Error in NNLearn instruction in Windows 10.0

For questions that don't fall into one of the categories above, such as working with the RATS interface, using Wizards, etc.

Error in NNLearn instruction in Windows 10.0

Unread postby MFB » Sun Jul 25, 2021 4:34 am

Hi

If you have longer lags and leads in your Rats program you shouldn't use NNLearn instruction in Winrats 10. Otherwise you get endless page-by-page output lists with the so-called "new coefficients" if you are using EPOCHS with TRACE options! So you cannot see the EPOCHS seperatly (because it runs so quickly) whether the procedure would converge or not. At the end it stops printing but you have no idea whether to wait any longer with the calculations or not. So you better use NNLearn with WinRats 8 if you want controll the EPOCHS step by step!!!

My conclusion is "NNLearn instruction is not compatible with WinRats 10". All of my inquiries on this problem at Estima were not answered!
MFB
 
Posts: 4
Joined: Sat Jul 24, 2021 10:03 am

Re: Error in NNLearn instruction in Windows 10.0

Unread postby PeterF » Mon Jul 26, 2021 2:03 pm

Hi,
I use the NNLearn instruction an a regular basis with RATS for Window Version 10.0 running under OS Windows 10 bzuks 21H1. it works perfectly well. If you want to see the output after each EPOCH, just set the value accordingly.

Best regards
PeterF
PeterF
 
Posts: 45
Joined: Thu Apr 12, 2012 2:03 pm

Re: Error in NNLearn instruction in Windows 10.0

Unread postby MFB » Wed Jul 28, 2021 10:44 am

Hi

Thank you for your answer. Which value you mean after EPOCH to set accordingly? You can only write MODE=EPOCH.

I use also Windows 10, Version 21H1 but with Intel i7-1165G7 Prozessor. When I use the same program with WinRATS 8.0 I don't have any problem. It happens only with the version WinRATS10???
MFB
 
Posts: 4
Joined: Sat Jul 24, 2021 10:03 am

Re: Error in NNLearn instruction in Windows 10.0

Unread postby PeterF » Wed Jul 28, 2021 1:53 pm

Hi,

you can set a number for the option Iters. With a low number, the program stops after these number of iterations, you should be able to follow the output provided if the trace option is selected. Another possible solution might be to send the output not to the screen, but to a text file, which can then be inspected.

Best regards
PeterF
PeterF
 
Posts: 45
Joined: Thu Apr 12, 2012 2:03 pm

Re: Error in NNLearn instruction in Windows 10.0

Unread postby MFB » Fri Jul 30, 2021 4:52 am

Rats_Output.RPF
WinRats10_Output NNlearn
(1.67 MiB) Downloaded 203 times
Hi

Unfortunately, such suggestions are not very suitable if you use a large number of lags in the program. I want to see step by step the epochs without displaying of iterations of the "new coefficients" -it never ends- and it doesn't stop (the partial output is attached). Whit Winrats 8.0 there are no listing of "new coefficients" in the output you only see the epochs separately. It is much easier. So you can control immediately on display whether it will converge or not.

The error can be also in the version. Can you please tell me which Winrats10 version you use? My version is "10.0c". It is strange that Estima does not comment on it. Since more than 30 years I use Winrats and NNLearn instruction. I never had such problems like that!! The customer service is no more the same before.
MFB
 
Posts: 4
Joined: Sat Jul 24, 2021 10:03 am

Re: Error in NNLearn instruction in Windows 10.0

Unread postby MFB » Sun Aug 08, 2021 3:52 am

The example of "Neural.RPF" in the User's Guide, Section 13.3 from Estima needs only 15 lines of output for NNLearn instruction which is produced
with an older WinRATS Version like 8.0, but the same example needs 805 lines of output when you produce it with the WinRATS Version 10.0!!!

Who are helped by the wasteful and useless "new coefficients" which take up a lot of memory and place with WinRATS 10.0?
Why doesn't Estima want to see this problem??? If Estima doesn't want to correct this in the NNLearn instruction, then it should at least correct it in the User's Guide example!

Neural.rpf output according to Estima User's Guide:

epochs = 1000, meanSqErr = 1.427339e-001
epochs = 2000, meanSqErr = 1.324815e-001
epochs = 3000, meanSqErr = 1.308288e-001
epochs = 4000, meanSqErr = 1.300869e-001
epochs = 5000, meanSqErr = 1.300869e-001
epochs = 6000, meanSqErr = 1.300869e-001
epochs = 7000, meanSqErr = 1.300869e-001
epochs = 8000, meanSqErr = 1.300869e-001
epochs = 9000, meanSqErr = 1.300869e-001
epochs = 10000, meanSqErr = 1.300869e-001
Convergence not achieved in 10000 epochs
Mean Squared Error = 1.300869e-001

Vote Actual Neural Net LPM Probit
No 35.000000 23.000000 17.000000 16.000000
Yes 60.000000 52.000000 51.000000 53.000000

Neural.rpf output according to WinRATS 10:

epochs = 100, thisMeanSqErr = 0.21005739 , bestMeanSqErr = 0.18985430 , score = 0.144864
New Coefficients:
0.013417 0.055122 0.178819 0.037164 -0.219234
-0.001526 0.244259 0.642468 -0.514137 0.051936
-0.046658 -0.239799 -0.019482 0.219144 -0.007227
-0.200648 -0.797147 0.473863 -0.098619 1.052868
-1.291479 0.235454 0.861039 0.208245 -0.649231
-0.181045 1.077702 2.782333 -1.875370
epochs = 200, thisMeanSqErr = 0.18470774 , bestMeanSqErr = 0.18470774 , score = 0.000000
New Coefficients:
-0.034068 0.044133 0.163055 0.060032 -0.228384
0.006588 0.256600 0.783510 -0.646963 0.129325
-0.030925 -0.217656 -0.055619 0.236516 -0.020263
-0.222819 -1.025421 0.675062 -0.253784 1.019051
-1.900448 0.199745 0.809450 0.300667 -0.694033
-0.141709 1.221508 3.387450 -2.375080
epochs = 300, thisMeanSqErr = 0.18251654 , bestMeanSqErr = 0.18251654 , score = 0.000000
New Coefficients:
-0.064406 0.047163 0.169131 0.069790 -0.236458
0.023481 0.266854 0.922516 -0.757708 0.192207
-0.037896 -0.233853 -0.091074 0.249037 -0.051841
-0.244180 -1.323810 0.814568 -0.282156 1.029910
-2.436500 0.216518 0.839800 0.366288 -0.718435
-0.036085 1.419207 3.980253 -2.660371
epochs = 400, thisMeanSqErr = 0.18164774 , bestMeanSqErr = 0.18164761 , score = 0.078811
New Coefficients:
-0.079743 0.048847 0.173173 0.076894 -0.242734
0.035235 0.280115 0.995137 -0.813127 0.225414
-0.045590 -0.272542 -0.134031 0.248479 -0.073193
-0.262563 -1.557065 0.896255 -0.312093 1.047357
-2.796695 0.232643 0.855394 0.405665 -0.734613
0.028227 1.564574 4.326915 -2.861833
epochs = 500, thisMeanSqErr = 0.18110176 , bestMeanSqErr = 0.18110176 , score = 0.000000
New Coefficients:
-0.095927 0.049719 0.169838 0.027996 -0.268335
0.042287 0.286915 1.012585 -0.859895 0.222330
-0.055543 -0.343665 -0.540036 0.091434 -0.089631
-0.249304 -1.904252 0.857978 -0.330117 1.054889
-3.150413 0.256097 0.840146 0.251991 -0.811169
0.070464 1.706100 4.442134 -3.014282
epochs = 600, thisMeanSqErr = 0.17576203 , bestMeanSqErr = 0.17576203 , score = 0.000000
New Coefficients:
-0.097219 0.077573 0.274635 -0.057172 -0.523329
0.052880 0.313286 1.040261 -0.871040 0.333609
0.191147 0.370098 -4.642521 -3.718288 -0.199451
-0.271064 -2.246883 0.715390 -0.329659 1.094094
-3.748651 0.399466 1.252122 -0.446372 -1.896018
0.105917 1.930851 4.531459 -3.065650
epochs = 700, thisMeanSqErr = 0.17468237 , bestMeanSqErr = 0.17263004 , score = 0.009780
New Coefficients:
-0.053398 0.125890 0.600662 -0.053958 -0.601788
0.058728 0.258911 1.107964 -0.836484 0.430050
0.543986 1.376814 -5.303962 -5.536503 -0.397794
-0.570660 -2.254577 0.714981 -0.207081 1.204765
-4.040428 0.705005 2.069156 -0.567339 -2.299719
0.122086 1.812924 4.705447 -2.959699
epochs = 800, thisMeanSqErr = 0.17102431 , bestMeanSqErr = 0.17071757 , score = 0.001694
New Coefficients:
-0.054537 0.151416 0.809184 -0.058455 -0.667573
0.056449 -0.014460 1.190178 -0.832685 0.589759
0.703860 2.037627 -5.487416 -7.172594 -0.528926
-1.169193 -2.262242 0.714655 -0.215993 1.281923
-4.266917 0.833886 2.470197 -0.722264 -2.637488
0.113392 1.466852 4.822743 -2.977986
epochs = 900, thisMeanSqErr = 0.16954435 , bestMeanSqErr = 0.16954435 , score = 0.000000
New Coefficients:
-0.036311 0.186363 1.014549 -0.061344 -0.743961
0.050950 -0.391450 1.258419 -0.804992 0.719807
0.825261 2.588267 -5.557248 -8.266255 -0.654979
-1.833515 -2.276301 0.712910 -0.198371 1.369017
-4.448864 0.943632 2.676405 -0.828918 -2.831104
0.092145 1.209715 4.905090 -2.972903
epochs = 1000, thisMeanSqErr = 0.16876095 , bestMeanSqErr = 0.16857819 , score = 0.010547
New Coefficients:
-0.012764 0.233922 1.257795 -0.096573 -0.831331
0.037543 -0.545660 1.364679 -0.761603 0.842941
0.925606 2.943163 -5.574860 -8.873752 -0.822883
-2.190296 -2.343416 0.703839 -0.164652 1.503191
-4.746403 1.051409 2.844334 -0.933740 -3.014515
0.033515 1.079702 4.952699 -2.973431
epochs = 1100, thisMeanSqErr = 0.16786849 , bestMeanSqErr = 0.16786849 , score = 0.000000
New Coefficients:
0.017093 0.290349 1.452630 -0.174695 -0.910144
0.014596 -0.869530 1.497142 -0.713763 0.913480
0.988703 3.216995 -5.575444 -9.335080 -0.972441
-2.537124 -2.410331 0.677010 -0.140123 1.606086
-4.999410 1.154963 2.987837 -0.997986 -3.143119
-0.041812 1.009301 4.976088 -2.972088
epochs = 1200, thisMeanSqErr = 0.16732542 , bestMeanSqErr = 0.16732542 , score = 0.000000
New Coefficients:
0.040573 0.337253 1.657169 -0.302404 -0.955165
-0.010300 -1.070588 1.593694 -0.671279 1.017989
1.043224 3.356634 -5.550787 -9.763313 -1.088846
-2.732544 -2.482349 0.664157 -0.117887 1.728036
-5.180781 1.236812 3.047300 -1.030936 -3.265659
-0.123293 0.978936 4.983239 -2.977945
epochs = 1300, thisMeanSqErr = 0.16681858 , bestMeanSqErr = 0.16681858 , score = 0.000000
New Coefficients:
0.054746 0.383174 1.790020 -0.404711 -0.988029
-0.049907 -1.321621 1.778596 -0.629381 1.121864
1.091695 3.428956 -5.433901 -10.146219 -1.287460
-2.885981 -2.515440 0.660909 -0.088217 1.852656
-5.366894 1.292142 3.100557 -1.090086 -3.399289
-0.266254 0.975549 4.985906 -2.984506
epochs = 1400, thisMeanSqErr = 0.16667178 , bestMeanSqErr = 0.16629371 , score = 0.001745
New Coefficients:
0.065694 0.460441 1.986607 -0.500836 -1.000491
-0.085735 -1.472285 1.982549 -0.592609 1.217554
1.165199 3.431911 -5.128452 -10.493730 -1.526912
-2.969369 -2.534116 0.659619 -0.050253 2.077123
-5.521182 1.447214 3.110806 -1.133185 -3.552228
-0.410726 0.980168 4.988650 -3.004577
epochs = 1500, thisMeanSqErr = 0.16590398 , bestMeanSqErr = 0.16574100 , score = 0.000506
New Coefficients:
0.066370 0.463901 2.022967 -0.560086 -1.003515
-0.138332 -1.602553 2.135940 -0.603121 1.347662
1.299454 3.400152 -3.876295 -10.815618 -1.760158
-3.004364 -2.529474 0.694002 -0.029310 2.106107
-5.648170 1.448565 3.069485 -1.143057 -3.728680
-0.609745 1.009427 4.988650 -3.016140
epochs = 1600, thisMeanSqErr = 0.16504751 , bestMeanSqErr = 0.16459410 , score = 0.001377
New Coefficients:
0.075051 0.486604 2.053803 -0.559214 -1.006175
-0.179954 -1.648027 2.277481 -0.573185 1.455585
1.508443 3.231607 -3.092061 -11.143389 -2.194286
-3.010941 -2.529475 0.832787 0.038150 2.243168
-5.726006 1.574419 2.891353 -1.091414 -3.942565
-0.922519 1.039716 4.988993 -3.011361
epochs = 1700, thisMeanSqErr = 0.16159823 , bestMeanSqErr = 0.16108946 , score = 0.003754
New Coefficients:
0.076467 0.490739 2.059732 -0.582927 -1.006384
-0.255335 -1.659625 2.368372 -0.540584 1.527112
1.976864 2.956849 -2.893439 -11.514651 -3.291642
-3.009342 -2.529195 1.071502 0.110911 2.401015
-5.679356 1.775117 2.656839 -1.090623 -4.127011
-1.595356 1.184661 5.023791 -3.000006
epochs = 1800, thisMeanSqErr = 0.15397800 , bestMeanSqErr = 0.15397800 , score = 0.000000
New Coefficients:
0.079907 0.492633 2.045842 -0.590298 -1.016890
-0.287058 -1.663177 2.639902 -0.520579 1.701458
2.699687 3.089930 -2.775654 -12.001123 -5.469545
-2.969577 -2.513740 1.359017 0.196511 2.550609
-5.720012 1.924780 2.434439 -1.088117 -4.398651
-3.060057 1.769581 5.114918 -2.908877
epochs = 1900, thisMeanSqErr = 0.14985554 , bestMeanSqErr = 0.14952312 , score = 0.015160
New Coefficients:
0.066754 0.482545 1.932889 -0.666955 -1.063406
-0.266380 -1.677544 2.819681 -0.518303 1.923287
3.331198 3.540681 -2.725057 -12.406145 -7.216479
-3.121477 -2.531991 1.532169 0.368834 2.805376
-6.005527 1.978082 2.349299 -1.144352 -4.759035
-3.939048 2.167371 5.219372 -2.833065
epochs = 2000, thisMeanSqErr = 0.14763980 , bestMeanSqErr = 0.14710875 , score = 0.001805
New Coefficients:
0.035926 0.482162 1.870350 -0.738243 -1.070373
-0.195894 -1.747063 2.993789 -0.517254 2.200076
3.685798 3.845892 -2.725802 -12.741072 -8.100299
-3.435585 -2.693270 1.604251 0.455962 3.007582
-6.361198 1.990826 2.324642 -1.226648 -5.081776
-4.512234 2.350432 5.253657 -2.819673
epochs = 2100, thisMeanSqErr = 0.14578005 , bestMeanSqErr = 0.14561858 , score = 0.001606
New Coefficients:
0.046074 0.483502 1.865124 -0.778007 -1.064434
-0.162245 -1.770814 3.068442 -0.515313 2.324156
3.744394 3.897599 -2.752174 -13.148288 -8.485336
-3.590435 -2.844018 1.636610 0.689961 3.250614
-6.614274 1.996849 2.330536 -1.304175 -5.369603
-4.808841 2.420675 5.275385 -2.812593
epochs = 2200, thisMeanSqErr = 0.14478398 , bestMeanSqErr = 0.14437152 , score = 0.002373
New Coefficients:
0.028606 0.482263 1.862723 -0.817836 -1.057300
-0.136378 -1.799342 3.117913 -0.516472 2.498755
3.803142 3.947764 -2.799167 -13.552269 -8.721868
-3.716553 -2.956289 1.673267 0.794105 3.375831
-6.949615 1.993767 2.320355 -1.404318 -5.657252
-5.149159 2.477702 5.271332 -2.816683
epochs = 2300, thisMeanSqErr = 0.14325265 , bestMeanSqErr = 0.14324641 , score = 0.001672
New Coefficients:
0.013229 0.481381 1.866578 -0.833478 -1.006723
-0.097764 -1.839346 3.183477 -0.515683 2.632871
3.836428 3.980223 -2.847230 -13.937450 -8.951133
-3.850186 -3.097864 1.690757 0.964578 3.558497
-7.208638 1.999433 2.314674 -1.509218 -5.933866
-5.413725 2.537909 5.273420 -2.814726
epochs = 2400, thisMeanSqErr = 0.14207522 , bestMeanSqErr = 0.14207522 , score = 0.000000
New Coefficients:
-0.022165 0.470225 1.895433 -0.838714 -0.922202
-0.046481 -1.874834 3.292404 -0.513065 2.844824
3.878280 4.011824 -2.894634 -14.298574 -9.143326
-3.946973 -3.297522 1.727516 1.084688 3.747994
-7.576773 2.002191 2.313280 -1.624745 -6.271928
-5.699610 2.617768 5.273042 -2.815082
epochs = 2500, thisMeanSqErr = 0.14094843 , bestMeanSqErr = 0.14094843 , score = 0.000000
New Coefficients:
-0.056455 0.443295 1.964705 -0.830408 -0.772553
0.022293 -1.903884 3.453602 -0.512547 3.065752
3.925290 4.062893 -2.973364 -14.658344 -9.371281
-4.128569 -3.598627 1.786069 1.204739 3.951356
-7.832889 2.009147 2.305893 -1.772274 -6.626680
-5.941676 2.666344 5.265188 -2.818865
epochs = 2600, thisMeanSqErr = 0.14069560 , bestMeanSqErr = 0.13998151 , score = 0.004511
New Coefficients:
-0.093377 0.392033 2.027744 -0.786658 -0.537346
0.101614 -1.930082 3.681273 -0.503994 3.211760
3.956511 4.096929 -3.045846 -14.998384 -9.588906
-4.246512 -3.778868 1.839588 1.364627 4.123567
-8.062919 2.015568 2.313005 -1.935486 -6.984647
-6.160876 2.702028 5.256679 -2.818309
epochs = 2700, thisMeanSqErr = 0.13875936 , bestMeanSqErr = 0.13875936 , score = 0.000000
New Coefficients:
-0.209221 0.251959 2.115651 -0.183708 0.110991
0.227757 -1.950705 3.944203 -0.510347 3.375118
3.986155 4.144197 -3.085812 -15.189246 -9.783016
-4.401628 -3.962575 1.909545 1.448879 4.289291
-8.305932 2.031160 2.301316 -2.361739 -7.468147
-6.435265 2.746577 5.216247 -2.829171
epochs = 2800, thisMeanSqErr = 0.13770691 , bestMeanSqErr = 0.13770691 , score = 0.000000
New Coefficients:
-0.387822 0.014193 2.212446 0.398427 0.521555
0.419280 -1.953995 4.283000 -0.506844 3.501120
4.016789 4.178700 -3.068185 -15.375372 -9.988095
-4.563768 -4.130500 2.000156 1.553386 4.435032
-8.469718 2.077181 2.271921 -2.660104 -7.741974
-6.669372 2.802947 5.196634 -2.830389
epochs = 2900, thisMeanSqErr = 0.13683261 , bestMeanSqErr = 0.13670529 , score = 0.000466
New Coefficients:
-0.597945 -0.231237 2.357859 0.734483 0.749781
0.663654 -1.951606 4.719275 -0.501979 3.594059
4.057968 4.209637 -3.057374 -15.577879 -10.219860
-4.745534 -4.263843 2.090054 1.611086 4.591836
-8.576279 2.149361 2.234143 -2.776740 -7.901738
-6.852532 2.860709 5.145517 -2.826870
epochs = 3000, thisMeanSqErr = 0.13579520 , bestMeanSqErr = 0.13579520 , score = 0.000000
New Coefficients:
-0.787106 -0.431496 2.525931 0.916294 0.961642
0.876911 -1.953580 5.049603 -0.498974 3.686179
4.098457 4.224469 -3.055160 -15.784441 -10.344834
-4.850465 -4.362421 2.155088 1.656874 4.717519
-8.691935 2.190174 2.196894 -2.845850 -8.065702
-7.054380 2.903718 5.072612 -2.833674
epochs = 3100, thisMeanSqErr = 0.13503723 , bestMeanSqErr = 0.13501266 , score = 0.000789
New Coefficients:
-0.940043 -0.572317 2.664176 0.976596 1.083186
1.094771 -1.954190 5.376390 -0.497781 3.740035
4.139158 4.231922 -3.057486 -15.982817 -10.492792
-4.970942 -4.418649 2.193677 1.701629 4.852428
-8.780376 2.243682 2.178933 -2.876694 -8.172619
-7.268994 2.931307 5.002159 -2.848135
epochs = 3200, thisMeanSqErr = 0.13513405 , bestMeanSqErr = 0.13425147 , score = 0.005126
New Coefficients:
-1.090983 -0.708696 2.763467 1.020793 1.159925
1.310855 -1.945014 5.719822 -0.494600 3.796044
4.181842 4.241965 -3.076818 -16.170658 -10.663493
-5.069373 -4.480604 2.232778 1.793523 5.041785
-8.874053 2.325307 2.164771 -2.911178 -8.276957
-7.479011 2.970266 4.917866 -2.873127
epochs = 3300, thisMeanSqErr = 0.13352438 , bestMeanSqErr = 0.13352438 , score = 0.000000
New Coefficients:
-1.248968 -0.844767 2.841263 1.045358 1.202842
1.441271 -1.923664 6.014863 -0.494988 3.848500
4.235604 4.256463 -3.095810 -16.349126 -10.809299
-5.167976 -4.512790 2.266982 1.813833 5.163822
-8.962764 2.357542 2.151641 -2.943655 -8.372370
-7.678130 2.995800 4.829728 -2.904346
epochs = 3400, thisMeanSqErr = 0.13275115 , bestMeanSqErr = 0.13275115 , score = 0.000000
New Coefficients:
-1.375229 -0.919050 2.891161 1.055532 1.209365
1.519439 -1.896408 6.244808 -0.480766 3.911910
4.264726 4.270238 -3.108110 -16.519612 -10.916341
-5.260367 -4.580754 2.305693 1.855524 5.371872
-9.054465 2.388950 2.127122 -2.981132 -8.469177
-7.837109 3.012930 4.651655 -2.942955
epochs = 3500, thisMeanSqErr = 0.13446182 , bestMeanSqErr = 0.13195088 , score = 0.013956
New Coefficients:
-1.485857 -0.982893 2.922244 1.059829 1.196805
1.596925 -1.852155 6.440766 -0.429614 3.969171
4.286040 4.275397 -3.118148 -16.674679 -11.007359
-5.322181 -4.686318 2.341264 2.000647 5.685403
-9.165524 2.490611 2.114386 -3.009154 -8.562812
-8.033240 3.035013 4.402755 -2.961018
epochs = 3600, thisMeanSqErr = 0.13123811 , bestMeanSqErr = 0.13123811 , score = 0.000000
New Coefficients:
-1.609585 -1.028439 2.960872 1.063017 1.148666
1.668070 -1.803646 6.646356 -0.436384 4.021417
4.314379 4.283462 -3.132395 -16.818689 -11.095999
-5.388998 -4.730407 2.373543 1.956278 5.864419
-9.274224 2.470717 2.102496 -3.051332 -8.644902
-8.210935 3.057556 4.224226 -2.998185
epochs = 3700, thisMeanSqErr = 0.13058311 , bestMeanSqErr = 0.13042929 , score = 0.004025
New Coefficients:
-1.702683 -1.068993 2.978231 1.063321 1.075776
1.753333 -1.750162 6.847566 -0.418792 4.067924
4.323423 4.285552 -3.136651 -16.953987 -11.183547
-5.434890 -4.779592 2.400977 2.013985 6.128603
-9.379136 2.504552 2.091667 -3.080937 -8.724776
-8.440365 3.071654 3.982321 -3.032676
epochs = 3800, thisMeanSqErr = 0.12964726 , bestMeanSqErr = 0.12964726 , score = 0.000000
New Coefficients:
-1.822233 -1.104908 2.996316 1.063302 1.020764
1.828545 -1.693536 7.115413 -0.392661 4.118459
4.344097 4.286463 -3.140729 -17.082479 -11.284139
-5.483039 -4.862400 2.423755 2.053140 6.475146
-9.483190 2.551797 2.082949 -3.110377 -8.790428
-8.616489 3.083473 3.762870 -3.056791
epochs = 3900, thisMeanSqErr = 0.12903770 , bestMeanSqErr = 0.12899513 , score = 0.007197
New Coefficients:
-1.943782 -1.136000 3.032782 1.063165 0.981328
1.900305 -1.619977 7.309175 -0.395797 4.164941
4.369564 4.288763 -3.150229 -17.204401 -11.366968
-5.520364 -4.890093 2.450024 2.082923 6.609795
-9.580185 2.549277 2.075307 -3.147195 -8.842885
-8.780340 3.088227 3.600947 -3.104944
epochs = 4000, thisMeanSqErr = 0.12870920 , bestMeanSqErr = 0.12830371 , score = 0.001580
New Coefficients:
-2.083939 -1.189662 3.066565 1.062054 0.935373
1.978927 -1.561864 7.505476 -0.385758 4.232567
4.398254 4.295354 -3.160417 -17.320044 -11.443641
-5.564976 -4.949510 2.480613 2.125346 6.861520
-9.725882 2.585141 2.070156 -3.178113 -8.929215
-9.026429 3.093768 3.355480 -3.177146
epochs = 4100, thisMeanSqErr = 0.12766718 , bestMeanSqErr = 0.12766718 , score = 0.000000
New Coefficients:
-2.153205 -1.182025 3.116498 1.060404 0.918245
2.053891 -1.522699 7.687215 -0.374143 4.262049
4.415042 4.302718 -3.178194 -17.431823 -11.521458
-5.603280 -5.019614 2.492323 2.163059 7.089589
-9.753542 2.607776 2.066556 -3.206338 -8.958446
-9.144504 3.093679 3.162167 -3.176538
epochs = 4200, thisMeanSqErr = 0.12717351 , bestMeanSqErr = 0.12711362 , score = 0.002127
New Coefficients:
-2.246063 -1.196727 3.131217 1.056763 0.891888
2.085070 -1.492658 7.803970 -0.354526 4.301705
4.435422 4.309477 -3.183747 -17.533552 -11.573787
-5.647661 -5.073242 2.502927 2.255966 7.263560
-9.857685 2.647497 2.066683 -3.236943 -9.011028
-9.281643 3.095695 2.994934 -3.217384
epochs = 4300, thisMeanSqErr = 0.12650157 , bestMeanSqErr = 0.12649143 , score = 0.008525
New Coefficients:
-2.343422 -1.217320 3.166272 1.050457 0.868906
2.126163 -1.437563 8.000878 -0.342364 4.360103
4.464259 4.323413 -3.200653 -17.632053 -11.663208
-5.683630 -5.150077 2.520534 2.294993 7.500834
-9.930065 2.669514 2.062158 -3.269659 -9.062176
-9.416548 3.095524 2.792859 -3.255419
epochs = 4400, thisMeanSqErr = 0.12784716 , bestMeanSqErr = 0.12587835 , score = 0.010108
New Coefficients:
-2.443930 -1.253666 3.188112 1.041297 0.851403
2.200901 -1.393690 8.352097 -0.321115 4.417260
4.486226 4.335167 -3.213768 -17.725378 -11.788110
-5.730385 -5.231561 2.540880 2.375146 7.774910
-10.013243 2.703751 2.061292 -3.291721 -9.096824
-9.561744 3.095278 2.562058 -3.287413
epochs = 4500, thisMeanSqErr = 0.12593054 , bestMeanSqErr = 0.12536160 , score = 0.009601
New Coefficients:
-2.529770 -1.273521 3.206451 1.030328 0.833781
2.218282 -1.349659 8.416189 -0.319374 4.475600
4.506614 4.345529 -3.227981 -17.814001 -11.837304
-5.803606 -5.305262 2.555580 2.416602 7.949728
-10.076015 2.717790 2.060040 -3.313810 -9.136047
-9.693225 3.094878 2.397320 -3.324633
epochs = 4600, thisMeanSqErr = 0.12538060 , bestMeanSqErr = 0.12482394 , score = 0.003092
New Coefficients:
-2.682875 -1.355538 3.221525 1.013817 0.819622
2.265838 -1.310230 8.561730 -0.329589 4.574893
4.542245 4.358296 -3.241985 -17.899033 -11.909465
-5.825891 -5.359217 2.597462 2.428762 8.069671
-10.234903 2.715179 2.058742 -3.332620 -9.177430
-9.871826 3.093121 2.121357 -3.413311
epochs = 4700, thisMeanSqErr = 0.12421562 , bestMeanSqErr = 0.12421562 , score = 0.000000
New Coefficients:
-2.731874 -1.325532 3.251763 0.998680 0.809325
2.320625 -1.257794 8.740540 -0.320028 4.605592
4.565618 4.374498 -3.258192 -17.969510 -12.002815
-5.876718 -5.458899 2.592467 2.502288 8.305941
-10.244043 2.766116 2.057568 -3.350281 -9.223379
-10.029631 3.093225 1.949702 -3.400395
epochs = 4800, thisMeanSqErr = 0.12397169 , bestMeanSqErr = 0.12370795 , score = 0.012240
New Coefficients:
-2.827823 -1.338226 3.260547 0.959960 0.803193
2.359638 -1.210117 8.854439 -0.316423 4.655400
4.573740 4.379824 -3.268496 -17.990043 -12.057077
-5.909531 -5.526374 2.608923 2.582268 8.474572
-10.325281 2.808509 2.060493 -3.375219 -9.256899
-10.225892 3.093149 1.727349 -3.439130
epochs = 4900, thisMeanSqErr = 0.12319765 , bestMeanSqErr = 0.12319593 , score = 0.000028
New Coefficients:
-2.909075 -1.344995 3.278495 0.942591 0.794583
2.436501 -1.167430 9.009214 -0.315150 4.719891
4.588934 4.383558 -3.285381 -18.010755 -12.119023
-5.922382 -5.617301 2.619197 2.626577 8.686604
-10.400259 2.828325 2.062749 -3.390241 -9.288065
-10.386192 3.090147 1.533738 -3.458708
epochs = 5000, thisMeanSqErr = 0.12273710 , bestMeanSqErr = 0.12270419 , score = 0.000267
New Coefficients:
-2.981369 -1.354281 3.297401 0.909717 0.788393
2.472572 -1.126470 9.160215 -0.313350 4.776355
4.599924 4.389837 -3.298569 -18.018616 -12.181064
-5.959457 -5.704119 2.633060 2.686918 8.918698
-10.485481 2.853364 2.065261 -3.412017 -9.351584
-10.565285 3.089038 1.356029 -3.497465
epochs = 5100, thisMeanSqErr = 0.12254097 , bestMeanSqErr = 0.12226526 , score = 0.001751
New Coefficients:
-3.053512 -1.360213 3.324356 0.881097 0.788135
2.524142 -1.083846 9.315086 -0.311353 4.845539
4.608445 4.393264 -3.315511 -18.023878 -12.251207
-5.980081 -5.813851 2.641613 2.785400 9.161882
-10.584908 2.900172 2.068200 -3.432524 -9.415688
-10.729658 3.084952 1.198614 -3.515930
epochs = 5200, thisMeanSqErr = 0.12211440 , bestMeanSqErr = 0.12190921 , score = 0.001121
New Coefficients:
-3.148651 -1.398685 3.338006 0.852615 0.786534
2.556855 -1.059662 9.364303 -0.311584 4.935902
4.623221 4.398934 -3.339316 -18.031254 -12.289480
-6.004833 -5.871310 2.665594 2.797766 9.203807
-10.710773 2.894852 2.069910 -3.456258 -9.462948
-10.882476 3.080002 1.023577 -3.573410
epochs = 5300, thisMeanSqErr = 0.12157876 , bestMeanSqErr = 0.12157876 , score = 0.000000
New Coefficients:
-3.197361 -1.378548 3.366650 0.840845 0.786559
2.587012 -1.032288 9.501714 -0.311490 4.975199
4.625114 4.407000 -3.363018 -18.040926 -12.333433
-6.040733 -5.956640 2.667095 2.851595 9.338870
-10.733392 2.912005 2.075662 -3.475562 -9.498672
-10.977758 3.073173 0.924055 -3.580596
epochs = 5400, thisMeanSqErr = 0.12114753 , bestMeanSqErr = 0.12114753 , score = 0.000000
New Coefficients:
-3.254276 -1.395623 3.377910 0.828559 0.786722
2.614834 -1.001095 9.630213 -0.312104 5.061210
4.631377 4.413633 -3.374765 -18.050422 -12.384094
-6.060582 -6.069784 2.676932 2.895975 9.551878
-10.831573 2.933151 2.079323 -3.497778 -9.565916
-11.127517 3.072054 0.739055 -3.603643
epochs = 5500, thisMeanSqErr = 0.12075016 , bestMeanSqErr = 0.12073454 , score = 0.000191
New Coefficients:
-3.321507 -1.406009 3.402308 0.820129 0.787324
2.658294 -0.952112 9.768777 -0.314271 5.123677
4.635221 4.417592 -3.390306 -18.051824 -12.431406
-6.074879 -6.178691 2.693721 2.952949 9.732511
-10.927281 2.966533 2.094803 -3.517146 -9.628535
-11.312875 3.070360 0.608105 -3.628448
epochs = 5600, thisMeanSqErr = 0.12045981 , bestMeanSqErr = 0.12045981 , score = 0.000000
New Coefficients:
-3.369762 -1.411363 3.417291 0.798881 0.790827
2.696909 -0.927013 9.841887 -0.315156 5.184111
4.635338 4.420396 -3.423387 -18.054665 -12.469226
-6.103843 -6.251473 2.701193 2.995318 9.832732
-11.017223 2.984155 2.096178 -3.568413 -9.685787
-11.461188 3.062507 0.512890 -3.638198
epochs = 5700, thisMeanSqErr = 0.12026985 , bestMeanSqErr = 0.12012712 , score = 0.000917
New Coefficients:
-3.457359 -1.424363 3.436532 0.792411 0.790894
2.713425 -0.902851 9.951351 -0.318890 5.276966
4.638894 4.428704 -3.452460 -18.059480 -12.494965
-6.134142 -6.363215 2.734234 3.042650 9.976407
-11.136393 2.998950 2.104953 -3.592861 -9.736402
-11.552764 3.059897 0.356689 -3.707884
epochs = 5800, thisMeanSqErr = 0.11988465 , bestMeanSqErr = 0.11986940 , score = 0.001104
New Coefficients:
-3.486070 -1.430104 3.456304 0.786840 0.791843
2.728066 -0.878439 10.063102 -0.317064 5.328709
4.639277 4.438132 -3.482566 -18.066120 -12.518245
-6.153065 -6.479780 2.741055 3.097791 10.071798
-11.157907 3.019275 2.117191 -3.618202 -9.764297
-11.629990 3.056706 0.274627 -3.698577
epochs = 5900, thisMeanSqErr = 0.11961775 , bestMeanSqErr = 0.11961775 , score = 0.000000
New Coefficients:
-3.532929 -1.442999 3.467595 0.779233 0.793173
2.743004 -0.850556 10.170969 -0.319965 5.391018
4.639234 4.447732 -3.511159 -18.074199 -12.548763
-6.168452 -6.587898 2.758961 3.135657 10.165763
-11.219025 3.030950 2.130529 -3.653209 -9.800181
-11.738954 3.050971 0.178485 -3.711144
epochs = 6000, thisMeanSqErr = 0.11952277 , bestMeanSqErr = 0.11933514 , score = 0.003839
New Coefficients:
-3.582038 -1.456937 3.473236 0.774097 0.794955
2.775316 -0.825597 10.289696 -0.323758 5.467025
4.638812 4.452245 -3.531084 -18.083697 -12.593661
-6.179856 -6.701825 2.770543 3.172948 10.335113
-11.295578 3.052094 2.138593 -3.682605 -9.850868
-11.860249 3.047784 0.071971 -3.727283
epochs = 6100, thisMeanSqErr = 0.11917050 , bestMeanSqErr = 0.11907205 , score = 0.000413
New Coefficients:
-3.646010 -1.468158 3.484372 0.771714 0.798134
2.798059 -0.798029 10.356391 -0.331174 5.556456
4.638410 4.457733 -3.550929 -18.094474 -12.623227
-6.195203 -6.804577 2.796274 3.212752 10.413081
-11.394690 3.070741 2.146077 -3.705016 -9.911015
-11.995427 3.044803 -0.051929 -3.746779
epochs = 6200, thisMeanSqErr = 0.11882001 , bestMeanSqErr = 0.11882001 , score = 0.000000
New Coefficients:
-3.668434 -1.475480 3.496154 0.768471 0.800746
2.819989 -0.777758 10.443254 -0.336015 5.614628
4.637594 4.461775 -3.572540 -18.106370 -12.651826
-6.211590 -6.912547 2.801147 3.261280 10.538995
-11.455328 3.094913 2.156559 -3.741943 -9.957229
-12.106807 3.041661 -0.130780 -3.758683
epochs = 6300, thisMeanSqErr = 0.11861220 , bestMeanSqErr = 0.11858193 , score = 0.001439
New Coefficients:
-3.700627 -1.485873 3.505272 0.767094 0.801478
2.843780 -0.760309 10.534252 -0.341744 5.683961
4.634977 4.467390 -3.600819 -18.119437 -12.677122
-6.225752 -7.025761 2.815350 3.290800 10.666885
-11.523876 3.110633 2.167690 -3.776808 -10.002173
-12.200251 3.038114 -0.224128 -3.771161
epochs = 6400, thisMeanSqErr = 0.11834694 , bestMeanSqErr = 0.11834694 , score = 0.000000
New Coefficients:
-3.744771 -1.496964 3.518655 0.766403 0.801296
2.850764 -0.740080 10.611073 -0.349518 5.757587
4.631401 4.473370 -3.620927 -18.133361 -12.685012
-6.244675 -7.149996 2.842374 3.323323 10.752653
-11.600924 3.132614 2.181391 -3.798431 -10.049912
-12.291894 3.035267 -0.311968 -3.792825
epochs = 6500, thisMeanSqErr = 0.11811722 , bestMeanSqErr = 0.11811722 , score = 0.000000
New Coefficients:
-3.775978 -1.503447 3.524051 0.765504 0.803312
2.875776 -0.711560 10.694564 -0.354364 5.811315
4.616530 4.476916 -3.638554 -18.147808 -12.712367
-6.251119 -7.247942 2.862094 3.384521 10.877912
-11.684847 3.168743 2.203187 -3.843575 -10.106483
-12.452090 3.028843 -0.381849 -3.799051
epochs = 6600, thisMeanSqErr = 0.11791536 , bestMeanSqErr = 0.11791536 , score = 0.000000
New Coefficients:
-3.800184 -1.511422 3.529890 0.764935 0.803715
2.898042 -0.689939 10.759761 -0.360355 5.858648
4.613298 4.479258 -3.664790 -18.162693 -12.718570
-6.256348 -7.333043 2.878568 3.408868 10.986930
-11.770484 3.185991 2.217076 -3.875300 -10.168375
-12.562937 3.022461 -0.454866 -3.808706
epochs = 6700, thisMeanSqErr = 0.11798611 , bestMeanSqErr = 0.11771457 , score = 0.001694
New Coefficients:
-3.829615 -1.519524 3.536057 0.764474 0.803953
2.911822 -0.673853 10.821648 -0.366603 5.908526
4.606431 4.481458 -3.688814 -18.177922 -12.723679
-6.262549 -7.434337 2.897114 3.450781 11.089461
-11.862140 3.202802 2.232530 -3.922864 -10.217506
-12.669534 3.017085 -0.532261 -3.819331
epochs = 6800, thisMeanSqErr = 0.11757709 , bestMeanSqErr = 0.11751206 , score = 0.002426
New Coefficients:
-3.861179 -1.533441 3.542339 0.764267 0.803904
2.923639 -0.653906 10.886240 -0.379034 5.964988
4.599305 4.484259 -3.709595 -18.193524 -12.728637
-6.267057 -7.506397 2.917973 3.466575 11.172410
-11.950148 3.225589 2.244578 -3.945889 -10.280290
-12.782237 3.009723 -0.606828 -3.834541
epochs = 6900, thisMeanSqErr = 0.11732474 , bestMeanSqErr = 0.11732474 , score = 0.000000
New Coefficients:
-3.883195 -1.537299 3.552911 0.763936 0.803165
2.937025 -0.637474 10.942727 -0.381997 6.032165
4.585798 4.485984 -3.724916 -18.209316 -12.732382
-6.270985 -7.624455 2.944045 3.493780 11.268089
-12.033514 3.242907 2.261626 -3.975352 -10.330996
-12.879005 2.995782 -0.675339 -3.839612
epochs = 7000, thisMeanSqErr = 0.11717194 , bestMeanSqErr = 0.11714521 , score = 0.001253
New Coefficients:
-3.897772 -1.542594 3.558998 0.763834 0.802334
2.946494 -0.620765 11.000493 -0.385794 6.072545
4.576297 4.487620 -3.760966 -18.225071 -12.735103
-6.279317 -7.718074 2.965881 3.536943 11.365376
-12.103371 3.268290 2.280540 -4.030807 -10.382484
-12.980963 2.983888 -0.733269 -3.845340
epochs = 7100, thisMeanSqErr = 0.11694944 , bestMeanSqErr = 0.11694944 , score = 0.000000
New Coefficients:
-3.921689 -1.558859 3.565617 0.763717 0.798512
2.951814 -0.607322 11.063515 -0.397805 6.124098
4.569181 4.490103 -3.792075 -18.241352 -12.735626
-6.281977 -7.811213 2.993178 3.569369 11.450221
-12.198662 3.290569 2.296516 -4.060613 -10.443119
-13.082064 2.977786 -0.813632 -3.853936
epochs = 7200, thisMeanSqErr = 0.11685426 , bestMeanSqErr = 0.11675288 , score = 0.001801
New Coefficients:
-3.945664 -1.566323 3.569491 0.763563 0.793748
2.960948 -0.589154 11.136601 -0.407569 6.172598
4.552042 4.491600 -3.806266 -18.257549 -12.738187
-6.283994 -7.913777 3.020290 3.636336 11.551096
-12.282433 3.327853 2.321476 -4.082719 -10.489174
-13.200240 2.970533 -0.881747 -3.859939
epochs = 7300, thisMeanSqErr = 0.11658028 , bestMeanSqErr = 0.11657380 , score = 0.004024
New Coefficients:
-3.961769 -1.577008 3.572976 0.763481 0.787723
2.972542 -0.576400 11.185705 -0.413908 6.222960
4.543219 4.492605 -3.816583 -18.273951 -12.736179
-6.285468 -8.016555 3.049401 3.667784 11.619804
-12.361376 3.344869 2.342339 -4.103649 -10.534724
-13.302410 2.958660 -0.959504 -3.863394
epochs = 7400, thisMeanSqErr = 0.11644091 , bestMeanSqErr = 0.11641339 , score = 0.000293
New Coefficients:
-3.972098 -1.589211 3.575300 0.763463 0.781785
2.980502 -0.565374 11.235263 -0.416897 6.266724
4.534124 4.492724 -3.831871 -18.290205 -12.735330
-6.287096 -8.119152 3.074513 3.717991 11.714759
-12.443218 3.370216 2.363866 -4.142685 -10.589414
-13.392987 2.939785 -1.021830 -3.866961
epochs = 7500, thisMeanSqErr = 0.11625396 , bestMeanSqErr = 0.11625396 , score = 0.000000
New Coefficients:
-3.981293 -1.601323 3.576740 0.763461 0.774261
2.984498 -0.553885 11.269627 -0.423760 6.316399
4.522678 4.493055 -3.848415 -18.306898 -12.734826
-6.288952 -8.196872 3.103960 3.738405 11.775608
-12.528038 3.383928 2.375349 -4.170520 -10.637118
-13.502780 2.922499 -1.086029 -3.869821
epochs = 7600, thisMeanSqErr = 0.11610217 , bestMeanSqErr = 0.11608661 , score = 0.000793
New Coefficients:
-3.996890 -1.617398 3.578636 0.763460 0.768023
2.993353 -0.544685 11.332848 -0.433332 6.363347
4.509011 4.493022 -3.870901 -18.324484 -12.731997
-6.291900 -8.302711 3.127529 3.781529 11.860290
-12.601946 3.413662 2.395845 -4.198356 -10.687271
-13.593305 2.916683 -1.144032 -3.876822
epochs = 7700, thisMeanSqErr = 0.11592637 , bestMeanSqErr = 0.11592637 , score = 0.000000
New Coefficients:
-4.010477 -1.632880 3.579295 0.763455 0.759752
2.999874 -0.532597 11.375853 -0.441798 6.420944
4.498944 4.493002 -3.896448 -18.341874 -12.727943
-6.294821 -8.410543 3.163022 3.806122 11.934062
-12.694845 3.433803 2.418997 -4.227088 -10.734562
-13.697884 2.902371 -1.211359 -3.879009
epochs = 7800, thisMeanSqErr = 0.11574570 , bestMeanSqErr = 0.11574570 , score = 0.000000
New Coefficients:
-4.021803 -1.644568 3.579459 0.763450 0.752578
3.002776 -0.526127 11.438976 -0.448616 6.480761
4.490755 4.493002 -3.916388 -18.359607 -12.722192
-6.296907 -8.541216 3.201002 3.848800 12.005499
-12.771320 3.461853 2.442084 -4.259842 -10.771908
-13.765243 2.898092 -1.280124 -3.881220
epochs = 7900, thisMeanSqErr = 0.11557861 , bestMeanSqErr = 0.11557861 , score = 0.000000
New Coefficients:
-4.045088 -1.671716 3.579475 0.763461 0.745055
3.003984 -0.514080 11.534022 -0.460651 6.543339
4.471752 4.492469 -3.943114 -18.377764 -12.717542
-6.301281 -8.675149 3.241284 3.881027 12.076884
-12.842475 3.487398 2.466769 -4.299196 -10.809696
-13.835633 2.895128 -1.336206 -3.882724
epochs = 8000, thisMeanSqErr = 0.11633138 , bestMeanSqErr = 0.11540241 , score = 0.005217
New Coefficients:
-4.078166 -1.712905 3.579429 0.763498 0.737562
3.005602 -0.504593 11.606109 -0.480891 6.624998
4.464523 4.491444 -3.980931 -18.396520 -12.701550
-6.306680 -8.807503 3.334373 3.916360 12.126736
-12.961574 3.518712 2.494034 -4.340055 -10.849337
-13.933331 2.890332 -1.504772 -3.903597
epochs = 8100, thisMeanSqErr = 0.11878320 , bestMeanSqErr = 0.11523562 , score = 0.020111
New Coefficients:
-4.120736 -1.760599 3.577799 0.763509 0.723792
3.009928 -0.496821 11.663001 -0.507119 6.708617
4.449382 4.487657 -3.990650 -18.414941 -12.681752
-6.308254 -8.914566 3.357466 3.962311 12.205453
-13.112778 3.548652 2.522631 -4.354437 -10.906230
-14.031283 2.881466 -1.550842 -3.903498
epochs = 8200, thisMeanSqErr = 0.11507659 , bestMeanSqErr = 0.11507659 , score = 0.000000
New Coefficients:
-4.075058 -1.737984 3.573741 0.763514 0.714557
3.016247 -0.487387 11.707128 -0.478897 6.694418
4.432488 4.481490 -3.998834 -18.433658 -12.691231
-6.309949 -9.008275 3.351286 4.004204 12.278894
-13.098617 3.576194 2.545867 -4.368122 -10.943651
-14.136189 2.859171 -1.533328 -3.891655
epochs = 8300, thisMeanSqErr = 0.11493965 , bestMeanSqErr = 0.11492856 , score = 0.000568
New Coefficients:
-4.078851 -1.755906 3.566123 0.763515 0.705985
3.022291 -0.470430 11.749186 -0.483705 6.724965
4.418350 4.475190 -4.013943 -18.452438 -12.680989
-6.314015 -9.079952 3.371701 4.047696 12.348380
-13.180093 3.604718 2.567921 -4.398992 -10.994498
-14.220822 2.813786 -1.589148 -3.893171
epochs = 8400, thisMeanSqErr = 0.11481007 , bestMeanSqErr = 0.11475256 , score = 0.000470
New Coefficients:
-4.087207 -1.776077 3.560666 0.763522 0.693155
3.022526 -0.463160 11.825423 -0.492353 6.783177
4.401994 4.463411 -4.025358 -18.471227 -12.661224
-6.317137 -9.231315 3.411156 4.111256 12.445688
-13.265485 3.650492 2.609156 -4.433639 -11.036851
-14.297075 2.804690 -1.659535 -3.893612
epochs = 8500, thisMeanSqErr = 0.11461057 , bestMeanSqErr = 0.11461057 , score = 0.000000
New Coefficients:
-4.094049 -1.796704 3.531757 0.763533 0.682994
3.024958 -0.451419 11.852969 -0.497004 6.815135
4.389211 4.454259 -4.034757 -18.489917 -12.649650
-6.318625 -9.282867 3.435803 4.138448 12.488386
-13.355932 3.662283 2.622503 -4.465436 -11.094396
-14.413780 2.797913 -1.721595 -3.894466
epochs = 8600, thisMeanSqErr = 0.11452255 , bestMeanSqErr = 0.11446474 , score = 0.001579
New Coefficients:
-4.097159 -1.819215 3.500086 0.763545 0.673402
3.028395 -0.430653 11.894337 -0.501942 6.851277
4.371920 4.442067 -4.047435 -18.508684 -12.629385
-6.320768 -9.363028 3.461471 4.186140 12.552921
-13.465172 3.689959 2.644542 -4.505741 -11.147692
-14.513875 2.782859 -1.775080 -3.895746
epochs = 8700, thisMeanSqErr = 0.11436381 , bestMeanSqErr = 0.11431855 , score = 0.000905
New Coefficients:
-4.106853 -1.837589 3.473277 0.763568 0.664508
3.031626 -0.411829 11.943448 -0.508092 6.896056
4.355236 4.425689 -4.067755 -18.527483 -12.610827
-6.324045 -9.456243 3.495204 4.226142 12.613696
-13.545485 3.713014 2.670860 -4.549956 -11.196849
-14.622242 2.760396 -1.836354 -3.896575
epochs = 8800, thisMeanSqErr = 0.11419746 , bestMeanSqErr = 0.11414738 , score = 0.000219
New Coefficients:
-4.110617 -1.872321 3.436722 0.763599 0.649277
3.033293 -0.401023 12.002443 -0.512697 6.935005
4.338638 4.407460 -4.082792 -18.540901 -12.581706
-6.325907 -9.567421 3.531656 4.268505 12.694534
-13.648747 3.756951 2.707140 -4.572542 -11.249907
-14.712959 2.754245 -1.910492 -3.899292
epochs = 8900, thisMeanSqErr = 0.11401083 , bestMeanSqErr = 0.11401083 , score = 0.000000
New Coefficients:
-4.115856 -1.878581 3.387811 0.763608 0.641874
3.034681 -0.366809 12.052363 -0.516345 6.963523
4.321047 4.388834 -4.094601 -18.550440 -12.564867
-6.327249 -9.652526 3.560718 4.301827 12.759716
-13.709174 3.781373 2.733117 -4.589374 -11.285365
-14.805188 2.748348 -1.950580 -3.898758
epochs = 9000, thisMeanSqErr = 0.11391141 , bestMeanSqErr = 0.11386583 , score = 0.000379
New Coefficients:
-4.122005 -1.895557 3.328444 0.763612 0.632458
3.039354 -0.339477 12.115232 -0.520346 6.995187
4.295832 4.366040 -4.110479 -18.560164 -12.540893
-6.329021 -9.750169 3.588275 4.360778 12.849628
-13.789906 3.821240 2.765303 -4.616026 -11.331321
-14.892735 2.742350 -2.005443 -3.898978
epochs = 9100, thisMeanSqErr = 0.11377725 , bestMeanSqErr = 0.11373403 , score = 0.000352
New Coefficients:
-4.126461 -1.918140 3.253548 0.763613 0.622121
3.039472 -0.303078 12.143407 -0.523138 7.021328
4.285067 4.342580 -4.129058 -18.570030 -12.515800
-6.330901 -9.799221 3.615708 4.389533 12.888867
-13.884874 3.835715 2.780212 -4.650847 -11.387894
-14.987925 2.729258 -2.072403 -3.900246
epochs = 9200, thisMeanSqErr = 0.11358343 , bestMeanSqErr = 0.11357328 , score = 0.000045
New Coefficients:
-4.133549 -1.936210 3.226261 0.763615 0.612882
3.040832 -0.291915 12.223729 -0.529489 7.064766
4.257332 4.314414 -4.143298 -18.580253 -12.493958
-6.332154 -9.935400 3.648445 4.436879 12.994105
-13.969557 3.889557 2.828955 -4.691060 -11.423944
-15.080332 2.717840 -2.132726 -3.899616
epochs = 9300, thisMeanSqErr = 0.11343882 , bestMeanSqErr = 0.11343882 , score = 0.000000
New Coefficients:
-4.137473 -1.966844 3.075744 0.763626 0.600832
3.041193 -0.257623 12.247315 -0.531921 7.098745
4.249387 4.295118 -4.153084 -18.590375 -12.467142
-6.332563 -9.986101 3.675971 4.479779 13.020324
-14.058672 3.901836 2.839251 -4.730933 -11.474242
-15.163945 2.717295 -2.197064 -3.899452
epochs = 9400, thisMeanSqErr = 0.11330566 , bestMeanSqErr = 0.11330566 , score = 0.000000
New Coefficients:
-4.140169 -1.981745 3.009063 0.763637 0.590847
3.041341 -0.209706 12.292943 -0.533569 7.118826
4.226547 4.271518 -4.166691 -18.600841 -12.445348
-6.333178 -10.053040 3.697490 4.541661 13.090159
-14.133383 3.930083 2.864531 -4.771352 -11.522944
-15.251194 2.714850 -2.251429 -3.898516
epochs = 9500, thisMeanSqErr = 0.11320629 , bestMeanSqErr = 0.11317679 , score = 0.000130
New Coefficients:
-4.145172 -2.003720 2.903469 0.763646 0.581034
3.041261 -0.164325 12.326400 -0.536612 7.154296
4.212286 4.247898 -4.183153 -18.611452 -12.408653
-6.333950 -10.107013 3.726362 4.581959 13.137749
-14.233079 3.948582 2.883019 -4.811832 -11.574882
-15.348683 2.704583 -2.325704 -3.899141
epochs = 9600, thisMeanSqErr = 0.11309784 , bestMeanSqErr = 0.11305212 , score = 0.000375
New Coefficients:
-4.146489 -2.019686 2.820113 0.763650 0.570865
3.041236 -0.124873 12.376607 -0.536847 7.166561
4.188028 4.220870 -4.201325 -18.622248 -12.397726
-6.334825 -10.178977 3.742017 4.654148 13.215717
-14.298181 3.983594 2.911118 -4.851699 -11.621836
-15.422087 2.686350 -2.373508 -3.896757
epochs = 9700, thisMeanSqErr = 0.11292550 , bestMeanSqErr = 0.11292550 , score = 0.000000
New Coefficients:
-4.155471 -2.041837 2.789794 0.763656 0.559627
3.040288 -0.112768 12.425598 -0.537152 7.206761
4.186238 4.187327 -4.216106 -18.633113 -12.369295
-6.335496 -10.276311 3.776883 4.663193 13.241489
-14.374648 3.996809 2.938976 -4.888691 -11.644283
-15.469889 2.672312 -2.432458 -3.896958
epochs = 9800, thisMeanSqErr = 0.11373681 , bestMeanSqErr = 0.11277198 , score = 0.005439
New Coefficients:
-4.162485 -2.063593 2.739856 0.763668 0.550358
3.033642 -0.105158 12.510189 -0.539505 7.251779
4.158230 4.155163 -4.223029 -18.644219 -12.349024
-6.335700 -10.494393 3.815758 4.739752 13.334699
-14.461332 4.048954 2.983781 -4.905957 -11.682745
-15.532037 2.670055 -2.501538 -3.868136
epochs = 9900, thisMeanSqErr = 0.11265225 , bestMeanSqErr = 0.11263790 , score = 0.000614
New Coefficients:
-4.168031 -2.088597 2.690415 0.763676 0.536009
3.024925 -0.095072 12.529319 -0.541249 7.302606
4.163133 4.133609 -4.225637 -18.655129 -12.329312
-6.335737 -10.505290 3.858951 4.734169 13.357380
-14.534925 4.055467 2.995957 -4.909010 -11.706744
-15.584114 2.669429 -2.581545 -3.882775
epochs = 10000, thisMeanSqErr = 0.11251315 , bestMeanSqErr = 0.11251315 , score = 0.000000
New Coefficients:
-4.169151 -2.106678 2.591690 0.763680 0.528659
3.016469 -0.076209 12.564792 -0.541301 7.333308
4.149681 4.106112 -4.229009 -18.666131 -12.315604
-6.335838 -10.585431 3.883550 4.769675 13.417539
-14.603712 4.082716 3.015415 -4.913999 -11.749152
-15.643453 2.669251 -2.640960 -3.871758
Convergence not achieved in 10000 epochs
Mean Squared Error = 1.125132e-01

Vote Actual Neural Net LPM Probit
No 35.000000 26.000000 17.000000 16.000000
Yes 60.000000 56.000000 51.000000 53.000000
MFB
 
Posts: 4
Joined: Sat Jul 24, 2021 10:03 am


Return to Other RATS Usage Questions

Who is online

Users browsing this forum: No registered users and 1 guest