Neural Network Java output

D:\java\MinShip\my-minship-app>mvn package

[INFO] Scanning for projects...

[INFO]

[INFO] Using the builder org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder with a thread count of 1

[INFO]

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[INFO] Building my-minship-app 1.0-SNAPSHOT

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[INFO]

[INFO] --- maven-resources-plugin:2.6:resources (default-resources) @ my-minship-app ---

[WARNING] Using platform encoding (Cp1252 actually) to copy filtered resources, i.e. build is platform dependent!

[INFO] skip non existing resourceDirectory D:\java\MinShip\my-minship-app\src\main\resources

[INFO]

[INFO] --- maven-compiler-plugin:2.5.1:compile (default-compile) @ my-minship-app ---

[WARNING] File encoding has not been set, using platform encoding Cp1252, i.e. build is platform dependent!

[INFO] Compiling 1 source file to D:\java\MinShip\my-minship-app\target\classes

[INFO]

[INFO] --- maven-resources-plugin:2.6:testResources (default-testResources) @ my-minship-app ---

[WARNING] Using platform encoding (Cp1252 actually) to copy filtered resources, i.e. build is platform dependent!

[INFO] skip non existing resourceDirectory D:\java\MinShip\my-minship-app\src\test\resources

[INFO]

[INFO] --- maven-compiler-plugin:2.5.1:testCompile (default-testCompile) @ my-minship-app ---

[INFO] Nothing to compile - all classes are up to date

[INFO]

[INFO] --- maven-surefire-plugin:2.12.4:test (default-test) @ my-minship-app ---

[INFO] Surefire report directory: D:\java\MinShip\my-minship-app\target\surefire-reports

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T E S T S

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Running com.smkltd.app.AppTest

Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 0.01 sec

Results :

Tests run: 1, Failures: 0, Errors: 0, Skipped: 0

[INFO]

[INFO] --- maven-jar-plugin:2.4:jar (default-jar) @ my-minship-app ---

[INFO] Building jar: D:\java\MinShip\my-minship-app\target\my-minship-app-1.0-SNAPSHOT.jar

[INFO]

[INFO] --- onejar-maven-plugin:1.4.4:one-jar (default) @ my-minship-app ---

[INFO] Using One-Jar to create a single-file distribution

[INFO] Implementation Version: 1.0-SNAPSHOT

[INFO] Using One-Jar version: 0.97

[INFO] More info on One-Jar: http://one-jar.sourceforge.net/

[INFO] License for One-Jar: http://one-jar.sourceforge.net/one-jar-license.txt

[INFO] One-Jar file: D:\java\MinShip\my-minship-app\target\my-minship-app-1.0-SNAPSHOT.one-jar.jar

[INFO] ------------------------------------------------------------------------

[INFO] BUILD SUCCESS

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[INFO] Total time: 2.481 s

[INFO] Finished at: 2015-01-26T05:56:55-05:00

[INFO] Final Memory: 14M/489M

[INFO] ------------------------------------------------------------------------

D:\java\MinShip\my-minship-app>java -cp target/* com.smkltd.app.NeuralNetwork

A couple of Hops: CurrentState.transpose()

-1.0000 -1.0000 1.0000 -1.0000 1.0000 1.0000 1.0000 -1.0000 -1.0000 1.0000

-1.0000 0.0000 1.0000 1.0000 1.0000 1.0000 0.0000 -1.0000 0.0000 1.0000

1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 1.0000 1.0000

1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 1.0000 1.0000 1.0000 1.0000

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

..................

Coupled Langevin Equations with corrrelated Gaussian white noise

Continuous time model expected value of neuron CurrentState.transpose()

dZ = N(Zdt + AdW)

-1.0000 -1.0000 1.0000 -1.0000 1.0000 1.0000 1.0000 -1.0000 -1.0000 1.0000

-0.9749 -0.9509 0.9755 -0.9351 0.9674 0.9671 0.9512 -0.9668 -0.9506 1.0000

-0.9497 -0.9035 0.9518 -0.8736 0.9361 0.9350 0.9049 -0.9339 -0.9025 1.0000

-0.9243 -0.8577 0.9290 -0.8154 0.9061 0.9038 0.8608 -0.9013 -0.8555 1.0000

-0.8990 -0.8136 0.9070 -0.7603 0.8773 0.8734 0.8190 -0.8690 -0.8097 1.0000

-0.8736 -0.7711 0.8857 -0.7082 0.8498 0.8439 0.7794 -0.8370 -0.7650 1.0000

-0.8483 -0.7301 0.8652 -0.6588 0.8235 0.8152 0.7418 -0.8054 -0.7214 1.0000

-0.8231 -0.6906 0.8453 -0.6120 0.7984 0.7874 0.7062 -0.7740 -0.6789 1.0000

-0.7980 -0.6525 0.8261 -0.5678 0.7743 0.7605 0.6725 -0.7431 -0.6375 1.0000

-0.7731 -0.6158 0.8075 -0.5259 0.7514 0.7344 0.6407 -0.7124 -0.5970 1.0000

-0.7484 -0.5805 0.7895 -0.4863 0.7295 0.7091 0.6106 -0.6821 -0.5576 1.0000

-0.7239 -0.5464 0.7721 -0.4488 0.7086 0.6847 0.5822 -0.6522 -0.5191 1.0000

-0.6997 -0.5137 0.7552 -0.4133 0.6887 0.6611 0.5555 -0.6226 -0.4816 1.0000

-0.6757 -0.4821 0.7389 -0.3797 0.6697 0.6384 0.5304 -0.5934 -0.4451 1.0000

-0.6521 -0.4518 0.7230 -0.3479 0.6517 0.6165 0.5067 -0.5646 -0.4094 1.0000

-0.6288 -0.4226 0.7076 -0.3179 0.6345 0.5954 0.4845 -0.5361 -0.3746 1.0000

-0.6058 -0.3945 0.6927 -0.2894 0.6183 0.5751 0.4637 -0.5080 -0.3406 1.0000

-0.5831 -0.3674 0.6783 -0.2625 0.6029 0.5556 0.4443 -0.4803 -0.3075 1.0000

-0.5608 -0.3414 0.6643 -0.2370 0.5882 0.5368 0.4261 -0.4529 -0.2753 1.0000

-0.5389 -0.3164 0.6507 -0.2128 0.5744 0.5189 0.4092 -0.4259 -0.2438 1.0000

-0.5174 -0.2924 0.6375 -0.1900 0.5613 0.5017 0.3935 -0.3993 -0.2131 1.0000

-0.4962 -0.2693 0.6247 -0.1683 0.5490 0.4852 0.3789 -0.3731 -0.1831 1.0000

-0.4755 -0.2470 0.6123 -0.1478 0.5374 0.4695 0.3654 -0.3472 -0.1539 1.0000

-0.4551 -0.2257 0.6002 -0.1284 0.5264 0.4545 0.3529 -0.3217 -0.1254 1.0000

-0.4352 -0.2052 0.5886 -0.1099 0.5162 0.4402 0.3414 -0.2966 -0.0976 1.0000

-0.4157 -0.1854 0.5772 -0.0924 0.5065 0.4267 0.3309 -0.2718 -0.0705 1.0000

-0.3965 -0.1665 0.5663 -0.0758 0.4975 0.4138 0.3213 -0.2475 -0.0441 1.0000

.................. iter = 26

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

.................. iter = 795

Printing row normalized PKQTC for MyNeuralNetwork

0.2500 0.2500 0.2500 0.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 0.2500 0.2500 0.2500 0.2500 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 0.2500 0.2500 0.2500 0.2500 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 0.0000 0.3333 0.3333 0.0000 0.3333 0.0000 0.0000 0.0000

0.0000 0.0000 0.0000 0.0000 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667

0.0000 0.0000 0.0000 0.0000 0.0000 0.3333 0.3333 0.3333 0.0000 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.5000 0.0000 0.5000 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.3333 0.3333 0.3333

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000

Printing N = sysmat for MyNeuralNetwork

-0.7500 0.2500 0.2500 0.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 -0.7500 0.2500 0.2500 0.2500 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 -0.7500 0.2500 0.2500 0.2500 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 0.0000 -0.6667 0.3333 0.0000 0.3333 0.0000 0.0000 0.0000

0.0000 0.0000 0.0000 0.0000 -0.8333 0.1667 0.1667 0.1667 0.1667 0.1667

0.0000 0.0000 0.0000 0.0000 0.0000 -0.6667 0.3333 0.3333 0.0000 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -0.5000 0.0000 0.5000 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -0.6667 0.3333 0.3333

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -0.5000 0.5000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Printing Gaussian white noise covariance matrix A.AT for MyNeuralNetwork

12.0156 -2.3722 -2.4423 -7.4692 0.0412 -6.1687 -2.7423 -2.1067 2.6456 -1.4936

-2.3722 7.8723 -3.8264 -0.3203 2.9152 -0.5710 2.8053 7.7808 3.2032 -1.5101

-2.4423 -3.8264 11.1944 0.8502 -2.2400 2.4936 0.9158 -3.9065 -4.9597 -2.3018

-7.4692 -0.3203 0.8502 11.0899 -0.1852 3.9205 5.9056 0.0376 -3.2538 5.5353

0.0412 2.9152 -2.2400 -0.1852 7.9969 -3.7415 0.2663 2.4438 5.1206 2.1550

-6.1687 -0.5710 2.4936 3.9205 -3.7415 8.2336 4.0974 -2.9308 -2.3097 1.3805

-2.7423 2.8053 0.9158 5.9056 0.2663 4.0974 11.5493 0.4381 -0.6725 2.8608

-2.1067 7.7808 -3.9065 0.0376 2.4438 -2.9308 0.4381 15.4952 5.5176 -0.4693

2.6456 3.2032 -4.9597 -3.2538 5.1206 -2.3097 -0.6725 5.5176 8.8133 1.4219

-1.4936 -1.5101 -2.3018 5.5353 2.1550 1.3805 2.8608 -0.4693 1.4219 8.5184

determinant = 346070.2923369596

condition = 1532.465320130049

Printing covariance matrix NA.ATNT for MyNeuralNetwork iter = 338

12.8379 1.0883 0.7567 -4.1529 1.8275 -1.7177 -3.8501 -2.2672 2.3939 0.0000

1.0883 6.5857 -3.1202 -2.4945 -0.3594 -2.9055 -1.4218 3.8959 2.8277 0.0000

0.7567 -3.1202 7.5896 -0.5197 -0.1590 1.4107 0.8643 0.6560 0.1925 0.0000

-4.1529 -2.4945 -0.5197 4.6175 -2.3856 1.9757 1.8252 -0.0792 -2.8351 0.0000

1.8275 -0.3594 -0.1590 -2.3856 5.7989 -1.7547 -2.4812 -1.1977 1.3142 0.0000

-1.7177 -2.9055 1.4107 1.9757 -1.7547 6.2432 0.9453 -3.8328 -1.6390 0.0000

-3.8501 -1.4218 0.8643 1.8252 -2.4812 0.9453 5.4269 -0.3520 -2.7311 0.0000

-2.2672 3.8959 0.6560 -0.0792 -1.1977 -3.8328 -0.3520 6.8848 1.9465 0.0000

2.3939 2.8277 0.1925 -2.8351 1.3142 -1.6390 -2.7311 1.9465 3.6219 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Printing covariance matrix <DZ.DZT> for MyNeuralNetwork iter = 338

Initial covariance matrix <DZ0.DZ0T> assumed to be identity matrix

9.5834 2.1616 2.2400 -1.3273 2.1164 -0.0369 -0.7052 0.3939 2.8498 1.0000

2.1616 5.1558 0.1991 0.2325 1.8645 0.4677 1.1906 4.3040 3.0889 1.0000

2.2400 0.1991 7.4414 2.0205 1.6492 3.4752 2.2454 1.2154 0.8347 1.0000

-1.3273 0.2325 2.0205 4.9554 0.2460 3.1340 2.7386 0.6488 -1.2503 1.0000

2.1164 1.8645 1.6492 0.2460 4.6502 0.3376 0.4782 1.4893 2.5493 1.0000

-0.0369 0.4677 3.4752 3.1340 0.3376 5.9637 2.8838 -0.3212 0.1046 1.0000

-0.7052 1.1906 2.2454 2.7386 0.4782 2.8838 5.5067 1.5939 0.0798 1.0000

0.3939 4.3040 1.2154 0.6488 1.4893 -0.3212 1.5939 7.5152 3.7033 1.0000

2.8498 3.0889 0.8347 -1.2503 2.5493 0.1046 0.0798 3.7033 4.6219 1.0000

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

N<DZ.DZT> + <DZ.DZT>NT + NA.ATNT = 0.00000000

determinant = 42397.420299500765

condition = 64.49475448984984

MyKron rank = 99

Printing covariance matrix <DZ.DZT>particular for MyNeuralNetwork.lyapunovEq()

Null matrix <DZ.DZT> for MyNeuralNetwork.lyapunovEq() is square matrix of 1's

Equilibrium points are const*null + particular

8.5834 1.1616 1.2400 -2.3273 1.1164 -1.0369 -1.7052 -0.6061 1.8498 -0.0000

1.1616 4.1558 -0.8009 -0.7675 0.8645 -0.5323 0.1906 3.3040 2.0889 -0.0000

1.2400 -0.8009 6.4414 1.0205 0.6492 2.4752 1.2454 0.2154 -0.1653 -0.0000

-2.3273 -0.7675 1.0205 3.9554 -0.7540 2.1340 1.7386 -0.3512 -2.2503 -0.0000

1.1164 0.8645 0.6492 -0.7540 3.6502 -0.6624 -0.5218 0.4893 1.5493 -0.0000

-1.0369 -0.5323 2.4752 2.1340 -0.6624 4.9637 1.8838 -1.3212 -0.8954 -0.0000

-1.7052 0.1906 1.2454 1.7386 -0.5218 1.8838 4.5067 0.5939 -0.9202 -0.0000

-0.6061 3.3040 0.2154 -0.3512 0.4893 -1.3212 0.5939 6.5152 2.7033 -0.0000

1.8498 2.0889 -0.1653 -2.2503 1.5493 -0.8954 -0.9202 2.7033 3.6219 -0.0000

-0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.0000

Checking

sysmat.times(particular).plus(particular.times(sysmat.transpose())).times(-1)

12.8379 1.0883 0.7567 -4.1529 1.8275 -1.7177 -3.8501 -2.2672 2.3939 -0.0000

1.0883 6.5857 -3.1202 -2.4945 -0.3594 -2.9055 -1.4218 3.8959 2.8277 -0.0000

0.7567 -3.1202 7.5896 -0.5197 -0.1590 1.4107 0.8643 0.6560 0.1925 -0.0000

-4.1529 -2.4945 -0.5197 4.6175 -2.3856 1.9757 1.8252 -0.0792 -2.8351 -0.0000

1.8275 -0.3594 -0.1590 -2.3856 5.7989 -1.7547 -2.4812 -1.1977 1.3142 -0.0000

-1.7177 -2.9055 1.4107 1.9757 -1.7547 6.2432 0.9453 -3.8328 -1.6390 -0.0000

-3.8501 -1.4218 0.8643 1.8252 -2.4812 0.9453 5.4269 -0.3520 -2.7311 -0.0000

-2.2672 3.8959 0.6560 -0.0792 -1.1977 -3.8328 -0.3520 6.8848 1.9465 -0.0000

2.3939 2.8277 0.1925 -2.8351 1.3142 -1.6390 -2.7311 1.9465 3.6219 -0.0000

-0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000

D:\java\MinShip\my-minship-app>

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