neural network to predict queue eta on 2b2t
this is both, the methods used to implement parsing and minor data analysis are
exposed, feel free to use _2b2q for analysing queue data csv files. for documentation
run cargo doc
or just read the source code yourself
USAGE:
_2b2q <SUBCOMMAND>
OPTIONS:
-h, --help Print help information
SUBCOMMANDS:
help Print this message or the help of the given subcommand(s)
new creates a new neural network with specified layers at specified path, if using
`--dir` the model's file will be named after the layers
stat prints the current estimation of the specified models neatly organized to the
terminal
train trains the specified neural network on the data
creates a new neural network with specified layers at specified path, if using
`--dir` the model's file will be named after the layers
USAGE:
_2b2q new [OPTIONS] <--path <PATH>|--dir <DIR>> [LAYERS]...
ARGS:
<LAYERS>... layers which the neural net should have, for example: 10-6-2-4-1
OPTIONS:
-d, --dir <DIR> directory in which to place the model file
-f, --force force replacement of existing model file
-h, --help Print help information
-p, --path <PATH> path at which to place the model file
prints the current estimation of the specified models neatly organized to the terminal
USAGE:
_2b2q stat <DATA_DIR> [MODELS]...
ARGS:
<DATA_DIR> directory from which to read `stat`ing data
<MODELS>... models which to include in comparison
OPTIONS:
-h, --help Print help information
trains the specified neural network on the data
WARNING: changes apply immediately, make a backup if you are worried about it messing up
USAGE:
_2b2q train [OPTIONS] <DATA_DIR> <MODEL>
ARGS:
<DATA_DIR>
directory from which to read training data
<MODEL>
OPTIONS:
-e, --epochs <EPOCHS>
train specified amound of epochs per iteration
-h, --help
Print help information
-l, --loop <loop>
whether to loop after halt condition is reached conflicts with mse as error rate is not
expected to decrease
[default: true]
-l, --logging <logging>
enable or disable logging
[default: true]
-l, --logging-err-rate <LOGGING_ERR_RATE>
whether to log error rate after how many batch iterations
-m, --mse <MSE>
train until specified error rate is achieved
-m, --momentum <MOMENTUM>
momentum used by RustNN (don't change without reason)
[default: 0.1]
-r, --rate <RATE>
rate used for backpropagation by RustNN (don't change without reason)
[default: 0.3]
-t, --timer <TIMER>
train for specified amount of seconds per iteration