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HELP.txt
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--- SVR.DEM by G. Ch. Miliaresis
7th of January 2018
DESCRIPTION
@author: gmiliar (George Ch. Miliaresis)
Dimensonality reduction for DEMs (SVR.DEM) by G.Ch. Miliaresis
Ver. 2017.02 winpython implementation, (https://winpython.github.io/)
Details in https://github.com/miliaresis
https://sites.google.com/site/miliaresisg/
CONTENTS:
1. Processing options, active data header ('dataDEM2') and data
2. module: dmr_data_headers
3. module: dmr_myf
4. History file (test run)
5. Convergence (classification after k-means clustering)
_____________________________________________________________________
111111111111111111111111111111111111111111111111111111111111111111111
*********************************************************************
Processing options:
TIFF import options ['PIL', 'SKITimage']
Clustering options ['Kmeans', 'Kmeans refined by NBG']
DISPLAY ACTIVE DATA HEADER
---> ALOS, SRTM, ASTER GDEMs, 1 arc sec, Lat/Lon, WGS84, EGM96
Labels for x-axis, y-axis of images/histograms:
['H, m', 'Longitude,DD', 'Latitude, DD']
Geographic extent of data: [54.2362, 54.681, 27.1107, 27.5555]
AXES legends & Tables headers for rows & columns
['A', 'S', 'G']
['ALOS', 'SRTM', 'ASTER']
Domain of histograms, data: 301 2210 Rdata: -25 25 m
Subdir with images or vector files= data
Clustering method: Kmeans refined by NBG
Method for TIF file import: PIL
Data headers available: ['dataDEM2']
IMPORT/READ DATA FILES
Files EXTENSION= .tif DIR: data
FILENAMES: ['MASK', '01', '02', '03'] (names are case sensitive)
data\MASK.tif (1601, 1601)
data\01.tif (1601, 1601)
data\02.tif (1601, 1601)
data\03.tif (1601, 1601)
_____________________________________________________________________
222222222222222222222222222222222222222222222222222222222222222222222
*********************************************************************
Help on module dmr_data_headers:
NAME
dmr_data_headers - Created on 20th of December, 2017
----------------------------------------------------
TO LOAD your data, define a header in the file svr_data_headers.py.
---------------------------------------------------------
FUNCTIONS
dataDEM2(clustering_options, tiff_import_options)
ALOS, SRTM, ASTER GDEMs
phead(xy, ML, x2, x3, Lmn, Lmx, Rmn, Rmx, LDIR, T, cm)
PRINT DATA HEADER.
DATA files stored in a subdir named data within the dir where the
3 scripts are stored.
The tif image filenames (in the data dir) are fixed :
MASK [0, 1 for data], & 01, 02, 03 for the 3 DEMs (ALOS, SRTM, ASTER)
THE NAMES ARE CASE SENSITIVE and they are
determined automatically from the script (as well as the dimension of
the feature space -> length of tics list), so you should preserve them
in your data dir.
FILE
d:\run_z\dmr_data_headers.py
_____________________________________________________________________
333333333333333333333333333333333333333333333333333333333333333333333
*********************************************************************
Help on module dmr_myf:
NAME
dmr_myf - Created on 20th of December, 2017
DESCRIPTION
@author: gmiliar (George Ch. Miliaresis)
Dimensonality reduction for DEMs (SVR.DEM) by G.Ch. Miliaresis
Ver. 2017.02 winpython implementation, (https://winpython.github.io/)
Details in https://github.com/miliaresis
https://sites.google.com/site/miliaresisg/
FUNCTIONS
CreateMask_fromCluster(c)
Create mask matrix from cluster image matrix
ImplementSVR_MG(data, Labelmonth1, f)
main calls to SVR_MG
Kmeans_init(number_of_clusters)
Kmeans initialization
MainRun(data, rows, cols, GeoExtent, FigureLabels, LabelLST, LabelLSTxls, Hmin, Hmax, HRmin, HRmax, Clustering_method, clustering_options)
Main run module of SVR-mg.py
Processing_constants()
TIF import options (in function tiff_to_np in dim_myf)
if PIL then Image from PIL is used
if SKITimage then skimage.io is used
CLUSTERING & CLASSIFICATION OPTIONS:
K-means clustering
K-means clustering refined by Naive Bayes Gaussian classification
Reconstruct_matrix(evmat, LST)
Inverse transform keep pc-1 only
Reconstruct_matrix2(evmat, LST)
Inverse transform keep pc2 & pc3 only
centroids_visualize(data, figuretitle, Lx, MDLabel)
Visualize centroids
clusterRefineNBG(CM, centroid, iteration, centroid_variance, bb)
Clustering refinements by NBG,
display mean standardized divergence (n*n)-n, n=clusters
clustering_Kmeans(data, LabelLST, maxC, maxNBG, f, FigureLabels, Clustering_method)
Kmeans clustering
clustering_Kmeans_by_NBG(data, ML2, maxC, maxNBG, f, MDLabel, Clustering_method)
Kmeans clustering refined by NBG -density, display mean divergence
compute_descriptive_stats(RLST, x, lst_or_rlst)
compute mean, st.dev, kurtosis, skew
covariance_matrix(LST2)
Compoute variance-covariance matrix
create_data_files(data)
Read data file, create sub-matrices
creatematrix(rows, cols, ids, labels)
vector to image matrix
crosscorrelate(LST)
compute the crosscorrelation matrix
data_imv_read(LfilesDIR, featuredimension, T)
Main Data FILE (individual images read)
dem_differences_RMS(R)
Compute RMS of elevation differences among DEM pairs
dem_differences_absoulte_mean(R)
Compute absolute mean of elevation differences among DEM pairs
dem_differences_mean(R)
Compute mean of elevation differences among DEM pairs
dem_differences_stdev(R)
Compute st.dev of elevation differences among DEM pairs
descriptive_stats_RLST(data, LABELmonths3, Lx, f, lst_or_rlst)
Compute, display & save to xlsx descriptive statistics for Rdata
display_LST(rows, cols, xyrange, data, x, f, MDLabel)
display data images and save to png/tiff files
display_RLST(rows, cols, xyrange, data, RLST, x, f, MDLabel)
display Rdata images and save to png/tif files
display_save_clusterimage(rows, cols, xyrange, data, labels, f, w, MDLabe)
covert vector cluster labels to image, plot it & save to tif
display_save_maskimage(xyrange, c, MDLabel)
convert vector cluster labels to image, plot
dummyvar_fcheck()
assign dummy variables if file donot exist (to exit from return var
filenames_of_images(k)
Defines the filenames of images MASK, 01, 02, 03
findcreatenewpath()
Creates a new (non exisiting) path within the data/script-path where
the output files are stored. The path name is ...\outX where X is
a number determined automatically by this script
findpaths_data2csv(data)
find newpath to store outputs, change to newpath data dir
historyfile()
Track (save to file) the user inputs and the file outputs
input_screen_int(xstring, xmin, xmax)
input an integer X from screen in the range min<=X<=xmax
input_screen_str_yn(xstring)
input a string X from screen y, Y, n, N
pcanew(LST)
compute eigevalues, & eigenvectors
plotmatrix(c, xyrange, lut, name1, yesno, MDLabel)
plot a matrix
printHST(RLST, Fstring, xmin, xmax, x, f, MDLabel)
print histogram of LST/RLST
printNPP(RLST, x, f, lst_or_rlst)
print normal propability plot
printRLST_correlation(data, x)
write Rdata cross correlation matrix to xls file
print_RMS(Reconstruct, x, filename2, f)
Write elevation difference stats among DEM pairs to xls file
prn_xls_centroids(workbook, Centroids, LabelLST)
write Centroids matrix to a sheet of an excel workbook
prn_xls_cluster_membership(workbook, CLlabels)
compute & write cluster membership to excel file
prn_xls_divergence(workbook, Diverg)
write Divergence matrix to a sheet of an excel workbook
prn_xls_sigma(workbook, sigma, LabelLST)
write Sigma matrix to a sheet of an excel workbook
prnxls_confuse(workbook, data2)
Add confusion matrix to an xls sheet within a workbook
program_constants()
program constants:
maxC = Maximum number of clusters
maxNBG = Maximum number of NBG refinements
readdatafiles(filename, rows1, cols1, continue1, T)
Read SVR 2-d tif file & convert it 1-dto numpy array
readdatafiles0(filename, continue1, T)
Read image 2-d tif file & convert it 1-d to numpy array
readimagetiff(Ldatafiles, T)
Read individual tiff images - convert data
retranslatebymean(LST, RLST)
RETranslate a matrix by mean vector (per columns)
savematrix2image(c, name1)
save image to tif file
savevector_to_CSV(c, name1, f)
save vector data (derived from input images) to CSV files
sortdescent(evs, evmat)
sort eigenvalues-eigenvectors in descenting eigenvalue magnitude
standardize_matrix2(A)
standardize a 2-d matrix per columns
tiff_to_np(filename, T)
Read/Import tiff file
translatebymean(LST)
Translate a matrix by mean (per columns)
write2classconvergece(a, iteration)
Save mean inertia convergence to xlsx file
xlspca(data, data1, data2, data3, x)
write correlation matrix, eigen-vectors/values to xls file
FILE
d:\run_z\dmr_myf.py
______________________________________________________________
44444444444444444444444444444444444444444444444444444444444444
**************************************************************
HISTORY FILE: Test RUN
date: 2018-01-06 time = 1515222785.0923355
_history.txt tracks user selections & output files
Dimensionality reduction-DEM Selective Variance Reduction by
George Ch. Miliaresis (https://about.me/miliaresis)
Details in https://github.com/miliaresis [Repository SVR.DEM]
https://sites.google.com/site/miliaresisg/
Output data files are stored to : D:\run_z\out10
DISPLAY:descriptives, NPPs, images & histograms
SAVE vector data to CSV file (1st col = mask ID): vectors.csv
SAVE DEM comparisons: _initial_DEMS_DIF_stats.xlsx
Compute, display descriptive statistics
Write Rdata stats to descriptives_LST.xlsx
Save absolute kurtosis & skew to abs_kurtosis_skew.png
VISUALIZE & SAVE (png) the data images
L1_ALOS
L2_SRTM
L3_ASTER
Display & write NPP files
NPP_H1.png
NPP_H2.png
NPP_H3.png
DISPLAY & PRINT histograms for LST data
H_LST1.png
H_LST2.png
H_LST3.png
SVR IMPLEMENTATION
Compute cross correlation matrix
Compute eigenvalues & eigenvectors
Write xlsx file: pca.xlsx
---> Reconstruct from PC2 & PC3
SAVE DEM comparisons: _Reconstruted_DEMS_DIF_stats.xlsx
Compute, display descriptive statistics
Write Rdata stats to descriptives_RLST.xlsx
Save absolute kurtosis & skew to abs_kurtosis_skew.png
Display & write NPP files
NPP_RH1.png
NPP_RH2.png
NPP_RH3.png
VISUALIZE & SAVE (png/tif) the Rdata images
R1_ALOS
R2_SRTM
R3_ASTER
DISPLAY & PRINT histograms for RLST data
H_RLST1.png
H_RLST2.png
H_RLST3.png
Save clustering outputs to _clustering_output_tables.xlsx
Save centroids to centroids.png
Save sigma to Sigma.png
VISUALIZE cluster image & save to Clusters.png
Save to Clustermap.tif, & Clustermap.mat
_____________________________________________________________________
555555555555555555555555555555555555555555555555555555555555555555555
*********************************************************************
It might be non appropriate to fully reprocess / refine the K-means
clustering results with NBG classification (for DEM residual
information interpretation).
So just consider the example below as a convergence case study.
The percent 83.1 % at end, compares the initial cluster
map (formed by K-means clustering) to the final classified map
(formed by the 300 refinement iterations).
Convergence
1st: K-means clustering
Number of clusters in [2, 100]: 7
Number of NBG refinements in [5, 500]: 300
2nd:refine by NBG classification, MAX iterations: 300
no % vectors mean(st.divergence)
1 3.8322 ( 98228 ) 1.970045
2 3.8680 ( 99144 ) 2.344321
3 3.7974 ( 97334 ) 2.720994
4 3.7136 ( 95187 ) 3.082857
5 3.6144 ( 92644 ) 3.433549
6 3.4902 ( 89462 ) 3.784132
7 3.3796 ( 86625 ) 4.134461
8 3.2780 ( 84022 ) 4.484277
9 3.1763 ( 81414 ) 4.842610
10 3.0579 ( 78381 ) 5.226605
11 2.9298 ( 75096 ) 5.605404
12 2.8112 ( 72057 ) 5.975465
13 2.6951 ( 69080 ) 6.331822
14 2.5565 ( 65528 ) 6.671078
15 2.4303 ( 62294 ) 6.997241
16 2.3125 ( 59274 ) 7.307593
17 2.2193 ( 56885 ) 7.599233
18 2.1265 ( 54507 ) 7.872763
19 2.0165 ( 51687 ) 8.139425
20 1.9320 ( 49522 ) 8.389498
21 1.8551 ( 47550 ) 8.639150
22 1.7667 ( 45283 ) 8.878778
23 1.6890 ( 43293 ) 9.107852
24 1.6113 ( 41301 ) 9.322245
25 1.5160 ( 38857 ) 9.528048
26 1.4489 ( 37138 ) 9.727802
27 1.3635 ( 34950 ) 9.915437
28 1.2941 ( 33171 ) 10.094210
29 1.2371 ( 31710 ) 10.266284
30 1.1701 ( 29991 ) 10.435603
31 1.1116 ( 28493 ) 10.591728
32 1.0603 ( 27177 ) 10.741273
33 1.0153 ( 26024 ) 10.886203
34 0.9784 ( 25078 ) 11.025478
35 0.9373 ( 24024 ) 11.164367
36 0.8796 ( 22547 ) 11.298581
37 0.8226 ( 21085 ) 11.427965
38 0.7723 ( 19796 ) 11.543464
39 0.7316 ( 18753 ) 11.653255
40 0.6978 ( 17887 ) 11.753414
41 0.6748 ( 17296 ) 11.846644
42 0.6343 ( 16258 ) 11.937559
43 0.6090 ( 15611 ) 12.024399
44 0.5760 ( 14764 ) 12.109000
45 0.5440 ( 13945 ) 12.194221
46 0.5118 ( 13119 ) 12.276543
47 0.4843 ( 12414 ) 12.355780
48 0.4641 ( 11895 ) 12.426950
49 0.4466 ( 11447 ) 12.496063
50 0.4244 ( 10877 ) 12.563139
51 0.4047 ( 10374 ) 12.629435
52 0.3830 ( 9816 ) 12.692104
53 0.3771 ( 9666 ) 12.751669
54 0.3498 ( 8966 ) 12.810042
55 0.3307 ( 8476 ) 12.866677
56 0.3261 ( 8358 ) 12.923041
57 0.3098 ( 7940 ) 12.973538
58 0.2991 ( 7666 ) 13.022035
59 0.2936 ( 7526 ) 13.069765
60 0.2837 ( 7273 ) 13.114295
61 0.2828 ( 7249 ) 13.158304
62 0.2703 ( 6928 ) 13.201694
63 0.2678 ( 6863 ) 13.242992
64 0.2637 ( 6758 ) 13.286976
65 0.2616 ( 6705 ) 13.330512
66 0.2522 ( 6465 ) 13.372618
67 0.2460 ( 6306 ) 13.411799
68 0.2408 ( 6172 ) 13.450974
69 0.2271 ( 5822 ) 13.489082
70 0.2215 ( 5678 ) 13.523622
71 0.2206 ( 5655 ) 13.556506
72 0.2077 ( 5325 ) 13.590721
73 0.2119 ( 5432 ) 13.621196
74 0.2060 ( 5281 ) 13.652963
75 0.2029 ( 5200 ) 13.683631
76 0.1998 ( 5120 ) 13.714323
77 0.1926 ( 4937 ) 13.743423
78 0.1950 ( 4998 ) 13.770019
79 0.1875 ( 4805 ) 13.798033
80 0.1859 ( 4766 ) 13.823070
81 0.1857 ( 4759 ) 13.846213
82 0.1836 ( 4707 ) 13.872561
83 0.1839 ( 4715 ) 13.901870
84 0.1777 ( 4556 ) 13.931639
85 0.1811 ( 4641 ) 13.959283
86 0.1752 ( 4492 ) 13.986151
87 0.1742 ( 4464 ) 14.012049
88 0.1763 ( 4518 ) 14.039788
89 0.1710 ( 4384 ) 14.067708
90 0.1747 ( 4478 ) 14.094331
91 0.1696 ( 4346 ) 14.123367
92 0.1647 ( 4222 ) 14.149519
93 0.1714 ( 4394 ) 14.175403
94 0.1698 ( 4352 ) 14.203116
95 0.1648 ( 4224 ) 14.229827
96 0.1642 ( 4210 ) 14.255418
97 0.1612 ( 4131 ) 14.279488
98 0.1577 ( 4043 ) 14.306159
99 0.1524 ( 3906 ) 14.328890
100 0.1574 ( 4034 ) 14.353011
101 0.1472 ( 3772 ) 14.381336
102 0.1472 ( 3772 ) 14.406240
103 0.1443 ( 3699 ) 14.431170
104 0.1419 ( 3637 ) 14.456814
105 0.1458 ( 3736 ) 14.480170
106 0.1461 ( 3745 ) 14.505843
107 0.1387 ( 3556 ) 14.537208
108 0.1404 ( 3599 ) 14.566828
109 0.1356 ( 3476 ) 14.598140
110 0.1408 ( 3608 ) 14.625006
111 0.1307 ( 3349 ) 14.651316
112 0.1301 ( 3336 ) 14.675957
113 0.1321 ( 3386 ) 14.701783
114 0.1306 ( 3348 ) 14.725024
115 0.1292 ( 3311 ) 14.750317
116 0.1269 ( 3252 ) 14.772545
117 0.1234 ( 3162 ) 14.797587
118 0.1267 ( 3247 ) 14.816332
119 0.1262 ( 3236 ) 14.833959
120 0.1261 ( 3231 ) 14.852833
121 0.1285 ( 3294 ) 14.871717
122 0.1297 ( 3324 ) 14.893318
123 0.1314 ( 3367 ) 14.915886
124 0.1302 ( 3337 ) 14.935499
125 0.1328 ( 3405 ) 14.955018
126 0.1255 ( 3216 ) 14.977937
127 0.1273 ( 3263 ) 14.997817
128 0.1143 ( 2930 ) 15.012885
129 0.1145 ( 2934 ) 15.027802
130 0.1151 ( 2949 ) 15.048034
131 0.1092 ( 2800 ) 15.069110
132 0.1039 ( 2662 ) 15.088591
133 0.1001 ( 2566 ) 15.103214
134 0.0945 ( 2422 ) 15.117571
135 0.0959 ( 2457 ) 15.130576
136 0.0959 ( 2459 ) 15.139713
137 0.0981 ( 2514 ) 15.153871
138 0.0937 ( 2402 ) 15.165603
139 0.1023 ( 2621 ) 15.178544
140 0.0996 ( 2553 ) 15.195005
141 0.1038 ( 2661 ) 15.206446
142 0.0998 ( 2558 ) 15.218920
143 0.0978 ( 2506 ) 15.234213
144 0.0979 ( 2509 ) 15.244497
145 0.0996 ( 2553 ) 15.257131
146 0.1012 ( 2595 ) 15.273167
147 0.0963 ( 2469 ) 15.288724
148 0.0983 ( 2519 ) 15.300674
149 0.0979 ( 2510 ) 15.318656
150 0.0941 ( 2413 ) 15.330871
151 0.0999 ( 2561 ) 15.345144
152 0.0958 ( 2456 ) 15.361047
153 0.0946 ( 2426 ) 15.380945
154 0.0933 ( 2391 ) 15.393009
155 0.0948 ( 2430 ) 15.405712
156 0.0886 ( 2272 ) 15.422163
157 0.0881 ( 2257 ) 15.436250
158 0.0882 ( 2261 ) 15.448760
159 0.0835 ( 2139 ) 15.465442
160 0.0780 ( 1999 ) 15.476118
161 0.0800 ( 2050 ) 15.487984
162 0.0755 ( 1934 ) 15.497851
163 0.0729 ( 1868 ) 15.509905
164 0.0749 ( 1920 ) 15.518320
165 0.0689 ( 1766 ) 15.528145
166 0.0749 ( 1919 ) 15.535023
167 0.0618 ( 1585 ) 15.544282
168 0.0692 ( 1773 ) 15.554198
169 0.0661 ( 1693 ) 15.565681
170 0.0666 ( 1706 ) 15.574715
171 0.0640 ( 1640 ) 15.588891
172 0.0628 ( 1610 ) 15.598602
173 0.0628 ( 1609 ) 15.610842
174 0.0613 ( 1570 ) 15.622923
175 0.0608 ( 1559 ) 15.632100
176 0.0540 ( 1384 ) 15.641688
177 0.0615 ( 1577 ) 15.649015
178 0.0584 ( 1497 ) 15.660235
179 0.0524 ( 1342 ) 15.664813
180 0.0555 ( 1422 ) 15.673304
181 0.0513 ( 1315 ) 15.680562
182 0.0517 ( 1325 ) 15.682169
183 0.0535 ( 1372 ) 15.691580
184 0.0497 ( 1275 ) 15.699567
185 0.0491 ( 1259 ) 15.704506
186 0.0478 ( 1225 ) 15.711535
187 0.0464 ( 1189 ) 15.718195
188 0.0542 ( 1389 ) 15.720039
189 0.0537 ( 1377 ) 15.721503
190 0.0506 ( 1296 ) 15.726994
191 0.0543 ( 1393 ) 15.728065
192 0.0495 ( 1268 ) 15.730046
193 0.0455 ( 1167 ) 15.735765
194 0.0492 ( 1261 ) 15.737827
195 0.0418 ( 1072 ) 15.739133
196 0.0471 ( 1208 ) 15.744028
197 0.0471 ( 1208 ) 15.747054
198 0.0455 ( 1166 ) 15.747289
199 0.0490 ( 1255 ) 15.754310
200 0.0459 ( 1177 ) 15.762384
201 0.0437 ( 1121 ) 15.764128
202 0.0479 ( 1227 ) 15.770675
203 0.0431 ( 1105 ) 15.777470
204 0.0431 ( 1104 ) 15.780740
205 0.0432 ( 1108 ) 15.788257
206 0.0426 ( 1091 ) 15.796996
207 0.0361 ( 926 ) 15.800046
208 0.0419 ( 1074 ) 15.800899
209 0.0445 ( 1140 ) 15.805731
210 0.0387 ( 991 ) 15.812625
211 0.0446 ( 1144 ) 15.815506
212 0.0426 ( 1091 ) 15.818214
213 0.0390 ( 999 ) 15.826902
214 0.0421 ( 1078 ) 15.831557
215 0.0382 ( 978 ) 15.837976
216 0.0418 ( 1071 ) 15.843068
217 0.0349 ( 895 ) 15.851153
218 0.0298 ( 763 ) 15.856223
219 0.0312 ( 799 ) 15.859011
220 0.0324 ( 830 ) 15.861681
221 0.0408 ( 1045 ) 15.864957
222 0.0334 ( 857 ) 15.871507
223 0.0363 ( 931 ) 15.873839
224 0.0335 ( 859 ) 15.881175
225 0.0356 ( 912 ) 15.886148
226 0.0332 ( 850 ) 15.894594
227 0.0291 ( 746 ) 15.900502
228 0.0301 ( 771 ) 15.905046
229 0.0313 ( 802 ) 15.910283
230 0.0297 ( 762 ) 15.915292
231 0.0286 ( 732 ) 15.918836
232 0.0299 ( 766 ) 15.922403
233 0.0309 ( 792 ) 15.927755
234 0.0299 ( 767 ) 15.936226
235 0.0307 ( 787 ) 15.945598
236 0.0291 ( 747 ) 15.953744
237 0.0333 ( 854 ) 15.961416
238 0.0330 ( 845 ) 15.974147
239 0.0318 ( 814 ) 15.981704
240 0.0335 ( 858 ) 15.987721
241 0.0288 ( 739 ) 15.997911
242 0.0254 ( 652 ) 16.007595
243 0.0217 ( 557 ) 16.016525
244 0.0276 ( 707 ) 16.023349
245 0.0298 ( 764 ) 16.031961
246 0.0256 ( 657 ) 16.041337
247 0.0263 ( 673 ) 16.043987
248 0.0267 ( 684 ) 16.046332
249 0.0268 ( 688 ) 16.052024
250 0.0289 ( 742 ) 16.056477
251 0.0259 ( 665 ) 16.059819
252 0.0252 ( 647 ) 16.061851
253 0.0251 ( 644 ) 16.061987
254 0.0228 ( 585 ) 16.061693
255 0.0229 ( 587 ) 16.061162
256 0.0268 ( 687 ) 16.066249
257 0.0231 ( 593 ) 16.068953
258 0.0244 ( 625 ) 16.070274
259 0.0248 ( 636 ) 16.070099
260 0.0216 ( 554 ) 16.072281
261 0.0238 ( 609 ) 16.078714
262 0.0246 ( 630 ) 16.085496
263 0.0247 ( 632 ) 16.089612
264 0.0229 ( 588 ) 16.090993
265 0.0224 ( 575 ) 16.090011
266 0.0215 ( 551 ) 16.090361
267 0.0214 ( 548 ) 16.095471
268 0.0182 ( 466 ) 16.101468
269 0.0214 ( 548 ) 16.106102
270 0.0208 ( 532 ) 16.108473
271 0.0206 ( 527 ) 16.109966
272 0.0220 ( 564 ) 16.108609
273 0.0204 ( 522 ) 16.107681
274 0.0163 ( 417 ) 16.111305
275 0.0182 ( 466 ) 16.114304
276 0.0188 ( 483 ) 16.118800
277 0.0230 ( 589 ) 16.122662
278 0.0197 ( 504 ) 16.124731
279 0.0242 ( 621 ) 16.125532
280 0.0186 ( 476 ) 16.126810
281 0.0184 ( 472 ) 16.127973
282 0.0178 ( 456 ) 16.128690
283 0.0167 ( 428 ) 16.129534
284 0.0213 ( 547 ) 16.132516
285 0.0210 ( 539 ) 16.135663
286 0.0229 ( 588 ) 16.137391
287 0.0181 ( 464 ) 16.137173
288 0.0158 ( 404 ) 16.138154
289 0.0146 ( 373 ) 16.137111
290 0.0142 ( 363 ) 16.139087
291 0.0178 ( 456 ) 16.138476
292 0.0179 ( 458 ) 16.138262
293 0.0158 ( 404 ) 16.140455
294 0.0183 ( 470 ) 16.142718
295 0.0180 ( 462 ) 16.145703
296 0.0194 ( 497 ) 16.151119
297 0.0211 ( 540 ) 16.153308
298 0.0183 ( 470 ) 16.154665
299 0.0175 ( 448 ) 16.155528
300 0.0134 ( 344 ) 16.155097
Save mean inertia convergence to file: convergence_NBG.xlsx
NBG iterations: 300 output file: _clustering_output_tables.xlsx
Centroids, Sigma, Divergence, Occurence, Confusion Matrix
Confusion of KMEANS versus F I N A L NBG
Reclassified by NBG 83.1381 percent ( 2130996 )