1 / 11

Relevancy Interval pTree k-Means ( ripm ) Find mean, std of each class and column of TrainSet

P A j >c =P j,m o m ...o k+1 P j,k o i is AND iff b i =1, k is rightmost bit position with bit-value "0", ops are right binding. c = b m ... b k+1 ... b 0. sLN sWD pLN pWD SEPAL_LENGTH SEPAL_WIDTH PEDAL_LENGTH PEDAL_WIDTH.

rmcbride
Télécharger la présentation

Relevancy Interval pTree k-Means ( ripm ) Find mean, std of each class and column of TrainSet

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. PAj>c=Pj,m om...ok+1Pj,koi is AND iff bi=1, k is rightmost bit position with bit-value "0", ops are right binding. c = bm ... bk+1 ... b0 sLN sWD pLN pWD SEPAL_LENGTH SEPAL_WIDTH PEDAL_LENGTH PEDAL_WIDTH se 54 37 15 2 0 1 1 0 1 1 0 1 0 0 1 0 1 0 0 0 1 1 1 1 0 0 0 1 0 se 48 34 16 2 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 se 48 30 14 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 se 43 30 11 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 se 58 40 12 2 0 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 ve 50 20 35 10 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 0 ve 59 30 42 15 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 ve 60 22 40 10 0 1 1 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 1 0 ve 61 29 47 14 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 0 ve 56 29 36 13 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 vi 65 32 51 20 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 vi 64 27 53 19 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 vi 68 30 55 21 1 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 vi 57 25 50 20 0 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 vi 58 28 51 24 0 1 1 1 0 1 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 se 51 35 14 2 0 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 1 0 se 49 30 14 2 0 1 1 0 0 0 1 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 1 0 se 47 32 13 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 se 46 31 15 2 0 1 0 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 se 5 36 14 2 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 se 54 39 17 4 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 se 46 34 14 3 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 1 1 se 50 34 15 2 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 se 44 29 14 2 0 1 0 1 1 0 0 0 1 1 1 0 1 0 0 0 1 1 1 0 0 0 0 1 0 se 49 31 15 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 ve 70 32 47 14 1 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0 1 1 1 0 ve 64 32 45 15 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 69 31 49 15 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 1 0 1 1 1 1 ve 55 23 40 13 0 1 1 0 1 1 1 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 1 ve 65 28 46 15 1 0 0 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 1 1 ve 57 28 45 13 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 0 1 ve 63 33 47 16 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 ve 49 24 33 10 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 ve 66 29 46 13 1 0 0 0 0 1 0 0 1 1 1 0 1 0 1 0 1 1 1 0 0 1 1 0 1 ve 52 27 39 14 0 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 MEANS 44.1 33.1 14.5 2.2 setosa sepalLN sepalWD pedalLN pedalWD 61 28.7 43.7 13.8 versicolor 65.7 29.4 57.7 20.4 virginica STDs 13.3 2.9 1.0 0.7 setosa vi 63 33 60 25 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 0 1 vi 58 27 51 19 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 1 1 vi 71 30 59 21 1 0 0 0 1 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 0 1 0 1 vi 63 29 56 18 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 1 0 vi 65 30 58 22 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 1 0 vi 76 30 66 21 1 0 0 1 1 0 0 0 1 1 1 1 0 1 0 0 0 0 1 0 1 0 1 0 1 vi 49 25 45 17 0 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 1 1 0 0 0 1 vi 73 29 63 18 1 0 0 1 0 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 0 vi 67 25 58 18 1 0 0 0 0 1 1 0 1 1 0 0 1 0 1 1 1 0 1 0 1 0 0 1 0 vi 72 36 61 25 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 1 1 1 0 0 1 6.89 3.2 4.6 1.6 versicolor 7.6 3.2 5.7 2.8 virginica Relevancy Interval pTree k-Means (ripm) Find mean, std of each class and column of TrainSet For each column (feature) and each class, define left_relevancy_pt (lrp) = mean - x * std, right_relevancy_pt (rrp) = mean + x * std and (ri) relevancy_interval, [lrp, rrp]class,column A column has "high_relevancy" for class_k iff the gaps (which can be negative?) between the class_k_ri and the other class_ri's is large (size of these gaps (# stds) determines the level of relevancy. Given 10 tuples from each class as TrainSet (1st 10) For x = 2, calculate: Take 5 from each class asTestSet,remove class and predict sepalWD is irrelevant since the means are close and even 1 std radius makes all 3 ri's highly overlapping. Similarly, sepalLN is fairly irrelevant. pedLN and pedWD are certainly relevant for setosa/~setosa classification. pedLN and pedWD may be redundant (correlated?). It can't hurt to use both. We can note that pedLN is not as relevant in ~setosa for distinguishing ve/vi as is pedWD. Therefore a good minimal choice would be to use pedWD and 2*STD for ve/vi classification also.

  2.  7  15.9 Greater than 7 Greater than 15.9 in NOTse | ve ve ve ve ve vi vi vi vi vi spLN spWD pdLN pdWD ve 50 20 35 10 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 0 ve 59 30 42 15 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 ve 60 22 40 10 0 1 1 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 1 0 ve 61 29 47 14 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 0 ve 56 29 36 13 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 vi 65 32 51 20 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 vi 64 27 53 19 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 vi 68 30 55 21 1 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 vi 57 25 50 20 0 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 vi 58 28 51 24 0 1 1 1 0 1 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 The method is 100% accurate for this small example. Next we'll evaluate it on the full 120 tuple TrainingSet. For 2 stds, we get se/~se from pWD. We use a ~midpt cutoff = 7. 0.7, 3.7 se 10.6, 17 ve 14.8, 25.9 vi spLN spWD pdLN pdWD pedal pedal WDlrp, WDrrp 2*std se 54 37 15 2 0 1 1 0 1 1 0 1 0 0 1 0 1 0 0 0 1 1 1 1 0 0 0 1 0 se 48 34 16 2 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 se 48 30 14 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 se 43 30 11 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 se 58 40 12 2 0 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 ve 50 20 35 10 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 0 ve 59 30 42 15 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 ve 60 22 40 10 0 1 1 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 1 0 ve 61 29 47 14 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 0 ve 56 29 36 13 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 vi 65 32 51 20 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 vi 64 27 53 19 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 vi 68 30 55 21 1 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 vi 57 25 50 20 0 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 vi 58 28 51 24 0 1 1 1 0 1 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 Next we use pWD ripm on NOTse to differentiate ve and vi.Use cutoffs, pWD = (17+14.8)/2 = 15.9

  3. pWDcutoff=7 for se/NOTse spLN spWD pdLN pdWD versicolor 50 20 35 10 versicolor 59 30 42 15 versicolor 60 22 40 10 versicolor 61 29 47 14 versicolor 56 29 36 13 versicolor 67 31 44 14 versicolor 56 30 45 15 versicolor 58 27 41 10 versicolor 62 22 45 15 versicolor 56 25 39 11 versicolor 59 32 48 18 versicolor 61 28 40 13 versicolor 63 25 49 15 versicolor 61 28 47 12 versicolor 64 29 43 13 versicolor 66 30 44 14 versicolor 68 28 48 14 versicolor 67 30 50 17 versicolor 60 29 45 15 versicolor 57 26 35 10 versicolor 55 24 38 11 versicolor 55 24 37 10 versicolor 58 27 39 12 versicolor 60 27 51 16 versicolor 54 30 45 15 versicolor 60 34 45 16 versicolor 67 31 47 15 versicolor 63 23 44 13 versicolor 56 30 41 13 versicolor 55 25 40 13 versicolor 55 26 44 12 versicolor 61 30 46 14 versicolor 58 26 40 12 versicolor 50 23 33 10 versicolor 56 27 42 13 versicolor 57 30 42 12 versicolor 57 29 42 13 versicolor 62 29 43 13 versicolor 51 25 30 11 versicolor 57 28 41 13 spLN spWD pdLN pdWD spLN spWD pdLN pdWD virginica 65 32 51 20 virginica 64 27 53 19 virginica 68 30 55 21 virginica 57 25 50 20 virginica 58 28 51 24 virginica 64 32 53 23 virginica 65 30 55 18 virginica 77 38 67 22 virginica 77 26 69 23 virginica 60 22 50 15 virginica 69 32 57 23 virginica 56 28 49 20 virginica 77 28 67 20 virginica 63 27 49 18 virginica 67 33 57 21 virginica 72 32 60 18 virginica 62 28 48 18 virginica 61 30 49 18 virginica 64 28 56 21 virginica 72 30 58 16 virginica 74 28 61 19 virginica 79 38 64 20 virginica 64 28 56 22 virginica 63 28 51 15 virginica 61 26 56 14 virginica 77 30 61 23 virginica 63 34 56 24 virginica 64 31 55 18 virginica 60 30 58 18 virginica 69 31 54 21 virginica 67 31 56 24 virginica 69 31 51 23 virginica 58 27 51 19 virginica 68 32 59 23 virginica 67 33 57 25 virginica 67 30 52 23 virginica 63 25 50 19 virginica 65 30 52 20 virginica 62 34 54 23 virginica 59 30 51 18 setosa 54 37 15 2 setosa 48 34 16 2 setosa 48 30 14 1 setosa 43 30 11 1 setosa 58 40 12 2 setosa 57 44 15 4 setosa 54 39 13 4 setosa 51 35 14 3 setosa 57 38 17 3 setosa 51 38 15 3 setosa 54 34 17 2 setosa 51 37 15 4 setosa 46 36 10 2 setosa 51 33 17 5 setosa 48 34 19 2 setosa 50 30 16 2 setosa 50 34 16 4 setosa 52 35 15 2 setosa 52 34 14 2 setosa 47 32 16 2 setosa 48 31 16 2 setosa 54 34 15 4 setosa 52 41 15 1 setosa 55 42 14 2 setosa 49 31 15 1 setosa 50 32 12 2 setosa 55 35 13 2 setosa 49 31 15 1 setosa 44 30 13 2 setosa 51 34 15 2 setosa 50 35 13 3 setosa 45 23 13 3 setosa 44 32 13 2 setosa 50 35 16 6 setosa 51 38 19 4 setosa 48 30 14 3 setosa 51 38 16 2 setosa 46 32 14 2 setosa 53 37 15 2 setosa 50 33 14 2 >7 NOTse >7 NOTse 7 se 100% correct

  4. pWDcutoff=15.9 for ve/vi on NOTse. spLN spWD pdLN pdWD versicolor 50 20 35 10 versicolor 59 30 42 15 versicolor 60 22 40 10 versicolor 61 29 47 14 versicolor 56 29 36 13 versicolor 67 31 44 14 versicolor 56 30 45 15 versicolor 58 27 41 10 versicolor 62 22 45 15 versicolor 56 25 39 11 versicolor 59 32 48 18 versicolor 61 28 40 13 versicolor 63 25 49 15 versicolor 61 28 47 12 versicolor 64 29 43 13 versicolor 66 30 44 14 versicolor 68 28 48 14 versicolor 67 30 50 17 versicolor 60 29 45 15 versicolor 57 26 35 10 versicolor 55 24 38 11 versicolor 55 24 37 10 versicolor 58 27 39 12 versicolor 60 27 51 16 versicolor 54 30 45 15 versicolor 60 34 45 16 versicolor 67 31 47 15 versicolor 63 23 44 13 versicolor 56 30 41 13 versicolor 55 25 40 13 versicolor 55 26 44 12 versicolor 61 30 46 14 versicolor 58 26 40 12 versicolor 50 23 33 10 versicolor 56 27 42 13 versicolor 57 30 42 12 versicolor 57 29 42 13 versicolor 62 29 43 13 versicolor 51 25 30 11 versicolor 57 28 41 13 spLN spWD pdLN pdWD virginica 65 32 51 20 virginica 64 27 53 19 virginica 68 30 55 21 virginica 57 25 50 20 virginica 58 28 51 24 virginica 64 32 53 23 virginica 65 30 55 18 virginica 77 38 67 22 virginica 77 26 69 23 virginica 60 22 50 15 virginica 69 32 57 23 virginica 56 28 49 20 virginica 77 28 67 20 virginica 63 27 49 18 virginica 67 33 57 21 virginica 72 32 60 18 virginica 62 28 48 18 virginica 61 30 49 18 virginica 64 28 56 21 virginica 72 30 58 16 virginica 74 28 61 19 virginica 79 38 64 20 virginica 64 28 56 22 virginica 63 28 51 15 virginica 61 26 56 14 virginica 77 30 61 23 virginica 63 34 56 24 virginica 64 31 55 18 virginica 60 30 58 18 virginica 69 31 54 21 virginica 67 31 56 24 virginica 69 31 51 23 virginica 58 27 51 19 virginica 68 32 59 23 virginica 67 33 57 25 virginica 67 30 52 23 virginica 63 25 50 19 virginica 65 30 52 20 virginica 62 34 54 23 virginica 59 30 51 18  15.9 so classified as ve and correctly, except the 4 red cases >>15.9 so classified as vi correctly except for 3 red cases (120-7)/120 = 94.1% correct with one epoch requiring only two mask pTree evaluations using the Fei Pan formula.

  5. spLN spWD pdLN pdWD versicolor 50 20 35 10 versicolor 59 30 42 15 versicolor 60 22 40 10 versicolor 61 29 47 14 versicolor 56 29 36 13 versicolor 67 31 44 14 versicolor 56 30 45 15 versicolor 58 27 41 10 versicolor 62 22 45 15 versicolor 56 25 39 11 versicolor 59 32 48 18 versicolor 61 28 40 13 versicolor 63 25 49 15 versicolor 61 28 47 12 versicolor 64 29 43 13 versicolor 66 30 44 14 versicolor 68 28 48 14 versicolor 67 30 50 17 versicolor 60 29 45 15 versicolor 57 26 35 10 versicolor 55 24 38 11 versicolor 55 24 37 10 versicolor 58 27 39 12 versicolor 60 27 51 16 versicolor 54 30 45 15 versicolor 60 34 45 16 versicolor 67 31 47 15 versicolor 63 23 44 13 versicolor 56 30 41 13 versicolor 55 25 40 13 versicolor 55 26 44 12 versicolor 61 30 46 14 versicolor 58 26 40 12 versicolor 50 23 33 10 versicolor 56 27 42 13 versicolor 57 30 42 12 versicolor 57 29 42 13 versicolor 62 29 43 13 versicolor 51 25 30 11 versicolor 57 28 41 13 spLN spWD pdLN pdWD virginica 65 32 51 20 virginica 64 27 53 19 virginica 68 30 55 21 virginica 57 25 50 20 virginica 58 28 51 24 virginica 64 32 53 23 virginica 65 30 55 18 virginica 77 38 67 22 virginica 77 26 69 23 virginica 60 22 50 15 virginica 69 32 57 23 virginica 56 28 49 20 virginica 77 28 67 20 virginica 63 27 49 18 virginica 67 33 57 21 virginica 72 32 60 18 virginica 62 28 48 18 virginica 61 30 49 18 virginica 64 28 56 21 virginica 72 30 58 16 virginica 74 28 61 19 virginica 79 38 64 20 virginica 64 28 56 22 virginica 63 28 51 15 virginica 61 26 56 14 virginica 77 30 61 23 virginica 63 34 56 24 virginica 64 31 55 18 virginica 60 30 58 18 virginica 69 31 54 21 virginica 67 31 56 24 virginica 69 31 51 23 virginica 58 27 51 19 virginica 68 32 59 23 virginica 67 33 57 25 virginica 67 30 52 23 virginica 63 25 50 19 virginica 65 30 52 20 virginica 62 34 54 23 virginica 59 30 51 18 17 so classified as ve and correctly, except the 1 red cases >>17 correct except for 4 reds MNs 44.1 33.1 14.5 2.2 se sepalLN sepalWD pedalLN pedalWD 61 28.7 43.7 13.8 ve 65.7 29.4 57.7 20.4 vi STDs 13.3 2.9 1.0 0.7 se 6.89 3.2 4.6 1.6 ve 7.6 3.2 5.7 2.8 vi Examining means and stds again for NONse, ve classification using pdWD, we see that 2 stds right from the ve-mean still does not overlap with 1 std left from the vi mean. Thus this might be a better cutoff choice (namely, 13.8+2*1.6 = 17) (120-5)/120 = 95.8% correct with one epoch requiring only two mask pTree evaluations using the Fei Pan formula.

  6. Going back to se/~se classification, even though we get 100% accuracy, how do we know that without answers? spLN spWD pdLN pdWD versicolor 50 20 35 10 versicolor 59 30 42 15 versicolor 60 22 40 10 versicolor 61 29 47 14 versicolor 56 29 36 13 versicolor 67 31 44 14 versicolor 56 30 45 15 versicolor 58 27 41 10 versicolor 62 22 45 15 versicolor 56 25 39 11 versicolor 59 32 48 18 versicolor 61 28 40 13 versicolor 63 25 49 15 versicolor 61 28 47 12 versicolor 64 29 43 13 versicolor 66 30 44 14 versicolor 68 28 48 14 versicolor 67 30 50 17 versicolor 60 29 45 15 versicolor 57 26 35 10 versicolor 55 24 38 11 versicolor 55 24 37 10 versicolor 58 27 39 12 versicolor 60 27 51 16 versicolor 54 30 45 15 versicolor 60 34 45 16 versicolor 67 31 47 15 versicolor 63 23 44 13 versicolor 56 30 41 13 versicolor 55 25 40 13 versicolor 55 26 44 12 versicolor 61 30 46 14 versicolor 58 26 40 12 versicolor 50 23 33 10 versicolor 56 27 42 13 versicolor 57 30 42 12 versicolor 57 29 42 13 versicolor 62 29 43 13 versicolor 51 25 30 11 versicolor 57 28 41 13 spLN spWD pdLN pdWD spLN spWD pdLN pdWD virginica 65 32 51 20 virginica 64 27 53 19 virginica 68 30 55 21 virginica 57 25 50 20 virginica 58 28 51 24 virginica 64 32 53 23 virginica 65 30 55 18 virginica 77 38 67 22 virginica 77 26 69 23 virginica 60 22 50 15 virginica 69 32 57 23 virginica 56 28 49 20 virginica 77 28 67 20 virginica 63 27 49 18 virginica 67 33 57 21 virginica 72 32 60 18 virginica 62 28 48 18 virginica 61 30 49 18 virginica 64 28 56 21 virginica 72 30 58 16 virginica 74 28 61 19 virginica 79 38 64 20 virginica 64 28 56 22 virginica 63 28 51 15 virginica 61 26 56 14 virginica 77 30 61 23 virginica 63 34 56 24 virginica 64 31 55 18 virginica 60 30 58 18 virginica 69 31 54 21 virginica 67 31 56 24 virginica 69 31 51 23 virginica 58 27 51 19 virginica 68 32 59 23 virginica 67 33 57 25 virginica 67 30 52 23 virginica 63 25 50 19 virginica 65 30 52 20 virginica 62 34 54 23 virginica 59 30 51 18 setosa 54 37 15 2 setosa 48 34 16 2 setosa 48 30 14 1 setosa 43 30 11 1 setosa 58 40 12 2 setosa 57 44 15 4 setosa 54 39 13 4 setosa 51 35 14 3 setosa 57 38 17 3 setosa 51 38 15 3 setosa 54 34 17 2 setosa 51 37 15 4 setosa 46 36 10 2 setosa 51 33 17 5 setosa 48 34 19 2 setosa 50 30 16 2 setosa 50 34 16 4 setosa 52 35 15 2 setosa 52 34 14 2 setosa 47 32 16 2 setosa 48 31 16 2 setosa 54 34 15 4 setosa 52 41 15 1 setosa 55 42 14 2 setosa 49 31 15 1 setosa 50 32 12 2 setosa 55 35 13 2 setosa 49 31 15 1 setosa 44 30 13 2 setosa 51 34 15 2 setosa 50 35 13 3 setosa 45 23 13 3 setosa 44 32 13 2 setosa 50 35 16 6 setosa 51 38 19 4 setosa 48 30 14 3 setosa 51 38 16 2 setosa 46 32 14 2 setosa 53 37 15 2 setosa 50 33 14 2 >7 ~se >7 ~se 7 se MEANS 44.1 33.1 14.5 2.2 setosa sepalLN sepalWD pedalLN pedalWD 61 28.7 43.7 13.8 versicolor 65.7 29.4 57.7 20.4 virginica STDs 13.3 2.9 1.0 0.7 setosa 6.89 3.2 4.6 1.6 versicolor 7.6 3.2 5.7 2.8 virginica A better algorithm might look for radii of 3 stds (99.7% for normal distr). For se/~se, on pWD, we can go 4.94 stds before there is interval overlap (99.99995% confidence with a cutoff of 5.89). Thus we can be completely confident in se/~se. For ve/~ve in ~se, using pWD, can go 1.5 stds (cutoff 16.2) before ri overlap (~87%). From the previous slide we see that we get 6 incorrectly classified iris samples. 6/80 = 92% so that is very close to what is expected (must be fairly normally distributed data). From this TrainingSetMeansStdsTable we can also isolate those that are suspicious in terms of their classification. Iff a sample falls in the overlap of an ri intervals of a relevant attribute, then the radius of those intervals (# of stds used) plus the relevance of the attribute in general, constitute a way to measure the suspiciousness of the prediction. Suspicious predictions might be subjected to other methods such as CNN or ARM or ????

  7. The pTree-k-Means-Classification-Vanilla (pkmc-v) algorithm is as follows: In the TrainingSet, 1. For each attribute, calc the mean for each class and for each attribute, sort classes asc. 2. Calculate gaps (diff of consec. means) and all relative_gaps, rg's, (gap/mn) 3. Use the max of the sum of a mean's 2 rg's to identify the best attribute and the best class_k/NOT_class_k classification (which should be the current most unambiguous classification.) 4. Repeat 3 above on NOT_class_k until NOT_class_k is empty. 5. Repeat 1,2,3,4 until means stop changing (much). The pTree-k-Means-Classification-Divisive-means-only (pkmc-dm) algorithm is as follows: In the TrainingSet 1. For each attribute, calc the mean for each class, sort classes ascending by mean. 2. Calculate mean_gaps (diff of consecutive means) and each relative_mean_gap, (rmg), (mean_gaps/mean) 3. Choose class (and attr) with max rmg. Use Fei Pan's formula with c = mean  gap/2 to separate that class from NOTclass 4. Repeat 3 above on NOTclass until NOTclass is empty. 5. Repeat 1,2,3,4 until means stop changing (much). The pTree-k-Means-Classification-Divisive-means-stds (pkmc-dms) algorithm is as follows: In the TrainingSet 1. For each attribute, calc the mean for each class, sort classes ascending by mean. 2. Calc mean_gaps (difference of consecutive means) and each relative_mean_gap, rmg, (mean_gap/mean) (in stds) 3. In each gap, find the number, x, of stds so that cutpoint= mean1+x*std1=mean2-std2 (solve a system of two simple linear equations). Choose mean with max x, divide it;s cluster into two using Fei Pan Cutoff = cutpoint. 4. Repeat 3 above until all clusters are singleton classes. 5. Repeat 1,2,3,4 until means stop changing (much). The pTree-k-Means-Classification-Relevancy-Intervals (pkmc-ri) algorithm is as follows: 1. Calculate all means and stds for all feature columns and classes in a training set. 2. In each column, calculate max radii s.t. there is zero gap between each consecutive relevancy interval. 3. Add adjacent interval radii (either side of a given mean) to get the set of consecutive-interval radii (cir). Sort cir descending. Use the max cir to identify the first class_k/NOT_class_k classification (which should be the most unambiguous classification.) 4. Repeat 2 and 3 above on NOT_class_k until NOT_class_k is empty. 5. Repeat 1,2,3,4 until means stop changing (much).

  8. se 51 35 14 2 0 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 1 0 se 49 30 14 2 0 1 1 0 0 0 1 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 1 0 se 47 32 13 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 se 46 31 15 2 0 1 0 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 se 5 36 14 2 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 se 54 39 17 4 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 se 46 34 14 3 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 1 1 se 50 34 15 2 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 se 44 29 14 2 0 1 0 1 1 0 0 0 1 1 1 0 1 0 0 0 1 1 1 0 0 0 0 1 0 se 49 31 15 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 se 54 37 15 2 0 1 1 0 1 1 0 1 0 0 1 0 1 0 0 0 1 1 1 1 0 0 0 1 0 se 48 34 16 2 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 se 48 30 14 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 se 43 30 11 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 se 58 40 12 2 0 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 se 57 44 15 4 0 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 1 0 0 se 54 39 13 4 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 0 1 1 0 1 0 0 1 0 0 se 51 35 14 3 0 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 1 1 se 57 38 17 3 0 1 1 1 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1 1 se 51 38 15 3 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 1 1 se 54 34 17 2 0 1 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 se 51 37 15 4 0 1 1 0 0 1 1 1 0 0 1 0 1 0 0 0 1 1 1 1 0 0 1 0 0 se 46 36 10 2 0 1 0 1 1 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 se 51 33 17 5 0 1 1 0 0 1 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 se 48 34 19 2 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 se 50 30 16 2 0 1 1 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 se 50 34 16 4 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 se 52 35 15 2 0 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 1 0 se 52 34 14 2 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 1 0 se 47 32 16 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 se 48 31 16 2 0 1 1 0 0 0 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 1 0 se 54 34 15 4 0 1 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 1 0 0 se 52 41 15 1 0 1 1 0 1 0 0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 se 55 42 14 2 0 1 1 0 1 1 1 1 0 1 0 1 0 0 0 0 1 1 1 0 0 0 0 1 0 se 49 31 15 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 se 50 32 12 2 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 se 55 35 13 2 0 1 1 0 1 1 1 1 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 se 49 31 15 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 se 44 30 13 2 0 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 1 0 1 0 0 0 1 0 se 51 34 15 2 0 1 1 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 se 50 35 13 3 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 1 se 45 23 13 3 0 1 0 1 1 0 1 0 1 0 1 1 1 0 0 0 1 1 0 1 0 0 0 1 1 se 44 32 13 2 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 se 50 35 16 6 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 1 0 se 51 38 19 4 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 se 48 30 14 3 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 1 1 se 51 38 16 2 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 se 46 32 14 2 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 se 53 37 15 2 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 0 1 1 1 1 0 0 0 1 0 se 50 33 14 2 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 ve 70 32 47 14 1 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0 1 1 1 0 ve 64 32 45 15 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 69 31 49 15 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 1 0 1 1 1 1 ve 55 23 40 13 0 1 1 0 1 1 1 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 1 ve 65 28 46 15 1 0 0 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 1 1 ve 57 28 45 13 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 0 1 ve 63 33 47 16 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 ve 49 24 33 10 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 ve 66 29 46 13 1 0 0 0 0 1 0 0 1 1 1 0 1 0 1 0 1 1 1 0 0 1 1 0 1 ve 52 27 39 14 0 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 vi 63 33 60 25 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 0 1 vi 58 27 51 19 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 1 1 vi 71 30 59 21 1 0 0 0 1 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 0 1 0 1 vi 63 29 56 18 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 1 0 vi 65 30 58 22 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 1 0 vi 76 30 66 21 1 0 0 1 1 0 0 0 1 1 1 1 0 1 0 0 0 0 1 0 1 0 1 0 1 vi 49 25 45 17 0 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 1 1 0 0 0 1 vi 73 29 63 18 1 0 0 1 0 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 0 vi 67 25 58 18 1 0 0 0 0 1 1 0 1 1 0 0 1 0 1 1 1 0 1 0 1 0 0 1 0 vi 72 36 61 25 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 1 1 1 0 0 1 sepalLN sepalWD pedalLN pedalWD MEANS se 44.1 33.1 14.5 2.2 ve 61 28.7 42.7 13.8 vi 65.7 29.4 57.7 20.4 vi 65 32 51 20 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 vi 64 27 53 19 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 vi 68 30 55 21 1 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 vi 57 25 50 20 0 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 vi 58 28 51 24 0 1 1 1 0 1 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 vi 64 32 53 23 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 1 0 1 1 1 vi 65 30 55 18 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 1 0 vi 77 38 67 22 1 0 0 1 1 0 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 1 0 vi 77 26 69 23 1 0 0 1 1 0 1 0 1 1 0 1 0 1 0 0 0 1 0 1 1 0 1 1 1 vi 60 22 50 15 0 1 1 1 1 0 0 0 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 1 1 vi 69 32 57 23 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 1 vi 56 28 49 20 0 1 1 1 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 0 1 0 0 vi 77 28 67 20 1 0 0 1 1 0 1 0 1 1 1 0 0 1 0 0 0 0 1 1 1 0 1 0 0 vi 63 27 49 18 0 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 1 1 0 0 1 0 vi 67 33 57 21 1 0 0 0 0 1 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 0 1 0 1 vi 72 32 60 18 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 1 0 0 1 0 vi 62 28 48 18 0 1 1 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 vi 61 30 49 18 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 0 0 1 1 0 0 1 0 vi 64 28 56 21 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 1 0 1 vi 72 30 58 16 1 0 0 1 0 0 0 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 0 0 0 vi 74 28 61 19 1 0 0 1 0 1 0 0 1 1 1 0 0 0 1 1 1 1 0 1 1 0 0 1 1 vi 79 38 64 20 1 0 0 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 0 0 vi 64 28 56 22 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 1 1 0 vi 63 28 51 15 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 1 0 1 1 1 1 vi 61 26 56 14 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 0 0 0 1 1 1 0 vi 77 30 61 23 1 0 0 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 1 vi 63 34 56 24 0 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 0 1 1 0 0 0 vi 64 31 55 18 1 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 vi 60 30 18 18 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 1 0 1 0 0 1 0 vi 69 31 54 21 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 0 1 0 1 vi 67 31 56 24 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 0 vi 69 31 51 23 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 1 vi 58 27 51 19 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 1 1 vi 68 32 59 23 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 vi 67 33 57 25 1 0 0 0 0 1 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 1 vi 67 30 52 23 1 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 1 1 vi 63 25 50 19 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 0 1 1 vi 65 30 52 20 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 0 0 vi 62 34 54 23 0 1 1 1 1 1 0 1 0 0 0 1 0 0 1 1 0 1 1 0 1 0 1 1 1 vi 59 30 51 18 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 0 ordered sLN mns se 44.1 ve 61 vi 65.7 ordered sWD mns ve 28.7 vi 29.4 se 33.1 ordered pLN mns se 14.5 ve 43.7 vi 57.7 ordered pWD mns se 2.2 ve 13.8 vi 20.4 se_ve 16.9 ve_vi 0.7 se_ve 29.2 se_ve 11.6 GAPS ve_vi 4.7 vi_se 3.7 ve_vi 14 ve_vi 6.6 se_ve-se .38 ve_vi-ve .02 se_ve-se 2.01 se_ve-se 5.27 se_ve-ve .28 ve_vi-vi .02 se_ve-ve 0.67 se_ve-ve 0.84 ve_vi-ve .08 vi_se-vi .13 ve_vi-ve 0.32 ve_vi-ve 0.48 ve_vi-vi .07 vi_se-se .11 ve_vi-vi 0.24 ve_vi-vi 0.32 RelativeGaps ve 50 20 35 10 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 0 ve 59 30 42 15 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 ve 60 22 40 10 0 1 1 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 1 0 ve 61 29 47 14 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 0 ve 56 29 36 13 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 ve 67 31 44 14 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 0 1 1 1 0 ve 56 30 45 15 0 1 1 1 0 0 0 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 58 27 41 10 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 0 ve 62 22 45 15 0 1 1 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 56 25 39 11 0 1 1 1 0 0 0 0 1 1 0 0 1 0 1 0 0 1 1 1 0 1 0 1 1 ve 59 32 48 18 0 1 1 1 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 ve 61 28 40 13 0 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 0 1 1 0 1 ve 63 25 49 15 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 ve 61 28 47 12 0 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 1 1 1 0 1 1 0 0 ve 64 29 43 13 1 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 1 0 1 ve 66 30 44 14 1 0 0 0 0 1 0 0 1 1 1 1 0 0 1 0 1 1 0 0 0 1 1 1 0 ve 68 28 48 14 1 0 0 0 1 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 0 ve 67 30 50 17 1 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 1 ve 60 29 45 15 0 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 0 1 0 1 1 1 1 ve 57 26 35 10 0 1 1 1 0 0 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 1 0 ve 55 24 38 11 0 1 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 ve 55 24 37 10 0 1 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 ve 58 27 39 12 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 0 ve 60 27 51 16 0 1 1 1 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 ve 54 30 45 15 0 1 1 0 1 1 0 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 60 34 45 16 0 1 1 1 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0 ve 67 31 47 15 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 ve 63 23 44 13 0 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 1 ve 56 30 41 13 0 1 1 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 0 1 ve 55 25 40 13 0 1 1 0 1 1 1 0 1 1 0 0 1 0 1 0 1 0 0 0 0 1 1 0 1 ve 55 26 44 12 0 1 1 0 1 1 1 0 1 1 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 ve 61 30 46 14 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1 0 ve 58 26 40 12 0 1 1 1 0 1 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 ve 50 23 33 10 0 1 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 1 0 ve 56 27 42 13 0 1 1 1 0 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 1 ve 57 30 42 12 0 1 1 1 0 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 0 0 ve 57 29 42 13 0 1 1 1 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 ve 62 29 43 13 0 1 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 1 0 1 ve 51 25 30 11 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 0 1 0 1 1 ve 57 28 41 13 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 pWD_se_ve-se has the max RelativeGap and there is no mean on the other side of se, so we start with se/~se classification using threshold = se_mean-gap/2 = 2.2 + 5.27/2 = 4.84 pkmc-v pg 1

  9. vi 65 32 51 20 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 vi 64 27 53 19 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 vi 68 30 55 21 1 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 vi 57 25 50 20 0 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 vi 58 28 51 24 0 1 1 1 0 1 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 vi 64 32 53 23 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 1 0 1 1 1 vi 65 30 55 18 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 1 0 vi 77 38 67 22 1 0 0 1 1 0 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 1 0 vi 77 26 69 23 1 0 0 1 1 0 1 0 1 1 0 1 0 1 0 0 0 1 0 1 1 0 1 1 1 vi 60 22 50 15 0 1 1 1 1 0 0 0 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 1 1 vi 69 32 57 23 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 1 vi 56 28 49 20 0 1 1 1 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 0 1 0 0 vi 77 28 67 20 1 0 0 1 1 0 1 0 1 1 1 0 0 1 0 0 0 0 1 1 1 0 1 0 0 vi 63 27 49 18 0 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 1 1 0 0 1 0 vi 67 33 57 21 1 0 0 0 0 1 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 0 1 0 1 vi 72 32 60 18 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 1 0 0 1 0 vi 62 28 48 18 0 1 1 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 vi 61 30 49 18 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 0 0 1 1 0 0 1 0 vi 64 28 56 21 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 1 0 1 vi 72 30 58 16 1 0 0 1 0 0 0 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 0 0 0 vi 74 28 61 19 1 0 0 1 0 1 0 0 1 1 1 0 0 0 1 1 1 1 0 1 1 0 0 1 1 vi 79 38 64 20 1 0 0 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 0 0 vi 64 28 56 22 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 1 1 0 vi 63 28 51 15 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 1 0 1 1 1 1 vi 61 26 56 14 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 0 0 0 1 1 1 0 vi 77 30 61 23 1 0 0 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 1 vi 63 34 56 24 0 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 0 1 1 0 0 0 vi 64 31 55 18 1 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 vi 60 30 18 18 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 1 0 1 0 0 1 0 vi 69 31 54 21 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 0 1 0 1 vi 67 31 56 24 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 0 vi 69 31 51 23 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 1 vi 58 27 51 19 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 1 1 vi 68 32 59 23 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 vi 67 33 57 25 1 0 0 0 0 1 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 1 vi 67 30 52 23 1 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 1 1 vi 63 25 50 19 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 0 1 1 vi 65 30 52 20 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 0 0 vi 62 34 54 23 0 1 1 1 1 1 0 1 0 0 0 1 0 0 1 1 0 1 1 0 1 0 1 1 1 vi 59 30 51 18 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 0 ve 70 32 47 14 1 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0 1 1 1 0 ve 64 32 45 15 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 69 31 49 15 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 1 0 1 1 1 1 ve 55 23 40 13 0 1 1 0 1 1 1 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 1 ve 65 28 46 15 1 0 0 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 1 1 ve 57 28 45 13 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 0 1 ve 63 33 47 16 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 ve 49 24 33 10 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 ve 66 29 46 13 1 0 0 0 0 1 0 0 1 1 1 0 1 0 1 0 1 1 1 0 0 1 1 0 1 ve 52 27 39 14 0 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 vi 63 33 60 25 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 0 1 vi 58 27 51 19 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 1 1 vi 71 30 59 21 1 0 0 0 1 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 0 1 0 1 vi 63 29 56 18 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 1 0 vi 65 30 58 22 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 1 0 vi 76 30 66 21 1 0 0 1 1 0 0 0 1 1 1 1 0 1 0 0 0 0 1 0 1 0 1 0 1 vi 49 25 45 17 0 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 1 1 0 0 0 1 vi 73 29 63 18 1 0 0 1 0 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 0 vi 67 25 58 18 1 0 0 0 0 1 1 0 1 1 0 0 1 0 1 1 1 0 1 0 1 0 0 1 0 vi 72 36 61 25 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 1 1 1 0 0 1 sepalLN sepalWD pedalLN pedalWD ve 61 28.7 42.7 13.8 vi 65.7 29.4 57.7 20.4 MEANS ordered sLN mns ve 61 vi 65.7 ordered sWD mns ve 28.7 vi 29.4 ordered pLN mns ve 43.7 vi 57.7 ordered pWD mns ve 13.8 vi 20.4 ve 50 20 35 10 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 0 ve 59 30 42 15 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 ve 60 22 40 10 0 1 1 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 1 0 ve 61 29 47 14 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 0 ve 56 29 36 13 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 ve 67 31 44 14 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 0 1 1 1 0 ve 56 30 45 15 0 1 1 1 0 0 0 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 58 27 41 10 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 0 ve 62 22 45 15 0 1 1 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 56 25 39 11 0 1 1 1 0 0 0 0 1 1 0 0 1 0 1 0 0 1 1 1 0 1 0 1 1 ve 59 32 48 18 0 1 1 1 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 ve 61 28 40 13 0 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 0 1 1 0 1 ve 63 25 49 15 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 ve 61 28 47 12 0 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 1 1 1 0 1 1 0 0 ve 64 29 43 13 1 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 1 0 1 ve 66 30 44 14 1 0 0 0 0 1 0 0 1 1 1 1 0 0 1 0 1 1 0 0 0 1 1 1 0 ve 68 28 48 14 1 0 0 0 1 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 0 ve 67 30 50 17 1 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 1 ve 60 29 45 15 0 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 0 1 0 1 1 1 1 ve 57 26 35 10 0 1 1 1 0 0 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 1 0 ve 55 24 38 11 0 1 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 ve 55 24 37 10 0 1 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 ve 58 27 39 12 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 0 ve 60 27 51 16 0 1 1 1 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 ve 54 30 45 15 0 1 1 0 1 1 0 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 60 34 45 16 0 1 1 1 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0 ve 67 31 47 15 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 ve 63 23 44 13 0 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 1 ve 56 30 41 13 0 1 1 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 0 1 ve 55 25 40 13 0 1 1 0 1 1 1 0 1 1 0 0 1 0 1 0 1 0 0 0 0 1 1 0 1 ve 55 26 44 12 0 1 1 0 1 1 1 0 1 1 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 ve 61 30 46 14 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1 0 ve 58 26 40 12 0 1 1 1 0 1 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 ve 50 23 33 10 0 1 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 1 0 ve 56 27 42 13 0 1 1 1 0 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 1 ve 57 30 42 12 0 1 1 1 0 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 0 0 ve 57 29 42 13 0 1 1 1 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 ve 62 29 43 13 0 1 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 1 0 1 ve 51 25 30 11 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 0 1 0 1 1 ve 57 28 41 13 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 se_ve 16.9ve_vi 0.7se_ve 29.2 se_ve 11.6 ve_vi 4.7 ve_vi 0.7 ve_vi 14 ve_vi 6.6 GAP ve_vi .08 ve_vi .02 ve_vi .36 ve_vi .47 RelativeGaps pWDve_vi has the max RelativeGap so for ve/~ve (aka ve/vi) classification use threshold= e_mean-gap/2=13.8+6.6/2 = 16 74/80 accuracy here Overall, 114/120 = 95% pkmc-v pg 2

  10. se 51 35 14 2 0 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 1 0 se 49 30 14 2 0 1 1 0 0 0 1 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 1 0 se 47 32 13 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 se 46 31 15 2 0 1 0 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 se 5 36 14 2 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 se 54 39 17 4 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 se 46 34 14 3 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 1 1 se 50 34 15 2 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 se 44 29 14 2 0 1 0 1 1 0 0 0 1 1 1 0 1 0 0 0 1 1 1 0 0 0 0 1 0 se 49 31 15 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 se 54 37 15 2 0 1 1 0 1 1 0 1 0 0 1 0 1 0 0 0 1 1 1 1 0 0 0 1 0 se 48 34 16 2 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 se 48 30 14 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 se 43 30 11 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 se 58 40 12 2 0 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 se 57 44 15 4 0 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 1 0 0 se 54 39 13 4 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 0 1 1 0 1 0 0 1 0 0 se 51 35 14 3 0 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 1 1 se 57 38 17 3 0 1 1 1 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1 1 se 51 38 15 3 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 1 1 se 54 34 17 2 0 1 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 se 51 37 15 4 0 1 1 0 0 1 1 1 0 0 1 0 1 0 0 0 1 1 1 1 0 0 1 0 0 se 46 36 10 2 0 1 0 1 1 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 se 51 33 17 5 0 1 1 0 0 1 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 se 48 34 19 2 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 se 50 30 16 2 0 1 1 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 se 50 34 16 4 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 se 52 35 15 2 0 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 1 0 se 52 34 14 2 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 1 0 se 47 32 16 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 se 48 31 16 2 0 1 1 0 0 0 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 1 0 se 54 34 15 4 0 1 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 1 0 0 se 52 41 15 1 0 1 1 0 1 0 0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 se 55 42 14 2 0 1 1 0 1 1 1 1 0 1 0 1 0 0 0 0 1 1 1 0 0 0 0 1 0 se 49 31 15 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 se 50 32 12 2 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 se 55 35 13 2 0 1 1 0 1 1 1 1 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 se 49 31 15 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 se 44 30 13 2 0 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 1 0 1 0 0 0 1 0 se 51 34 15 2 0 1 1 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 se 50 35 13 3 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 1 se 45 23 13 3 0 1 0 1 1 0 1 0 1 0 1 1 1 0 0 0 1 1 0 1 0 0 0 1 1 se 44 32 13 2 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 se 50 35 16 6 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 1 0 se 51 38 19 4 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 se 48 30 14 3 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 1 1 se 51 38 16 2 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 se 46 32 14 2 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 se 53 37 15 2 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 0 1 1 1 1 0 0 0 1 0 se 50 33 14 2 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 pkmc-dm p1 ve 70 32 47 14 1 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0 1 1 1 0 ve 64 32 45 15 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 69 31 49 15 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 1 0 1 1 1 1 ve 55 23 40 13 0 1 1 0 1 1 1 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 1 ve 65 28 46 15 1 0 0 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 1 1 ve 57 28 45 13 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 0 1 ve 63 33 47 16 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 ve 49 24 33 10 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 ve 66 29 46 13 1 0 0 0 0 1 0 0 1 1 1 0 1 0 1 0 1 1 1 0 0 1 1 0 1 ve 52 27 39 14 0 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 pWD_se_ve-se th=(13.8+2.2)/2 =8 divides {se,ve,vi} into {se}, {ve,vi}. vi 63 33 60 25 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 0 1 vi 58 27 51 19 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 1 1 vi 71 30 59 21 1 0 0 0 1 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 0 1 0 1 vi 63 29 56 18 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 1 0 vi 65 30 58 22 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 1 0 vi 76 30 66 21 1 0 0 1 1 0 0 0 1 1 1 1 0 1 0 0 0 0 1 0 1 0 1 0 1 vi 49 25 45 17 0 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 1 1 0 0 0 1 vi 73 29 63 18 1 0 0 1 0 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 0 vi 67 25 58 18 1 0 0 0 0 1 1 0 1 1 0 0 1 0 1 1 1 0 1 0 1 0 0 1 0 vi 72 36 61 25 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 1 1 1 0 0 1 1. For each attr, calc mean for each class, sort classes asc by mean. 2. Calc mean_gaps (diff of consec means). For each mean, relative_mean_gap, rmg, (mn_gap/mn) (in stds?) 3. Choose max rmg, divide cluster using Cutoff = midpt of gap 4. Repeat 3 above on NOT_class_k until every cluster is 1 class. vi 65 32 51 20 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 vi 64 27 53 19 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 vi 68 30 55 21 1 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 vi 57 25 50 20 0 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 vi 58 28 51 24 0 1 1 1 0 1 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 vi 64 32 53 23 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 1 0 1 1 1 vi 65 30 55 18 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 1 0 vi 77 38 67 22 1 0 0 1 1 0 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 1 0 vi 77 26 69 23 1 0 0 1 1 0 1 0 1 1 0 1 0 1 0 0 0 1 0 1 1 0 1 1 1 vi 60 22 50 15 0 1 1 1 1 0 0 0 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 1 1 vi 69 32 57 23 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 1 vi 56 28 49 20 0 1 1 1 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 0 1 0 0 vi 77 28 67 20 1 0 0 1 1 0 1 0 1 1 1 0 0 1 0 0 0 0 1 1 1 0 1 0 0 vi 63 27 49 18 0 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 1 1 0 0 1 0 vi 67 33 57 21 1 0 0 0 0 1 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 0 1 0 1 vi 72 32 60 18 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 1 0 0 1 0 vi 62 28 48 18 0 1 1 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 vi 61 30 49 18 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 0 0 1 1 0 0 1 0 vi 64 28 56 21 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 1 0 1 vi 72 30 58 16 1 0 0 1 0 0 0 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 0 0 0 vi 74 28 61 19 1 0 0 1 0 1 0 0 1 1 1 0 0 0 1 1 1 1 0 1 1 0 0 1 1 vi 79 38 64 20 1 0 0 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 0 0 vi 64 28 56 22 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 1 1 0 vi 63 28 51 15 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 1 0 1 1 1 1 vi 61 26 56 14 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 0 0 0 1 1 1 0 vi 77 30 61 23 1 0 0 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 1 vi 63 34 56 24 0 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 0 1 1 0 0 0 vi 64 31 55 18 1 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 vi 60 30 18 18 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 1 0 1 0 0 1 0 vi 69 31 54 21 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 0 1 0 1 vi 67 31 56 24 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 0 vi 69 31 51 23 1 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 1 vi 58 27 51 19 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 1 1 vi 68 32 59 23 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 vi 67 33 57 25 1 0 0 0 0 1 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 1 vi 67 30 52 23 1 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 1 1 vi 63 25 50 19 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 0 1 1 vi 65 30 52 20 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 0 0 vi 62 34 54 23 0 1 1 1 1 1 0 1 0 0 0 1 0 0 1 1 0 1 1 0 1 0 1 1 1 vi 59 30 51 18 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 0 sepalLN sepalWD pedalLN pedalWD MEANS se 44.1 33.1 14.5 2.2 ve 61 28.7 42.7 13.8 vi 65.7 29.4 57.7 20.4 sorted sLN mns se 44.1 ve 61 vi 65.7 sorted sWD mns ve 28.7 vi 29.4 se 33.1 sorted pLN mns se 14.5 ve 43.7 vi 57.7 sorted pWD mns se 2.2 ve 13.8 vi 20.4 se_ve 16.9 ve_vi 0.7 se_ve 29.2 se_ve 11.6 Mean ve_vi 4.7 vi_se 3.7 ve_vi 14 ve_vi 6.6 Gaps ve 50 20 35 10 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 0 ve 59 30 42 15 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 ve 60 22 40 10 0 1 1 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 1 0 ve 61 29 47 14 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 0 ve 56 29 36 13 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 ve 67 31 44 14 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 0 1 1 1 0 ve 56 30 45 15 0 1 1 1 0 0 0 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 58 27 41 10 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 0 ve 62 22 45 15 0 1 1 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 56 25 39 11 0 1 1 1 0 0 0 0 1 1 0 0 1 0 1 0 0 1 1 1 0 1 0 1 1 ve 59 32 48 18 0 1 1 1 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 ve 61 28 40 13 0 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 0 1 1 0 1 ve 63 25 49 15 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 ve 61 28 47 12 0 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 1 1 1 0 1 1 0 0 ve 64 29 43 13 1 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 1 0 1 ve 66 30 44 14 1 0 0 0 0 1 0 0 1 1 1 1 0 0 1 0 1 1 0 0 0 1 1 1 0 ve 68 28 48 14 1 0 0 0 1 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 0 ve 67 30 50 17 1 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 1 ve 60 29 45 15 0 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 0 1 0 1 1 1 1 ve 57 26 35 10 0 1 1 1 0 0 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 1 0 ve 55 24 38 11 0 1 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 ve 55 24 37 10 0 1 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 ve 58 27 39 12 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 0 ve 60 27 51 16 0 1 1 1 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 ve 54 30 45 15 0 1 1 0 1 1 0 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 ve 60 34 45 16 0 1 1 1 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0 ve 67 31 47 15 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 ve 63 23 44 13 0 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 1 ve 56 30 41 13 0 1 1 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 0 1 ve 55 25 40 13 0 1 1 0 1 1 1 0 1 1 0 0 1 0 1 0 1 0 0 0 0 1 1 0 1 ve 55 26 44 12 0 1 1 0 1 1 1 0 1 1 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 ve 61 30 46 14 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1 0 ve 58 26 40 12 0 1 1 1 0 1 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 ve 50 23 33 10 0 1 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 1 0 ve 56 27 42 13 0 1 1 1 0 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 1 ve 57 30 42 12 0 1 1 1 0 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 0 0 ve 57 29 42 13 0 1 1 1 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 ve 62 29 43 13 0 1 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 1 0 1 ve 51 25 30 11 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 0 1 0 1 1 ve 57 28 41 13 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 gap mnrmg se_ve-se .38 ve_vi-ve .02 se_ve-se 2.01 se_ve-se 5.27 se_ve-ve .28 ve_vi-vi .02 se_ve-ve 0.67 se_ve-ve 0.84 ve_vi-ve .08 vi_se-vi .13 ve_vi-ve 0.32 ve_vi-ve 0.48 ve_vi-vi .07 vi_se-se .11 ve_vi-vi 0.24 ve_vi-vi 0.32 pWD_ve_vi-ve th=0.48 divides {ve,vi} into {ve}, {vi}. Done

  11. pkmc-dm p2 1. For each attr, calc mean for each class, sort classes asc by mean. 2. Calc mean_gaps (diff of consec means). For each mean, relative_mean_gap, rmg, (mn_gap/mn) (in stds?) 3. Choose max rmg, divide cluster using Cutoff = mean  rmg/2 4. Repeat 3 above on NOT_class_k until every cluster is 1 class. 5. Repeat 1,2,3,4 until means top changing (much?). In the DoD problem we have 7 classes {RedCars, WhiteCars, BlueCars, Grass, Pavement, Shadows, Trees}? Thus on the first sub-epoch (first time through 1,2,3,4) we separate the 7 into two clusters (e.g., of 3 and 4 resp.). Then we apply 5 to both clusters, just as is done in the DIANA hierarchical clustering methods.

More Related