gravityfargo

* = hardware playback not supported. Settings will be saved to software only.

If your device isn't on this list, it isn't currently supported by ckb-next. Pull requests welcome.

If your device is listed as Experimental, this means that support for it is not enabled by default, and is incomplete, or possibly even broken. If you wish to proceed, you can read here on how to enable support for it.

Please open a bug report if you find one of these variants incompatible or an Enhancement issue if your variants is compatible.

Keyboards

| FAMILY | MODEL | Status | DEVICE ID |

gravityfargo

Enable Experimental Device Support

In order to use newly supported devices marked as experimental, you will need to modify your daemon's unit file to start with the required flag for experimental device support.

1. Verify the ckb-next daemon is running on your system.

systemctl status ckb-next-daemon.service

gravityfargo

# Import required modules
import os
import random

# Define paths for the train, test and firstlast data files
ratio = "50-50"
trainFile = "/mnt/Storage/VMShare/" + ratio + "/train.dat"
testFile = "/mnt/Storage/VMShare/" + ratio + "/test.dat"
firstLastFile = "/mnt/Storage/VMShare/" + ratio + "/firstlast.dat"

gravityfargo

# Import required modules
import os

# Set the header of the data file
# Date;Time;Global_active_power;Global_reactive_power;Voltage;Global_intensity;Sub_metering_1;Sub_metering_2;Sub_metering_3

def main():
    # Open the data file and read its contents
    contents = openFile('household_power_consumption.txt')

gravityfargo

% Normalize the data to percentage of maximum value
nathan_training_error = nathan_training_error / max(nathan_training_error) * 100;
nathan_testing_error = nathan_testing_error / max(nathan_testing_error) * 100;
nathan_validation_error = nathan_validation_error / max(nathan_validation_error) * 100;

asmaa_training_error = asmaa_training_error / max(asmaa_training_error) * 100;
asmaa_testing_error = asmaa_testing_error / max(asmaa_testing_error) * 100;
asmaa_validation_error = asmaa_validation_error / max(asmaa_validation_error) * 100;

joseph_training_error = joseph_training_error / max(joseph_training_error) * 100;

gravityfargo

% Define ratio labels
Ratio = {'10/90', '20/80', '30/70', '40/60', '50/50', '60/40', '70/30', '80/20', '90/10'};

% Convert to categorical
RatioCat = categorical(Ratio);

% Adaptive Backpropagation
TrainingError_AdaptiveBackpropagation = [0.0838717, 0.0772489, 0.0677041, 0.0685053, 0.0694665, 0.0660926, 0.0657811, 0.0646259, 0.644504];
TestingError_AdaptiveBackpropagation = [8.658576, 7.694914, 6.696497, 6.895893, 6.978141, 6.622154, 6.420111, 6.4860063, 6.266074];
ValidationError_AdaptiveBackpropagation = [8.833146, 7.8823543, 6.8417497, 7.025997, 7.08813, 6.7571335, 6.585877, 6.6701446, 6.468319];