test6.save

高效的k-means算法实现

	<stage: 369 curr: 0.05937 best: 0.0506 >
	<stage: 370 curr: 0.07824 best: 0.0506 >
	<stage: 371 curr: 0.05007 best: 0.0506 >
	<stage: 372 curr: 0.0535 best: 0.05007 >
	<stage: 373 curr: 0.06426 best: 0.05007 >
	<stage: 374 curr: 0.0643 best: 0.05007 >
	<stage: 375 curr: 0.05644 best: 0.05007 >
	<stage: 376 curr: 0.05692 best: 0.05007 >
	<stage: 377 curr: 0.05388 best: 0.05007 >
	<stage: 378 curr: 0.06124 best: 0.05007 >
	<stage: 379 curr: 0.05867 best: 0.05007 >
	<stage: 380 curr: 0.05258 best: 0.05007 >
	<stage: 381 curr: 0.07373 best: 0.05007 >
	<stage: 382 curr: 0.05713 best: 0.05007 >
	<stage: 383 curr: 0.05957 best: 0.05007 >
	<stage: 384 curr: 0.05896 best: 0.05007 >
	<stage: 385 curr: 0.0605 best: 0.05007 >
	<stage: 386 curr: 0.06483 best: 0.05007 >
	<stage: 387 curr: 0.05685 best: 0.05007 >
	<stage: 388 curr: 0.05775 best: 0.05007 >
	<stage: 389 curr: 0.07129 best: 0.05007 >
	<stage: 390 curr: 0.06742 best: 0.05007 >
	<stage: 391 curr: 0.07022 best: 0.05007 >
	<stage: 392 curr: 0.05705 best: 0.05007 >
	<stage: 393 curr: 0.05833 best: 0.05007 >
	<stage: 394 curr: 0.05521 best: 0.05007 >
	<stage: 395 curr: 0.05856 best: 0.05007 >
	<stage: 396 curr: 0.05822 best: 0.05007 >
	<stage: 397 curr: 0.05283 best: 0.05007 >
	<stage: 398 curr: 0.05391 best: 0.05007 >
	<stage: 399 curr: 0.05685 best: 0.05007 >
	<stage: 400 curr: 0.06204 best: 0.05007 >
	<stage: 401 curr: 0.05413 best: 0.05007 >
	<stage: 402 curr: 0.05127 best: 0.05007 >
	<stage: 403 curr: 0.05464 best: 0.05007 >
	<stage: 404 curr: 0.06525 best: 0.05007 >
	<stage: 405 curr: 0.05458 best: 0.05007 >
	<stage: 406 curr: 0.05402 best: 0.05007 >
	<stage: 407 curr: 0.08161 best: 0.05007 >
	<stage: 408 curr: 0.05758 best: 0.05007 >
	<stage: 409 curr: 0.07327 best: 0.05007 >
	<stage: 410 curr: 0.07339 best: 0.05007 >
	<stage: 411 curr: 0.06029 best: 0.05007 >
	<stage: 412 curr: 0.07257 best: 0.05007 >
	<stage: 413 curr: 0.05945 best: 0.05007 >
	<stage: 414 curr: 0.05518 best: 0.05007 >
	<stage: 415 curr: 0.05664 best: 0.05007 >
	<stage: 416 curr: 0.08999 best: 0.05007 >
	<stage: 417 curr: 0.05696 best: 0.05007 >
	<stage: 418 curr: 0.06327 best: 0.05007 >
	<stage: 419 curr: 0.06513 best: 0.05007 >
	<stage: 420 curr: 0.05909 best: 0.05007 >
	<stage: 421 curr: 0.0574 best: 0.05007 >
	<stage: 422 curr: 0.05295 best: 0.05007 >
	<stage: 423 curr: 0.07154 best: 0.05007 >
	<stage: 424 curr: 0.06897 best: 0.05007 >
	<stage: 425 curr: 0.05989 best: 0.05007 >
	<stage: 426 curr: 0.05728 best: 0.05007 >
	<stage: 427 curr: 0.0491 best: 0.05007 >
	<stage: 428 curr: 0.05782 best: 0.0491 >
	<stage: 429 curr: 0.07021 best: 0.0491 >
	<stage: 430 curr: 0.05422 best: 0.0491 >
	<stage: 431 curr: 0.05737 best: 0.0491 >
	<stage: 432 curr: 0.05551 best: 0.0491 >
	<stage: 433 curr: 0.0628 best: 0.0491 >
	<stage: 434 curr: 0.0574 best: 0.0491 >
	<stage: 435 curr: 0.05767 best: 0.0491 >
	<stage: 436 curr: 0.05175 best: 0.0491 >
	<stage: 437 curr: 0.04989 best: 0.0491 >
	<stage: 438 curr: 0.05652 best: 0.0491 >
	<stage: 439 curr: 0.05934 best: 0.0491 >
	<stage: 440 curr: 0.05837 best: 0.0491 >
	<stage: 441 curr: 0.06623 best: 0.0491 >
	<stage: 442 curr: 0.05585 best: 0.0491 >
	<stage: 443 curr: 0.05837 best: 0.0491 >
	<stage: 444 curr: 0.05456 best: 0.0491 >
	<stage: 445 curr: 0.05329 best: 0.0491 >
	<stage: 446 curr: 0.0639 best: 0.0491 >
	<stage: 447 curr: 0.05725 best: 0.0491 >
	<stage: 448 curr: 0.06151 best: 0.0491 >
	<stage: 449 curr: 0.06296 best: 0.0491 >
	<stage: 450 curr: 0.05914 best: 0.0491 >
	<stage: 451 curr: 0.05639 best: 0.0491 >
	<stage: 452 curr: 0.06362 best: 0.0491 >
	<stage: 453 curr: 0.05974 best: 0.0491 >
	<stage: 454 curr: 0.06846 best: 0.0491 >
	<stage: 455 curr: 0.05668 best: 0.0491 >
	<stage: 456 curr: 0.05566 best: 0.0491 >
	<stage: 457 curr: 0.05848 best: 0.0491 >
	<stage: 458 curr: 0.05286 best: 0.0491 >
	<stage: 459 curr: 0.05581 best: 0.0491 >
	<stage: 460 curr: 0.05176 best: 0.0491 >
	<stage: 461 curr: 0.05668 best: 0.0491 >
	<stage: 462 curr: 0.06338 best: 0.0491 >
	<stage: 463 curr: 0.06838 best: 0.0491 >
	<stage: 464 curr: 0.06788 best: 0.0491 >
	<stage: 465 curr: 0.06892 best: 0.0491 >
	<stage: 466 curr: 0.05875 best: 0.0491 >
	<stage: 467 curr: 0.05912 best: 0.0491 >
	<stage: 468 curr: 0.06942 best: 0.0491 >
	<stage: 469 curr: 0.06982 best: 0.0491 >
	<stage: 470 curr: 0.05371 best: 0.0491 >
	<stage: 471 curr: 0.05762 best: 0.0491 >
	<stage: 472 curr: 0.06754 best: 0.0491 >
	<stage: 473 curr: 0.06165 best: 0.0491 >
	<stage: 474 curr: 0.05505 best: 0.0491 >
	<stage: 475 curr: 0.05471 best: 0.0491 >
	<stage: 476 curr: 0.06011 best: 0.0491 >
	<stage: 477 curr: 0.05201 best: 0.0491 >
	<stage: 478 curr: 0.05331 best: 0.0491 >
	<stage: 479 curr: 0.05144 best: 0.0491 >
	<stage: 480 curr: 0.05796 best: 0.0491 >
	<stage: 481 curr: 0.08121 best: 0.0491 >
	<stage: 482 curr: 0.0735 best: 0.0491 >
	<stage: 483 curr: 0.05527 best: 0.0491 >
	<stage: 484 curr: 0.05345 best: 0.0491 >
	<stage: 485 curr: 0.05351 best: 0.0491 >
	<stage: 486 curr: 0.06443 best: 0.0491 >
	<stage: 487 curr: 0.06571 best: 0.0491 >
	<stage: 488 curr: 0.05093 best: 0.0491 >
	<stage: 489 curr: 0.06185 best: 0.0491 >
	<stage: 490 curr: 0.07339 best: 0.0491 >
	<stage: 491 curr: 0.06228 best: 0.0491 >
	<stage: 492 curr: 0.05929 best: 0.0491 >
	<stage: 493 curr: 0.05881 best: 0.0491 >
	<stage: 494 curr: 0.05811 best: 0.0491 >
	<stage: 495 curr: 0.05141 best: 0.0491 >
	<stage: 496 curr: 0.06258 best: 0.0491 >
	<stage: 497 curr: 0.06085 best: 0.0491 >
	<stage: 498 curr: 0.0707 best: 0.0491 >
	<stage: 499 curr: 0.06772 best: 0.0491 >
	<stage: 500 curr: 0.07077 best: 0.0491 >

[k-means completed:
  n_stages      = 500
  total_time    = 8.21 sec
  init_time     = 0.31 sec
  stage_time    = 0.0158 sec/stage_(excl_init) 0.01642 sec/stage_(incl_init)
  average_distort = 0.0491
  (Final Center Points:
       0	[  -0.1212    0.666 ] dist =    21.57
       1	[   0.9831   0.5234 ] dist =    17.72
       2	[    0.982    1.012 ] dist =     19.7
       3	[     0.11  0.07749 ] dist =    12.13
       4	[   0.8727  0.03036 ] dist =    30.27
       5	[   0.2707    1.123 ] dist =    20.96
       6	[   -1.039 -0.09599 ] dist =     46.4
       7	[  -0.5144   0.1237 ] dist =    24.61
       8	[   0.6733  -0.4367 ] dist =    31.13
       9	[  -0.7092   0.3895 ] dist =    10.94
      10	[   0.3701  -0.1128 ] dist =    20.72
      11	[   0.6708  -0.9253 ] dist =    32.64
      12	[  -0.7373   0.6641 ] dist =    16.17
      13	[   0.2054  -0.8493 ] dist =    52.53
      14	[    0.121   0.4113 ] dist =    17.79
      15	[ -0.01971   0.9475 ] dist =    30.09
      16	[  -0.6262    1.358 ] dist =    16.16
      17	[    0.721   0.4391 ] dist =    16.24
      18	[    1.058  -0.5247 ] dist =    29.71
      19	[  -0.0151  -0.2786 ] dist =     23.5
  )
]
  (Cluster assignments:
    Point  Center  Squared Dist
    -----  ------  ------------
       0      14     0.005515
       1      13     0.002939
       2      17     0.006373
       3       4      0.01502
       4       1     0.002279
       5      19       0.0178
       6      14      0.04198
       7       1      0.03274
       8       1      0.03505
       9       6      0.02626
      10      17      0.02679
      11       9      0.02255
      12       4      0.02016
      13      13      0.05309
      14      16     0.007526
      15       5     0.002583
      16       6       0.1935
      17       8      0.03773
      18       8      0.03743
      19       5      0.01448
      20       0        0.101
      21       9       0.0127
      22       7      0.01048
      23       8      0.01411
      24      11      0.01717
      25       5        0.019
      26       3       0.1028
      27       3      0.03362
      28      15      0.01852
      29      10       0.0566
      30       0      0.06989
      31       8      0.02497
      32       1      0.01294
      33       5      0.02567
      34      16       0.1129
      35      13       0.2655
      36       0     0.006661
      37       7     0.001886
      38      14      0.03771
      39      11      0.08584
      40      17      0.09736
      41      16      0.05422
      42      15       0.0969
      43       6      0.03398
      44       3      0.07573
      45       8     0.006358
      46       8      0.07134
      47       1      0.00947
      48       1      0.01309
      49       4     0.003801
      50       7      0.01282
      51       4      0.05284
      52       0      0.02256
      53      12      0.03025
      54      18       0.0148
      55      16      0.06575
      56      11      0.06897
      57       1       0.1348
      58      15      0.08017
      59      10      0.03748
      60      17      0.04042
      61      10       0.0107
      62      18       0.1069
      63       4      0.02158
      64       8     0.000741
      65       5     0.006903
      66       4      0.02007
      67       8        0.025
      68      18      0.08164
      69       5       0.1133
      70       6      0.09419
      71      10        0.092
      72      16       0.2158
      73       5      0.05502
      74       4     0.006785
      75       0       0.1385
      76      14      0.09823
      77      17      0.02904
      78      10     0.003618
      79      12      0.02176
      80       7     0.004747
      81       5       0.0121
      82       3      0.02088
      83       9      0.02449
      84      14     0.004214
      85       1     0.007954
      86      14      0.02256
      87      13      0.04395
      88       4     0.006045
      89      16      0.08084
      90       0      0.02126
      91       6    0.0009835
      92       8      0.01008
      93      18     0.009971
      94       0       0.0736
      95      18     0.005482
      96       4      0.00225
      97      14      0.03889
      98      13      0.06302
      99      10       0.0617
     100      11      0.02638
     101       0      0.04229
     102       1       0.1257
     103      15      0.03146
     104       5       0.0297
     105       6      0.02748
     106       2      0.08913
     107       0      0.02546
     108       4      0.01722
     109       8      0.01711
     110      12      0.04656
     111      15        0.125
     112      10      0.02778
     113      15      0.01717
     114       8      0.04302
     115      10       0.0383
     116      13        0.234
     117       2      0.03868
     118      17       0.1493
     119       0      0.02254
     120      12      0.00481
     121       0       0.1076
     122      12       0.1459
     123      19      0.06944
     124       6      0.00966
     125       8      0.01231
     126       1      0.05221
     127      11      0.07815
     128      12      0.09045
     129      11      0.01206
     130       8      0.05049
     131       0      0.04868
     132       2       0.0292
     133       8      0.01114
     134      14      0.04294
     135       7      0.01455
     136       2       0.1317
     137       6       0.2544
     138      15    0.0005152
     139      10      0.04505
     140       7     0.002798
     141      10      0.09988
     142      19     0.003458
     143      15      0.01134
     144       5      0.03498
     145       7      0.01648
     146      15     0.002043
     147       8      0.04137
     148      15      0.02992
     149      19      0.08324
     150       4        0.032
     151      12      0.05769
     152       0       0.0202
     153      15     0.006607
     154       4        0.083
     155      19       0.0271
     156       3      0.01869
     157       0       0.0485
     158      15     0.007089
     159      13      0.05905
     160      15    0.0008903
     161       4      0.08986
     162      10      0.05361
     163      16      0.06962
     164      15      0.03287
     165      19      0.01184
     166       2      0.01428
     167       0      0.07085
     168      14     0.008384
     169       8      0.02246
     170      12      0.05734
     171      18      0.07662
     172      15      0.07525
     173      15      0.02273
     174       4      0.03372
     175      11      0.03246
     176      15     0.009456
     177       8      0.06577
     178      13      0.06627
     179       7      0.04755
     180      15     0.007388
     181      10       0.1357
     182       5     0.007072
     183      12      0.06627
     184       6      0.06939
     185      18     0.006873
     186       8     0.001294
     187       7      0.02725
     188      13      0.07512
     189      18       0.1537
     190       0      0.03292
     191       5      0.02935
     192       3       0.0132
     193       7       0.0808
     194       1     0.003662
     195       8    0.0004748
     196       4      0.02981
     197       7      0.09402
     198      13      0.09745
     199      13       0.1421
     200      12      0.02712
     201      10      0.01833
     202       3     0.008191
     203      10      0.05868
     204      17      0.08493
     205       3      0.03208
     206       2       0.1158
     207       0      0.01188
     208       5      0.03642
     209      13     0.008323
     210      15      0.03768
     211       5      0.04592
     212       7       0.2641
     213      18      0.06537
     214       8      0.01779
     215       0      0.02362
     216       6      0.06179
     217       7      0.09155
     218      18      0.07247
     219      15       0.1058
     220       3      0.02245
     221       5      0.02823
     222      13     0.005141
     223       4     0.004418
     224       7      0.06843
     225       8      0.03257
     226      10       0.0539
     227       6       0.1926
     228       0      0.01357
     229       4      0.05414