├── kin8nm ├── results │ ├── results.png │ ├── results_green.png │ ├── results_orange.png │ ├── results_orange2.png │ ├── black_blue_green_red.m │ ├── sequential.m │ └── results.csv ├── run_once.py ├── libsvm2vw.py ├── config.pb ├── rmse.py ├── output2csv.py ├── main.py └── data │ └── validation.libsvm.txt ├── madelon ├── results │ ├── results1.png │ ├── results2.png │ ├── results2_green.png │ ├── results2_orange.png │ ├── black_blue_green_red.m │ ├── sequential.m │ ├── results1.csv │ └── results2.csv ├── run_once.py ├── libsvm2vw.py ├── config.pb ├── auc.py ├── acc.py ├── output2csv.py └── main.py ├── .gitattributes ├── README.md ├── LICENSE └── .gitignore /kin8nm/results/results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zygmuntz/the-secret-of-the-big-guys/HEAD/kin8nm/results/results.png -------------------------------------------------------------------------------- /madelon/results/results1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zygmuntz/the-secret-of-the-big-guys/HEAD/madelon/results/results1.png -------------------------------------------------------------------------------- /madelon/results/results2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zygmuntz/the-secret-of-the-big-guys/HEAD/madelon/results/results2.png -------------------------------------------------------------------------------- /kin8nm/run_once.py: -------------------------------------------------------------------------------- 1 | from main import * 2 | 3 | params = { 'centers': [ 2600 ], 'rbf_param': [ 0.42 ] } 4 | 5 | run_test( params ) -------------------------------------------------------------------------------- /kin8nm/results/results_green.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zygmuntz/the-secret-of-the-big-guys/HEAD/kin8nm/results/results_green.png -------------------------------------------------------------------------------- /kin8nm/results/results_orange.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zygmuntz/the-secret-of-the-big-guys/HEAD/kin8nm/results/results_orange.png -------------------------------------------------------------------------------- /kin8nm/results/results_orange2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zygmuntz/the-secret-of-the-big-guys/HEAD/kin8nm/results/results_orange2.png -------------------------------------------------------------------------------- /madelon/results/results2_green.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zygmuntz/the-secret-of-the-big-guys/HEAD/madelon/results/results2_green.png -------------------------------------------------------------------------------- /madelon/results/results2_orange.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zygmuntz/the-secret-of-the-big-guys/HEAD/madelon/results/results2_orange.png -------------------------------------------------------------------------------- /madelon/run_once.py: -------------------------------------------------------------------------------- 1 | from main import * 2 | 3 | params = { 'centers': [ 2867 ], 'rbf_param': [ 0.627209109103 ] } 4 | 5 | run_test( params ) -------------------------------------------------------------------------------- /kin8nm/libsvm2vw.py: -------------------------------------------------------------------------------- 1 | import sys 2 | 3 | input_file = sys.argv[1] 4 | output_file = sys.argv[2] 5 | 6 | i = open( input_file ) 7 | o = open( output_file, 'wb' ) 8 | 9 | for line in i: 10 | line = line.replace( ' ', ' |m ', 1 ) 11 | o.write( line ) -------------------------------------------------------------------------------- /madelon/libsvm2vw.py: -------------------------------------------------------------------------------- 1 | import sys 2 | 3 | input_file = sys.argv[1] 4 | output_file = sys.argv[2] 5 | 6 | i = open( input_file ) 7 | o = open( output_file, 'wb' ) 8 | 9 | for line in i: 10 | line = line.replace( ' ', ' |m ', 1 ) 11 | o.write( line ) -------------------------------------------------------------------------------- /kin8nm/config.pb: -------------------------------------------------------------------------------- 1 | language: PYTHON 2 | name: "main" 3 | 4 | variable { 5 | name: "centers" 6 | type: INT 7 | size: 1 8 | min: 1500 9 | max: 4000 10 | } 11 | 12 | variable { 13 | name: "rbf_param" 14 | type: FLOAT 15 | size: 1 16 | min: 0.001 17 | max: 1 18 | } 19 | 20 | 21 | 22 | 23 | -------------------------------------------------------------------------------- /madelon/config.pb: -------------------------------------------------------------------------------- 1 | language: PYTHON 2 | name: "main" 3 | 4 | variable { 5 | name: "centers" 6 | type: INT 7 | size: 1 8 | min: 1000 9 | max: 4000 10 | } 11 | 12 | variable { 13 | name: "rbf_param" 14 | type: FLOAT 15 | size: 1 16 | min: 0.4 17 | max: 1.5 18 | } 19 | 20 | 21 | 22 | 23 | -------------------------------------------------------------------------------- /kin8nm/results/black_blue_green_red.m: -------------------------------------------------------------------------------- 1 | results = csvread( 'results.csv' ); 2 | 3 | err = results(:,1); 4 | 5 | red = err < 0.087; 6 | green = err < 0.9; 7 | blue = err < 0.10; 8 | black = err >= 0.10; 9 | 10 | plot( results( black, 2 ), results( black, 3 ), 'ko' ) 11 | hold on; 12 | 13 | plot( results( blue, 2 ), results( blue, 3 ), 'bo' ) 14 | plot( results( green, 2 ), results( green, 3 ), 'go' ) 15 | plot( results( red, 2 ), results( red, 3 ), 'ro' ) 16 | 17 | xlabel( 'clusters') 18 | ylabel( 'rbf param' ) 19 | 20 | hold off; 21 | 22 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | *.sln merge=union 7 | *.csproj merge=union 8 | *.vbproj merge=union 9 | *.fsproj merge=union 10 | *.dbproj merge=union 11 | 12 | # Standard to msysgit 13 | *.doc diff=astextplain 14 | *.DOC diff=astextplain 15 | *.docx diff=astextplain 16 | *.DOCX diff=astextplain 17 | *.dot diff=astextplain 18 | *.DOT diff=astextplain 19 | *.pdf diff=astextplain 20 | *.PDF diff=astextplain 21 | *.rtf diff=astextplain 22 | *.RTF diff=astextplain 23 | -------------------------------------------------------------------------------- /madelon/results/black_blue_green_red.m: -------------------------------------------------------------------------------- 1 | results = csvread( 'results2.csv' ); 2 | 3 | err = results(:,1); 4 | 5 | % arbitrarily chosen values 6 | red = err < 0.038; 7 | green = err < 0.044; 8 | blue = err < 0.05; 9 | black = err >= 0.05; 10 | 11 | plot( results( black, 2 ), results( black, 3 ), 'ko' ) 12 | hold on; 13 | 14 | plot( results( blue, 2 ), results( blue, 3 ), 'bo' ) 15 | plot( results( green, 2 ), results( green, 3 ), 'go' ) 16 | plot( results( red, 2 ), results( red, 3 ), 'ro' ) 17 | 18 | xlabel( 'clusters') 19 | ylabel( 'selected_clusters' ) 20 | 21 | hold off; 22 | 23 | -------------------------------------------------------------------------------- /madelon/auc.py: -------------------------------------------------------------------------------- 1 | 'compute AUC from VW validation and predictions file' 2 | 3 | import sys, csv, math 4 | from ml_metrics import auc 5 | 6 | test_file = sys.argv[1] 7 | predictions_file = sys.argv[2] 8 | 9 | test_reader = csv.reader( open( test_file ), delimiter = " " ) 10 | p_reader = csv.reader( open( predictions_file ), delimiter = "\n" ) 11 | 12 | ys = [] 13 | ps = [] 14 | 15 | for p_line in p_reader: 16 | test_line = test_reader.next() 17 | 18 | p = float( p_line[0] ) 19 | p = math.tanh( p ) 20 | ps.append( p ) 21 | 22 | y = float( test_line[0] ) 23 | ys.append( y ) 24 | 25 | AUC = auc( ys, ps ) 26 | 27 | #print "%s %s" % ( test_file, predictions_file ) 28 | print "AUC: %s" % ( AUC ) 29 | print "error: %s" % ( 1 - AUC ) 30 | print -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | The secret of the big guys 2 | ========================== 3 | 4 | See [http://fastml.com/the-secret-of-the-big-guys/](http://fastml.com/the-secret-of-the-big-guys/) for description. 5 | 6 | If interested, also check out [sofia-ml with sparse RBF mapping](https://github.com/zygmuntz/sofia-ml-mod), described in the article. 7 | 8 | kin8nm - Spearmint experiment for the kin8nm dataset 9 | madelon - Spearmint experiment for the madelon dataset 10 | 11 | The code in these folders can also be run independently of Spearmint, using `run_once.py`. Either way, you will need sofia-ml and Vowpal Wabbit installed. We use VW because we like it, although there is also a linear learner in sofia-ml, so you can do both clustering and supervised learning just using sofia-ml. 12 | 13 | 14 | -------------------------------------------------------------------------------- /madelon/acc.py: -------------------------------------------------------------------------------- 1 | 'compute accuracy from VW validation and predictions file' 2 | 3 | import sys, csv, math 4 | from ml_metrics import auc 5 | 6 | test_file = sys.argv[1] 7 | predictions_file = sys.argv[2] 8 | 9 | test_reader = csv.reader( open( test_file ), delimiter = " " ) 10 | p_reader = csv.reader( open( predictions_file ), delimiter = "\n" ) 11 | 12 | n = 0 13 | t = 0 14 | 15 | for p_line in p_reader: 16 | test_line = test_reader.next() 17 | n += 1 18 | 19 | p = float( p_line[0] ) 20 | y = int( test_line[0] ) 21 | if y * p > 0: 22 | t += 1 23 | else: 24 | pass 25 | # print ( "%s / %s\t\t%s / %s" % ( y, p, t, n )) 26 | 27 | acc = 1.0 * t / n 28 | 29 | #print "%s %s" % ( test_file, predictions_file ) 30 | print "accuracy: %s" % ( acc ) 31 | print "error: %s" % ( 1 - acc ) 32 | print -------------------------------------------------------------------------------- /kin8nm/rmse.py: -------------------------------------------------------------------------------- 1 | 'compute RMSE from VW validation and predictions file' 2 | 3 | import sys, csv, math 4 | 5 | test_file = sys.argv[1] 6 | predictions_file = sys.argv[2] 7 | 8 | test_reader = csv.reader( open( test_file ), delimiter = " " ) 9 | p_reader = csv.reader( open( predictions_file ), delimiter = "\n" ) 10 | 11 | squared_diffs = [] 12 | n = 0 13 | 14 | for p_line in p_reader: 15 | test_line = test_reader.next() 16 | n += 1 17 | 18 | p = float( p_line[0] ) 19 | y = float( test_line[0] ) 20 | #print "%s / %s" % ( y, p ) 21 | 22 | squared_diff = math.pow( y - p, 2 ) 23 | squared_diffs.append( squared_diff ) 24 | 25 | squared_diffs = sum( squared_diffs ) 26 | MSE = squared_diffs / n 27 | RMSE = math.sqrt( MSE ) 28 | 29 | #print "%s %s" % ( test_file, predictions_file ) 30 | print "RMSE: %s" % ( RMSE ) 31 | print -------------------------------------------------------------------------------- /madelon/results/sequential.m: -------------------------------------------------------------------------------- 1 | results = csvread( 'results2.csv' ); 2 | 3 | err = results(:,1); 4 | 5 | best = err < 0.038; 6 | good = err < 0.044; 7 | mediocre = err < 0.05; 8 | bad = err >= 0.05; 9 | 10 | best_color = [ 217/255 71/255 1/255 ]; 11 | good_color = [ 253/255 141/255 60/255 ]; 12 | mediocre_color = [ 253/255 190/255 133/255 ]; 13 | bad_color = [ 254/255 237/255 222/255 ]; 14 | 15 | %{ 16 | best_color = [ 35/255 139/255 69/255 ]; 17 | good_color = [ 116/255 196/255 118/255 ]; 18 | mediocre_color = [ 186/255 228/255 179/255 ]; 19 | bad_color = [ 237/255 248/255 233/255 ]; 20 | %} 21 | 22 | plot( results( bad, 2 ), results( bad, 3 ), 'o', 'Color', bad_color, 'MarkerFaceColor', bad_color ) 23 | hold on; 24 | 25 | plot( results( mediocre, 2 ), results( mediocre, 3 ), 'o', 'Color', mediocre_color, 'MarkerFaceColor', mediocre_color ) 26 | plot( results( good, 2 ), results( good, 3 ), 'o', 'Color', good_color, 'MarkerFaceColor', good_color ) 27 | plot( results( best, 2 ), results( best, 3 ), 'o', 'Color', best_color, 'MarkerFaceColor', best_color ) 28 | 29 | xlabel( 'clusters') 30 | ylabel( 'rbf gamma' ) 31 | 32 | hold off; 33 | 34 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2013 Zygmunt Zając 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /kin8nm/results/sequential.m: -------------------------------------------------------------------------------- 1 | % a new version of plotting code, using schemes from colorbrewer2.com 2 | 3 | results = csvread( 'results.csv' ); 4 | 5 | err = results(:,1); 6 | 7 | best = err < 0.087; 8 | good = err < 0.95; 9 | mediocre = err < 0.2; 10 | bad = err >= 0.2; 11 | 12 | % orange 1 13 | best_color = [ 217/255 71/255 1/255 ]; 14 | good_color = [ 253/255 141/255 60/255 ]; 15 | mediocre_color = [ 253/255 190/255 133/255 ]; 16 | bad_color = [ 254/255 237/255 222/255 ]; 17 | 18 | % green 19 | best_color = [ 35/255 139/255 69/255 ]; 20 | good_color = [ 116/255 196/255 118/255 ]; 21 | mediocre_color = [ 186/255 228/255 179/255 ]; 22 | bad_color = [ 237/255 248/255 233/255 ]; 23 | 24 | % orange 2 25 | best_color = [ 204/255 76/255 2/255 ]; 26 | good_color = [ 254/255 153/255 41/255 ]; 27 | mediocre_color = [ 254/255 217/255 142/255 ]; 28 | bad_color = [ 255/255 255/255 212/255 ]; 29 | 30 | 31 | plot( results( bad, 2 ), results( bad, 3 ), 'o', 'Color', bad_color, 'MarkerFaceColor', bad_color ) 32 | hold on; 33 | 34 | plot( results( mediocre, 2 ), results( mediocre, 3 ), 'o', 'Color', mediocre_color, 'MarkerFaceColor', mediocre_color ) 35 | plot( results( good, 2 ), results( good, 3 ), 'o', 'Color', good_color, 'MarkerFaceColor', good_color ) 36 | plot( results( best, 2 ), results( best, 3 ), 'o', 'Color', best_color, 'MarkerFaceColor', best_color ) 37 | 38 | xlabel( 'clusters') 39 | ylabel( 'rbf gamma' ) 40 | 41 | hold off; 42 | 43 | -------------------------------------------------------------------------------- /madelon/results/results1.csv: -------------------------------------------------------------------------------- 1 | 0.371622222222,5,0.01 2 | 0.0411444444444,1002,0.505 3 | 0.042,2000,1.0 4 | 0.0430444444444,2000,1.0 5 | 0.355561111111,5,1.0 6 | 0.336588888889,2000,0.01 7 | 0.0369222222222,2000,0.680885043374 8 | 0.0391777777778,2000,0.494307247201 9 | 0.0421333333333,1925,1.0 10 | 0.0414777777778,2000,1.0 11 | 0.0460777777778,2000,1.0 12 | 0.0445777777778,1405,1.0 13 | 0.0390555555556,1499,0.620149802092 14 | 0.0400666666667,1411,0.460722433771 15 | 0.0431,1678,0.872806501167 16 | 0.0359666666667,2000,0.587542284487 17 | 0.0359333333333,1652,0.544055967009 18 | 0.0398666666667,1282,0.54090517142 19 | 0.0384,1855,0.576651487207 20 | 0.0357444444444,2000,0.59267040851 21 | 0.0385555555556,2000,0.593543556564 22 | 0.0387,1047,0.728165863766 23 | 0.0374333333333,1283,0.760038381428 24 | 0.0376555555556,1156,0.644563374003 25 | 0.0384,1727,0.518648223466 26 | 0.344861111111,5,0.460746004682 27 | 0.0401555555556,1166,0.853068766029 28 | 0.0417333333333,2000,0.802966561833 29 | 0.0377111111111,2000,0.596597474413 30 | 0.0406,1190,0.720796556616 31 | 0.0413444444444,979,1.0 32 | 0.0380555555556,1572,0.536643183384 33 | 0.0472555555556,1087,0.343528910738 34 | 0.0373666666667,2000,0.585818653631 35 | 0.0367444444444,1751,0.660649710354 36 | 0.0373555555556,1729,0.596713712233 37 | 0.0375666666667,1622,0.71209864528 38 | 0.0434555555556,1135,1.0 39 | 0.0402666666667,936,0.856503135688 40 | 0.0354888888889,1861,0.636979977336 41 | 0.562088888889,1858,0.653780754506 42 | -------------------------------------------------------------------------------- /madelon/output2csv.py: -------------------------------------------------------------------------------- 1 | import os, re, csv 2 | 3 | # regular expressions for capturing the interesting quantities 4 | centers_pattern = '^centers: (\d+)$' 5 | selected_centers_pattern = '^rbf param: ([0-9.]+)$' # misleading name! 6 | res_pattern = '^error: +([0-9.]+$)' 7 | 8 | search_dir = "output" 9 | results_file = '../results.csv' 10 | 11 | os.chdir( search_dir ) 12 | files = filter( os.path.isfile, os.listdir( '.' )) 13 | #files = [ os.path.join( search_dir, f ) for f in files ] # add path to each file 14 | files.sort( key=lambda x: os.path.getmtime( x )) 15 | 16 | results = [] 17 | 18 | for file in files: 19 | f = open( file ) 20 | contents = f.read() 21 | 22 | # centers 23 | matches = re.search( centers_pattern, contents, re.M ) 24 | try: 25 | centers = matches.group( 1 ) 26 | except AttributeError: 27 | print "centers error 1: %s" % ( contents ) 28 | continue 29 | 30 | # selected centers 31 | matches = re.search( selected_centers_pattern, contents, re.M ) 32 | try: 33 | selected_centers = matches.group( 1 ) 34 | except AttributeError: 35 | print "selected error 1: %s" % ( contents ) 36 | continue 37 | 38 | # rmse 39 | matches = re.search( res_pattern, contents, re.M ) 40 | try: 41 | res = matches.group( 1 ) 42 | except AttributeError: 43 | print "matches error 2: %s" % ( contents ) 44 | continue 45 | 46 | results.append(( res, centers, selected_centers )) 47 | 48 | writer = csv.writer( open( results_file, 'wb' )) 49 | for result in results: 50 | writer.writerow( result ) -------------------------------------------------------------------------------- /kin8nm/output2csv.py: -------------------------------------------------------------------------------- 1 | import os, re, csv 2 | 3 | # regular expressions for capturing the interesting quantities 4 | centers_pattern = '^centers: (\d+)$' 5 | selected_centers_pattern = '^rbf param: ([0-9.]+)$' # not really selected_centers, but rather rbf param - var name is misleading 6 | res_pattern = '^RMSE: +([0-9.]+$)' 7 | 8 | search_dir = "output" 9 | results_file = '../results.csv' 10 | 11 | os.chdir( search_dir ) 12 | files = filter( os.path.isfile, os.listdir( '.' )) 13 | #files = [ os.path.join( search_dir, f ) for f in files ] # add path to each file 14 | files.sort( key=lambda x: os.path.getmtime( x )) 15 | 16 | results = [] 17 | 18 | for file in files: 19 | f = open( file ) 20 | contents = f.read() 21 | 22 | # centers 23 | matches = re.search( centers_pattern, contents, re.M ) 24 | try: 25 | centers = matches.group( 1 ) 26 | except AttributeError: 27 | print "centers error 1: %s" % ( contents ) 28 | continue 29 | 30 | # selected centers 31 | matches = re.search( selected_centers_pattern, contents, re.M ) 32 | try: 33 | selected_centers = matches.group( 1 ) 34 | except AttributeError: 35 | print "selected error 1: %s" % ( contents ) 36 | continue 37 | 38 | # rmse 39 | matches = re.search( res_pattern, contents, re.M ) 40 | try: 41 | res = matches.group( 1 ) 42 | except AttributeError: 43 | print "matches error 2: %s" % ( contents ) 44 | continue 45 | 46 | results.append(( res, centers, selected_centers )) 47 | 48 | writer = csv.writer( open( results_file, 'wb' )) 49 | for result in results: 50 | writer.writerow( result ) -------------------------------------------------------------------------------- /kin8nm/results/results.csv: -------------------------------------------------------------------------------- 1 | 0.240703416449,1500,0.001 2 | 0.0878533908172,2750,0.5005 3 | 0.105823223196,3981,0.997378112793 4 | 0.213100725346,3991,0.00490234375 5 | 0.113086300894,1882,0.999115875244 6 | 0.0990988974094,3206,0.820919006348 7 | 0.100372029803,1503,0.674825012207 8 | 0.0880232257995,4000,0.600375610352 9 | 0.0966535015852,2356,0.707751037598 10 | 0.0881633895711,3442,0.589034423828 11 | 0.0944571036678,3996,0.741408752441 12 | 0.0898449549733,3137,0.598546386719 13 | 0.119474789095,1503,0.988231994629 14 | 0.0871386412984,2132,0.501475585937 15 | 0.0865107040386,3552,0.46312286377 16 | 0.0871361334435,3736,0.534218688965 17 | 0.0877902308249,3062,0.442208740234 18 | 0.0872132177149,4000,0.467177642822 19 | 0.0895814405084,1503,0.455440124512 20 | 0.0901447039921,1835,0.494463562012 21 | 0.0869618830822,3371,0.491658752441 22 | 0.0866466506161,2387,0.432148010254 23 | 0.0883225361357,2403,0.485012573242 24 | 0.108591634064,2824,0.999603668213 25 | 0.087402433941,3770,0.504036499023 26 | 0.088075184148,4000,0.529859039307 27 | 0.0881940750632,2148,0.422544586182 28 | 0.0878813324829,3491,0.492207519531 29 | 0.0861801665819,2650,0.421599487305 30 | 0.0856027085781,2654,0.417026428223 31 | 0.0876190755021,2779,0.361814361572 32 | 0.0873312288889,2694,0.417941040039 33 | 0.0863460516134,3485,0.366844726563 34 | 0.0890568736429,3680,0.405959625244 35 | 0.0867971856991,3289,0.380137084961 36 | 0.088256114924,3326,0.416965454102 37 | 0.0864968872721,2537,0.415258178711 38 | 0.0884605729029,2576,0.418123962402 39 | 0.0873113131235,3162,0.322211669922 40 | 0.0871047262519,2994,0.381082183838 41 | 0.0876308900681,3310,0.359954650879 42 | 0.088776589335,2824,0.412453369141 43 | 0.0879055956001,2521,0.331662658691 44 | 0.0873489434011,2461,0.401660949707 45 | 0.0901902275476,2903,0.257822998047 46 | 0.088577648674,3025,0.34605255127 47 | 0.0899829625198,3712,0.620801940918 48 | 0.0859732168095,3468,0.419953186035 49 | 0.0871677938542,3462,0.424953063965 50 | 0.088372616351,3452,0.413428955078 51 | 0.0889128778501,2568,0.39013684082 52 | 0.0883926682,1753,0.35946685791 53 | 0.44300454033,1950,0.399648803711 54 | -------------------------------------------------------------------------------- /kin8nm/main.py: -------------------------------------------------------------------------------- 1 | # python spearmint_sync.py --method=GPEIChooser kin8nm 2 | 3 | import sys, subprocess, re, os 4 | from math import exp 5 | 6 | def get_validation_loss( data ): 7 | pattern = 'RMSE: ([0-9.]+)' 8 | matches = re.search( pattern, data ) 9 | 10 | validation_loss = float( matches.group( 1 )) 11 | return validation_loss 12 | 13 | def run_test( params ): 14 | 15 | #debug_o = open( 'debug', 'wb' ) 16 | #print >> debug_o, params 17 | 18 | centers = params["centers"][0] 19 | rbf_param = params["rbf_param"][0] 20 | #print >> debug_o, parameters 21 | 22 | # find centers 23 | cmd = "sofia-kmeans --k %s --init_type optimized_kmeans_pp --opt_type mini_batch_kmeans --mini_batch_size 100 --iterations 500 --objective_after_init --objective_after_training --training_file data/train.libsvm.txt --model_out data/model_sofia --dimensionality 9" % ( centers ) 24 | os.system( cmd ) 25 | 26 | # map train 27 | cmd = "sofia-kmeans --model_in data/model_sofia --test_file data/train.libsvm.txt --cluster_mapping_out data/mapped_train.libsvm.txt --cluster_mapping_type rbf_kernel --cluster_mapping_param %s --cluster_mapping_threshold 0.01" % ( rbf_param ) 28 | os.system( cmd ) 29 | 30 | # map validation 31 | cmd = "sofia-kmeans --model_in data/model_sofia --test_file data/validation.libsvm.txt --cluster_mapping_out data/mapped_validation.libsvm.txt --cluster_mapping_type rbf_kernel --cluster_mapping_param %s --cluster_mapping_threshold 0.001" % ( rbf_param ) 32 | os.system( cmd ) 33 | 34 | # train 2 vw 35 | cmd = "python libsvm2vw.py data/mapped_train.libsvm.txt data/mapped_train.vw" 36 | os.system( cmd ) 37 | 38 | # validation 2 vw 39 | cmd = "python libsvm2vw.py data/mapped_validation.libsvm.txt data/mapped_validation.vw" 40 | os.system( cmd ) 41 | 42 | # train vw 43 | cmd = "vw -d data/mapped_train.vw -f data/model_vw -c -k --passes 100" 44 | os.system( cmd ) 45 | 46 | # predict vw 47 | cmd = "vw -t -d data/mapped_validation.vw -i data/model_vw -p data/p.txt" 48 | os.system( cmd ) 49 | 50 | # python rmse.py data/sparse_validation.vw data/p.txt 51 | data = subprocess.check_output( ['python', 'rmse.py', 'data/mapped_validation.vw', 'data/p.txt' ] ) 52 | validation_loss = get_validation_loss( data ) 53 | 54 | print 'RMSE: ', validation_loss 55 | print 56 | 57 | return validation_loss 58 | 59 | def main( job_id, params ): 60 | print 'Job id:', str( job_id ) 61 | print "centers: %s" % ( params['centers'][0] ) 62 | print "rbf param: %s" % ( params['rbf_param'][0] ) 63 | 64 | return run_test( params ) 65 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | ################# 2 | ## Eclipse 3 | ################# 4 | 5 | *.pydevproject 6 | .project 7 | .metadata 8 | bin/ 9 | tmp/ 10 | *.tmp 11 | *.bak 12 | *.swp 13 | *~.nib 14 | local.properties 15 | .classpath 16 | .settings/ 17 | .loadpath 18 | 19 | # External tool builders 20 | .externalToolBuilders/ 21 | 22 | # Locally stored "Eclipse launch configurations" 23 | *.launch 24 | 25 | # CDT-specific 26 | .cproject 27 | 28 | # PDT-specific 29 | .buildpath 30 | 31 | 32 | ################# 33 | ## Visual Studio 34 | ################# 35 | 36 | ## Ignore Visual Studio temporary files, build results, and 37 | ## files generated by popular Visual Studio add-ons. 38 | 39 | # User-specific files 40 | *.suo 41 | *.user 42 | *.sln.docstates 43 | 44 | # Build results 45 | [Dd]ebug/ 46 | [Rr]elease/ 47 | *_i.c 48 | *_p.c 49 | *.ilk 50 | *.meta 51 | *.obj 52 | *.pch 53 | *.pdb 54 | *.pgc 55 | *.pgd 56 | *.rsp 57 | *.sbr 58 | *.tlb 59 | *.tli 60 | *.tlh 61 | *.tmp 62 | *.vspscc 63 | .builds 64 | *.dotCover 65 | 66 | ## TODO: If you have NuGet Package Restore enabled, uncomment this 67 | #packages/ 68 | 69 | # Visual C++ cache files 70 | ipch/ 71 | *.aps 72 | *.ncb 73 | *.opensdf 74 | *.sdf 75 | 76 | # Visual Studio profiler 77 | *.psess 78 | *.vsp 79 | 80 | # ReSharper is a .NET coding add-in 81 | _ReSharper* 82 | 83 | # Installshield output folder 84 | [Ee]xpress 85 | 86 | # DocProject is a documentation generator add-in 87 | DocProject/buildhelp/ 88 | DocProject/Help/*.HxT 89 | DocProject/Help/*.HxC 90 | DocProject/Help/*.hhc 91 | DocProject/Help/*.hhk 92 | DocProject/Help/*.hhp 93 | DocProject/Help/Html2 94 | DocProject/Help/html 95 | 96 | # Click-Once directory 97 | publish 98 | 99 | # Others 100 | [Bb]in 101 | [Oo]bj 102 | sql 103 | TestResults 104 | *.Cache 105 | ClientBin 106 | stylecop.* 107 | ~$* 108 | *.dbmdl 109 | Generated_Code #added for RIA/Silverlight projects 110 | 111 | # Backup & report files from converting an old project file to a newer 112 | # Visual Studio version. Backup files are not needed, because we have git ;-) 113 | _UpgradeReport_Files/ 114 | Backup*/ 115 | UpgradeLog*.XML 116 | 117 | 118 | 119 | ############ 120 | ## Windows 121 | ############ 122 | 123 | # Windows image file caches 124 | Thumbs.db 125 | 126 | # Folder config file 127 | Desktop.ini 128 | 129 | 130 | ############# 131 | ## Python 132 | ############# 133 | 134 | *.py[co] 135 | 136 | # Packages 137 | *.egg 138 | *.egg-info 139 | dist 140 | build 141 | eggs 142 | parts 143 | bin 144 | var 145 | sdist 146 | develop-eggs 147 | .installed.cfg 148 | 149 | # Installer logs 150 | pip-log.txt 151 | 152 | # Unit test / coverage reports 153 | .coverage 154 | .tox 155 | 156 | #Translations 157 | *.mo 158 | 159 | #Mr Developer 160 | .mr.developer.cfg 161 | 162 | # Mac crap 163 | .DS_Store 164 | -------------------------------------------------------------------------------- /madelon/main.py: -------------------------------------------------------------------------------- 1 | # python spearmint_sync.py --method=GPEIOptChooser madelon_sofia_vw_rbf 2 | 3 | import sys, subprocess, re, os 4 | from math import exp 5 | 6 | def get_validation_loss( data ): 7 | pattern = 'error: ([0-9.]+)' 8 | matches = re.search( pattern, data ) 9 | 10 | validation_loss = float( matches.group( 1 )) 11 | return validation_loss 12 | 13 | def get_validation_acc( data ): 14 | pattern = 'accuracy: ([0-9.]+)' 15 | matches = re.search( pattern, data ) 16 | 17 | acc = float( matches.group( 1 )) 18 | return acc 19 | 20 | 21 | def run_test( params ): 22 | 23 | #debug_o = open( 'debug', 'wb' ) 24 | #print >> debug_o, params 25 | 26 | centers = params["centers"][0] 27 | rbf_param = params["rbf_param"][0] 28 | #print >> debug_o, parameters 29 | 30 | # find centers 31 | # dimensionality w/label (+1) 32 | cmd = "sofia-kmeans --k %s --init_type optimized_kmeans_pp --opt_type mini_batch_kmeans --mini_batch_size 100 --iterations 500 --objective_after_init --objective_after_training --training_file data/all.libsvm.txt --model_out data/model_sofia --dimensionality 13" % ( centers ) 33 | os.system( cmd ) 34 | 35 | # map train 36 | cmd = "sofia-kmeans --model_in data/model_sofia --test_file data/train.libsvm.txt --cluster_mapping_out data/mapped_train.libsvm.txt --cluster_mapping_type rbf_kernel --cluster_mapping_param %s" % ( rbf_param ) 37 | os.system( cmd ) 38 | 39 | # map validation 40 | cmd = "sofia-kmeans --model_in data/model_sofia --test_file data/validation.libsvm.txt --cluster_mapping_out data/mapped_validation.libsvm.txt --cluster_mapping_type rbf_kernel --cluster_mapping_param %s" % ( rbf_param ) 41 | os.system( cmd ) 42 | 43 | # map test 44 | #cmd = "sofia-kmeans --model_in data/model_sofia --test_file data/test.libsvm.txt --cluster_mapping_out data/mapped_test.libsvm.txt" 45 | #os.system( cmd ) 46 | 47 | ### 48 | 49 | # train 2 vw 50 | cmd = "python libsvm2vw.py data/mapped_train.libsvm.txt data/mapped_train.vw" 51 | os.system( cmd ) 52 | 53 | # validation 2 vw 54 | cmd = "python libsvm2vw.py data/mapped_validation.libsvm.txt data/mapped_validation.vw" 55 | os.system( cmd ) 56 | 57 | ### 58 | 59 | # train vw 60 | cmd = "vw -d data/mapped_train.vw -f data/model_vw -c -k --passes 100 --loss_function logistic" 61 | os.system( cmd ) 62 | 63 | # predict vw 64 | cmd = "vw -t -d data/mapped_validation.vw -i data/model_vw -p data/p.txt --loss_function logistic" 65 | os.system( cmd ) 66 | 67 | # python rmse.py data/sparse_validation.vw data/p.txt 68 | data = subprocess.check_output( ['python', 'auc.py', 'data/mapped_validation.vw', 'data/p.txt' ] ) 69 | validation_loss = get_validation_loss( data ) 70 | 71 | #data = subprocess.check_output( ['python', 'acc.py', 'data/mapped_validation.vw', 'data/p.txt' ] ) 72 | #validation_acc = get_validation_acc( data ) 73 | 74 | print 'error: ', validation_loss 75 | #print 'acc: ', validation_acc 76 | print 77 | 78 | return validation_loss 79 | 80 | def main( job_id, params ): 81 | print 'Job id:', str( job_id ) 82 | print "centers: %s" % ( params['centers'][0] ) 83 | print "rbf param: %s" % ( params['rbf_param'][0] ) 84 | 85 | return run_test( params ) 86 | -------------------------------------------------------------------------------- /madelon/results/results2.csv: -------------------------------------------------------------------------------- 1 | 0.0409777777778,1000,0.4 2 | 0.0410222222222,2500,0.95 3 | 0.0546333333333,4000,1.5 4 | 0.0423333333333,1000,0.4 5 | 0.0387777777778,4000,0.4 6 | 0.0485333333333,1000,1.5 7 | 0.0374444444444,4000,0.4 8 | 0.0411555555556,2826,0.4 9 | 0.039,4000,0.4 10 | 0.0370555555556,4000,0.655423420033 11 | 0.0357888888889,4000,0.65546501265 12 | 0.0402666666667,1000,0.816379786706 13 | 0.0357888888889,4000,0.718958780775 14 | 0.0376777777778,4000,0.700703896022 15 | 0.0380444444444,4000,0.691556781383 16 | 0.0469,1000,1.17412488756 17 | 0.0371333333333,4000,0.616191100775 18 | 0.0395444444444,4000,0.635325445936 19 | 0.0346111111111,2867,0.627209109103 20 | 0.0404111111111,4000,0.4 21 | 0.0424222222222,1988,0.917840576172 22 | 0.0360666666667,2829,0.614928773092 23 | 0.0357111111111,1000,0.632968323593 24 | 0.0373666666667,2261,0.624796027093 25 | 0.0379222222222,1000,0.635086268885 26 | 0.0366333333333,2587,0.613039954696 27 | 0.0505,4000,1.04370321302 28 | 0.0375111111111,2390,0.705773685492 29 | 0.0364,2721,0.596043134076 30 | 0.0421444444444,1979,0.868493652344 31 | 0.0382444444444,2022,0.85439453125 32 | 0.0466222222222,3085,1.00001831055 33 | 0.0408333333333,1567,0.453106689453 34 | 0.0381222222222,3901,0.734149169922 35 | 0.0407,2072,0.8705078125 36 | 0.037,4000,0.537302439595 37 | 0.0424222222222,3387,0.954766845703 38 | 0.0371555555556,2283,0.665969848633 39 | 0.0354666666667,3311,0.599200074233 40 | 0.0357222222222,3215,0.590867561898 41 | 0.0400111111111,3387,0.734350585938 42 | 0.0507277777778,1563,1.38270874023 43 | 0.0403,3064,0.832708740234 44 | 0.0501222222222,3814,1.10770874023 45 | 0.0369888888889,2314,0.557708740234 46 | 0.0441444444444,1939,0.970208740234 47 | 0.0397,3439,0.420208740234 48 | 0.0563555555556,3533,1.49472961426 49 | 0.0546555555556,2689,1.24520874023 50 | 0.0397222222222,1000,0.652217602672 51 | 0.0400777777778,3201,0.585403442383 52 | 0.0478666666667,3951,1.41040344238 53 | 0.0419111111111,2450,0.860403442383 54 | 0.0492555555556,2075,1.27290344238 55 | 0.0379777777778,4000,0.573050245957 56 | 0.0373,1188,0.695208740234 57 | 0.0418555555556,2826,0.997903442383 58 | 0.0536555555556,1282,1.27958374023 59 | 0.0436666666667,1231,0.963528442383 60 | 0.0384444444444,2732,0.413528442383 61 | 0.0481888888889,3482,1.23852844238 62 | 0.0371777777778,1982,0.688528442383 63 | 0.0526388888889,2357,1.37602844238 64 | 0.0403777777778,3857,0.826028442383 65 | 0.0474111111111,3107,1.10102844238 66 | 0.0389888888889,1606,0.551028442383 67 | 0.0539388888889,1419,1.30727844238 68 | 0.0407333333333,2919,0.757278442383 69 | 0.0469611111111,3670,1.03227844238 70 | 0.0372777777778,3058,0.473583984375 71 | 0.0418333333333,1794,1.16977844238 72 | 0.0493111111111,3809,1.29858398438 73 | 0.0400111111111,1180,0.628338623047 74 | 0.0433333333333,1044,0.894778442383 75 | 0.0489888888889,1133,1.47052612305 76 | 0.0354888888889,3994,0.559588623047 77 | 0.0384777777778,2533,0.452200317383 78 | 0.0479,3283,1.27720031738 79 | 0.0393444444444,1782,0.727200317383 80 | 0.0383,2308,0.748583984375 81 | 0.0497611111111,3384,1.19552612305 82 | 0.0478444444444,2908,1.13970031738 83 | 0.0363,1884,0.645526123047 84 | 0.0511888888889,1595,1.34595031738 85 | 0.0420333333333,3095,0.795950317383 86 | 0.0497111111111,3845,1.07095031738 87 | 0.0492666666667,2259,1.05802612305 88 | 0.0481444444444,1970,1.20845031738 89 | 0.0372222222222,3759,0.508026123047 90 | 0.0501666666667,3009,1.33302612305 91 | 0.0414777777778,1220,0.933450317383 92 | 0.0463555555556,1313,1.44907531738 93 | 0.0390444444444,1509,0.783026123047 94 | 0.0509222222222,3564,1.17407531738 95 | 0.0377,2064,0.624075317383 96 | 0.0588666666667,1933,1.43608398438 97 | 0.0392888888889,3433,0.886083984375 98 | 0.0554777777778,3189,1.31157531738 99 | 0.0428555555556,1688,0.761575317383 100 | 0.0417444444444,1501,1.10532531738 101 | 0.0377444444444,4000,0.559615255606 102 | 0.0371777777778,3001,0.555325317383 103 | 0.0509333333333,3947,1.40177612305 104 | 0.0398666666667,2251,0.830325317383 105 | 0.0483111111111,1876,1.24282531738 106 | 0.0373,3377,0.692825317383 107 | 0.0413333333333,2626,0.967825317383 108 | 0.0408888888889,1126,0.417825317383 109 | 0.0485888888889,1173,1.26001281738 110 | 0.0358444444444,2673,0.710012817383 111 | 0.0459777777778,3423,0.985012817383 112 | 0.0389,1923,0.435012817383 113 | 0.0439111111111,2298,1.12251281738 114 | 0.0368444444444,3799,0.572512817383 115 | 0.0463555555556,3048,1.39751281738 116 | 0.0390111111111,1548,0.847512817383 117 | 0.0488666666667,1735,1.05376281738 118 | 0.0371555555556,3236,0.503762817383 119 | 0.0518555555556,3986,1.32876281738 120 | 0.0375333333333,2486,0.778762817383 121 | 0.0521222222222,2110,1.46626281738 122 | 0.0415777777778,3611,0.916262817383 123 | 0.036,2024,0.683190917969 124 | 0.0394777777778,1360,0.641262817383 125 | 0.0445333333333,1266,1.15688781738 126 | 0.039,2767,0.606887817383 127 | 0.0564055555556,3517,1.43188781738 128 | 0.0377666666667,2017,0.881887817383 129 | 0.0509555555556,2392,1.29438781738 130 | 0.0383333333333,3892,0.744387817383 131 | 0.0474333333333,3142,1.01938781738 132 | 0.0407444444444,1642,0.469387817383 133 | 0.0479166666667,1454,1.36313781738 134 | 0.0369888888889,2955,0.813137817383 135 | 0.0468444444444,3705,1.08813781738 136 | 0.0496444444444,2683,1.16108398438 137 | 0.0359666666667,2204,0.538137817383 138 | 0.0424555555556,1829,0.950637817383 139 | 0.0397,3330,0.400637817383 140 | 0.0441555555556,2579,1.22563781738 141 | 0.0366111111111,1079,0.675637817383 142 | 0.04715,1055,1.40610656738 143 | 0.0419777777778,2556,0.856106567383 144 | 0.0475111111111,3306,1.13110656738 145 | 0.0370777777778,1806,0.581106567383 146 | 0.0430888888889,2181,0.993606567383 147 | 0.0369111111111,3681,0.443606567383 148 | 0.0534555555556,2931,1.26860656738 149 | 0.0385888888889,1431,0.718606567383 150 | 0.0464222222222,1618,1.19985656738 151 | 0.0372666666667,3119,0.649856567383 152 | 0.0502777777778,3869,1.47485656738 153 | 0.0411222222222,2368,0.924856567383 154 | 0.0497222222222,1993,1.33735656738 155 | 0.0401333333333,3494,0.787356567383 156 | 0.0429666666667,2744,1.06235656738 157 | 0.0404777777778,1243,0.512356567383 158 | 0.0481,1337,1.02798156738 159 | 0.0377222222222,2837,0.477981567383 160 | 0.0495222222222,3588,1.30298156738 161 | 0.0427444444444,2087,0.752981567383 162 | 0.0531333333333,2462,1.44048156738 163 | -------------------------------------------------------------------------------- /kin8nm/data/validation.libsvm.txt: -------------------------------------------------------------------------------- 1 | 1.0426291 1:-1.1845123 2:-0.30847449 3:-0.080024553 4:-1.521214 5:0.088574597 6:-0.096783148 7:-0.18618583 8:-1.3866696 2 | 0.8528549 1:0.17493348 2:-0.90282173 3:-0.3710041 4:-0.7621634 5:-0.44167289 6:0.71061882 7:-0.19889709 8:-1.422196 3 | 1.1605411 1:-0.20037867 2:-0.5307344 3:-1.361403 4:-1.5661481 5:0.63224943 6:-0.20885547 7:1.1797135 8:-0.20080303 4 | 0.74152645 1:1.0310845 2:-0.082002541 3:-1.2519808 4:0.22291916 5:-0.6589026 6:0.77777502 7:0.95615484 8:0.20680374 5 | 0.85680827 1:1.155561 2:1.1693638 3:0.4430786 4:1.417156 5:0.66124222 6:-0.46871404 7:-0.48551777 8:-0.24288656 6 | 0.98412045 1:-0.59235426 2:1.4771964 3:-0.28240109 4:-0.86088572 5:0.17999484 6:-0.90582612 7:1.523828 8:0.9894415 7 | 0.60887038 1:0.0091030553 2:-0.76680414 3:0.63133861 4:1.2694334 5:-0.18044583 6:-0.061585928 7:0.71398804 8:1.0934046 8 | 0.1598116 1:0.48174648 2:1.1217509 3:1.0665344 4:0.70526164 5:-1.3149333 6:0.55303227 7:-0.12108917 8:-0.33958088 9 | 0.87304188 1:1.1704253 2:-1.4203336 3:-0.66985287 4:-0.61630658 5:-1.4848034 6:0.35472859 7:1.027863 8:0.66489041 10 | 0.39646365 1:0.94850184 2:-0.30283171 3:1.1807699 4:-0.81538137 5:1.4465208 6:1.2740674 7:1.0940395 8:0.30407523 11 | 0.88095717 1:0.82506353 2:1.424193 3:-0.74812389 4:-1.3569353 5:-0.35132006 6:0.95279822 7:0.35880534 8:-0.66697001 12 | 0.8056081 1:-0.15867039 2:1.5703312 3:-0.99867638 4:-1.4643619 5:-0.79574842 6:0.68676072 7:-0.024359828 8:-0.12546194 13 | 0.82609617 1:-0.77881022 2:0.027686773 3:0.49168422 4:0.97568221 5:1.170734 6:0.28834976 7:-1.4724769 8:-0.75449923 14 | 0.78361273 1:-1.2916605 2:-0.35363455 3:1.2262338 4:0.55774463 5:0.57354046 6:0.24231856 7:-1.432081 8:0.47546514 15 | 0.90976303 1:1.5598181 2:-0.82466194 3:0.18029455 4:-0.23426728 5:-0.12511044 6:-0.47734902 7:0.069971296 8:-0.75686509 16 | 0.89839244 1:-1.2266692 2:-1.0867687 3:-0.24218089 4:-1.3372774 5:1.5633194 6:-0.25044885 7:-1.1539335 8:-0.39998776 17 | 0.90528959 1:0.054519505 2:1.2658265 3:-0.099836079 4:-1.3904924 5:1.3703378 6:1.0087177 7:0.23928143 8:-0.64957728 18 | 1.038361 1:-0.014509235 2:-0.50180728 3:-0.93131784 4:-0.84231014 5:0.38965722 6:-0.17285596 7:0.9281213 8:-0.79512537 19 | 0.40101153 1:1.4148163 2:1.4338339 3:1.4959888 4:0.86738604 5:0.93323801 6:1.3527371 7:0.55218314 8:1.0433354 20 | 0.23659981 1:-0.89702576 2:1.5448228 3:0.37951963 4:-0.77252648 5:0.58337692 6:-1.5677124 7:-1.452681 8:-0.65650124 21 | 0.24862364 1:0.19122203 2:-1.4271463 3:1.1533876 4:0.34982512 5:-0.094713463 6:-0.0044326952 7:1.248294 8:-0.54290862 22 | 0.78008234 1:1.0675134 2:1.2225798 3:-1.3119543 4:-1.4777884 5:-0.26553697 6:-0.4412614 7:-0.88910839 8:-0.65092445 23 | 0.20315809 1:-0.54344233 2:-1.4501276 3:0.45193733 4:1.0091097 5:-1.1893696 6:0.53735183 7:0.99527806 8:0.21181306 24 | 1.3042058 1:-1.2112907 2:-1.2069807 3:-0.79009976 4:-0.051490107 5:0.66040332 6:0.92509037 7:0.10049762 8:1.3775325 25 | 1.2284387 1:-1.4676134 2:-1.3124386 3:-1.3430827 4:0.39109121 5:0.93579487 6:0.076296979 7:-1.3872601 8:0.38713881 26 | 0.42946711 1:1.3321041 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1.0898701 1:-1.2201177 2:-0.97715687 3:0.16528821 4:-0.57558674 5:-0.38830722 6:0.026137079 7:-0.45687137 8:-0.10556314 798 | 0.9831384 1:0.39154951 2:0.56704421 3:-0.3465013 4:-0.5025089 5:0.21875397 6:-1.0294617 7:0.88323148 8:0.023557732 799 | 0.92250499 1:1.4029724 2:-0.95600374 3:-0.13794273 4:0.50707909 5:-0.12346081 6:-0.10809892 7:-0.80419484 8:-1.2615242 800 | 0.75909329 1:-0.87527321 2:-0.22652564 3:1.1871041 4:-0.36390364 5:-0.1248917 6:-1.1601901 7:-0.27295734 8:0.32425061 801 | 0.62620086 1:0.46183478 2:0.53591828 3:1.241104 4:-1.2261555 5:0.44278492 6:-0.11317833 7:-0.62794672 8:0.55628155 802 | 1.1771503 1:-0.81827533 2:0.23241664 3:-0.93982183 4:0.85252476 5:-0.54677756 6:-1.5245681 7:-1.2907258 8:0.044265241 803 | 0.96657347 1:0.80498378 2:0.65290878 3:-0.52788187 4:1.227423 5:-1.0200085 6:-0.58597363 7:-1.0902488 8:-0.18817552 804 | 1.0957796 1:-1.2287218 2:-1.0027339 3:0.24694776 4:-0.92926245 5:0.65198279 6:-1.1067218 7:0.55310091 8:-0.57331298 805 | 0.41266335 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