test4.save

高效的k-means算法实现

	<stage: 365 curr: 0.00563 best: 0.0004198 >
	<stage: 366 curr: 0.0004268 best: 0.0004198 >
	<stage: 367 curr: 0.002152 best: 0.0004198 >
	<stage: 368 curr: 0.0004258 best: 0.0004198 >
	<stage: 369 curr: 0.00652 best: 0.0004198 >
	<stage: 370 curr: 0.005765 best: 0.0004198 >
	<stage: 371 curr: 0.007703 best: 0.0004198 >
	<stage: 372 curr: 0.0006622 best: 0.0004198 >
	<stage: 373 curr: 0.00065 best: 0.0004198 >
	<stage: 374 curr: 0.005631 best: 0.0004198 >
	<stage: 375 curr: 0.0004176 best: 0.0004198 >
	<stage: 376 curr: 0.008268 best: 0.0004176 >
	<stage: 377 curr: 0.01878 best: 0.0004176 >
	<stage: 378 curr: 0.002843 best: 0.0004176 >
	<stage: 379 curr: 0.01565 best: 0.0004176 >
	<stage: 380 curr: 0.006508 best: 0.0004176 >
	<stage: 381 curr: 0.002738 best: 0.0004176 >
	<stage: 382 curr: 0.01564 best: 0.0004176 >
	<stage: 383 curr: 0.003072 best: 0.0004176 >
	<stage: 384 curr: 0.0003043 best: 0.0004176 >
	<stage: 385 curr: 0.007587 best: 0.0003043 >
	<stage: 386 curr: 0.01553 best: 0.0003043 >
	<stage: 387 curr: 0.006402 best: 0.0003043 >
	<stage: 388 curr: 0.002622 best: 0.0003043 >
	<stage: 389 curr: 0.01003 best: 0.0003043 >
	<stage: 390 curr: 0.0004036 best: 0.0003043 >
	<stage: 391 curr: 0.0003383 best: 0.0003043 >
	<stage: 392 curr: 0.002735 best: 0.0003043 >
	<stage: 393 curr: 0.0004124 best: 0.0003043 >
	<stage: 394 curr: 0.0005505 best: 0.0003043 >
	<stage: 395 curr: 0.0004274 best: 0.0003043 >
	<stage: 396 curr: 0.005148 best: 0.0003043 >
	<stage: 397 curr: 0.0004066 best: 0.0003043 >
	<stage: 398 curr: 0.008159 best: 0.0003043 >
	<stage: 399 curr: 0.01003 best: 0.0003043 >
	<stage: 400 curr: 0.002958 best: 0.0003043 >
	<stage: 401 curr: 0.005511 best: 0.0003043 >
	<stage: 402 curr: 0.002625 best: 0.0003043 >
	<stage: 403 curr: 0.01004 best: 0.0003043 >
	<stage: 404 curr: 0.005777 best: 0.0003043 >
	<stage: 405 curr: 0.002734 best: 0.0003043 >
	<stage: 406 curr: 0.0004261 best: 0.0003043 >
	<stage: 407 curr: 0.006402 best: 0.0003043 >
	<stage: 408 curr: 0.00387 best: 0.0003043 >
	<stage: 409 curr: 0.002035 best: 0.0003043 >
	<stage: 410 curr: 0.00055 best: 0.0003043 >
	<stage: 411 curr: 0.002212 best: 0.0003043 >
	<stage: 412 curr: 0.005147 best: 0.0003043 >
	<stage: 413 curr: 0.005512 best: 0.0003043 >
	<stage: 414 curr: 0.0003891 best: 0.0003043 >
	<stage: 415 curr: 0.008143 best: 0.0003043 >
	<stage: 416 curr: 0.0002984 best: 0.0003043 >
	<stage: 417 curr: 0.006394 best: 0.0002984 >
	<stage: 418 curr: 0.005773 best: 0.0002984 >
	<stage: 419 curr: 0.002727 best: 0.0002984 >
	<stage: 420 curr: 0.005136 best: 0.0002984 >
	<stage: 421 curr: 0.0004025 best: 0.0002984 >
	<stage: 422 curr: 0.0003701 best: 0.0002984 >
	<stage: 423 curr: 0.0002957 best: 0.0002984 >
	<stage: 424 curr: 0.002716 best: 0.0002957 >
	<stage: 425 curr: 0.002606 best: 0.0002957 >
	<stage: 426 curr: 0.0005339 best: 0.0002957 >
	<stage: 427 curr: 0.005774 best: 0.0002957 >
	<stage: 428 curr: 0.006384 best: 0.0002957 >
	<stage: 429 curr: 0.0004224 best: 0.0002957 >
	<stage: 430 curr: 0.002724 best: 0.0002957 >
	<stage: 431 curr: 0.01878 best: 0.0002957 >
	<stage: 432 curr: 0.007804 best: 0.0002957 >
	<stage: 433 curr: 0.002725 best: 0.0002957 >
	<stage: 434 curr: 0.0005378 best: 0.0002957 >
	<stage: 435 curr: 0.005496 best: 0.0002957 >
	<stage: 436 curr: 0.006399 best: 0.0002957 >
	<stage: 437 curr: 0.002023 best: 0.0002957 >
	<stage: 438 curr: 0.01552 best: 0.0002957 >
	<stage: 439 curr: 0.002949 best: 0.0002957 >
	<stage: 440 curr: 0.0005409 best: 0.0002957 >
	<stage: 441 curr: 0.01552 best: 0.0002957 >
	<stage: 442 curr: 0.007858 best: 0.0002957 >
	<stage: 443 curr: 0.0004268 best: 0.0002957 >
	<stage: 444 curr: 0.0003924 best: 0.0002957 >
	<stage: 445 curr: 0.01877 best: 0.0002957 >
	<stage: 446 curr: 0.0002964 best: 0.0002957 >
	<stage: 447 curr: 0.004814 best: 0.0002957 >
	<stage: 448 curr: 0.008137 best: 0.0002957 >
	<stage: 449 curr: 0.005508 best: 0.0002957 >
	<stage: 450 curr: 0.004819 best: 0.0002957 >
	<stage: 451 curr: 0.008151 best: 0.0002957 >
	<stage: 452 curr: 0.0004449 best: 0.0002957 >
	<stage: 453 curr: 0.004775 best: 0.0002957 >
	<stage: 454 curr: 0.00203 best: 0.0002957 >
	<stage: 455 curr: 0.006394 best: 0.0002957 >
	<stage: 456 curr: 0.00221 best: 0.0002957 >
	<stage: 457 curr: 0.009144 best: 0.0002957 >
	<stage: 458 curr: 0.01878 best: 0.0002957 >
	<stage: 459 curr: 0.0002979 best: 0.0002957 >
	<stage: 460 curr: 0.006394 best: 0.0002957 >
	<stage: 461 curr: 0.006392 best: 0.0002957 >
	<stage: 462 curr: 0.0002961 best: 0.0002957 >
	<stage: 463 curr: 0.00815 best: 0.0002957 >
	<stage: 464 curr: 0.004813 best: 0.0002957 >
	<stage: 465 curr: 0.0004218 best: 0.0002957 >
	<stage: 466 curr: 0.00173 best: 0.0002957 >
	<stage: 467 curr: 0.003865 best: 0.0002957 >
	<stage: 468 curr: 0.00221 best: 0.0002957 >
	<stage: 469 curr: 0.01528 best: 0.0002957 >
	<stage: 470 curr: 0.0004257 best: 0.0002957 >
	<stage: 471 curr: 0.0003979 best: 0.0002957 >
	<stage: 472 curr: 0.003865 best: 0.0002957 >
	<stage: 473 curr: 0.01553 best: 0.0002957 >
	<stage: 474 curr: 0.000398 best: 0.0002957 >
	<stage: 475 curr: 0.002206 best: 0.0002957 >
	<stage: 476 curr: 0.002714 best: 0.0002957 >
	<stage: 477 curr: 0.0003974 best: 0.0002957 >
	<stage: 478 curr: 0.002952 best: 0.0002957 >
	<stage: 479 curr: 0.0003247 best: 0.0002957 >
	<stage: 480 curr: 0.0002958 best: 0.0002957 >
	<stage: 481 curr: 0.002024 best: 0.0002957 >
	<stage: 482 curr: 0.005507 best: 0.0002957 >
	<stage: 483 curr: 0.006397 best: 0.0002957 >
	<stage: 484 curr: 0.00295 best: 0.0002957 >
	<stage: 485 curr: 0.002028 best: 0.0002957 >
	<stage: 486 curr: 0.00272 best: 0.0002957 >
	<stage: 487 curr: 0.002211 best: 0.0002957 >
	<stage: 488 curr: 0.002948 best: 0.0002957 >
	<stage: 489 curr: 0.01877 best: 0.0002957 >
	<stage: 490 curr: 0.006392 best: 0.0002957 >
	<stage: 491 curr: 0.004814 best: 0.0002957 >
	<stage: 492 curr: 0.004812 best: 0.0002957 >
	<stage: 493 curr: 0.003864 best: 0.0002957 >
	<stage: 494 curr: 0.005773 best: 0.0002957 >
	<stage: 495 curr: 0.01002 best: 0.0002957 >
	<stage: 496 curr: 0.005507 best: 0.0002957 >
	<stage: 497 curr: 0.01552 best: 0.0002957 >
	<stage: 498 curr: 0.002029 best: 0.0002957 >
	<stage: 499 curr: 0.004816 best: 0.0002957 >
	<stage: 500 curr: 0.002616 best: 0.0002957 >

[k-means completed:
  n_stages      = 500
  total_time    = 0.72 sec
  init_time     = 0.03 sec
  stage_time    = 0.00138 sec/stage_(excl_init) 0.00144 sec/stage_(incl_init)
  average_distort = 0.0002957
  (Final Center Points:
       0	[   0.9195   0.4727 ] dist =  0.01022
       1	[   0.5716  -0.3746 ] dist =  0.02445
       2	[  0.06363   0.8993 ] dist =  0.01569
       3	[    0.294 -0.08563 ] dist =  0.02304
       4	[  0.07251  -0.8238 ] dist = 0.009392
       5	[   0.9164   0.8788 ] dist =  0.01072
       6	[  -0.2958   0.1774 ] dist = 0.008057
       7	[   -0.718   0.2776 ] dist =  0.01214
       8	[   0.1735  0.04778 ] dist =  0.01159
       9	[   0.9142  -0.1995 ] dist =  0.02455
      10	[   0.1231   0.8797 ] dist =   0.0155
      11	[  -0.5105   0.9874 ] dist =  0.01839
      12	[   0.1824  -0.6496 ] dist =  0.01662
      13	[   0.9169   -0.382 ] dist =  0.01229
      14	[    0.762  -0.9376 ] dist =  0.01489
      15	[   0.1421    0.931 ] dist =  0.01578
      16	[  -0.0855   0.6241 ] dist =  0.01279
      17	[   0.6821  -0.5637 ] dist =  0.01629
      18	[   0.7491   0.1654 ] dist = 0.007499
      19	[   -0.986  -0.2227 ] dist =  0.01578
  )
]
  (Cluster assignments:
    Point  Center  Squared Dist
    -----  ------  ------------
       0       2    0.0006121
       1       4    7.407e-05
       2       0    4.087e-05
       3       9    0.0006335
       4       0    0.0001292
       5      12    0.0003457
       6      16    0.0001942
       7       0            0
       8       0    0.0003069
       9      19    0.0001373
      10      18    0.0004954
      11       7    0.0001868
      12       9    0.0004907
      13       4     0.000339
      14      11    0.0004969
      15      15    4.448e-06
      16      19    0.0002035
      17      12    0.0006329
      18       1    0.0001354
      19      15    5.858e-05
      20      11    3.248e-05
      21       7    0.0002211
      22       6    7.995e-05
      23      17    0.0002762
      24      14    0.0003826
      25       0     0.001812
      26       8    0.0001474
      27       3    0.0004415
      28      10    8.461e-05
      29       8    4.405e-05
      30      16    0.0001686
      31       1    0.0001377
      32       0    0.0001678
      33       2     1.74e-05
      34      11    7.187e-05
      35       4     0.000419
      36      16    5.401e-05
      37       6    0.0001243
      38       6    0.0001658
      39      14    0.0005687
      40       5    0.0002633
      41      11    0.0004958
      42      15    0.0002702
      43      19    2.444e-05
      44       6    4.271e-05
      45       9    0.0002101
      46      12      0.00048
      47       5    0.0001563
      48      18    0.0001374
      49      18    0.0001289
      50       6    0.0001321
      51       9    0.0002901
      52      15    0.0005731
      53       7     0.000338
      54       1    0.0001717
      55      11    9.045e-05
      56      14    0.0007814
      57       0    0.0004327
      58      15    0.0002829
      59       8    5.419e-05
      60      18    0.0003862
      61       8    9.475e-06
      62      14            0
      63       0    8.881e-05
      64      17    0.0001669
      65      15    8.484e-06
      66       9    0.0004613
      67       1    0.0001294
      68      17     0.001048
      69      15    0.0001947
      70      19    0.0004472
      71      13    0.0008955
      72      11      0.00108
      73      10    0.0006377
      74       9     0.001036
      75      11    3.061e-05
      76      15    0.0004183
      77       5    0.0004544
      78       3    0.0003163
      79      11    0.0005367
      80       7     0.000338
      81      15     8.23e-06
      82       8    0.0001641
      83       7    0.0002911
      84      16    0.0005692
      85       5    0.0001678
      86      16    0.0005341
      87       4    0.0001133
      88       0    0.0001821
      89      11    0.0001165
      90      16    0.0001447
      91      19    0.0002091
      92      17    0.0002032
      93      13    0.0005486
      94       7    0.0007908
      95      13     0.000348
      96       9    0.0006814
      97      15    0.0005333
      98       4    4.964e-05
      99       3    0.0001352
     100      14    0.0005239
     101      16    0.0001765
     102       5    0.0004059
     103      15     0.000104
     104      10    0.0003267
     105      19    5.583e-05
     106       5    0.0004393
     107      15    0.0008124
     108       3     0.001189
     109       1    8.208e-06
     110      11    0.0005144
     111      11      2.5e-05
     112       8    0.0001004
     113      15    0.0004236
     114      13     0.000148
     115       3    0.0001885
     116       4    0.0003939
     117       5    1.746e-05
     118      15    0.0003663
     119      10    0.0004211
     120       7    0.0004315
     121      11     4.54e-05
     122      11    5.785e-05
     123       4    0.0002611
     124      19    6.856e-05
     125       1    0.0002233
     126       5    0.0001864
     127      12     0.000408
     128       7    0.0003851
     129       1    9.357e-05
     130      13    0.0002239
     131      11    0.0001218
     132       5    0.0001644
     133       1    0.0001941
     134       8     0.000384
     135       6    0.0003547
     136       5    0.0002225
     137      19    0.0004738
     138      15    0.0002562
     139       8    3.051e-05
     140       6    0.0001635
     141       1     0.000376
     142      12    0.0004574
     143      15    0.0001024
     144       2    0.0002566
     145       6    5.371e-05
     146      11    0.0002317
     147       1    4.127e-06
     148      10    0.0006012
     149      12     0.000112
     150       9     0.000793
     151       7    0.0003581
     152       2    0.0005254
     153       2     0.000363
     154       9    0.0001895
     155       1    0.0009555
     156       8    0.0002585
     157      11    0.0002546
     158      15    0.0003528
     159       4    0.0002443
     160      10     0.000213
     161       9    0.0009212
     162       3    0.0006948
     163      11    0.0004989
     164      10     0.000573
     165       8    0.0004222
     166       5    5.752e-05
     167      11    5.656e-05
     168       8    0.0003034
     169      13    2.794e-05
     170      11    0.0002038
     171      17    0.0003588
     172      11    0.0001571
     173      15    0.0002597
     174       9    0.0006413
     175      14    9.902e-05
     176      15    0.0001227
     177      17      1.7e-07
     178       1    0.0002722
     179       6    0.0001281
     180       2    0.0001463
     181       1    0.0004308
     182      15    6.023e-05
     183      11    0.0003318
     184      19            0
     185       9    2.358e-05
     186       1    5.834e-05
     187       6    2.457e-05
     188       4    8.239e-05
     189      17     0.001398
     190      16    0.0001205
     191      10    4.032e-05
     192       8    6.392e-05
     193       6     2.26e-05
     194       0    4.712e-05
     195       1    7.735e-05
     196       9     0.000392
     197       6    1.879e-05
     198       4    6.361e-05
     199       4    0.0001971
     200      11    0.0003987
     201       8    1.243e-06
     202       3    7.368e-05
     203       8    6.119e-05
     204       5    0.0001052
     205       8    0.0001294
     206       5    0.0003246
     207      16    3.378e-05
     208      15     3.86e-05
     209      12    7.595e-05
     210      15    0.0001614
     211      15    5.548e-05
     212      19     0.001387
     213      17    0.0008504
     214       1    0.0001957
     215      15    0.0003943
     216      19    0.0005631
     217       6    5.081e-06
     218       3     0.002069
     219      15    0.0008358
     220       3    9.916e-05
     221       2    2.045e-05