test4.save

高效的k-means算法实现

------------------------------------------------------------
kmltest: KMlocal (k-means clustering by local search)
    Version: 1.7 (Use at your own risk)
    Copyright: David M. Mount.
    Latest Revision: August 10, 2005.
------------------------------------------------------------

stats = stage
[Generating Data Points:
  data_size     = 1000
  dim           = 2
  distribution  = clus_gauss
  std_dev       = 0.01
  colors        = 20
  (cluster centers regenerated)
  cluster_sep   = 3.309
]

[Run_k-means:
  k-means_alg      = swap
  data_size        = 1000
  kcenters         = 20
  dim              = 2
  max_tot_stage    = 500
  max_swaps        = 1
]
	<stage: 0 curr: 0.1404 best: 0.1404 >
	<stage: 1 curr: 0.1402 best: 0.1404 >
	<stage: 2 curr: 0.1402 best: 0.1402 >
	<stage: 3 curr: 0.1399 best: 0.1402 >
	<stage: 4 curr: 0.06783 best: 0.1399 >
	<stage: 5 curr: 0.0582 best: 0.06783 >
	<stage: 6 curr: 0.0582 best: 0.0582 >
	<stage: 7 curr: 0.05843 best: 0.0582 >
	<stage: 8 curr: 0.04021 best: 0.0582 >
	<stage: 9 curr: 0.04019 best: 0.04021 >
	<stage: 10 curr: 0.04016 best: 0.04019 >
	<stage: 11 curr: 0.04016 best: 0.04016 >
	<stage: 12 curr: 0.04031 best: 0.04016 >
	<stage: 13 curr: 0.04015 best: 0.04016 >
	<stage: 14 curr: 0.04672 best: 0.04015 >
	<stage: 15 curr: 0.04018 best: 0.04015 >
	<stage: 16 curr: 0.04012 best: 0.04015 >
	<stage: 17 curr: 0.03984 best: 0.04012 >
	<stage: 18 curr: 0.03979 best: 0.03984 >
	<stage: 19 curr: 0.1119 best: 0.03979 >
	<stage: 20 curr: 0.05794 best: 0.03979 >
	<stage: 21 curr: 0.04089 best: 0.03979 >
	<stage: 22 curr: 0.03976 best: 0.03979 >
	<stage: 23 curr: 0.03952 best: 0.03976 >
	<stage: 24 curr: 0.03952 best: 0.03952 >
	<stage: 25 curr: 0.03954 best: 0.03952 >
	<stage: 26 curr: 0.03954 best: 0.03952 >
	<stage: 27 curr: 0.04982 best: 0.03952 >
	<stage: 28 curr: 0.04125 best: 0.03952 >
	<stage: 29 curr: 0.04126 best: 0.03952 >
	<stage: 30 curr: 0.03389 best: 0.03952 >
	<stage: 31 curr: 0.03387 best: 0.03389 >
	<stage: 32 curr: 0.03361 best: 0.03387 >
	<stage: 33 curr: 0.03604 best: 0.03361 >
	<stage: 34 curr: 0.03362 best: 0.03361 >
	<stage: 35 curr: 0.03602 best: 0.03361 >
	<stage: 36 curr: 0.03702 best: 0.03361 >
	<stage: 37 curr: 0.03604 best: 0.03361 >
	<stage: 38 curr: 0.03363 best: 0.03361 >
	<stage: 39 curr: 0.03364 best: 0.03361 >
	<stage: 40 curr: 0.03086 best: 0.03361 >
	<stage: 41 curr: 0.01924 best: 0.03086 >
	<stage: 42 curr: 0.03693 best: 0.01924 >
	<stage: 43 curr: 0.02179 best: 0.01924 >
	<stage: 44 curr: 0.01926 best: 0.01924 >
	<stage: 45 curr: 0.01923 best: 0.01924 >
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	<stage: 50 curr: 0.01951 best: 0.01923 >
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	<stage: 56 curr: 0.02163 best: 0.01921 >
	<stage: 57 curr: 0.01923 best: 0.01921 >
	<stage: 58 curr: 0.02416 best: 0.01921 >
	<stage: 59 curr: 0.01921 best: 0.01921 >
	<stage: 60 curr: 0.03708 best: 0.01921 >
	<stage: 61 curr: 0.0192 best: 0.01921 >
	<stage: 62 curr: 0.01923 best: 0.0192 >
	<stage: 63 curr: 0.02988 best: 0.0192 >
	<stage: 64 curr: 0.02431 best: 0.0192 >
	<stage: 65 curr: 0.03707 best: 0.0192 >
	<stage: 66 curr: 0.01921 best: 0.0192 >
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	<stage: 68 curr: 0.02416 best: 0.0192 >
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	<stage: 115 curr: 0.013 best: 0.01298 >
	<stage: 116 curr: 0.01541 best: 0.01298 >
	<stage: 117 curr: 0.00827 best: 0.01298 >
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	<stage: 119 curr: 0.008261 best: 0.00827 >
	<stage: 120 curr: 0.02675 best: 0.008261 >
	<stage: 121 curr: 0.00826 best: 0.008261 >
	<stage: 122 curr: 0.008301 best: 0.00826 >
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	<stage: 125 curr: 0.008267 best: 0.00826 >
	<stage: 126 curr: 0.009919 best: 0.00826 >
	<stage: 127 curr: 0.01271 best: 0.00826 >
	<stage: 128 curr: 0.008253 best: 0.00826 >
	<stage: 129 curr: 0.009984 best: 0.008253 >
	<stage: 130 curr: 0.01299 best: 0.008253 >
	<stage: 131 curr: 0.03287 best: 0.008253 >
	<stage: 132 curr: 0.02185 best: 0.008253 >
	<stage: 133 curr: 0.006766 best: 0.008253 >
	<stage: 134 curr: 0.01374 best: 0.006766 >
	<stage: 135 curr: 0.008499 best: 0.006766 >
	<stage: 136 curr: 0.007941 best: 0.006766 >
	<stage: 137 curr: 0.00909 best: 0.006766 >
	<stage: 138 curr: 0.006645 best: 0.006766 >
	<stage: 139 curr: 0.006664 best: 0.006645 >
	<stage: 140 curr: 0.01362 best: 0.006645 >
	<stage: 141 curr: 0.006528 best: 0.006645 >
	<stage: 142 curr: 0.008713 best: 0.006528 >
	<stage: 143 curr: 0.00648 best: 0.006528 >
	<stage: 144 curr: 0.008906 best: 0.00648 >
	<stage: 145 curr: 0.01194 best: 0.00648 >
	<stage: 146 curr: 0.0311 best: 0.00648 >
	<stage: 147 curr: 0.008212 best: 0.00648 >
	<stage: 148 curr: 0.006482 best: 0.00648 >
	<stage: 149 curr: 0.0079 best: 0.00648 >
	<stage: 150 curr: 0.03101 best: 0.00648 >
	<stage: 151 curr: 0.01346 best: 0.00648 >
	<stage: 152 curr: 0.008801 best: 0.00648 >
	<stage: 153 curr: 0.0004493 best: 0.00648 >
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	<stage: 157 curr: 0.002073 best: 0.0004493 >
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	<stage: 160 curr: 0.0008079 best: 0.0004493 >
	<stage: 161 curr: 0.005285 best: 0.0004493 >
	<stage: 162 curr: 0.001864 best: 0.0004493 >
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