Usunięcie starych katalogów

w2/data_company.csv

@@ -1,97 +0,0 @@
-6.1101,17.592
-5.5277,9.1302
-8.5186,13.662
-7.0032,11.854
-5.8598,6.8233
-8.3829,11.886
-7.4764,4.3483
-8.5781,12
-6.4862,6.5987
-5.0546,3.8166
-5.7107,3.2522
-14.164,15.505
-5.734,3.1551
-8.4084,7.2258
-5.6407,0.71618
-5.3794,3.5129
-6.3654,5.3048
-5.1301,0.56077
-6.4296,3.6518
-7.0708,5.3893
-6.1891,3.1386
-20.27,21.767
-5.4901,4.263
-6.3261,5.1875
-5.5649,3.0825
-18.945,22.638
-12.828,13.501
-10.957,7.0467
-13.176,14.692
-22.203,24.147
-5.2524,-1.22
-6.5894,5.9966
-9.2482,12.134
-5.8918,1.8495
-8.2111,6.5426
-7.9334,4.5623
-8.0959,4.1164
-5.6063,3.3928
-12.836,10.117
-6.3534,5.4974
-5.4069,0.55657
-6.8825,3.9115
-11.708,5.3854
-5.7737,2.4406
-7.8247,6.7318
-7.0931,1.0463
-5.0702,5.1337
-5.8014,1.844
-11.7,8.0043
-5.5416,1.0179
-7.5402,6.7504
-5.3077,1.8396
-7.4239,4.2885
-7.6031,4.9981
-6.3328,1.4233
-6.3589,-1.4211
-6.2742,2.4756
-5.6397,4.6042
-9.3102,3.9624
-9.4536,5.4141
-8.8254,5.1694
-5.1793,-0.74279
-21.279,17.929
-14.908,12.054
-18.959,17.054
-7.2182,4.8852
-8.2951,5.7442
-10.236,7.7754
-5.4994,1.0173
-20.341,20.992
-10.136,6.6799
-7.3345,4.0259
-6.0062,1.2784
-7.2259,3.3411
-5.0269,-2.6807
-6.5479,0.29678
-7.5386,3.8845
-5.0365,5.7014
-10.274,6.7526
-5.1077,2.0576
-5.7292,0.47953
-5.1884,0.20421
-6.3557,0.67861
-9.7687,7.5435
-6.5159,5.3436
-8.5172,4.2415
-9.1802,6.7981
-6.002,0.92695
-5.5204,0.152
-5.0594,2.8214
-5.7077,1.8451
-7.6366,4.2959
-5.8707,7.2029
-5.3054,1.9869
-8.2934,0.14454
-13.394,9.0551
-5.4369,0.61705

w2/ex2data2.txt

@@ -1,118 +0,0 @@
-0.051267,0.69956,1
--0.092742,0.68494,1
--0.21371,0.69225,1
--0.375,0.50219,1
--0.51325,0.46564,1
--0.52477,0.2098,1
--0.39804,0.034357,1
--0.30588,-0.19225,1
-0.016705,-0.40424,1
-0.13191,-0.51389,1
-0.38537,-0.56506,1
-0.52938,-0.5212,1
-0.63882,-0.24342,1
-0.73675,-0.18494,1
-0.54666,0.48757,1
-0.322,0.5826,1
-0.16647,0.53874,1
--0.046659,0.81652,1
--0.17339,0.69956,1
--0.47869,0.63377,1
--0.60541,0.59722,1
--0.62846,0.33406,1
--0.59389,0.005117,1
--0.42108,-0.27266,1
--0.11578,-0.39693,1
-0.20104,-0.60161,1
-0.46601,-0.53582,1
-0.67339,-0.53582,1
--0.13882,0.54605,1
--0.29435,0.77997,1
--0.26555,0.96272,1
--0.16187,0.8019,1
--0.17339,0.64839,1
--0.28283,0.47295,1
--0.36348,0.31213,1
--0.30012,0.027047,1
--0.23675,-0.21418,1
--0.06394,-0.18494,1
-0.062788,-0.16301,1
-0.22984,-0.41155,1
-0.2932,-0.2288,1
-0.48329,-0.18494,1
-0.64459,-0.14108,1
-0.46025,0.012427,1
-0.6273,0.15863,1
-0.57546,0.26827,1
-0.72523,0.44371,1
-0.22408,0.52412,1
-0.44297,0.67032,1
-0.322,0.69225,1
-0.13767,0.57529,1
--0.0063364,0.39985,1
--0.092742,0.55336,1
--0.20795,0.35599,1
--0.20795,0.17325,1
--0.43836,0.21711,1
--0.21947,-0.016813,1
--0.13882,-0.27266,1
-0.18376,0.93348,0
-0.22408,0.77997,0
-0.29896,0.61915,0
-0.50634,0.75804,0
-0.61578,0.7288,0
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-0.76555,0.50219,0
-0.92684,0.3633,0
-0.82316,0.27558,0
-0.96141,0.085526,0
-0.93836,0.012427,0
-0.86348,-0.082602,0
-0.89804,-0.20687,0
-0.85196,-0.36769,0
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-0.35081,-0.57968,0
-0.28744,-0.76974,0
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-0.14919,-0.57968,0
--0.13306,-0.4481,0
--0.40956,-0.41155,0
--0.39228,-0.25804,0
--0.74366,-0.25804,0
--0.69758,0.041667,0
--0.75518,0.2902,0
--0.69758,0.68494,0
--0.4038,0.70687,0
--0.38076,0.91886,0
--0.50749,0.90424,0
--0.54781,0.70687,0
-0.10311,0.77997,0
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--0.10426,0.99196,0
--0.081221,1.1089,0
-0.28744,1.087,0
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-1.0709,0.10015,0
--0.046659,-0.57968,0
--0.23675,-0.63816,0
--0.15035,-0.36769,0
--0.49021,-0.3019,0
--0.46717,-0.13377,0
--0.28859,-0.060673,0
--0.61118,-0.067982,0
--0.66302,-0.21418,0
--0.59965,-0.41886,0
--0.72638,-0.082602,0
--0.83007,0.31213,0
--0.72062,0.53874,0
--0.59389,0.49488,0
--0.48445,0.99927,0
--0.0063364,0.99927,0
-0.63265,-0.030612,0

w2/iris.csv

@@ -1,151 +0,0 @@
-sl,sw,pl,pw,Gatunek
-5.2,3.4,1.4,0.2,Iris-setosa
-5.1,3.7,1.5,0.4,Iris-setosa
-6.7,3.1,5.6,2.4,Iris-virginica
-6.5,3.2,5.1,2.0,Iris-virginica
-4.9,2.5,4.5,1.7,Iris-virginica
-6.0,2.7,5.1,1.6,Iris-versicolor
-5.7,2.6,3.5,1.0,Iris-versicolor
-5.0,2.0,3.5,1.0,Iris-versicolor
-5.2,3.5,1.5,0.2,Iris-setosa
-4.8,3.0,1.4,0.1,Iris-setosa
-6.7,3.3,5.7,2.5,Iris-virginica
-6.1,3.0,4.9,1.8,Iris-virginica
-4.8,3.4,1.9,0.2,Iris-setosa
-5.8,2.8,5.1,2.4,Iris-virginica
-4.4,2.9,1.4,0.2,Iris-setosa
-7.2,3.0,5.8,1.6,Iris-virginica
-4.4,3.2,1.3,0.2,Iris-setosa
-5.0,3.5,1.3,0.3,Iris-setosa
-5.4,3.9,1.3,0.4,Iris-setosa
-7.7,2.8,6.7,2.0,Iris-virginica
-5.0,3.6,1.4,0.2,Iris-setosa
-6.2,2.8,4.8,1.8,Iris-virginica
-6.0,2.2,5.0,1.5,Iris-virginica
-7.4,2.8,6.1,1.9,Iris-virginica
-5.0,3.2,1.2,0.2,Iris-setosa
-6.7,3.1,4.4,1.4,Iris-versicolor
-6.7,3.1,4.7,1.5,Iris-versicolor
-5.6,2.7,4.2,1.3,Iris-versicolor
-5.6,2.5,3.9,1.1,Iris-versicolor
-6.3,3.3,4.7,1.6,Iris-versicolor
-5.1,3.4,1.5,0.2,Iris-setosa
-6.0,2.9,4.5,1.5,Iris-versicolor
-5.3,3.7,1.5,0.2,Iris-setosa
-5.6,2.9,3.6,1.3,Iris-versicolor
-5.5,2.5,4.0,1.3,Iris-versicolor
-5.5,2.4,3.7,1.0,Iris-versicolor
-4.4,3.0,1.3,0.2,Iris-setosa
-6.6,3.0,4.4,1.4,Iris-versicolor
-7.9,3.8,6.4,2.0,Iris-virginica
-5.7,2.8,4.1,1.3,Iris-versicolor
-5.8,2.7,4.1,1.0,Iris-versicolor
-6.5,2.8,4.6,1.5,Iris-versicolor
-6.1,2.8,4.7,1.2,Iris-versicolor
-5.1,3.8,1.9,0.4,Iris-setosa
-5.0,3.4,1.6,0.4,Iris-setosa
-5.5,2.6,4.4,1.2,Iris-versicolor
-5.0,3.4,1.5,0.2,Iris-setosa
-6.8,2.8,4.8,1.4,Iris-versicolor
-6.9,3.1,4.9,1.5,Iris-versicolor
-6.1,2.9,4.7,1.4,Iris-versicolor
-5.1,3.8,1.6,0.2,Iris-setosa
-6.4,3.2,5.3,2.3,Iris-virginica
-6.4,2.7,5.3,1.9,Iris-virginica
-5.7,2.8,4.5,1.3,Iris-versicolor
-5.8,2.6,4.0,1.2,Iris-versicolor
-4.7,3.2,1.6,0.2,Iris-setosa
-5.1,3.3,1.7,0.5,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-6.3,3.4,5.6,2.4,Iris-virginica
-5.1,3.8,1.5,0.3,Iris-setosa
-7.0,3.2,4.7,1.4,Iris-versicolor
-5.4,3.0,4.5,1.5,Iris-versicolor
-6.0,2.2,4.0,1.0,Iris-versicolor
-6.0,3.0,4.8,1.8,Iris-virginica
-6.2,2.9,4.3,1.3,Iris-versicolor
-5.6,3.0,4.1,1.3,Iris-versicolor
-4.9,3.0,1.4,0.2,Iris-setosa
-5.0,2.3,3.3,1.0,Iris-versicolor
-6.3,2.5,5.0,1.9,Iris-virginica
-4.8,3.4,1.6,0.2,Iris-setosa
-5.9,3.0,4.2,1.5,Iris-versicolor
-4.6,3.6,1.0,0.2,Iris-setosa
-5.0,3.5,1.6,0.6,Iris-setosa
-5.7,4.4,1.5,0.4,Iris-setosa
-5.0,3.0,1.6,0.2,Iris-setosa
-5.6,3.0,4.5,1.5,Iris-versicolor
-6.3,2.8,5.1,1.5,Iris-virginica
-5.2,2.7,3.9,1.4,Iris-versicolor
-5.9,3.2,4.8,1.8,Iris-versicolor
-7.7,3.0,6.1,2.3,Iris-virginica
-6.2,3.4,5.4,2.3,Iris-virginica
-6.4,2.9,4.3,1.3,Iris-versicolor
-6.5,3.0,5.5,1.8,Iris-virginica
-5.8,2.7,5.1,1.9,Iris-virginica
-6.9,3.2,5.7,2.3,Iris-virginica
-6.4,2.8,5.6,2.2,Iris-virginica
-4.7,3.2,1.3,0.2,Iris-setosa
-5.5,2.4,3.8,1.1,Iris-versicolor
-5.4,3.4,1.5,0.4,Iris-setosa
-7.2,3.6,6.1,2.5,Iris-virginica
-6.7,2.5,5.8,1.8,Iris-virginica
-6.1,3.0,4.6,1.4,Iris-versicolor
-6.0,3.4,4.5,1.6,Iris-versicolor
-6.3,2.7,4.9,1.8,Iris-virginica
-6.9,3.1,5.1,2.3,Iris-virginica
-5.5,3.5,1.3,0.2,Iris-setosa
-6.7,3.0,5.2,2.3,Iris-virginica
-4.6,3.1,1.5,0.2,Iris-setosa
-5.8,2.7,5.1,1.9,Iris-virginica
-6.4,3.1,5.5,1.8,Iris-virginica
-7.3,2.9,6.3,1.8,Iris-virginica
-4.8,3.0,1.4,0.3,Iris-setosa
-7.1,3.0,5.9,2.1,Iris-virginica
-5.9,3.0,5.1,1.8,Iris-virginica
-6.1,2.6,5.6,1.4,Iris-virginica
-5.4,3.9,1.7,0.4,Iris-setosa
-6.4,3.2,4.5,1.5,Iris-versicolor
-5.1,2.5,3.0,1.1,Iris-versicolor
-6.3,2.9,5.6,1.8,Iris-virginica
-7.2,3.2,6.0,1.8,Iris-virginica
-5.4,3.4,1.7,0.2,Iris-setosa
-4.6,3.2,1.4,0.2,Iris-setosa
-6.1,2.8,4.0,1.3,Iris-versicolor
-7.7,3.8,6.7,2.2,Iris-virginica
-5.7,2.9,4.2,1.3,Iris-versicolor
-5.1,3.5,1.4,0.2,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-6.5,3.0,5.2,2.0,Iris-virginica
-4.9,3.1,1.5,0.1,Iris-setosa
-6.3,2.3,4.4,1.3,Iris-versicolor
-6.2,2.2,4.5,1.5,Iris-versicolor
-5.7,3.8,1.7,0.3,Iris-setosa
-6.4,2.8,5.6,2.1,Iris-virginica
-4.9,2.4,3.3,1.0,Iris-versicolor
-5.7,2.5,5.0,2.0,Iris-virginica
-5.5,4.2,1.4,0.2,Iris-setosa
-6.7,3.0,5.0,1.7,Iris-versicolor
-5.0,3.3,1.4,0.2,Iris-setosa
-6.3,2.5,4.9,1.5,Iris-versicolor
-5.4,3.7,1.5,0.2,Iris-setosa
-7.7,2.6,6.9,2.3,Iris-virginica
-5.7,3.0,4.2,1.2,Iris-versicolor
-7.6,3.0,6.6,2.1,Iris-virginica
-4.8,3.1,1.6,0.2,Iris-setosa
-5.6,2.8,4.9,2.0,Iris-virginica
-4.5,2.3,1.3,0.3,Iris-setosa
-6.8,3.2,5.9,2.3,Iris-virginica
-6.3,3.3,6.0,2.5,Iris-virginica
-4.6,3.4,1.4,0.3,Iris-setosa
-5.8,2.7,3.9,1.2,Iris-versicolor
-5.5,2.3,4.0,1.3,Iris-versicolor
-5.2,4.1,1.5,0.1,Iris-setosa
-6.6,2.9,4.6,1.3,Iris-versicolor
-4.3,3.0,1.1,0.1,Iris-setosa
-6.8,3.0,5.5,2.1,Iris-virginica
-5.8,4.0,1.2,0.2,Iris-setosa
-5.1,3.5,1.4,0.3,Iris-setosa
-6.5,3.0,5.8,2.2,Iris-virginica
-6.9,3.1,5.4,2.1,Iris-virginica
-6.7,3.3,5.7,2.1,Iris-virginica

w2/polynomial_logistic.tsv

@@ -1,100 +0,0 @@
-1	0.25777005758108174	0.601012316037165
-1	0.3659669567447452	-0.11214686303429633
-0	0.49453050141627375	0.47110655546911206
-0	0.7029060372914113	-0.9225798301680093
-0	0.46658862037642423	-0.6226973935055724
-0	0.8793946243263941	-0.11408014657778076
-0	-0.3311850002119068	0.8444766749977881
-0	-0.5435170087333634	0.8851383010436487
-0	0.9197924083397226	0.41607011737177735
-0	0.28011742147804797	0.6143115673056148
-0	0.9475436344725683	-0.7830731144606005
-0	0.4904989452188586	0.649356142549592
-0	-0.865983500565505	0.9896361556274065
-0	-0.8579184997717257	0.3062253122060574
-0	0.08082005095746103	-0.7736760810964189
-0	-0.3363842450225085	-0.8802992880290186
-0	0.4748472924067402	0.9756949850919965
-0	-0.7956979203895616	0.8751067723304518
-1	0.06752895667287606	-0.7683056187589332
-0	-0.5825898275446799	0.8068359661366173
-1	0.1109238791315652	-0.2034825016864903
-0	0.5011542085506828	0.9366868642789181
-0	0.2011359606302785	0.4800561245801245
-1	-0.38620580274071115	0.4003933803256208
-1	-0.1722113915778094	0.3926707935387965
-0	0.6575404624823169	-0.7070032890943085
-1	-0.2832309098070882	0.034184675674787446
-1	-0.16828017341376333	-0.1628482245819587
-0	-0.6552618226108893	-0.3159705063754401
-0	-0.6466772083696701	-0.07116372625398881
-0	0.848711325640519	0.2132898335742659
-1	-0.35490315474701606	-0.0025105634256454845
-0	-0.36568446532837817	0.5637325774329354
-0	-0.5089179414092766	0.8086671779253405
-0	0.9609295951994559	-0.12114542304082354
-0	0.055563338045806265	0.8532855304613407
-0	-0.8937129542754998	-0.02555660184206876
-0	0.40678784672410284	-0.5480665560665205
-0	-0.7683896050204841	0.9475293644451854
-0	-0.515467982993429	-0.5389177617277066
-0	0.9693903475176826	-0.9765032993967369
-0	-0.5476549714934908	-0.018838768427513974
-0	0.5262277827151787	-0.9936327305281174
-1	0.9394838829593151	0.9962891110157359
-0	-0.935709119652979	-0.6940925482964921
-0	0.6161569745665239	-0.044448545050667976
-0	-0.08521587367561922	0.9636255303204684
-0	0.9073344675416231	-0.08813265618067079
-1	-0.1563237189794715	0.05022859605451302
-0	-0.9785642881644829	-0.5076719844587916
-0	-0.5494648865481802	-0.6044852696776528
-0	-0.7170122682018529	-0.6250685449461151
-1	0.5333872877810009	0.1395189003073396
-0	-0.49270328980187905	0.9081426529064955
-1	0.07777642690144848	-0.44188199856981347
-0	0.8328452661100116	0.5508441451500428
-1	-0.33275827507477573	-0.15434344174028314
-0	-0.9057550401714867	0.6324599729071743
-0	-0.8476574433184823	0.5739140088331203
-0	-0.37393930555231103	0.7361874446899226
-1	0.6610910543790163	0.0036185958785315275
-0	0.49147748571126004	-0.6155167984371757
-0	0.31992462553488177	-0.38253832622755657
-0	0.7398386519468336	-0.915886088774648
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-0	0.20470935356917574	-0.7551473328272353
-0	0.1877078820888327	-0.3377139504156679

w3/bayes_nasty.tsv

@@ -1,100 +0,0 @@
-0	0.7544830909519196	-0.7557810097698512
-0	-0.401040192413354	0.05087719368515575
-0	-0.14557860894357444	-0.9167657103778997
-0	0.15606723792840116	0.7049866105111644
-0	0.595321005153232	-0.3993704503800295
-0	-0.21608773803320203	-0.9473358133826528
-1	-0.9991678089071083	-0.8203462932941652
-0	-0.29270128006691776	0.8999761729296656
-0	-0.3744408488491382	0.5298017577688894
-0	-0.13262908466039347	0.016485142896286442
-1	-0.45382240999178936	-0.6292411536948919
-1	0.8466491727357144	0.7677619536810258
-0	0.5640604742334225	-0.6415301955014154
-1	0.7652661495157325	0.9042394673218532
-0	0.027488607545269383	-0.8561480245619784
-0	0.8937874814271918	-0.568262764805403
-1	0.05865567400417637	0.1271824506485506
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-1	0.8575290920021019	0.5995196615047915
-1	-0.5942919380814724	-0.9173657127389143
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-0	-0.5436468155122751	0.7321778365594638
-0	-0.0016772778866704918	0.9953499312779903
-0	-0.49022669174509304	0.7643365578090109
-0	0.9274390975500406	-0.16941260003761904
-0	0.517483672350449	-0.9259720728025793
-0	0.4993683788149732	-0.8086741166111076
-1	-0.8954705171891042	-0.8352780222016363
-1	-0.35723728886723927	-0.6472670626320902
-0	-0.45030919789416135	-0.014680291690282399
-1	-0.3149222964035554	-0.2363491678998142
-0	-0.21632030129179247	0.9937719759687991
-0	-0.3479296178713067	0.7754592480508431
-1	-0.39993029073188713	-0.4021302940990339
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-0	0.7221094132486976	-0.07187829685579739
-1	0.8767936705571608	0.9516806200255943
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-0	0.7860399993656908	0.2917387997774099
-0	-0.31357941345778184	0.8173465016744779
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-1	0.9180554343925105	0.7402607565839483
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-0	0.05178553367177474	0.024867950728887367
-0	0.6123243631772981	-0.9310030202911994

w3/create_bayes_nasty.py

@@ -1,26 +0,0 @@
-#!/usr/bin/python3
-# -*- coding:utf-8 -*-
-
-
-import csv
-import math
-import random
-import sys
-
-
-def create_bayes_nasty_data(filename):
-    with open(filename, 'w') as f:
-        writer = csv.writer(
-            f, delimiter=('\t' if filename.endswith('.tsv') else ','))
-        for i in range(100):
-            x = random.uniform(-1.0, 1.0)
-            y = random.uniform(-1.0, 1.0)
-            p = abs(x-y) / abs(x+y)
-            # for i in range(2):
-            #     p = -2 * p**3 + 3 * p**2
-            v = 1 if random.random() > p else 0
-            writer.writerow([v, x, y])
-
-
-if __name__ == '__main__':
-    create_bayes_nasty_data(sys.argv[1])

w3/iris.csv

@@ -1,151 +0,0 @@
-łod.dł.,łod.sz.,pł.dł.,pł.sz.,Gatunek
-5.2,3.4,1.4,0.2,Iris-setosa
-5.1,3.7,1.5,0.4,Iris-setosa
-6.7,3.1,5.6,2.4,Iris-virginica
-6.5,3.2,5.1,2.0,Iris-virginica
-4.9,2.5,4.5,1.7,Iris-virginica
-6.0,2.7,5.1,1.6,Iris-versicolor
-5.7,2.6,3.5,1.0,Iris-versicolor
-5.0,2.0,3.5,1.0,Iris-versicolor
-5.2,3.5,1.5,0.2,Iris-setosa
-4.8,3.0,1.4,0.1,Iris-setosa
-6.7,3.3,5.7,2.5,Iris-virginica
-6.1,3.0,4.9,1.8,Iris-virginica
-4.8,3.4,1.9,0.2,Iris-setosa
-5.8,2.8,5.1,2.4,Iris-virginica
-4.4,2.9,1.4,0.2,Iris-setosa
-7.2,3.0,5.8,1.6,Iris-virginica
-4.4,3.2,1.3,0.2,Iris-setosa
-5.0,3.5,1.3,0.3,Iris-setosa
-5.4,3.9,1.3,0.4,Iris-setosa
-7.7,2.8,6.7,2.0,Iris-virginica
-5.0,3.6,1.4,0.2,Iris-setosa
-6.2,2.8,4.8,1.8,Iris-virginica
-6.0,2.2,5.0,1.5,Iris-virginica
-7.4,2.8,6.1,1.9,Iris-virginica
-5.0,3.2,1.2,0.2,Iris-setosa
-6.7,3.1,4.4,1.4,Iris-versicolor
-6.7,3.1,4.7,1.5,Iris-versicolor
-5.6,2.7,4.2,1.3,Iris-versicolor
-5.6,2.5,3.9,1.1,Iris-versicolor
-6.3,3.3,4.7,1.6,Iris-versicolor
-5.1,3.4,1.5,0.2,Iris-setosa
-6.0,2.9,4.5,1.5,Iris-versicolor
-5.3,3.7,1.5,0.2,Iris-setosa
-5.6,2.9,3.6,1.3,Iris-versicolor
-5.5,2.5,4.0,1.3,Iris-versicolor
-5.5,2.4,3.7,1.0,Iris-versicolor
-4.4,3.0,1.3,0.2,Iris-setosa
-6.6,3.0,4.4,1.4,Iris-versicolor
-7.9,3.8,6.4,2.0,Iris-virginica
-5.7,2.8,4.1,1.3,Iris-versicolor
-5.8,2.7,4.1,1.0,Iris-versicolor
-6.5,2.8,4.6,1.5,Iris-versicolor
-6.1,2.8,4.7,1.2,Iris-versicolor
-5.1,3.8,1.9,0.4,Iris-setosa
-5.0,3.4,1.6,0.4,Iris-setosa
-5.5,2.6,4.4,1.2,Iris-versicolor
-5.0,3.4,1.5,0.2,Iris-setosa
-6.8,2.8,4.8,1.4,Iris-versicolor
-6.9,3.1,4.9,1.5,Iris-versicolor
-6.1,2.9,4.7,1.4,Iris-versicolor
-5.1,3.8,1.6,0.2,Iris-setosa
-6.4,3.2,5.3,2.3,Iris-virginica
-6.4,2.7,5.3,1.9,Iris-virginica
-5.7,2.8,4.5,1.3,Iris-versicolor
-5.8,2.6,4.0,1.2,Iris-versicolor
-4.7,3.2,1.6,0.2,Iris-setosa
-5.1,3.3,1.7,0.5,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-6.3,3.4,5.6,2.4,Iris-virginica
-5.1,3.8,1.5,0.3,Iris-setosa
-7.0,3.2,4.7,1.4,Iris-versicolor
-5.4,3.0,4.5,1.5,Iris-versicolor
-6.0,2.2,4.0,1.0,Iris-versicolor
-6.0,3.0,4.8,1.8,Iris-virginica
-6.2,2.9,4.3,1.3,Iris-versicolor
-5.6,3.0,4.1,1.3,Iris-versicolor
-4.9,3.0,1.4,0.2,Iris-setosa
-5.0,2.3,3.3,1.0,Iris-versicolor
-6.3,2.5,5.0,1.9,Iris-virginica
-4.8,3.4,1.6,0.2,Iris-setosa
-5.9,3.0,4.2,1.5,Iris-versicolor
-4.6,3.6,1.0,0.2,Iris-setosa
-5.0,3.5,1.6,0.6,Iris-setosa
-5.7,4.4,1.5,0.4,Iris-setosa
-5.0,3.0,1.6,0.2,Iris-setosa
-5.6,3.0,4.5,1.5,Iris-versicolor
-6.3,2.8,5.1,1.5,Iris-virginica
-5.2,2.7,3.9,1.4,Iris-versicolor
-5.9,3.2,4.8,1.8,Iris-versicolor
-7.7,3.0,6.1,2.3,Iris-virginica
-6.2,3.4,5.4,2.3,Iris-virginica
-6.4,2.9,4.3,1.3,Iris-versicolor
-6.5,3.0,5.5,1.8,Iris-virginica
-5.8,2.7,5.1,1.9,Iris-virginica
-6.9,3.2,5.7,2.3,Iris-virginica
-6.4,2.8,5.6,2.2,Iris-virginica
-4.7,3.2,1.3,0.2,Iris-setosa
-5.5,2.4,3.8,1.1,Iris-versicolor
-5.4,3.4,1.5,0.4,Iris-setosa
-7.2,3.6,6.1,2.5,Iris-virginica
-6.7,2.5,5.8,1.8,Iris-virginica
-6.1,3.0,4.6,1.4,Iris-versicolor
-6.0,3.4,4.5,1.6,Iris-versicolor
-6.3,2.7,4.9,1.8,Iris-virginica
-6.9,3.1,5.1,2.3,Iris-virginica
-5.5,3.5,1.3,0.2,Iris-setosa
-6.7,3.0,5.2,2.3,Iris-virginica
-4.6,3.1,1.5,0.2,Iris-setosa
-5.8,2.7,5.1,1.9,Iris-virginica
-6.4,3.1,5.5,1.8,Iris-virginica
-7.3,2.9,6.3,1.8,Iris-virginica
-4.8,3.0,1.4,0.3,Iris-setosa
-7.1,3.0,5.9,2.1,Iris-virginica
-5.9,3.0,5.1,1.8,Iris-virginica
-6.1,2.6,5.6,1.4,Iris-virginica
-5.4,3.9,1.7,0.4,Iris-setosa
-6.4,3.2,4.5,1.5,Iris-versicolor
-5.1,2.5,3.0,1.1,Iris-versicolor
-6.3,2.9,5.6,1.8,Iris-virginica
-7.2,3.2,6.0,1.8,Iris-virginica
-5.4,3.4,1.7,0.2,Iris-setosa
-4.6,3.2,1.4,0.2,Iris-setosa
-6.1,2.8,4.0,1.3,Iris-versicolor
-7.7,3.8,6.7,2.2,Iris-virginica
-5.7,2.9,4.2,1.3,Iris-versicolor
-5.1,3.5,1.4,0.2,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-6.5,3.0,5.2,2.0,Iris-virginica
-4.9,3.1,1.5,0.1,Iris-setosa
-6.3,2.3,4.4,1.3,Iris-versicolor
-6.2,2.2,4.5,1.5,Iris-versicolor
-5.7,3.8,1.7,0.3,Iris-setosa
-6.4,2.8,5.6,2.1,Iris-virginica
-4.9,2.4,3.3,1.0,Iris-versicolor
-5.7,2.5,5.0,2.0,Iris-virginica
-5.5,4.2,1.4,0.2,Iris-setosa
-6.7,3.0,5.0,1.7,Iris-versicolor
-5.0,3.3,1.4,0.2,Iris-setosa
-6.3,2.5,4.9,1.5,Iris-versicolor
-5.4,3.7,1.5,0.2,Iris-setosa
-7.7,2.6,6.9,2.3,Iris-virginica
-5.7,3.0,4.2,1.2,Iris-versicolor
-7.6,3.0,6.6,2.1,Iris-virginica
-4.8,3.1,1.6,0.2,Iris-setosa
-5.6,2.8,4.9,2.0,Iris-virginica
-4.5,2.3,1.3,0.3,Iris-setosa
-6.8,3.2,5.9,2.3,Iris-virginica
-6.3,3.3,6.0,2.5,Iris-virginica
-4.6,3.4,1.4,0.3,Iris-setosa
-5.8,2.7,3.9,1.2,Iris-versicolor
-5.5,2.3,4.0,1.3,Iris-versicolor
-5.2,4.1,1.5,0.1,Iris-setosa
-6.6,2.9,4.6,1.3,Iris-versicolor
-4.3,3.0,1.1,0.1,Iris-setosa
-6.8,3.0,5.5,2.1,Iris-virginica
-5.8,4.0,1.2,0.2,Iris-setosa
-5.1,3.5,1.4,0.3,Iris-setosa
-6.5,3.0,5.8,2.2,Iris-virginica
-6.9,3.1,5.4,2.1,Iris-virginica
-6.7,3.3,5.7,2.1,Iris-virginica

w3/tennis.tsv

@@ -1,15 +0,0 @@
-Day	Outlook	Humidity	Wind	Play
-1	Sunny	High	Weak	No
-2	Sunny	High	Strong	No
-3	Overcast	High	Weak	Yes
-4	Rain	High	Weak	Yes
-5	Rain	Normal	Weak	Yes
-6	Rain	Normal	Strong	No
-7	Overcast	Normal	Strong	Yes
-8	Sunny	High	Weak	No
-9	Sunny	Normal	Weak	Yes
-10	Rain	Normal	Weak	Yes
-11	Sunny	Normal	Strong	Yes
-12	Overcast	High	Strong	Yes
-13	Overcast	Normal	Weak	Yes
-14	Rain	High	Strong	No

w3/zumz3b.ipynb

@@ -1,847 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Uczenie maszynowe UMZ 2017/2018\n",
-    "# 3. Naiwny klasyfikator bayesowski, drzewa decyzyjne\n",
-    "### Część 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## 3.2. Drzewa decyzyjne"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Drzewa decyzyjne – przykład"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Przydatne importy\n",
-    "\n",
-    "import ipywidgets as widgets\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Day</th>\n",
-       "      <th>Outlook</th>\n",
-       "      <th>Humidity</th>\n",
-       "      <th>Wind</th>\n",
-       "      <th>Play</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Sunny</td>\n",
-       "      <td>High</td>\n",
-       "      <td>Weak</td>\n",
-       "      <td>No</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Sunny</td>\n",
-       "      <td>High</td>\n",
-       "      <td>Strong</td>\n",
-       "      <td>No</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Overcast</td>\n",
-       "      <td>High</td>\n",
-       "      <td>Weak</td>\n",
-       "      <td>Yes</td>\n",
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-       "      <th>3</th>\n",
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-       "      <td>Rain</td>\n",
-       "      <td>High</td>\n",
-       "      <td>Weak</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Rain</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>Weak</td>\n",
-       "      <td>Yes</td>\n",
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-       "    <tr>\n",
-       "      <th>5</th>\n",
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-       "      <td>Strong</td>\n",
-       "      <td>No</td>\n",
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-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7</td>\n",
-       "      <td>Overcast</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>Strong</td>\n",
-       "      <td>Yes</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8</td>\n",
-       "      <td>Sunny</td>\n",
-       "      <td>High</td>\n",
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-       "      <td>Weak</td>\n",
-       "      <td>Yes</td>\n",
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-       "      <td>11</td>\n",
-       "      <td>Sunny</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>Strong</td>\n",
-       "      <td>Yes</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>12</td>\n",
-       "      <td>Overcast</td>\n",
-       "      <td>High</td>\n",
-       "      <td>Strong</td>\n",
-       "      <td>Yes</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
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-       "      <td>Overcast</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>Weak</td>\n",
-       "      <td>Yes</td>\n",
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-       "      <td>14</td>\n",
-       "      <td>Rain</td>\n",
-       "      <td>High</td>\n",
-       "      <td>Strong</td>\n",
-       "      <td>No</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Day   Outlook Humidity    Wind Play\n",
-       "0     1     Sunny     High    Weak   No\n",
-       "1     2     Sunny     High  Strong   No\n",
-       "2     3  Overcast     High    Weak  Yes\n",
-       "3     4      Rain     High    Weak  Yes\n",
-       "4     5      Rain   Normal    Weak  Yes\n",
-       "5     6      Rain   Normal  Strong   No\n",
-       "6     7  Overcast   Normal  Strong  Yes\n",
-       "7     8     Sunny     High    Weak   No\n",
-       "8     9     Sunny   Normal    Weak  Yes\n",
-       "9    10      Rain   Normal    Weak  Yes\n",
-       "10   11     Sunny   Normal  Strong  Yes\n",
-       "11   12  Overcast     High  Strong  Yes\n",
-       "12   13  Overcast   Normal    Weak  Yes\n",
-       "13   14      Rain     High  Strong   No"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alldata = pandas.read_csv('tennis.tsv', sep='\\t')\n",
-    "alldata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Humidity': {'High', 'Normal'},\n",
-       " 'Outlook': {'Overcast', 'Rain', 'Sunny'},\n",
-       " 'Wind': {'Strong', 'Weak'}}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Dane jako lista słowników\n",
-    "data = alldata.T.to_dict().values()\n",
-    "features = ['Outlook', 'Humidity', 'Wind']\n",
-    "\n",
-    "# Możliwe wartości w poszczególnych kolumnach\n",
-    "values = {feature: set(row[feature] for row in data)\n",
-    "          for feature in features}\n",
-    "values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "* Czy John zagra w tenisa, jeżeli będzie padać, przy wysokiej wilgotności i silnym wietrze?\n",
-    "* Algorytm drzew decyzyjnych spróbuje _zrozumieć_ „taktykę” Johna.\n",
-    "* Wykorzystamy metodę „dziel i zwyciężaj”."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Podziel dane\n",
-    "def split(features, data):\n",
-    "    values = {feature: list(set(row[feature]\n",
-    "                                for row in data))\n",
-    "              for feature in features}\n",
-    "    if not features:\n",
-    "        return data\n",
-    "    return {val: split(features[1:],\n",
-    "                       [row for row in data\n",
-    "                        if row[features[0]] == val])\n",
-    "            for val in values[features[0]]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\tOutlook\tHumid\tWind\tPlay\n",
-      "Day 3:\tOvercast\tHigh\tWeak\tYes\n",
-      "Day 7:\tOvercast\tNormal\tStrong\tYes\n",
-      "Day 12:\tOvercast\tHigh\tStrong\tYes\n",
-      "Day 13:\tOvercast\tNormal\tWeak\tYes\n",
-      "\n",
-      "\tOutlook\tHumid\tWind\tPlay\n",
-      "Day 1:\tSunny\tHigh\tWeak\tNo\n",
-      "Day 2:\tSunny\tHigh\tStrong\tNo\n",
-      "Day 8:\tSunny\tHigh\tWeak\tNo\n",
-      "Day 9:\tSunny\tNormal\tWeak\tYes\n",
-      "Day 11:\tSunny\tNormal\tStrong\tYes\n",
-      "\n",
-      "\tOutlook\tHumid\tWind\tPlay\n",
-      "Day 4:\tRain\tHigh\tWeak\tYes\n",
-      "Day 5:\tRain\tNormal\tWeak\tYes\n",
-      "Day 6:\tRain\tNormal\tStrong\tNo\n",
-      "Day 10:\tRain\tNormal\tWeak\tYes\n",
-      "Day 14:\tRain\tHigh\tStrong\tNo\n"
-     ]
-    }
-   ],
-   "source": [
-    "split_data = split(['Outlook'], data)\n",
-    "\n",
-    "for outlook in values['Outlook']:\n",
-    "    print('\\n\\tOutlook\\tHumid\\tWind\\tPlay')\n",
-    "    for row in split_data[outlook]:\n",
-    "        print('Day {Day}:\\t{Outlook}\\t{Humidity}\\t{Wind}\\t{Play}'\n",
-    "              .format(**row))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "Obserwacja: John lubi grać, gdy jest pochmurnie.\n",
-    "\n",
-    "W pozostałych przypadkach podzielmy dane ponownie:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\tOutlook\tHumid\tWind\tPlay\n",
-      "Day 1:\tSunny\tHigh\tWeak\tNo\n",
-      "Day 2:\tSunny\tHigh\tStrong\tNo\n",
-      "Day 8:\tSunny\tHigh\tWeak\tNo\n",
-      "\n",
-      "\tOutlook\tHumid\tWind\tPlay\n",
-      "Day 9:\tSunny\tNormal\tWeak\tYes\n",
-      "Day 11:\tSunny\tNormal\tStrong\tYes\n"
-     ]
-    }
-   ],
-   "source": [
-    "split_data_sunny = split(['Outlook', 'Humidity'], data)\n",
-    "\n",
-    "for humidity in values['Humidity']:\n",
-    "    print('\\n\\tOutlook\\tHumid\\tWind\\tPlay')\n",
-    "    for row in split_data_sunny['Sunny'][humidity]:\n",
-    "        print('Day {Day}:\\t{Outlook}\\t{Humidity}\\t{Wind}\\t{Play}'\n",
-    "              .format(**row))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\tOutlook\tHumid\tWind\tPlay\n",
-      "Day 6:\tRain\tNormal\tStrong\tNo\n",
-      "Day 14:\tRain\tHigh\tStrong\tNo\n",
-      "\n",
-      "\tOutlook\tHumid\tWind\tPlay\n",
-      "Day 4:\tRain\tHigh\tWeak\tYes\n",
-      "Day 5:\tRain\tNormal\tWeak\tYes\n",
-      "Day 10:\tRain\tNormal\tWeak\tYes\n"
-     ]
-    }
-   ],
-   "source": [
-    "split_data_rain = split(['Outlook', 'Wind'], data)\n",
-    "\n",
-    "for wind in values['Wind']:\n",
-    "    print('\\n\\tOutlook\\tHumid\\tWind\\tPlay')\n",
-    "    for row in split_data_rain['Rain'][wind]:\n",
-    "        print('Day {Day}:\\t{Outlook}\\t{Humidity}\\t{Wind}\\t{Play}'\n",
-    "              .format(**row))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "* Outlook=\n",
-    "  * Overcast\n",
-    "    * → Playing\n",
-    "  * Sunny\n",
-    "    * Humidity=\n",
-    "      * High\n",
-    "        * → Not playing\n",
-    "      * Normal\n",
-    "        * → Playing\n",
-    "  * Rain\n",
-    "    * Wind=\n",
-    "      * Weak\n",
-    "        * → Playing\n",
-    "      * Strong\n",
-    "        * → Not playing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "* (9/5)\n",
-    "  * Outlook=Overcast (4/0)\n",
-    "    * YES\n",
-    "  * Outlook=Sunny (2/3)\n",
-    "    * Humidity=High (0/3)\n",
-    "      * NO\n",
-    "    * Humidity=Normal (2/0)\n",
-    "      * YES\n",
-    "  * Outlook=Rain (3/2)\n",
-    "    * Wind=Weak (3/0)\n",
-    "      * YES\n",
-    "    * Wind=Strong (0/2)\n",
-    "      * NO"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Algorytm ID3\n",
-    "\n",
-    "* podziel(węzeł, zbiór przykładów):\n",
-    "    1. A ← najlepszy atrybut do podziału zbioru przykładów\n",
-    "    1. Dla każdej wartości atrybutu A, utwórz nowy węzeł potomny\n",
-    "    1. Podziel zbiór przykładów na podzbiory według węzłów potomnych\n",
-    "    1. Dla każdego węzła potomnego i podzbioru:\n",
-    "        * jeżeli podzbiór jest jednolity: zakończ\n",
-    "        * w przeciwnym przypadku: podziel(węzeł potomny, podzbiór)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "Jak wybrać „najlepszy atrybut”?\n",
-    "* powinien zawierać jednolity podzbiór\n",
-    "* albo przynajmniej „w miarę jednolity”\n",
-    "\n",
-    "Skąd wziąć miarę „jednolitości” podzbioru?\n",
-    "* miara powinna być symetryczna (4/0 vs. 0/4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Entropia\n",
-    "\n",
-    "$$ H(S) = - p_{(+)} \\log p_{(+)} - p_{(-)} \\log p_{(-)} $$\n",
-    "\n",
-    "* $S$ – podzbiór przykładów\n",
-    "* $p_{(+)}$, $p_{(-)}$ – procent pozytywnych/negatywnych przykładów w $S$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "Entropię można traktować jako „liczbę bitów” potrzebną do sprawdzenia, czy losowo wybrany $x \\in S$ jest pozytywnym, czy negatywnym przykładem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "#### Entropia – przykład\n",
-    "\n",
-    "* (3 TAK / 3 NIE):\n",
-    "$$ H(S) = -\\frac{3}{6} \\log\\frac{3}{6} - \\frac{3}{6} \\log\\frac{3}{6} = 1 \\mbox{ bit} $$\n",
-    "* (4 TAK / 0 NIE):\n",
-    "$$ H(S) = -\\frac{4}{4} \\log\\frac{4}{4} - \\frac{0}{4} \\log\\frac{0}{4} = 0 \\mbox{ bitów} $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### _Information gain_\n",
-    "\n",
-    "_Information gain_ – różnica między entropią przed podziałem a entropią po podziale (podczas podziału entropia zmienia się):\n",
-    "\n",
-    "$$ \\mathop{\\rm Gain}(S,A) = H(S) - \\sum_{V \\in \\mathop{\\rm Values(A)}} \\frac{|S_V|}{|S|} H(S_V) $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "#### _Information gain_ – przykład\n",
-    "\n",
-    "$$ \\mathop{\\rm Gain}(S, Wind) = H(S) - \\frac{8}{14} H(S_{Wind={\\rm Weak}}) - \\frac{6}{14} H(S_{Wind={\\rm Strong}}) = \\\\\n",
-    "= 0.94 - \\frac{8}{14} \\cdot 0.81 - \\frac{6}{14} \\cdot 1.0 = 0.049 $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "* _Information gain_ jest całkiem sensowną heurystyką wskazującą, który atrybut jest najlepszy do dokonania podziału.\n",
-    "* **Ale**: _information gain_ przeszacowuje użyteczność atrybutów, które mają dużo różnych wartości.\n",
-    "* **Przykład**: gdybyśmy wybrali jako atrybut _datę_, otrzymalibyśmy bardzo duży _information gain_, ponieważ każdy podzbiór byłby jednolity, a nie byłoby to ani trochę użyteczne!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### _Information gain ratio_\n",
-    "\n",
-    "$$ \\mathop{\\rm GainRatio}(S, A) = \\frac{ \\mathop{\\rm Gain}(S, A) }{ -\\sum_{V \\in \\mathop{\\rm Values}(A)} \\frac{|S_V|}{|S|} \\log\\frac{|S_V|}{|S|} } $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "* _Information gain ratio_ może być lepszym wyborem heurystyki wskazującej najużyteczniejszy atrybut, jeżeli atrybuty mają wiele różnych wartości."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Drzewa decyzyjne a formuły logiczne\n",
-    "\n",
-    "Drzewo decyzyjne można pzekształcić na formułę logiczną w postaci normalnej (DNF):\n",
-    "\n",
-    "$$ Play={\\rm True} \\Leftrightarrow \\left( Outlook={\\rm Overcast} \\vee \\\\\n",
-    "( Outlook={\\rm Rain} \\wedge Wind={\\rm Weak} ) \\vee \\\\\n",
-    "( Outlook={\\rm Sunny} \\wedge Humidity={\\rm Normal} ) \\right) $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Klasyfikacja wieloklasowa przy użyciu drzew decyzyjnych\n",
-    "\n",
-    "Algorytm przebiega analogicznie, zmienia się jedynie wzór na entropię:\n",
-    "\n",
-    "$$ H(S) = -\\sum_{y \\in Y} p_{(y)} \\log p_{(y)} $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Skuteczność algorytmu ID3\n",
-    "\n",
-    "* Przyjmujemy, że wśród danych uczących nie ma duplikatów (tj. przykładów, które mają jednakowe cechy $x$, a mimo to należą do różnych klas $y$).\n",
-    "* Wówczas algorytm drzew decyzyjnych zawsze znajdzie rozwiązanie, ponieważ w ostateczności będziemy mieli węzły 1-elementowe na liściach drzewa."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Nadmierne dopasowanie drzew decyzyjnych\n",
-    "\n",
-    "* Zauważmy, że w miarę postępowania algorytmu dokładność przewidywań drzewa (_accuracy_) liczona na zbiorze uczącym dąży do 100% (i w ostateczności osiąga 100%, nawet kosztem jednoelementowych liści).\n",
-    "* Takie rozwiązanie niekoniecznie jest optymalne. Dokładność na zbiorze testowym może być dużo niższa, a to oznacza nadmierne dopasowanie."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Jak zapobiec nadmiernemu dopasowaniu?\n",
-    "\n",
-    "Aby zapobiegać nadmiernemu dopasowaniu drzew decyzyjnych, należy je przycinać (_pruning_)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "Można tego dokonywać na kilka sposobów:\n",
-    "* Można zatrzymywać procedurę podziału w pewnym momencie (np. kiedy podzbiory staja się zbyt małe).\n",
-    "* Można najpierw wykonać algorytm ID3 w całości, a następnie przyciąć drzewo, np. kierując się wynikami uzyskanymi na zbiorze walidacyjnym.\n",
-    "* Algorytm _sub-tree replacement pruning_ (algorytm zachłanny)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "#### _Sub-tree replacement pruning_\n",
-    "\n",
-    "1. Dla każdego węzła:\n",
-    "   1. Udaj, że usuwasz węzeł wraz z całym zaczepionym w nim poddrzewem.\n",
-    "   1. Dokonaj ewaluacji na zbiorze walidacyjnym.\n",
-    "1. Usuń węzeł, którego usunięcie daje największą poprawę wyniku.\n",
-    "1. Powtarzaj, dopóki usuwanie węzłów poprawia wynik."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Zalety drzew decyzyjnych\n",
-    "\n",
-    "* Zasadę działania drzew decyzyjnych łatwo zrozumieć człowiekowi.\n",
-    "* Atrybuty, które nie wpływają na wynik, mają _gain_ równy 0, zatem są od razu pomijane przez algorytm.\n",
-    "* Po zbudowaniu, drzewo decyzyjne jest bardzo szybkim klasyfikatorem (złożoność $O(d)$, gdzie $d$ jest głębokościa dzewa)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Wady drzew decyzyjnych\n",
-    "\n",
-    "* ID3 jest algorytmem zachłannym – może nie wskazać najlepszego drzewa.\n",
-    "* Nie da się otrzymać granic klas (_decision boundaries_), które nie są równoległe do osi wykresu."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## 3.3. Lasy losowe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Algorytm lasów losowych – idea\n",
-    "\n",
-    "* Algorytm lasów losowych jest rozwinięciem algorytmu ID3.\n",
-    "* Jest to bardzo wydajny algorytm klasyfikacji.\n",
-    "* Zamiast jednego, będziemy budować $k$ drzew."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Algorytm lasów losowych – budowa lasu\n",
-    "\n",
-    "1. Weź losowy podzbiór $S_r$ zbioru uczącego.\n",
-    "1. Zbuduj pełne (tj. bez przycinania) drzewo decyzyjne dla $S_r$, używając algorytmu ID3 z następującymi modyfikacjami:\n",
-    "   * podczas podziału używaj losowego $d$-elementowego podzbioru atrybutów,\n",
-    "   * obliczaj _gain_ względem $S_r$.\n",
-    "1. Powyższą procedurę powtórz $k$-krotnie, otrzymując $k$ drzew ($T_1, T_2, \\ldots, T_k$)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Algorytm lasów losowych – predykcja\n",
-    "\n",
-    "1. Sklasyfikuj $x$ według każdego z drzew $T_1, T_2, \\ldots, T_k$ z osobna.\n",
-    "1. Użyj głosowania większościowego: przypisz klasę przewidzianą przez najwięcej drzew."
-   ]
-  }
- ],
- "metadata": {
-  "celltoolbar": "Slideshow",
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.15rc1"
-  },
-  "livereveal": {
-   "start_slideshow_at": "selected",
-   "theme": "amu"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

w4/classification.tsv

@@ -1,20 +0,0 @@
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-1	-0.16225248935963799	0.4956311492381327
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-0	-0.5145933151394193	-0.804772931556422
-1	0.10024139618609662	0.018730576213300765
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-0	-0.5702785062714137	0.3522449667057965
-1	0.1848842019744028	0.4173729058647151
-0	0.24479522096847361	-0.7089634989586504
-0	0.6236644548417194	-0.1156611626471038
-0	-0.10592913601169007	-0.9980825893807068
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-0	-0.11951845657345062	-0.8219825834866892
-0	0.04590771234351232	-0.7564447785519108
-1	0.7967711966832445	-0.4352501064041614
-1	0.8053812216632301	0.1203107620499666

w4/create_polynomial_logistic.py

@@ -1,26 +0,0 @@
-#!/usr/bin/python3
-# -*- coding:utf-8 -*-
-
-
-import csv
-import math
-import random
-import sys
-
-
-def create_polynomial_logistic_data(filename):
-    with open(filename, 'w') as f:
-        writer = csv.writer(
-            f, delimiter=('\t' if filename.endswith('.tsv') else ','))
-        for i in range(50):
-            x = random.uniform(-1.0, 1.0)
-            y = random.uniform(-1.0, 1.0)
-            p = math.sqrt(x*x + y*y)
-            for i in range(1):
-                p = -2 * p**3 + 3 * p**2
-            v = 1 if random.random() > p else 0
-            writer.writerow([v, x, y])
-
-
-if __name__ == '__main__':
-    create_polynomial_logistic_data(sys.argv[1])

w4/data.tsv

@@ -1,50 +0,0 @@
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-0	-0.0623197964184079	0.8187639325432745
-0	-0.20572090815735944	-0.6655000969777327

w4/iris.csv

@@ -1,151 +0,0 @@
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-5.6,2.8,4.9,2.0,Iris-virginica
-4.5,2.3,1.3,0.3,Iris-setosa
-6.8,3.2,5.9,2.3,Iris-virginica
-6.3,3.3,6.0,2.5,Iris-virginica
-4.6,3.4,1.4,0.3,Iris-setosa
-5.8,2.7,3.9,1.2,Iris-versicolor
-5.5,2.3,4.0,1.3,Iris-versicolor
-5.2,4.1,1.5,0.1,Iris-setosa
-6.6,2.9,4.6,1.3,Iris-versicolor
-4.3,3.0,1.1,0.1,Iris-setosa
-6.8,3.0,5.5,2.1,Iris-virginica
-5.8,4.0,1.2,0.2,Iris-setosa
-5.1,3.5,1.4,0.3,Iris-setosa
-6.5,3.0,5.8,2.2,Iris-virginica
-6.9,3.1,5.4,2.1,Iris-virginica
-6.7,3.3,5.7,2.1,Iris-virginica

w5/iris.csv

@@ -1,151 +0,0 @@
-łod.dł.,łod.sz.,pł.dł.,pł.sz.,Gatunek
-5.2,3.4,1.4,0.2,Iris-setosa
-5.1,3.7,1.5,0.4,Iris-setosa
-6.7,3.1,5.6,2.4,Iris-virginica
-6.5,3.2,5.1,2.0,Iris-virginica
-4.9,2.5,4.5,1.7,Iris-virginica
-6.0,2.7,5.1,1.6,Iris-versicolor
-5.7,2.6,3.5,1.0,Iris-versicolor
-5.0,2.0,3.5,1.0,Iris-versicolor
-5.2,3.5,1.5,0.2,Iris-setosa
-4.8,3.0,1.4,0.1,Iris-setosa
-6.7,3.3,5.7,2.5,Iris-virginica
-6.1,3.0,4.9,1.8,Iris-virginica
-4.8,3.4,1.9,0.2,Iris-setosa
-5.8,2.8,5.1,2.4,Iris-virginica
-4.4,2.9,1.4,0.2,Iris-setosa
-7.2,3.0,5.8,1.6,Iris-virginica
-4.4,3.2,1.3,0.2,Iris-setosa
-5.0,3.5,1.3,0.3,Iris-setosa
-5.4,3.9,1.3,0.4,Iris-setosa
-7.7,2.8,6.7,2.0,Iris-virginica
-5.0,3.6,1.4,0.2,Iris-setosa
-6.2,2.8,4.8,1.8,Iris-virginica
-6.0,2.2,5.0,1.5,Iris-virginica
-7.4,2.8,6.1,1.9,Iris-virginica
-5.0,3.2,1.2,0.2,Iris-setosa
-6.7,3.1,4.4,1.4,Iris-versicolor
-6.7,3.1,4.7,1.5,Iris-versicolor
-5.6,2.7,4.2,1.3,Iris-versicolor
-5.6,2.5,3.9,1.1,Iris-versicolor
-6.3,3.3,4.7,1.6,Iris-versicolor
-5.1,3.4,1.5,0.2,Iris-setosa
-6.0,2.9,4.5,1.5,Iris-versicolor
-5.3,3.7,1.5,0.2,Iris-setosa
-5.6,2.9,3.6,1.3,Iris-versicolor
-5.5,2.5,4.0,1.3,Iris-versicolor
-5.5,2.4,3.7,1.0,Iris-versicolor
-4.4,3.0,1.3,0.2,Iris-setosa
-6.6,3.0,4.4,1.4,Iris-versicolor
-7.9,3.8,6.4,2.0,Iris-virginica
-5.7,2.8,4.1,1.3,Iris-versicolor
-5.8,2.7,4.1,1.0,Iris-versicolor
-6.5,2.8,4.6,1.5,Iris-versicolor
-6.1,2.8,4.7,1.2,Iris-versicolor
-5.1,3.8,1.9,0.4,Iris-setosa
-5.0,3.4,1.6,0.4,Iris-setosa
-5.5,2.6,4.4,1.2,Iris-versicolor
-5.0,3.4,1.5,0.2,Iris-setosa
-6.8,2.8,4.8,1.4,Iris-versicolor
-6.9,3.1,4.9,1.5,Iris-versicolor
-6.1,2.9,4.7,1.4,Iris-versicolor
-5.1,3.8,1.6,0.2,Iris-setosa
-6.4,3.2,5.3,2.3,Iris-virginica
-6.4,2.7,5.3,1.9,Iris-virginica
-5.7,2.8,4.5,1.3,Iris-versicolor
-5.8,2.6,4.0,1.2,Iris-versicolor
-4.7,3.2,1.6,0.2,Iris-setosa
-5.1,3.3,1.7,0.5,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-6.3,3.4,5.6,2.4,Iris-virginica
-5.1,3.8,1.5,0.3,Iris-setosa
-7.0,3.2,4.7,1.4,Iris-versicolor
-5.4,3.0,4.5,1.5,Iris-versicolor
-6.0,2.2,4.0,1.0,Iris-versicolor
-6.0,3.0,4.8,1.8,Iris-virginica
-6.2,2.9,4.3,1.3,Iris-versicolor
-5.6,3.0,4.1,1.3,Iris-versicolor
-4.9,3.0,1.4,0.2,Iris-setosa
-5.0,2.3,3.3,1.0,Iris-versicolor
-6.3,2.5,5.0,1.9,Iris-virginica
-4.8,3.4,1.6,0.2,Iris-setosa
-5.9,3.0,4.2,1.5,Iris-versicolor
-4.6,3.6,1.0,0.2,Iris-setosa
-5.0,3.5,1.6,0.6,Iris-setosa
-5.7,4.4,1.5,0.4,Iris-setosa
-5.0,3.0,1.6,0.2,Iris-setosa
-5.6,3.0,4.5,1.5,Iris-versicolor
-6.3,2.8,5.1,1.5,Iris-virginica
-5.2,2.7,3.9,1.4,Iris-versicolor
-5.9,3.2,4.8,1.8,Iris-versicolor
-7.7,3.0,6.1,2.3,Iris-virginica
-6.2,3.4,5.4,2.3,Iris-virginica
-6.4,2.9,4.3,1.3,Iris-versicolor
-6.5,3.0,5.5,1.8,Iris-virginica
-5.8,2.7,5.1,1.9,Iris-virginica
-6.9,3.2,5.7,2.3,Iris-virginica
-6.4,2.8,5.6,2.2,Iris-virginica
-4.7,3.2,1.3,0.2,Iris-setosa
-5.5,2.4,3.8,1.1,Iris-versicolor
-5.4,3.4,1.5,0.4,Iris-setosa
-7.2,3.6,6.1,2.5,Iris-virginica
-6.7,2.5,5.8,1.8,Iris-virginica
-6.1,3.0,4.6,1.4,Iris-versicolor
-6.0,3.4,4.5,1.6,Iris-versicolor
-6.3,2.7,4.9,1.8,Iris-virginica
-6.9,3.1,5.1,2.3,Iris-virginica
-5.5,3.5,1.3,0.2,Iris-setosa
-6.7,3.0,5.2,2.3,Iris-virginica
-4.6,3.1,1.5,0.2,Iris-setosa
-5.8,2.7,5.1,1.9,Iris-virginica
-6.4,3.1,5.5,1.8,Iris-virginica
-7.3,2.9,6.3,1.8,Iris-virginica
-4.8,3.0,1.4,0.3,Iris-setosa
-7.1,3.0,5.9,2.1,Iris-virginica
-5.9,3.0,5.1,1.8,Iris-virginica
-6.1,2.6,5.6,1.4,Iris-virginica
-5.4,3.9,1.7,0.4,Iris-setosa
-6.4,3.2,4.5,1.5,Iris-versicolor
-5.1,2.5,3.0,1.1,Iris-versicolor
-6.3,2.9,5.6,1.8,Iris-virginica
-7.2,3.2,6.0,1.8,Iris-virginica
-5.4,3.4,1.7,0.2,Iris-setosa
-4.6,3.2,1.4,0.2,Iris-setosa
-6.1,2.8,4.0,1.3,Iris-versicolor
-7.7,3.8,6.7,2.2,Iris-virginica
-5.7,2.9,4.2,1.3,Iris-versicolor
-5.1,3.5,1.4,0.2,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-6.5,3.0,5.2,2.0,Iris-virginica
-4.9,3.1,1.5,0.1,Iris-setosa
-6.3,2.3,4.4,1.3,Iris-versicolor
-6.2,2.2,4.5,1.5,Iris-versicolor
-5.7,3.8,1.7,0.3,Iris-setosa
-6.4,2.8,5.6,2.1,Iris-virginica
-4.9,2.4,3.3,1.0,Iris-versicolor
-5.7,2.5,5.0,2.0,Iris-virginica
-5.5,4.2,1.4,0.2,Iris-setosa
-6.7,3.0,5.0,1.7,Iris-versicolor
-5.0,3.3,1.4,0.2,Iris-setosa
-6.3,2.5,4.9,1.5,Iris-versicolor
-5.4,3.7,1.5,0.2,Iris-setosa
-7.7,2.6,6.9,2.3,Iris-virginica
-5.7,3.0,4.2,1.2,Iris-versicolor
-7.6,3.0,6.6,2.1,Iris-virginica
-4.8,3.1,1.6,0.2,Iris-setosa
-5.6,2.8,4.9,2.0,Iris-virginica
-4.5,2.3,1.3,0.3,Iris-setosa
-6.8,3.2,5.9,2.3,Iris-virginica
-6.3,3.3,6.0,2.5,Iris-virginica
-4.6,3.4,1.4,0.3,Iris-setosa
-5.8,2.7,3.9,1.2,Iris-versicolor
-5.5,2.3,4.0,1.3,Iris-versicolor
-5.2,4.1,1.5,0.1,Iris-setosa
-6.6,2.9,4.6,1.3,Iris-versicolor
-4.3,3.0,1.1,0.1,Iris-setosa
-6.8,3.0,5.5,2.1,Iris-virginica
-5.8,4.0,1.2,0.2,Iris-setosa
-5.1,3.5,1.4,0.3,Iris-setosa
-6.5,3.0,5.8,2.2,Iris-virginica
-6.9,3.1,5.4,2.1,Iris-virginica
-6.7,3.3,5.7,2.1,Iris-virginica