.39,8.6,84,4800,26,32 102 | 5118,2,93.7,156.9,63.4,53.7,2050,97,3.62,2.36,9,69,4900,31,36 103 | 7295,0,96.5,163.4,64,54.5,2010,92,2.91,3.41,9.2,76,6000,30,34 104 | 18420,-2,104.3,188.8,67.2,56.2,3045,130,3.62,3.15,7.5,162,5100,17,22 105 | 16845,-1,109.1,188.8,68.9,55.5,2952,141,3.78,3.15,9.5,114,5400,23,28 106 | 9960,0,97.2,172,65.4,52.5,2340,108,3.62,2.64,9,94,5200,26,32 107 | 25552,-1,110,190.9,70.3,56.5,3515,183,3.58,3.64,21.5,123,4350,22,25 108 | 6795,1,93.1,159.1,64.2,54.1,1905,91,3.03,3.15,9,68,5000,31,38 109 | 19045,-1,109.1,188.8,68.8,55.5,3049,141,3.78,3.15,8.7,160,5300,19,25 110 | 8845,0,96.5,175.4,65.2,54.1,2304,110,3.15,3.58,9,86,5800,27,33 111 | 16630,0,107.9,186.7,68.4,56.7,3075,120,3.46,3.19,8.4,97,5000,19,24 112 | 13499,0,100.4,184.6,66.5,55.1,3060,181,3.43,3.27,9,152,5200,19,25 113 | 13415,-1,104.3,188.8,67.2,57.5,3034,141,3.78,3.15,9.5,114,5400,23,28 114 | 5499,1,94.5,165.3,63.8,54.5,1889,97,3.15,3.29,9.4,69,5200,31,37 115 | 7129,1,93.7,150,64,52.6,1956,92,2.91,3.41,9.2,76,6000,30,34 116 | 7957,1,93.7,157.3,63.8,50.8,2128,98,3.03,3.39,7.6,102,5500,24,30 117 | 8778,0,95.7,169.7,63.6,59.1,3110,92,3.05,3.03,9,62,4800,27,32 118 | 8238,1,94.5,168.7,64,52.6,2204,98,3.19,3.03,9,70,4800,29,34 119 | 15510,2,99.1,186.6,66.5,56.1,2758,121,3.54,3.07,9.3,110,5250,21,28 120 | 7499,1,94.5,165.3,63.8,54.5,1971,97,3.15,3.29,9.4,69,5200,31,37 121 | 20970,0,101.2,176.8,64.8,54.3,2710,164,3.31,3.19,9,121,4250,21,28 122 | 5151,2,88.4,141.1,60.3,53.2,1488,61,2.91,3.03,9.5,48,5100,47,53 123 | 6989,1,96.3,172.4,65.4,51.6,2365,122,3.35,3.46,8.5,88,5000,25,32 124 | 10698,-1,102.4,175.6,66.5,54.9,2480,110,3.27,3.35,22.5,73,4500,30,33 125 | 6575,0,94.5,158.8,63.6,52,1909,90,3.03,3.11,9.6,70,5400,38,43 126 | 6479,2,86.6,144.6,63.9,50.8,1713,92,2.91,3.41,9.6,58,4800,49,54 127 | 7895,0,96.5,167.5,65.2,53.3,2236,110,3.15,3.58,9,86,5800,27,33 128 | 5399,1,93.7,150,64,52.6,1837,79,2.91,3.07,10.1,60,5500,38,42 129 | 14489,3,95.9,173.2,66.3,50.2,2926,156,3.59,3.86,7,145,5000,19,24 130 | 15040,3,99.1,186.6,66.5,56.1,2707,121,2.54,2.07,9.3,110,5250,21,28 131 | 22470,-1,109.1,188.8,68.9,55.5,3217,145,3.01,3.4,23,106,4800,26,27 132 | 9980,3,94.5,165.7,64,51.4,2221,109,3.19,3.4,8.5,90,5500,24,29 133 | 8449,2,98.4,176.2,65.6,52,2540,146,3.62,3.5,9.3,116,4800,24,30 134 | 9095,0,96.5,167.5,65.2,53.3,2289,110,3.15,3.58,9,86,5800,27,33 135 | 15998,3,102.9,183.5,67.7,52,3016,171,3.27,3.35,9.3,161,5200,19,24 136 | 8499,3,96.3,173,65.4,49.4,2328,122,3.35,3.46,8.5,88,5000,25,32 137 | 16500,1,94.5,171.2,65.5,52.4,2823,152,2.68,3.47,9,154,5000,19,26 138 | 18150,0,108,186.7,68.3,56,3130,134,3.61,3.21,7,142,5600,18,24 139 | 9989,2,98.4,176.2,65.6,52,2551,146,3.62,3.5,9.3,116,4800,24,30 140 | 11850,3,99.1,186.6,66.5,56.1,2658,121,3.54,3.07,9.31,110,5250,21,28 141 | 8058,1,94.5,168.7,64,52.6,2169,98,3.19,3.03,9,70,4800,29,34 142 | 18620,2,99.1,186.6,66.5,56.1,2847,121,3.54,3.07,9,160,5500,19,26 143 | 13860,0,114.2,198.9,68.4,58.7,3430,152,3.7,3.52,21,95,4150,25,25 144 | 11694,0,96.9,173.6,65.4,54.9,2650,108,3.62,2.64,7.7,111,4800,23,23 145 | 8921,-1,103.3,174.6,64.6,59.8,2535,122,3.35,3.46,8.5,88,5000,24,30 146 | 9538,1,94.5,168.7,64,52.6,2300,98,3.24,3.08,9.4,112,6600,26,29 147 | 35550,0,113,199.6,69.6,52.8,4066,258,3.63,4.17,8.1,176,4750,15,19 148 | 13200,0,107.9,186.7,68.4,56.7,3197,152,3.7,3.52,21,95,4150,28,33 149 | 11259,0,97,172,65.4,54.3,2510,108,3.62,2.64,7.7,111,4800,24,29 150 | 16900,0,107.9,186.7,68.4,56.7,3252,152,3.7,3.52,21,95,4150,28,33 151 | 7788,0,95.7,166.3,64.4,52.8,2275,110,3.27,3.35,22.5,56,4500,38,47 152 | 36000,0,102,191.7,70.6,47.8,3950,326,3.54,2.76,11.5,262,5000,13,17 153 | 17199,3,91.3,170.7,67.9,49.7,3071,181,3.43,3.27,9,160,5200,19,25 154 | 17950,0,107.9,186.7,68.4,56.7,3252,152,3.7,3.52,21,95,4150,28,33 155 | 7053,2,93.7,157.9,63.6,53.7,2120,108,3.62,2.64,8.7,73,4400,26,31 156 | 9959,3,96.3,173,65.4,49.4,2370,110,3.17,3.46,7.5,116,5500,23,30 157 | 18344,0,104.9,175,66.1,54.4,2700,134,3.43,3.64,22,72,4200,31,39 158 | 5389,2,93.7,157.3,64.4,50.8,1918,92,2.97,3.23,9.4,68,5500,37,41 159 | 34028,3,89.5,168.9,65,51.6,2756,194,3.74,2.9,9.5,207,5900,17,25 160 | 6229,1,93.7,157.3,63.8,50.6,1967,90,2.97,3.23,9.4,68,5500,31,38 161 | 7099,1,94.5,165.3,63.8,54.5,2017,103,2.99,3.47,21.9,55,4800,45,50 162 | 6095,1,93.1,159.1,64.2,54.1,1900,91,3.03,3.15,9,68,5000,31,38 163 | 7603,2,93.3,157.3,63.8,55.7,2240,108,3.62,2.64,8.7,73,4400,26,31 164 | 6229,1,93.7,157.3,63.8,50.6,1967,90,2.97,3.23,9.4,68,5500,31,38 165 | 15985,-2,104.3,188.8,67.2,56.2,2935,141,3.78,3.15,9.5,114,5400,24,28 166 | 17710,1,105.8,192.7,71.4,55.7,2844,136,3.19,3.4,8.5,110,5500,19,25 167 | 7775,2,97.3,171.7,65.5,55.7,2261,97,3.01,3.4,23,52,4800,37,46 168 | 8949,0,97.2,173.4,65.2,54.7,2324,120,3.33,3.47,8.5,97,5200,27,34 169 | 5572,1,93.7,157.3,63.8,50.8,1918,90,2.97,3.23,9.4,68,5500,37,41 170 | 8558,1,93.7,157.3,63.8,50.6,2191,98,3.03,3.39,7.6,102,5500,24,30 171 | 13295,0,100.4,180.2,66.9,55.1,2661,136,3.19,3.4,8.5,110,5500,19,24 172 | 12629,3,95.9,173.2,66.3,50.2,2833,156,3.58,3.86,7,145,5000,19,24 173 | 16558,3,102.9,183.5,67.7,52,2976,171,3.27,3.35,9.3,161,5200,20,24 174 | 13845,0,100.4,180.2,66.9,55.1,2579,97,3.01,3.4,23,68,4500,33,38 175 | 45400,1,112,199.2,72,55.4,3715,304,3.8,3.35,8,184,4500,14,16 176 | 5195,1,93.1,159.1,64.2,54.1,1890,91,3.03,3.15,9,68,5000,30,31 177 | 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4.33,0,0,0,2.638,1.401,0.632,0,0 4 | 7.019,9.23,11,0,5.799,2.93,0.486,0,0 5 | 6.723,9.23,11,0,5.453,2.887,0.495,0,0 6 | 5.979,9.23,11,0,4.068,2.758,0.695,0,0 7 | 6.064,215.34,327.629,3,0.189,4.677,1.333,0,4 8 | 7.337,9.23,11,0,2.723,2.321,1.165,0,0 9 | 4.1,0,0,0,3.267,2.318,0.963,0,0 10 | 3.941,0,0,0,2.067,1.8,1.25,0,0 11 | 3.809,0,0,0,2.746,1.667,1.4,0,0 12 | 4.34,0,0,0,3.124,1.8,1.417,0,0 13 | 3.93,12.03,18.011,1,0.817,1.8,1.488,1,0 14 | 7.371,69.67,97.435,0,3.122,3.723,1.26,0,2 15 | 3.78,23.79,31.059,0,0.094,1.334,1.115,1,1 16 | 4.112,92.14,107.097,1,4.275,4.174,1.174,1,1 17 | 3.533,0,0,0,2.851,1.989,0.938,0,0 18 | 4.632,0,0,0,1.115,1,0.588,0,0 19 | 7.693,35.53,47.145,0,4.579,3.875,1.124,0,1 20 | 0.673,17.07,25.145,0,0.202,1.225,1.66,0,0 21 | 3.323,34.14,50.29,0,0.076,1.654,1.493,0,0 22 | 2.955,67.51,103.973,1,3.204,3.344,0.938,0,1 23 | 4.216,49.33,85.839,2,1.06,2.272,1.007,1,1 24 | 2.593,122.22,155.543,2,1.406,3.511,1.456,5,2 25 | 2.272,18.46,22,0,1.262,2.147,1.576,0,0 26 | 5.96,3.24,3.124,0,9.148,5.497,1.559,1,0 27 | 6.537,12.03,18.011,1,6.728,4.392,1.554,1,0 28 | 7.1,54.57,41.028,0,5.65,4.026,1.575,0,0 29 | 6.424,118.22,238.562,5,2.201,3.83,1.214,0,0 30 | 4.355,37.3,67.828,1,3.23,2.63,1.166,0,1 31 | 5.721,3.24,3.124,0,4.48,3.258,1.31,1,0 32 | 5.102,12.47,14.124,0,3.827,3.362,1.373,1,0 33 | 3.718,0,0,0,2.226,1.265,0.57,0,0 34 | 3.577,0,14.96,0,2.026,1.265,0.722,0,0 35 | 6.458,45.61,28.269,0,3.012,2.557,0.901,1,0 36 | 2.352,12.03,18.011,1,1.587,1.654,1.732,1,0 37 | 2.958,86.81,121.412,3,0.709,2.938,1.416,2,0 38 | 5.44,132.42,196.332,2,4.478,4.984,1.087,4,0 39 | 4.838,40.46,85.367,2,5.255,3.001,0.485,0,0 40 | 6.858,0,0,0,5.453,2.837,0.735,0,0 41 | 6.36,20.23,42.683,1,3.921,2.494,1.229,0,0 42 | 4.301,40.46,85.367,2,3.306,2.832,0.917,0,0 43 | 3.772,0,0,0,3.259,2.048,1.111,0,0 44 | 4.179,26.02,32.897,2,2.193,2.031,0.992,1,0 45 | 3.061,17.07,25.145,0,1.947,2.005,1.276,0,0 46 | 4.867,0,0,0,3.267,2.119,1.25,0,0 47 | 5.373,0,0,0,3.267,2.119,1.25,0,0 48 | 3.522,0,0,0,3.374,2.118,1.204,0,0 49 | 0.55,17.07,25.145,0,-0.317,1.155,1.74,0,0 50 | 4.714,29.1,43.156,1,3.076,2.438,0.773,1,1 51 | 3.17,37.3,67.828,1,0.133,1.401,1.582,0,1 52 | 1.22,40.46,85.367,2,0.706,1.768,1.594,0,0 53 | 3.631,0,0,0,2.226,1.401,0.826,0,0 54 | 3.725,103.67,119.505,1,3.546,2.847,1.211,3,0 55 | 6,111.87,144.177,1,2.702,2.626,1.047,2,0 56 | 6.32,38.33,54.156,1,2.391,2.612,1.293,1,0 57 | 2.201,75.27,111.456,2,0.779,2.709,1.029,2,2 58 | 2.815,17.07,25.145,0,0.202,1.334,1.66,0,0 59 | 3.819,87.66,139.995,3,1.589,3.514,1.339,2,1 60 | 3.16,37.3,67.828,1,0.586,1.577,1.56,0,1 61 | 4.72,49.17,55.448,0,4.168,3.329,1.042,3,0 62 | 4.333,93.06,157.656,2,2.014,3.767,1.189,0,0 63 | 3.87,0,0,0,3.138,1.509,1.7,0,0 64 | 3.358,37.3,67.828,1,0.996,1.746,1.547,0,1 65 | 3.08,37.3,67.828,1,2.06,2.06,1.529,0,1 66 | 3.115,20.23,42.683,1,2.274,2.005,1.596,0,0 67 | 5.749,52.6,72.29,0,4.737,3.907,1.595,0,2 68 | 4.55,52.6,72.29,0,5.426,3.893,1.402,0,2 69 | 6.383,54.57,41.028,0,5.208,3.691,1.585,0,0 70 | 4.34,0,0,0,3.516,1.678,1.667,0,0 71 | 4.85,81.7,115.445,1,5.44,4.623,1.517,1,3 72 | 6.064,20.23,42.683,1,5.089,3.049,1.247,0,0 73 | 6.36,20.23,42.683,1,4.833,2.913,1.229,0,0 74 | 6.074,50.24,26.663,0,3.247,2.789,1.206,1,1 75 | 3.226,20.23,42.683,1,1.94,1.997,1.61,0,0 76 | 2.626,122.91,210.537,3,0.868,3.59,1.462,3,1 77 | 4.474,45.61,28.269,0,2.275,2.445,1.29,1,0 78 | 5.429,44.45,16.786,0,2.539,1.84,0.92,1,1 79 | 6.848,38.33,54.156,1,1.737,2.472,0.638,1,0 80 | 3.85,70.14,50.908,3,0.59,1.909,0.907,2,0 81 | 4.878,52.6,72.29,0,3.618,3.197,1.356,0,2 82 | 5.19,52.6,72.29,0,4.091,3.568,1.15,0,2 83 | 3.7,9.23,11,0,2.274,2.15,1.754,0,0 84 | 5.291,46.15,54.999,0,2.11,3.866,1.521,0,0 85 | 6.042,54.57,41.028,0,3.742,3.136,1.773,0,0 86 | 5.548,0,0,0,3.562,2.281,1.15,0,0 87 | 4.608,45.61,28.269,0,1.971,2.294,1.275,1,0 88 | 6.188,121.26,6.249,0,2.869,3.095,0.862,2,0 89 | 6.24,94.88,149.498,0,4.207,3.047,1.283,3,0 90 | 0.935,20.23,42.683,1,0.347,1.334,1.768,0,0 91 | 3.25,41.57,57.28,1,0.734,2.548,1.853,2,0 92 | 6.102,38.8,0,1,1.364,1.334,1.212,0,0 93 | 6.579,86.71,27.185,0,2.06,2.394,1.088,0,0 94 | 7.044,92.31,70.941,2,2.195,3.082,0.999,3,0 95 | 9.14,98.61,57.46,0,2.054,2.891,1.442,3,0 96 | 5.308,44.45,16.786,0,1.676,1.509,0.779,1,1 97 | 2.838,56.15,36.022,2,0.59,1.879,1.001,2,0 98 | 3.351,56.15,36.022,2,1.945,2.461,0.805,2,0 99 | 3.331,62.73,86.378,2,2.273,2.478,1.254,5,0 100 | 6.447,86.52,117.437,2,2.031,2.679,1.198,6,1 101 | 0.59,20.23,42.683,1,-0.172,1.155,1.911,0,0 102 | 5.03,107.65,178.917,0,4.219,3.945,1.078,1,2 103 | 2.827,26.3,36.145,0,0.996,1.723,1.805,0,1 104 | 2.899,52.6,72.29,0,0.787,2.223,1.8,0,2 105 | 8.349,138.26,86.133,0,0.491,2.854,1.146,0,2 106 | 3.603,52.6,72.29,0,2.577,2.707,1.313,0,2 107 | 2.668,52.33,62.438,0,0.567,2.239,1.542,1,1 108 | 1.73,9.23,11,0,0.8,1.509,2.003,0,0 109 | 9.141,100.39,91.775,0,2.682,2.903,1.823,1,0 110 | 6.442,110.96,13.843,0,3.089,3.015,0.889,0,0 111 | 8.564,95.37,54.336,0,2.433,2.915,1.394,2,0 112 | 8.15,115.41,71.512,0,2.223,2.903,1.295,1,0 113 | 8.849,82.48,37.55,0,3.213,2.806,1.172,1,0 114 | 8.207,110.96,13.843,0,1.971,2.52,1.012,0,0 115 | 3.072,64.63,90.3,1,1.651,2.785,1.346,1,2 116 | 5.036,21.26,29.011,1,2.826,2.709,1.184,1,0 117 | 7.483,76.43,6.922,0,2.723,2.553,0.835,0,0 118 | 5.562,38.8,0,1,0.845,1.155,1.177,0,0 119 | 4.437,45.61,28.269,0,1.579,2.397,1.162,1,0 120 | 7.128,110.96,13.843,0,1.321,2.402,0.988,0,0 121 | 3.277,193.91,332.83,5,-0.139,4.73,1.551,1,1 122 | 3.795,180.08,310.81,5,0.143,4.805,1.597,2,1 123 | 4.602,182.91,301.147,4,0.036,4.758,1.584,1,1 124 | 4.307,49.41,71.18,1,4.761,3.537,1.147,2,2 125 | 6.13,26.02,32.897,2,2.193,2.056,0.992,1,0 126 | 3.481,12.89,16.786,0,1.533,2.056,1.203,1,0 127 | 4.632,77.82,99.365,4,3.048,2.813,0.791,4,0 128 | 3.432,74.57,147.153,2,1.789,3.373,1.175,3,1 129 | 1.601,23.55,31.394,0,0.16,1.7,2.092,2,0 130 | 7.037,58.56,75.965,0,1.648,2.689,1.675,4,0 131 | 2.232,38.57,6.249,0,0.59,1.7,1.19,2,0 132 | 3.392,61.35,36.022,2,-0.273,1.475,0.876,2,0 133 | 4.858,95.96,6.249,0,0.454,2.072,0.8,2,0 134 | 6.059,121.26,6.249,0,0.454,2.227,0.677,2,0 135 | 7.378,140.02,119.209,4,-0.812,2.478,0.926,3,0 136 | 1.43,36.6,20.263,0,1.354,1.89,2.073,3,0 137 | 3.573,181.62,324.873,7,-0.392,3.629,0.863,2,1 138 | 6.43,181.62,324.873,7,-0.875,3.595,0.945,2,1 139 | 4.385,3.24,3.124,0,2.193,2.048,1.241,1,0 140 | 3.298,61.35,36.022,2,1.628,2.231,0.75,2,0 141 | 2.685,111.56,99.4,2,0.658,3.426,1.189,4,0 142 | 4.733,16.13,19.91,0,1.272,2.473,1.256,2,0 143 | 3.674,75.01,128.306,1,1.146,3.505,1.232,3,1 144 | 5.47,63.87,6.249,0,0.198,1.975,0.858,2,0 145 | 5.76,83.67,84.184,1,0.349,2.397,2.092,1,1 146 | 5.13,114.76,56.999,1,-0.755,2.214,1.033,1,1 147 | 5.12,96.3,102.044,1,-0.463,2.434,1.256,3,2 148 | 5.9,93.21,118.087,1,-0.147,2.355,1.644,2,0 149 | 5.551,75.99,73.774,1,0.787,2.266,1.185,2,0 150 | 6.52,47.56,65.155,1,1.459,2.72,1.265,1,0 151 | 6.883,56.79,76.155,1,1.064,2.72,1.346,1,0 152 | 5.942,38.33,54.156,1,2.222,2.725,1.012,1,0 153 | 3.979,70.14,50.908,3,-0.792,1.401,0.647,2,0 154 | 2.188,47.36,21.135,1,0.18,1.627,1.051,2,0 155 | 3.008,73.58,63.842,2,0.894,3.136,1.388,3,0 156 | 5.79,12.03,18.011,1,1.859,1.989,1.062,1,0 157 | 0.989,20.23,42.683,1,-0.814,1,2.353,0,0 158 | 5.742,69.48,142.262,0,4.096,3.884,1.169,2,1 159 | 6.756,65.82,80.765,0,3.692,3.883,1.087,3,0 160 | 4.33,164.9,144.333,4,0.16,3.323,0.833,4,0 161 | 4.652,76.66,108.311,2,2.087,3.468,1.137,2,0 162 | 3.77,52.6,72.29,0,2.005,2.468,1.281,0,2 163 | 5.475,35.53,47.145,0,3.967,3.368,1.533,0,1 164 | 3.536,55.84,75.414,0,1.907,2.892,1.439,1,2 165 | 4.339,29.54,39.269,0,3.026,2.828,1.243,1,1 166 | 0.59,29.46,53.683,1,-0.081,1.577,1.952,0,0 167 | 2.511,35.53,47.145,0,0.538,1.952,1.928,0,1 168 | 1.888,38.69,64.683,1,0.224,2.108,1.904,0,0 169 | 7.699,65.57,76.79,1,0.489,1.999,1.533,0,0 170 | 4.107,90.51,65.67,1,-0.759,1.929,1.402,1,1 171 | 6.618,87.43,55.411,2,-1.222,1.575,1.362,1,0 172 | 7.671,115.41,71.512,0,1.919,2.72,1.291,1,0 173 | 7.341,115.41,71.512,0,1.602,2.687,1.288,1,0 174 | 7.858,133.44,75.193,0,0.906,3.034,0.941,3,1 175 | 7.012,9.23,11,0,4.19,2.225,0.58,0,0 176 | 6.176,29.46,53.683,1,3.028,2.225,0.548,0,0 177 | 5.527,29.46,53.683,1,2.736,2.183,0.62,0,0 178 | 5.636,100.87,112.494,0,3.275,2.661,1.243,2,0 179 | 5.372,29.46,53.683,1,2.736,2.187,0.62,0,0 180 | 5.571,35.25,43.897,2,1.246,2.056,1.081,1,0 181 | 3.155,41.57,57.28,1,1.838,2.611,1.175,2,0 182 | 3.594,96.31,77.727,1,1.126,3.536,1.157,3,0 183 | 5.642,35.25,43.897,2,1.246,2.046,1.081,1,0 184 | 3.48,29.46,53.683,1,1.246,2.046,1.114,0,0 185 | 3.432,9.23,11,0,1.859,1.989,1.164,0,0 186 | 4.011,35.25,43.897,2,1.246,2.031,1.081,1,0 187 | 3.68,29.46,53.683,1,1.246,2.031,1.114,0,0 188 | 5.132,75.74,93.694,3,2.195,4.365,1.086,2,0 189 | 6.79,94.89,20.765,0,2.492,2.659,1.194,0,0 190 | 4.485,88.03,86.378,2,3.185,2.93,1.13,5,0 191 | 4.288,102.02,101.265,3,2.273,2.459,1.086,5,0 192 | 5.45,0,0,0,3.562,2.031,1.15,0,0 193 | 4.53,0,0,0,3.259,1.975,1.111,0,0 194 | 4.765,20.23,42.683,1,2.193,1.975,1.02,0,0 195 | 3.524,0,0,0,2.942,1.924,1.063,0,0 196 | 4.14,103.35,161.259,2,2.401,3.053,0.798,1,0 197 | 4.317,0,0,0,3.562,2.119,1.15,0,0 198 | 4.933,0,0,0,3.21,1.924,0.788,0,0 199 | 3.71,20.23,42.683,1,1.859,1.924,0.949,0,0 200 | 3.618,12.89,16.786,0,2.66,2.408,0.976,1,0 201 | 4.98,0,0,0,3.677,2.344,0.868,0,0 202 | 4.025,28.24,0,0,3.169,2.238,0.711,0,0 203 | 3.653,0,0,0,2.942,1.918,1.063,0,0 204 | 5.168,26.02,32.897,2,1.859,1.918,0.917,1,0 205 | 3.76,20.23,42.683,1,1.859,1.918,0.949,0,0 206 | 4.04,45.82,50.747,0,2.241,2.108,0.942,1,0 207 | 5.001,0,0,0,3.677,2.324,0.868,0,0 208 | 3.791,0,0,0,2.942,1.908,1.063,0,0 209 | 3.863,20.23,42.683,1,1.859,1.908,0.949,0,0 210 | 2.359,84.32,125.108,2,0.139,2.748,1.267,2,1 211 | 4.756,28.24,0,0,3.169,2.224,0.711,0,0 212 | 4.002,0,0,0,2.459,1.475,1.5,0,0 213 | 3.28,0,0,0,1.672,1.225,0.562,0,0 214 | 8.067,35.53,47.145,0,4.579,3.875,0.927,0,1 215 | 2.576,17.07,25.145,0,1.442,1.509,1.157,0,1 216 | 4.927,0,0,0,3.291,1.549,2,0,0 217 | 4.186,0,0,0,2.957,1.509,0.533,0,0 218 | 3.46,0,0,0,2.604,1.475,0.505,0,0 219 | 3.954,0,44.88,0,2.419,1.509,0.731,0,0 220 | 8.271,0,0,0,6.166,2.959,0.57,0,0 221 | 3.643,0,0,0,2.226,1.265,2.5,0,0 222 | 3.254,0,0,0,1.817,1.225,0.502,0,0 223 | 6.72,0,0,0,3.558,1.978,1,0,0 224 | 6.508,12.53,11,0,4.452,2.862,0.543,0,0 225 | 5.3,60.98,61.404,0,3.429,2.749,1.08,0,0 226 | 6.21,12.53,11,0,4.214,2.687,0.504,0,0 227 | 6.777,0,0,0,5.124,2.515,0.456,0,0 228 | 6.623,0,0,0,5.469,2.573,0.465,0,0 229 | 3.485,0,0,0,2.081,1.401,0.501,0,0 230 | 2.642,0,0,0,1.672,1.334,0.562,0,0 231 | 3.655,0,0,0,2.604,1.559,0.664,0,0 232 | 3.842,0,0,0,2.604,1.43,0.505,0,0 233 | 3.339,0,0,0,2.226,1.401,0.826,0,0 234 | 3.587,12.53,11,0,0.408,1.689,1.038,0,0 235 | 4.322,0,0,0,2.081,1.509,0.685,0,0 236 | 2.49,0,0,0,1.817,1.334,0.718,0,0 237 | 2.937,18.46,22,0,1.466,2.15,1.22,0,0 238 | 3.044,67.34,94.527,2,-0.297,1.604,1.682,0,0 239 | 5.6,0,0,0,4.634,2.018,0.461,0,0 240 | 4.75,0,0,0,4.063,1.96,0.499,0,0 241 | 5.56,20.23,42.683,1,3.909,2.12,0.281,0,0 242 | 7.61,0,0,0,6.232,2.728,0.493,0,0 243 | 8.78,0,0,0,6.47,2.805,0.469,0,0 244 | 5.632,20.23,42.683,1,3.046,2.027,0.602,0,0 245 | 4.874,0,0,0,4.634,2.027,0.461,0,0 246 | 4.495,20.23,42.683,1,3.314,2.027,0.409,0,0 247 | 4.674,0,0,0,4.926,2.074,0.478,0,0 248 | 4.761,26.02,32.897,2,3.314,2.031,0.366,1,0 249 | 4.864,20.23,42.683,1,3.314,2.031,0.409,0,0 250 | 8.44,0,0,0,6.47,2.821,0.469,0,0 251 | 5.64,0,0,0,5.472,2.676,0.577,0,0 252 | 5.878,55.05,61.747,0,4.296,3.055,0.603,1,0 253 | 4.434,45.82,50.747,0,3.11,2.165,0.478,1,0 254 | 4.257,92.35,174.455,1,3.84,3.373,0.802,1,1 255 | 5.691,20.23,42.683,1,2.461,1.975,0.737,0,0 256 | 5.42,0,0,0,3.795,1.975,0.657,0,0 257 | 4.713,39.94,104.289,0,4.281,3.357,1.002,3,0 258 | 4.83,0,0,0,4.063,1.975,0.499,0,0 259 | 5.43,26.02,32.897,2,2.729,1.975,0.462,1,0 260 | 4.797,20.23,42.683,1,2.729,1.975,0.5,0,0 261 | 4.66,45.82,50.747,0,3.11,2.16,0.478,1,0 262 | 6.989,0,0,0,5.989,2.733,0.529,0,0 263 | 4.742,26.02,32.897,2,2.729,1.975,0.462,1,0 264 | 6.109,29.46,53.683,1,3.842,2.887,0.614,0,0 265 | 4.268,45.82,50.747,0,2.842,2.154,0.726,1,0 266 | 6.65,75.13,100.774,3,4.512,3.662,0.702,5,0 267 | 4.138,0,0,0,3.478,1.924,0.575,0,0 268 | 4.992,41.57,57.28,1,3.18,3.292,1.014,2,0 269 | 4.711,42.52,44.313,0,4.746,2.905,0.916,0,0 270 | 4.31,45.82,50.747,0,2.509,2.119,0.617,1,0 271 | 6.209,0,0,0,5.472,2.703,0.577,0,0 272 | 5.626,0,0,0,4.416,2.605,0.723,0,0 273 | 8.16,0,0,0,5.989,2.758,0.529,0,0 274 | 6.99,0,0,0,5.989,2.712,0.529,0,0 275 | 6.212,30.18,48.531,0,4.27,3.354,0.867,3,0 276 | 5.5,0,0,0,3.945,2.344,0.678,0,0 277 | 4.304,0,0,0,3.478,1.918,0.575,0,0 278 | 3.84,45.82,50.747,0,2.509,2.108,0.617,1,0 279 | 5.641,0,0,0,4.416,2.591,0.723,0,0 280 | 5.009,0,0,0,3.945,2.324,0.678,0,0 281 | 3.918,0,0,0,2.876,1.857,0.699,0,0 282 | 4.787,0,0,0,3.478,1.908,0.575,0,0 283 | 2.572,72.68,73.324,1,0.403,2.355,1.424,4,1 284 | 4.9,22,73.15,0,3.983,3.431,0.951,1,0 285 | 3.028,61.82,73.324,0,0.736,2.405,1.546,4,1 286 | 2.018,0,59.84,0,1.817,1.43,1.988,0,0 287 | 3.716,65.72,101.271,2,-0.234,1.975,1.091,2,1 288 | 3.742,74.57,132.193,2,1.671,3.501,1.095,3,1 289 | 4.579,43.24,59.409,0,4.396,3.542,0.91,3,0 290 | 5.754,39.72,65.971,1,3.289,3.767,1.129,1,0 291 | 4.835,64.16,92.47,0,3.613,4.245,1.175,4,2 292 | 8.582,59.32,78.204,0,4.832,4.074,0.962,1,2 293 | 8.678,59.32,123.084,0,4.217,4.111,0.994,1,2 294 | 9.945,59.32,78.204,0,4.794,3.99,0.645,1,2 295 | 9.063,59.32,78.204,0,3.907,3.934,0.908,1,2 296 | 2.533,114.19,100.652,3,0.821,3.06,1.082,6,1 297 | 6.475,75.39,67.204,0,2.981,2.784,1.072,1,2 298 | 6.251,98.18,62.119,0,0.796,1.84,0.743,2,2 299 | 9.133,59.32,93.164,0,4.279,3.962,0.907,1,2 300 | 10.047,59.32,78.204,0,4.115,3.934,0.76,1,2 301 | 4.92,36.26,60.144,0,3.3,3.469,1.102,2,1 302 | 3.34,49.57,64.63,0,-0.259,1.989,1.103,3,1 303 | 2.928,237.75,223.047,8,0.083,3.249,1.04,7,1 304 | 2.649,43.09,58.042,2,-0.38,1.401,1.151,1,1 305 | 4.27,166.75,250.102,6,1.632,5.369,1.145,6,4 306 | 4.234,48.02,61.166,2,2.589,2.919,0.826,2,0 307 | 2.348,43.09,58.042,2,2.481,2.133,0.506,1,1 308 | 3.884,84.13,65.794,4,-1.434,1.225,0.335,2,0 309 | 3.476,70.14,50.908,3,1.294,2.203,0.591,2,0 310 | 3.133,26.02,32.897,2,0.202,1.334,1.347,1,0 311 | 3.358,52.04,65.794,4,-1.053,1.334,1.61,2,0 312 | 3.775,68.01,92.593,3,-0.283,2.17,0.893,3,1 313 | 5.16,26.02,32.897,2,2.729,1.979,0.462,1,0 314 | 5.43,26.02,32.897,2,3.314,2.018,0.366,1,0 315 | 6.409,26.02,32.897,2,2.127,1.924,0.597,1,0 316 | 4.263,52.04,65.794,4,0.893,1.918,0.732,2,0 317 | 5.371,26.02,32.897,2,1.506,1.857,0.815,1,0 318 | 2.072,90.37,131.635,3,0.332,2.706,1.313,3,1 319 | 4.404,129.62,165.834,6,2.703,3.078,0.775,7,0 320 | 2.332,12.53,11,0,-0.564,1.5,1.733,0,0 321 | 4.018,12.53,11,0,1.533,2.274,1.026,0,0 322 | 5.301,74.92,138.783,1,3.16,2.827,1.14,2,1 323 | 8.112,26.3,81.025,0,5.4,3.936,0.991,0,1 324 | 7.802,58.2,116.232,2,3.508,3.281,0.803,2,1 325 | 7.478,41.13,61.166,2,3.988,3.169,0.627,2,0 326 | 6.394,17.07,25.145,0,4.912,2.659,0.491,0,0 327 | 5.001,34.14,50.29,0,1.415,2.074,1.108,0,0 328 | 5.545,34.14,50.29,0,2.233,2.415,0.866,0,0 329 | 4.249,58.2,86.311,2,3.652,3.557,1.237,2,2 330 | 5.17,37.3,67.828,1,3.696,3.117,1.149,0,0 331 | 3.769,73.22,61.166,2,-1.202,1.795,0.599,2,1 332 | 3.508,120.73,148.22,1,0.096,2.964,1.16,4,1 333 | 1.188,41.13,61.166,2,-1.095,1.726,1.463,2,0 334 | 3.884,182.83,315.561,5,1.662,5.831,1.351,0,1 335 | 4.566,203.06,358.244,6,0.957,5.839,1.345,0,1 336 | 3.788,34.14,50.29,0,0.076,1.84,1.493,0,0 337 | 0.704,20.31,28.269,0,-0.273,1.401,2.073,1,1 338 | 3.669,77.76,153.195,3,0.559,2.137,0.767,0,0 339 | 3.976,17.07,25.145,0,1.769,1.989,0.927,0,0 340 | 4.024,66.05,93.43,1,1.348,2.09,0.784,1,0 341 | 4.403,32.67,47.536,0,3.223,2.773,0.881,2,0 342 | 5.512,54.57,41.028,0,4.789,3.58,1.505,0,0 343 | 4.225,87.08,111.608,1,1.749,3.666,1.085,3,0 344 | 4.072,42.52,44.313,0,1.473,2.241,1.158,0,0 345 | 4.57,42.52,44.313,0,2.777,2.643,1.055,0,0 346 | 6.075,54.74,39.247,0,3.115,2.706,0.946,0,0 347 | 4.221,80.74,61.166,2,-0.31,1.918,0.578,2,1 348 | 4.032,30.21,36.145,0,2.265,2.344,0.923,0,1 349 | 3.22,60.13,76.448,0,2.288,2.848,1.591,4,0 350 | 4.001,38.77,22.156,0,2.779,2.639,0.859,0,0 351 | 4.501,118.65,210.6,1,3.773,3.759,0.95,1,2 352 | 3.599,54.37,92.973,1,3.374,3.061,0.91,0,1 353 | 4.56,37.3,67.828,1,4.548,3.101,1.137,0,1 354 | 2.736,115.14,206.609,3,-1.169,2.333,1.496,1,3 355 | 3.047,46.53,78.828,1,2.086,2.455,0.941,0,1 356 | 3.783,46.53,78.828,1,2.354,2.455,0.743,0,1 357 | 4.187,104.06,189.34,3,1.929,3.197,1.021,0,2 358 | 3.068,57.53,110.512,2,1.643,2.09,0.814,0,1 359 | 3.644,76.21,117.511,3,1.119,2.25,0.375,2,1 360 | 4.01,50.94,76.132,1,3.77,3.091,1.081,3,0 361 | 3.917,67.79,107.839,2,2.347,3.243,1.4,1,1 362 | 3.902,84.58,129.736,4,0.925,3.178,1.263,2,1 363 | 3.277,81.95,157.061,4,1.358,3.325,1.248,1,0 364 | 4.305,57.28,71.694,3,2.124,3.455,1.069,2,0 365 | 3.961,94.83,178.34,3,1.661,3.391,0.942,0,0 366 | 1.82,20.23,42.683,1,0.8,1.401,1.698,0,0 367 | 3.002,20.23,42.683,1,1.209,1.43,0.476,0,0 368 | 2.957,52.49,103.377,3,-0.917,1.84,1.75,1,0 369 | 0.122,40.46,85.367,2,-1.053,1.334,1.827,0,0 370 | 1.07,29.46,53.683,1,-0.081,1.678,1.952,0,0 371 | 1.53,38.69,64.683,1,-0.129,2.15,1.971,0,0 372 | 3.077,336.43,551.098,16,-5.199,4.3,1.373,7,0 373 | 3.025,115.38,179.269,3,1.228,3.039,0.787,2,1 374 | 3.088,347.32,571.952,18,-6.446,4.624,1.39,5,0 375 | 3.177,97.99,195.878,4,-1.204,2.214,1.066,0,1 376 | 5.291,138.45,281.245,6,1.442,3.878,1.205,0,0 377 | 4.852,20.23,42.683,1,2.461,1.975,0.737,0,0 378 | 4.464,20.23,42.683,1,2.296,1.924,0.521,0,0 379 | 4.426,20.23,42.683,1,2.127,1.924,0.633,0,0 380 | 4.016,66.05,93.43,1,1.348,2.119,0.784,1,0 381 | 4.668,20.23,42.683,1,3.127,2.605,0.797,0,0 382 | 4.962,40.46,85.367,2,3.635,3.12,1.03,0,0 383 | 4.61,20.23,42.683,1,2.637,2.344,0.758,0,0 384 | 3.85,20.23,42.683,1,1.506,1.857,0.853,0,0 385 | 4.238,20.23,42.683,1,2.127,1.908,0.633,0,0 386 | 5.38,20.23,42.683,1,3.127,2.57,0.797,0,0 387 | 3.589,56.15,36.022,2,-0.665,1.726,0.676,2,0 388 | 4.932,61.52,41.93,0,3.447,2.307,0.662,1,2 389 | 6.099,44.45,16.786,0,3.648,2.427,0.757,1,1 390 | 6.54,44.45,16.786,0,3.33,2.281,0.701,1,1 391 | 6.399,88.9,33.571,0,4.405,2.456,0.568,2,2 392 | 6.131,44.45,16.786,0,3.265,2.134,0.637,1,1 393 | 4.56,64.18,0,0,-0.049,1.155,1.5,0,1 394 | 2.54,50.93,0,0,0.552,2.485,1.147,3,0 395 | 4.494,79.93,16.786,1,1.769,2.239,0.793,1,0 396 | 3.758,118.06,85.367,4,-0.081,1.768,1.061,0,0 397 | 4.168,86.34,105.331,1,1.616,2.97,0.983,5,0 398 | 3.85,165.15,185.897,0,0.575,2.608,1.809,3,0 399 | 3.339,119.33,134.931,0,0.289,2.587,1.989,2,0 400 | 5.441,91.64,101.494,0,2.239,2.296,0.884,2,0 401 | 3.138,134.73,159.536,2,1.649,2.527,0.818,3,1 402 | 4.79,111.87,144.177,1,1.748,2.377,0.839,2,0 403 | 4.39,137.46,152.241,0,2.359,2.527,0.825,3,0 404 | 3.43,157.69,194.924,1,1.543,2.527,0.647,3,0 405 | 3.978,45.82,50.747,0,2.574,2.168,1.014,1,0 406 | 4.489,71.84,83.644,2,1.969,2.168,0.531,2,0 407 | 3.718,91.64,101.494,0,2.239,2.334,0.884,2,0 408 | 5.402,91.64,101.494,0,2.507,2.334,0.543,2,0 409 | 4.606,111.87,144.177,1,1.395,2.334,0.716,2,0 410 | 3.591,91.64,101.494,0,1.886,2.287,0.769,2,0 411 | 4.558,45.82,50.747,0,2.574,2.141,1.014,1,0 412 | 4.611,45.82,50.747,0,2.842,2.141,0.726,1,0 413 | 4.62,45.82,50.747,0,3.11,2.141,0.478,1,0 414 | 3.615,45.82,50.747,0,1.887,2.048,0.842,1,0 415 | 4.14,45.82,50.747,0,2.241,2.09,0.942,1,0 416 | 3.575,0,0,0,3.958,2.488,0.802,0,0 417 | 5.01,7.76,0,0,2.122,2.7,1.053,2,0 418 | 5.433,9.23,11,0,3.395,2.692,0.941,0,0 419 | 4.909,0,0,0,3.876,2.539,0.837,0,0 420 | 4.7,15.79,0,1,2.859,2.614,0.752,1,0 421 | 5.069,15.79,0,1,1.834,2.176,0.81,1,0 422 | 6.004,0,0,0,5.551,3.231,0.689,0,0 423 | 6.281,0,0,0,4.76,2.868,0.7,0,0 424 | 5.406,0,0,0,4.332,2.7,0.758,0,0 425 | 6.7,0,0,0,4.332,2.727,0.758,0,0 426 | 4.737,13.14,9.507,0,3.743,2.995,0.783,0,0 427 | 6.027,28.24,0,0,4.76,2.995,0.65,0,0 428 | 4.147,0,0,0,3.386,2.284,0.805,0,0 429 | 3.711,12.89,16.786,0,2.066,2.284,0.867,1,0 430 | 4.851,12.89,16.786,0,3.051,2.727,0.8,1,0 431 | 3.53,12.89,16.786,0,2.066,2.284,0.867,1,0 432 | 3.619,132.86,33.571,0,2.892,3.487,0.572,2,0 433 | 5.597,28.24,0,0,3.876,2.614,0.652,0,0 434 | 3.357,28.24,0,0,2.851,2.176,0.656,0,0 435 | 1.815,12.89,16.786,0,0.468,1.8,1.038,1,0 436 | 2.421,28.24,0,0,1.173,1.667,0.667,0,0 437 | 5.054,79.63,77.875,2,1.326,3.024,1.126,5,0 438 | 3.89,0,0,0,2.734,1.857,0.831,0,0 439 | 4.779,278.8,413.191,5,-0.321,6.439,1.557,11,11 440 | 3.792,0,0,0,3.374,2.075,1.204,0,0 441 | 5.993,17.07,25.145,0,0.094,1.334,1.328,0,0 442 | 1.057,23.79,31.059,0,-0.317,1.155,1.403,1,1 443 | 4.886,26.3,36.145,0,2.754,2.454,1.421,0,1 444 | 5.541,43.37,61.29,0,3.626,3.373,1.111,0,1 445 | 3.638,58.11,32.897,2,-0.317,1.225,0.575,1,1 446 | 1.22,40.46,85.367,2,0.706,1.689,1.594,0,0 447 | 2.614,29.46,53.683,1,1.629,2.353,1.2,0,0 448 | 4.762,38.33,54.156,1,2.378,2.587,1.055,1,0 449 | 2.829,45.34,34.106,0,1.297,2.042,1.933,0,0 450 | 5.37,40.46,85.367,2,3.956,3.123,0.959,0,0 451 | 4.819,0,0,0,3.477,1.857,1.429,0,0 452 | 4.039,17.07,25.145,0,1.442,1.84,1.58,0,0 453 | 5.47,0,0,0,4.203,1.997,1.625,0,0 454 | 5.299,0,0,0,4.823,2.299,1.6,0,0 455 | 6.194,52.6,72.29,0,6.338,4.16,1.402,0,2 456 | 3.52,56.15,36.022,2,1.971,2.472,1.144,2,0 457 | 4.862,54.57,41.028,0,3.234,2.901,1.984,0,0 458 | 7.676,102.78,20.263,0,3.322,2.901,0.874,0,0 459 | 4.943,47.36,56.055,0,3.373,3.471,0.992,3,0 460 | 7.012,94.88,104.618,0,4.066,3.016,1.314,3,0 461 | 5.548,45.61,28.269,0,3.092,2.814,0.948,1,0 462 | 3.078,20.31,28.269,0,2.821,2.502,1.283,1,1 463 | 5.976,121.26,6.249,0,1.761,2.66,0.779,2,0 464 | 3.936,70.14,50.908,3,-0.273,1.577,0.756,2,0 465 | 3.118,12.03,18.011,1,0.8,1.509,1.846,1,0 466 | 5.46,12.03,18.011,1,2.193,2.134,1.117,1,0 467 | 3.601,62.73,86.378,2,2.59,2.546,1.277,5,0 468 | 3.63,88.03,86.378,2,2.273,2.575,1.159,5,0 469 | 2.09,26.3,36.145,0,0.586,1.577,1.881,0,1 470 | 5.762,107.02,52.33,0,0.64,2.614,1.233,1,0 471 | 6.56,54.57,41.028,0,4.449,2.988,1.497,0,0 472 | 8.113,82.67,47.447,1,3.747,2.935,1.513,1,0 473 | 9.15,129.95,30.53,0,1.145,2.637,1.444,0,0 474 | 4.502,0,0,0,2.942,1.989,1.063,0,0 475 | 1.983,91.49,117.376,5,-0.329,1.868,1.621,5,0 476 | 5.551,3.24,3.124,0,4.761,3.273,1.052,1,0 477 | 5.418,44.45,16.786,0,1.157,1.334,0.685,1,1 478 | 2.958,12.03,18.011,1,-0.172,1.155,2.177,1,0 479 | 6.964,75.99,73.774,1,0.057,2.093,1.123,2,1 480 | 4.91,47.56,65.155,1,1.57,2.644,1.376,1,0 481 | 3.825,56.15,36.022,2,-0.273,1.559,0.876,2,0 482 | 5.783,21.26,73.891,1,4.153,3.383,1.121,1,0 483 | 6.389,103.56,133.775,0,3.168,4.055,1.153,3,2 484 | 4.071,110.45,141.047,1,2.767,3.488,1.216,2,2 485 | 6.119,67.01,70.941,2,1.052,2.676,1.137,3,0 486 | 2.506,71.06,94.289,0,0.977,3.191,1.146,0,0 487 | 9.099,54.57,41.028,0,1.246,2.086,1.875,0,0 488 | 5.6,124.73,67.257,0,1.02,2.969,0.946,1,2 489 | 8.028,63.22,80.144,0,1.936,3.852,1.151,0,0 490 | 7.087,18.46,22,0,4.268,3.164,0.857,0,0 491 | 3.794,46.53,78.828,1,2.758,2.856,1.022,0,1 492 | 0.495,36.28,22.156,0,-0.317,1.225,1.333,0,0 493 | 4.823,7.76,0,0,1.586,2.674,1.166,2,0 494 | 6.19,0,0,0,4.588,2.744,0.807,0,0 495 | 4.3,0,0,0,3.259,1.979,1.111,0,0 496 | 4.01,45.82,50.747,0,2.241,2.119,0.942,1,0 497 | 4.038,20.23,42.683,1,2.193,1.96,1.02,0,0 498 | 3.002,0,0,0,2.608,1.857,1,0,0 499 | 5.312,26.02,32.897,2,1.859,1.908,0.917,1,0 500 | 5.513,12.53,11,0,4.452,2.716,0.49,0,0 501 | 3.492,0,0,0,2.226,1.401,0.57,0,0 502 | 2.608,0,0,0,2.226,1.509,0.826,0,0 503 | 2.778,9.23,11,0,1.587,1.84,1.05,0,0 504 | 2.659,0,0,0,1.364,1.155,0.576,0,0 505 | 5.389,0,0,0,4.092,2.12,0.53,0,0 506 | 5.559,26.02,32.897,2,3.617,2.074,0.288,1,0 507 | 5.76,20.23,42.683,1,3.617,2.074,0.34,0,0 508 | 5.61,20.23,42.683,1,3.617,2.074,0.34,0,0 509 | 5.55,0,0,0,4.366,2.031,0.57,0,0 510 | 4.251,45.82,50.747,0,3.11,2.154,0.478,1,0 511 | 7.509,0,0,0,6.232,2.766,0.493,0,0 512 | 6.67,0,0,0,5.472,2.657,0.577,0,0 513 | 5.02,50.94,76.132,1,4.013,3.218,1.104,3,0 514 | 4.821,32.67,45.055,0,3.355,3.097,0.889,2,1 515 | 5.422,0,0,0,4.416,2.57,0.723,0,0 516 | 6.21,47.58,62.119,0,3.133,2.324,0.467,2,2 517 | 3.839,126.44,152.848,3,-0.172,2.629,1.025,4,0 518 | 5.437,26.02,32.897,2,2.729,1.975,0.462,1,0 519 | 5.381,52.04,65.794,4,3.586,2.758,0.518,2,0 520 | 6.108,26.02,32.897,2,2.127,1.918,0.597,1,0 521 | 5.19,26.02,32.897,2,2.127,1.908,0.597,1,0 522 | 4.006,115.47,84.479,4,0.038,2.278,0.544,5,1 523 | 4.535,57.53,110.512,2,3.31,3.267,1.169,0,0 524 | 8.164,39.44,45.652,0,4.332,4.121,1.12,0,1 525 | 4.906,20.31,28.269,0,0.198,1.782,1.559,1,1 526 | 4.022,87.28,110.688,1,2.843,3.244,0.745,3,1 527 | 3.47,47.56,65.155,1,1.191,2.899,1.265,1,0 528 | 4.462,37.3,67.828,1,1.667,2.031,0.867,0,0 529 | 2.764,74.6,135.657,2,-0.45,1.823,1.075,0,2 530 | 3.474,46.53,78.828,1,2.378,2.472,0.988,0,1 531 | 3.103,155.68,277.562,4,-1.358,3.033,1.552,2,4 532 | 3.908,116.67,180.366,4,-1.96,2.021,1.83,1,1 533 | 4.121,49.33,85.839,2,3.988,3.047,0.694,1,1 534 | 2.579,20.23,42.683,1,0.347,1.334,0.961,0,0 535 | 2.959,23.47,45.808,1,-0.081,1.577,2.001,1,0 536 | 0.46,58.92,107.366,2,-0.937,2.299,1.936,0,0 537 | 6.11,20.23,42.683,1,3.617,2.074,0.34,0,0 538 | 5.461,20.23,42.683,1,3.314,2.027,0.409,0,0 539 | 3.531,56.15,36.022,2,3.112,2.997,0.701,2,0 540 | 5.922,105.86,19.91,0,0.623,2.116,0.508,2,0 541 | 3.64,45.82,50.747,0,2.509,2.09,0.617,1,0 542 | 6.173,0,0,0,4.76,2.859,0.7,0,0 543 | 4.651,24.06,35.776,2,3.326,2.837,0.849,2,0 544 | 3.953,9.23,11,0,3.275,2.727,0.874,0,0 545 | 6.219,0,0,0,5.165,3.111,0.732,0,0 546 | 4.995,13.14,9.507,0,2.859,2.614,0.827,0,0 547 | 2.48,0,0,0,2.255,1.8,0.917,0,0 548 | -------------------------------------------------------------------------------- /TEST/glmnetModel.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/enanomapper/RRegrs/e5194f812e949be7694023ede0e62e27264a13b1/TEST/glmnetModel.RData -------------------------------------------------------------------------------- /TEST/model.svmRadialReg.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/enanomapper/RRegrs/e5194f812e949be7694023ede0e62e27264a13b1/TEST/model.svmRadialReg.RData -------------------------------------------------------------------------------- /install.deps.R: -------------------------------------------------------------------------------- 1 | install.packages( 2 | c( 3 | "nnet", 4 | "gtools", "foreach", "iterators", "car", 5 | "gtable", "brglm", "profileModel", "proto","glmnet", 6 | "colorspace", "nlme", "nloptr", "digest", 7 | "MASS", "munsell", "stringr", 8 | "lattice", "codetools", "Rcpp", "Matrix", 9 | "plyr", "caret","corrplot", "data.table","corrplot" 10 | ), 11 | repos="http://cran.us.r-project.org/", 12 | lib="~/R_libs" 13 | ) 14 | --------------------------------------------------------------------------------