[unofficial] Statistical Machine Intelligence & Learning Engine (smile) develop environment
ref. https://docs.docker.com/engine/install/
ref. https://docs.docker.com/compose/install/
$ git clone https://github.com/syneart/smile_dev/
$ cd smile_dev
$ sudo docker-compose up -d
$ sudo docker-compose exec smile_dev /bin/bash
## In Container
# ./shell/src/universal/bin/jshell.sh ../example/segment-challenge.jsh
Then you will see
...
[ForkJoinPool.commonPool-worker-19] INFO smile.classification.RandomForest - Decision tree OOB accuracy: 92.67%
[ForkJoinPool.commonPool-worker-9] INFO smile.classification.RandomForest - Decision tree OOB accuracy: 88.89%
[ForkJoinPool.commonPool-worker-19] INFO smile.classification.RandomForest - Decision tree OOB accuracy: 93.99%
[ForkJoinPool.commonPool-worker-9] INFO smile.classification.RandomForest - Decision tree OOB accuracy: 91.89%
[ForkJoinPool.commonPool-worker-21] INFO smile.classification.RandomForest - Decision tree OOB accuracy: 90.80%
0.9604938271604938
ROW=truth and COL=predicted
class 0 | 124 | 0 | 0 | 0 | 1 | 0 | 0 |
class 1 | 0 | 110 | 0 | 0 | 0 | 0 | 0 |
class 2 | 3 | 0 | 115 | 1 | 3 | 0 | 0 |
class 3 | 2 | 0 | 0 | 106 | 2 | 0 | 0 |
class 4 | 2 | 0 | 10 | 5 | 109 | 0 | 0 |
class 5 | 0 | 0 | 0 | 0 | 0 | 94 | 0 |
class 6 | 1 | 0 | 1 | 1 | 0 | 0 | 120 |
{
fit time: 10803.319 ms,
score time: 193.036 ms,
validation data size: 1500,
error: 45,
accuracy: 97.00%
}
root@smile_dev:~/syneart/smile#
Like this
## In Container
# ./shell/src/universal/bin/jshell.sh
## then type "Hello, Jed!"
jshell> "Hello, Jed!"
You will get this result, like this
root@smile_dev:~/syneart/smile# ./shell/src/universal/bin/jshell.sh
| Welcome to JShell -- Version 11.0.14.1
| For an introduction type: /help intro
jshell> "Hello, Jed!"
$1 ==> "Hello, Jed!"
jshell>
You can find it at lib_export/ directory at host env
For more detailed instructions, please refer to the official - https://haifengl.github.io