reservoir-computing

Real time training and predict framework

This program is a framework of reservoir-computing that adopts multiple applications. It runs on basically PC(Ubuntu).

You might need to implement codes on a device like arduino for getting data from sensors.

Here is a sample code which runs on arduino for 4 sensors and 3 buttons. You can custom it for your applications.

puls_sensors_sample.ino

Usage

At first, you need to change the com port according to your environment in nail_conductor.py

   # Set com port as data streamer
   ds = data_streamer_serial.DataStreamer('COM3') # 

how to run a thumup detection with reservoir-computing

git clone https://github.com/recruit-tech/reservoir-computing.git
cd reservoir-computing/src
python nail_conductor.py

To quit the program, push [q] key on the screen or ctrl + c on the terminal.

You can also run a cardboard controller( famicom ) when you change code as follows in .env

#APPLICATION_NAME=app_thumbup
APPLICATION_NAME=app_famicom
#APPLICATION_NAME=app_balloon
#APPLICATION_NAME=app_volume

You will see the following images. (upper:training screen, lower: predict screen)

How to make YOUR APPs.

If you would like to make YOUR APPs, you only need to write 4 claasses which is in the orange frame on folliwing diagram.

---

Batch training and predict framework

batch_training_and_predict.py is batch program which train and predict and outputs a figure as follow.

Usage

how to run the batch program for thumup detection.

cd reservoir-computing/src
python batch_training_and_predict.py -csv_file output/train_log_20221101_115348.csv

---

Grid search for finding the best hyper parameters

grid_search.py is program which finds the best hyper parameters.

Usage

First of all, you need to change application to thumup_new as follows in .env

APPLICATION_NAME=app_thumbup_new

And then, run a sample program for grid search.

cd reservoir-computing/src
python grid_search.py -csv_file output/thumbup_nc_data.csv -cpu 4

Setting scope of hyper parameters.

Please find set_next_grid_search_params() in app_thumbup_new.py for reference. All you need is change following code if you'd like to change the range of hyper parameters.

   def set_next_grid_search_params(self):
               :
           for node in [800,900,1000]:
               for density in [0.4,]:
                   for input_scale in [0.004,]:
                       for rho in [0.6,0.7,0.8,0.9,1.0]:
                           for fb_scale in [None,]:
                               for leaking_rate in [0.1,]:
                                   for average_window in [1,]:
               :
       return True

Visualization the result of relationship between accuracy and hyperparameters with T-SNE.

python t_sne.py -csv_file output/results_grid_search_app_thumbup_new_2736.csv

You will see the following chart.

---

Arduino

arduino code for app_thumbup_new or two nail conductor devices.

nc_3input_with_sw.ino is a single nail conductor device. Supports button annotation. nc_6input.ino is two nail conductor devices(6 pd inputs) without button.

nc_3input_with_sw (for app_thumbup_new)

analog pin No. 0, 1, 2 (sensor input)
digital pin No. 2 (switch button）→ No resistor required (this is setting a pullup)

output sample

:
120,110,90,0
115,109,91,0
170,140,120,1 ← push switch
:

nc_6input (for app_famicom_new, app_smartphone)

analog pin No. 0, 1, 2, 3, 4, 5 (sensor input)

output sample

:
120,110,90,125,108,99
156,177,132,120,120,112
161,181,156,120,143,119
: