Test points, no noise, each row one data point (x y label)
  0.93047158  -0.58735839  0
 -0.09318738  -0.35513542  0
 -0.01862983   0.15940247  0
 -0.25893781   0.77230280  0
  0.06651694  -0.44380675  0
  0.11614690  -0.53281529  1
  0.62305945   0.46710380  1
 -0.47837698  -0.17173663  1
  0.79484501   0.03266162  1
  0.91101353   0.60275598  1
10 training points, no noise, each row one data point (x y label)
  0.42315697   0.39776041  0
 -0.55648940  -0.80334524  0
 -0.70938372  -0.88671224  0
 -0.25571690   0.63638755  0
 -0.18016825   0.69635998  0
  0.87428276   0.03285699  1
  0.36116465  -0.70610861  1
  0.17317828   0.91694791  1
 -0.78774349   0.89332025  1
  0.50867105  -0.45876274  1
100 training points, no noise, each row one data point (x y label)
  0.42315697   0.39776041  0
 -0.55648940  -0.80334524  0
 -0.70938372  -0.88671224  0
 -0.25571690   0.63638755  0
 -0.18016825   0.69635998  0
 -0.77141252  -0.22665415  0
 -0.95902921   0.10735963  0
 -0.02340465  -0.83723733  0
  0.87661231  -0.94863396  0
 -0.58564981   0.26724282  0
  0.79163199  -0.97791963  0
 -0.54380220  -0.51842322  0
 -0.40379235   0.73514751  0
 -0.93971335  -0.41395496  0
  0.17986934   0.43043834  0
  0.97038147  -0.18996119  0
 -0.48598846  -0.64919529  0
 -0.74235776   0.02880467  0
  0.23122016   0.41998223  0
 -0.68715159   0.25052820  0
 -0.95311875   0.01269156  0
 -0.34280280   0.51409728  0
  0.40539583   0.28001685  0
 -0.30502588  -0.88325273  0
  0.10838582  -0.26582374  0
  0.40837119   0.25728013  0
 -0.95721012  -0.57631257  0
 -0.15064238  -0.07928196  0
  0.40310363   0.64298194  0
 -0.15901133  -0.33005320  0
 -0.09061486   0.67460594  0
 -0.60123352   0.26666832  0
 -0.37870290   0.73141331  0
 -0.02845510   0.54310157  0
  0.25809927   0.36970502  0
 -0.76024106   0.00497302  0
 -0.19637091  -0.93902795  0
  0.15337676  -0.43254830  0
 -0.84704376  -0.46244169  0
 -0.97969512  -0.40043071  0
 -0.09039648  -0.34580166  0
 -0.81168744  -0.24930530  0
  0.15045320  -0.92581447  0
 -0.17679301  -0.81871084  0
  0.57070572  -0.21037801  0
 -0.83457542  -0.32942742  0
 -0.34978162  -0.88916431  0
 -0.62336048   0.45084226  0
 -0.90131530  -0.86105955  0
  0.05877979  -0.24536635  0
  0.87428276   0.03285699  1
  0.36116465  -0.70610861  1
  0.17317828   0.91694791  1
 -0.78774349   0.89332025  1
  0.50867105  -0.45876274  1
 -0.13476573  -0.47719062  1
  0.86723725  -0.10969997  1
  0.30373024  -0.51277305  1
 -0.43751085   0.36572068  1
  0.01551693   0.33442375  1
  0.65678985  -0.50705650  1
  0.77254998   0.25264164  1
  0.41590456  -0.60573917  1
 -0.03887295   0.21029738  1
 -0.10249067  -0.47616617  1
  0.06081964  -0.06965817  1
  0.45865717   0.80699673  1
 -0.11586401   0.96010066  1
 -0.11896093   0.48236933  1
  0.66439810   0.52780932  1
 -0.39904036  -0.10572616  1
 -0.90138630   0.85490938  1
 -0.62365149   0.89412103  1
 -0.05360652  -0.05578501  1
  0.72191366   0.97607750  1
  0.36498769  -0.64937765  1
  0.12919986   0.85977034  1
  0.83777587   0.45036865  1
 -0.82140009   0.50692582  1
  0.87396101  -0.23781657  1
  0.11063715   0.31117926  1
  0.60797160  -0.32344551  1
 -0.24594540   0.44659975  1
  0.75719537  -0.01118749  1
 -0.38133703   0.21168499  1
 -0.33857230  -0.36573657  1
  0.74992667  -0.19616205  1
 -0.44441085   0.00937092  1
 -0.24210794   0.94679566  1
  0.00198823  -0.00942331  1
 -0.58635161   0.83517493  1
 -0.52580213  -0.06150265  1
  0.17330021   0.01295142  1
 -0.61191511   0.02286853  1
 -0.41955260  -0.14599639  1
  0.45973439   0.69279872  1
 -0.14376267  -0.78457989  1
  0.87859040   0.70985452  1
  0.86693050  -0.08017292  1
  0.04685601   0.22308341  1
1000 training points, no noise, each row one data point (x y label)
  0.42315697   0.39776041  0
 -0.55648940  -0.80334524  0
 -0.70938372  -0.88671224  0
 -0.25571690   0.63638755  0
 -0.18016825   0.69635998  0
 -0.77141252  -0.22665415  0
 -0.95902921   0.10735963  0
 -0.02340465  -0.83723733  0
  0.87661231  -0.94863396  0
 -0.58564981   0.26724282  0
  0.79163199  -0.97791963  0
 -0.54380220  -0.51842322  0
 -0.40379235   0.73514751  0
 -0.93971335  -0.41395496  0
  0.17986934   0.43043834  0
  0.97038147  -0.18996119  0
 -0.48598846  -0.64919529  0
 -0.74235776   0.02880467  0
  0.23122016   0.41998223  0
 -0.68715159   0.25052820  0
 -0.95311875   0.01269156  0
 -0.34280280   0.51409728  0
  0.40539583   0.28001685  0
 -0.30502588  -0.88325273  0
  0.10838582  -0.26582374  0
  0.40837119   0.25728013  0
 -0.95721012  -0.57631257  0
 -0.15064238  -0.07928196  0
  0.40310363   0.64298194  0
 -0.15901133  -0.33005320  0
 -0.09061486   0.67460594  0
 -0.60123352   0.26666832  0
 -0.37870290   0.73141331  0
 -0.02845510   0.54310157  0
  0.25809927   0.36970502  0
 -0.76024106   0.00497302  0
 -0.19637091  -0.93902795  0
  0.15337676  -0.43254830  0
 -0.84704376  -0.46244169  0
 -0.97969512  -0.40043071  0
 -0.09039648  -0.34580166  0
 -0.81168744  -0.24930530  0
  0.15045320  -0.92581447  0
 -0.17679301  -0.81871084  0
  0.57070572  -0.21037801  0
 -0.83457542  -0.32942742  0
 -0.34978162  -0.88916431  0
 -0.62336048   0.45084226  0
 -0.90131530  -0.86105955  0
  0.05877979  -0.24536635  0
  0.46748386   0.41693455  0
  0.80091496  -0.82653768  0
  0.56610305  -0.83377187  0
 -0.77674858   0.16190370  0
 -0.97305858   0.05901691  0
  0.05362495  -0.97565124  0
 -0.19498525  -0.35410982  0
 -0.61050028  -0.87014385  0
 -0.67137130  -0.91903676  0
  0.60203603   0.30052736  0
  0.49376118  -0.12525417  0
 -0.50856729   0.32853972  0
 -0.08427744   0.08563698  0
 -0.27766497   0.04886289  0
  0.46486255  -0.15692208  0
 -0.97732667  -0.40511918  0
  0.41228613  -0.34543454  0
 -0.75407026  -0.93693940  0
  0.73560839  -0.67916658  0
  0.39357741  -0.89309834  0
  0.71516847  -0.90156688  0
 -0.29609299   0.53002080  0
 -0.61044680   0.56398108  0
  0.36468779  -0.83208278  0
  0.39508928   0.56615448  0
 -0.63493352   0.18210043  0
  0.40619919  -0.14086231  0
 -0.94730163  -0.89519119  0
 -0.38244021  -0.97826221  0
 -0.69487930   0.60621018  0
  0.12110259  -0.44462560  0
 -0.63688264   0.38928524  0
 -0.68089465  -0.99685913  0
 -0.83272086  -0.04491688  0
 -0.56168125  -0.79370518  0
 -0.70330085  -0.80195726  0
  0.59821917  -0.91319724  0
 -0.54296819   0.54994962  0
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  0.45765426   0.54249555  0
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  0.26604738  -0.31824771  0
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  0.11092547  -0.29045254  0
  0.33091795   0.67664791  0
  0.98564434  -0.66084349  0
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Test points, noise stddev=0.01, each row one data point (x y label)
  0.93737004  -0.58660206  0
 -0.11559069  -0.35107285  0
 -0.01257261   0.15276278  0
 -0.26711440   0.77686594  0
  0.07120978  -0.43732097  0
  0.10795707  -0.53130215  1
  0.61464893   0.48233349  1
 -0.49294933  -0.18517285  1
  0.79976576   0.02836410  1
  0.89009672   0.59981495  1
10 training points, noise stddev=0.01, each row one data point (x y label)
  0.41967241   0.39549188  0
 -0.55097199  -0.78695184  0
 -0.71368460  -0.86993833  0
 -0.26430437   0.62497234  0
 -0.17638073   0.69505466  0
  0.88071728   0.02614885  1
  0.36952486  -0.72431852  1
  0.16954682   0.93420487  1
 -0.78263106   0.90273318  1
  0.51176417  -0.46056985  1
100 training points, noise stddev=0.01, each row one data point (x y label)
  0.44093266   0.39537265  0
 -0.54832202  -0.79928055  0
 -0.68766173  -0.89551045  0
 -0.27742551   0.64478911  0
 -0.19661349   0.68511544  0
 -0.77972708  -0.23981771  0
 -0.96292908   0.09604260  0
 -0.02635057  -0.83944581  0
  0.89202578  -0.95185172  0
 -0.59969702   0.26700723  0
  0.78930063  -0.99170761  0
 -0.52206698  -0.51196430  0
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  0.17814754   0.43456393  0
  0.96844527  -0.19975713  0
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  0.23217894   0.41212994  0
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  0.39893723   0.28747002  0
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  0.88709298   0.03420472  1
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  0.35799597  -0.65720346  1
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  0.84436077   0.44683647  1
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  0.61697704  -0.32179153  1
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  0.76102046  -0.01094100  1
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  0.74703033  -0.20666239  1
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  0.19679243   0.02755563  1
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  0.45728038   0.69645652  1
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  0.89025756   0.72150323  1
  0.85384687  -0.09033067  1
  0.04890038   0.23492645  1
1000 training points, noise stddev=0.01, each row one data point (x y label)
  0.42770568   0.38900540  0
 -0.57535385  -0.80899386  0
 -0.70851472  -0.90112803  0
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  0.79124446  -0.98000644  0
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 -0.41230668   0.74302951  0
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  0.18251099   0.44711069  0
  0.97206681  -0.17043291  0
 -0.49422207  -0.64241318  0
 -0.73840334   0.02165473  0
  0.21292314   0.42740380  0
 -0.68813029   0.24742850  0
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 -0.32677985   0.51948153  0
  0.40493872   0.27646910  0
 -0.30451325  -0.89321221  0
  0.11012089  -0.26150496  0
  0.40283857   0.27128494  0
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  0.38340740   0.65234988  0
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  0.26766050   0.37134012  0
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  0.15390490  -0.41801001  0
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Test points, noise stddev=0.05, each row one data point (x y label)
  0.95569616  -0.56440331  0
 -0.09664224  -0.35513804  0
  0.00786230   0.09300472  0
 -0.36262368   0.73534721  0
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  0.12293221  -0.54296111  1
  0.69160007   0.40399315  1
 -0.48046897  -0.16428286  1
  0.77923474   0.08517163  1
  0.85058662   0.65768269  1
10 training points, noise stddev=0.05, each row one data point (x y label)
  0.43942362   0.44410239  0
 -0.50792903  -0.77909921  0
 -0.71378824  -0.86222864  0
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  0.88598361   0.04297662  1
  0.38077017  -0.73955077  1
  0.18494151   0.87432385  1
 -0.80176837   0.90748141  1
  0.55043587  -0.44337307  1
100 training points, noise stddev=0.05, each row one data point (x y label)
  0.47764221   0.33397448  0
 -0.55784441  -0.72341757  0
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  0.11752434   0.09500486  1
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  0.72921600  -0.34436876  1
  0.94295807  -0.12592946  1
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  0.71352419   0.63664570  1
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  0.69291368  -0.38621533  1
  0.13550080   0.28412882  1
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  0.92671733   0.83261672  1
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  0.01286824  -0.55618987  1
  0.71005917  -0.23395555  1
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  0.96464669   0.87992132  1
  0.76430606   0.01256002  1
 -0.39439113  -0.60109908  1
  0.19894939  -0.74156221  1
  0.45808462   0.85039951  1
  0.56250586  -0.60795712  1
  0.52151085  -0.39202536  1
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  0.14951754  -0.45749360  1
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  0.04276935  -0.86733879  1
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  0.12345208   0.14316064  1
Test points, noise stddev=0.1, each row one data point (x y label)
  0.98492804  -0.65979712  0
 -0.02188405  -0.36430634  0
 -0.08549925   0.12008403  0
 -0.11094020   0.74543732  0
 -0.02427910  -0.39360373  0
  0.11734506  -0.47845876  1
  0.67133186   0.45276063  1
 -0.37049430  -0.02898489  1
  0.71189827   0.12972317  1
  0.87712452   0.57264287  1
10 training points, noise stddev=0.1, each row one data point (x y label)
  0.53529558   0.30721658  0
 -0.53482005  -0.92295625  0
 -0.92528621  -0.88948854  0
 -0.39628364   0.63136061  0
 -0.14705824   0.74774129  0
  0.99124929   0.12209426  1
  0.38219167  -0.73737919  1
  0.09452080   0.94407052  1
 -0.83806229   0.80777753  1
  0.35499010  -0.32778934  1
100 training points, noise stddev=0.1, each row one data point (x y label)
  0.49983129   0.36733811  0
 -0.52745716  -0.84543695  0
 -0.88093873  -1.07159277  0
 -0.20905228   0.58240391  0
 -0.07736061   0.74068003  0
 -0.76529890  -0.38790888  0
 -0.95971107  -0.01261170  0
 -0.08757510  -0.90280708  0
  0.87800597  -1.11557029  0
 -0.41410549   0.31712062  0
  0.78597816  -0.96701395  0
 -0.62010125  -0.58472025  0
 -0.28900863   0.60324654  0
 -1.03660559  -0.54263453  0
  0.10507056   0.44536232  0
  0.94137125  -0.11395577  0
 -0.61799425  -0.71973663  0
 -0.74537278  -0.02077561  0
  0.41535134   0.42913383  0
 -0.71403223   0.22535911  0
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  0.34499813   0.25089202  0
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  0.04204787  -0.40803106  0
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  0.97457227   0.00981859  1
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  0.63580103   0.89609523  1
  0.22270620  -0.48159843  1
  0.02520555   0.72568967  1
  0.90317218   0.47505260  1
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  0.78760293  -0.22669300  1
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  0.73806728  -0.51465326  1
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  0.94910371   0.09730964  1
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  0.63323405  -0.19211432  1
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  0.19315401   0.08165079  1
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  0.63450996   0.64023153  1
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  0.84812811   0.74902846  1
  0.62727863  -0.00545855  1
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1000 training points, noise stddev=0.1, each row one data point (x y label)
  0.47196881   0.29002269  0
 -0.64729482  -0.63717847  0
 -0.55197316  -1.01899979  0
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  0.99622978  -1.11649645  0
 -0.76672447   0.38153980  0
  0.79291934  -1.01548549  0
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  0.21052455   0.41789483  0
  1.02212085  -0.07904970  0
 -0.52714273  -0.61526808  0
 -0.57661967   0.01681051  0
  0.21766631   0.47608351  0
 -0.64433826   0.43415386  0
 -1.01502266  -0.06346452  0
 -0.31564431   0.44774193  0
  0.40908530   0.25728335  0
 -0.38753924  -1.13122842  0
  0.07203454  -0.38093925  0
  0.33537803   0.11511898  0
 -1.04601501  -0.53004481  0
 -0.18743034   0.07643219  0
  0.33498574   0.45415710  0
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  0.01858418   0.71167245  0
  0.11564922   0.47153494  0
 -0.73324877  -0.08171733  0
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  0.14959967  -0.52067563  0
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  0.05694913  -0.44851802  0
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