gauss1.pat

径向基RBF多维空间插值算法

No. of patterns     : 100.000000
No. of input units  : 1.000000
No. of output units : 1.000000

# 1.000000
0.000000
3.032653

# 2.000000
0.000985
3.038630

# 3.000000
0.041631
3.284563

# 4.000000
0.176643
4.056489

# 5.000000
0.364602
4.819994

# 6.000000
0.091331
3.580193

# 7.000000
0.092298
3.585850

# 8.000000
0.487217
4.998366

# 9.000000
0.526750
4.992849

# 10.000000
0.454433
4.979280

# 11.000000
0.233178
4.336426

# 12.000000
0.831292
4.014566

# 13.000000
0.931731
3.444070

# 14.000000
0.568060
4.953893

# 15.000000
0.556094
4.968633

# 16.000000
0.050832
3.339877

# 17.000000
0.767051
4.335362

# 18.000000
0.018915
3.147322

# 19.000000
0.252360
4.422860

# 20.000000
0.298197
4.608900

# 21.000000
0.875981
3.768642

# 22.000000
0.531557
4.990052

# 23.000000
0.920261
3.512048

# 24.000000
0.515431
4.997619

# 25.000000
0.810429
4.123511

# 26.000000
0.188420
4.117615

# 27.000000
0.886314
3.709734

# 28.000000
0.570614
4.950385

# 29.000000
0.076775
3.494528

# 30.000000
0.815274
4.098588

# 31.000000
0.984891
3.124266

# 32.000000
0.118352
3.736417

# 33.000000
0.893906
3.666048

# 34.000000
0.784484
4.252793

# 35.000000
0.100916
3.636066

# 36.000000
0.253311
4.427020

# 37.000000
0.019842
3.152935

# 38.000000
0.378377
4.854246

# 39.000000
0.678877
4.690052

# 40.000000
0.680923
4.683153

# 41.000000
0.752707
4.400493

# 42.000000
0.006229
3.070435

# 43.000000
0.624407
4.847600

# 44.000000
0.126462
3.782470

# 45.000000
0.617550
4.863712

# 46.000000
0.770928
4.317315

# 47.000000
0.186536
4.107926

# 48.000000
0.496722
4.999893

# 49.000000
0.509500
4.999097

# 50.000000
0.315939
4.672438

# 51.000000
0.701703
4.609273

# 52.000000
0.367247
4.826835

# 53.000000
0.876312
3.766762

# 54.000000
0.370995
4.836316

# 55.000000
0.525252
4.993627

# 56.000000
0.427835
4.948192

# 57.000000
0.542743
4.981763

# 58.000000
0.667979
4.725646

# 59.000000
0.171142
4.027487

# 60.000000
0.830779
4.017293

# 61.000000
0.861905
3.847744

# 62.000000
0.769221
4.325284

# 63.000000
0.052579
3.350359

# 64.000000
0.966730
3.234146

# 65.000000
0.334284
4.732787

# 66.000000
0.082097
3.525955

# 67.000000
0.270315
4.499324

# 68.000000
0.074262
3.479654

# 69.000000
0.676226
4.698893

# 70.000000
0.810159
4.124898

# 71.000000
0.464211
4.987208

# 72.000000
0.569185
4.952363

# 73.000000
0.512203
4.998511

# 74.000000
0.586510
4.925717

# 75.000000
0.738759
4.461239

# 76.000000
0.871689
3.792907

# 77.000000
0.577967
4.939579

# 78.000000
0.964704
3.246375

# 79.000000
0.333529
4.730415

# 80.000000
0.392828
4.886451

# 81.000000
0.176928
4.057985

# 82.000000
0.111869
3.699311

# 83.000000
0.852681
3.898806

# 84.000000
0.811172
4.119707

# 85.000000
0.954887
3.305517

# 86.000000
0.904283
3.605816

# 87.000000
0.138915
3.852312

# 88.000000
0.686187
4.665087

# 89.000000
0.126987
3.785436

# 90.000000
0.004090
3.057461

# 91.000000
0.000503
3.035703

# 92.000000
0.192952
4.140766

# 93.000000
0.586780
4.925256

# 94.000000
0.374959
4.846067

# 95.000000
0.026786
3.194961

# 96.000000
0.464765
4.987600

# 97.000000
0.785343
4.248630

# 98.000000
0.442235
4.966743

# 99.000000
0.287904
4.569795

# 100.000000
0.853353
3.895108