I think you need to use the fit or fit_transform function before you predict the classes and use the Binarizer. Have a look at scikit's official documentation for the same.
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html
I require my Arduino Due to be able to plot a square wave with a predetermined duty cycle and time period, without the use of delay() or millis() or any such function that keeps the processor idle unnecessarily. Hence, I've decided to go for interrupts and found a lot of resources on implementing it on the Uno, but none for the Due.
Can someone shed light on how I can set up an interrupt ( I guess two will be required here) from the inbuilt timers in the Due?
@sreu13
fit and fit_transform are actually inbuilt functions found in the scikit-learn library. So I'd suggest you fit your model with the available data using those functions whose application can be found on scikit-learn's documentation and then proceed with the Binarizer.
Hey @kowshik1729, why don't you use a five of the touch keys(shown below), form them up in the shape of a cross and get the sequential data across two of those sensors to detect a left/right or up/down swipe?
https://robokits.co.in/sensors/touch-sensor/capacitive-touch-module-based-on-ttp223
@salmanfaris Thanks! I'll definitely have a look at these!
I require my Arduino Due to be able to plot a square wave with a predetermined duty cycle and time period, without the use of delay() or millis() or any such function that keeps the processor idle unnecessarily. Hence, I've decided to go for interrupts and found a lot of resources on implementing it on the Uno, but none for the Due.
Can someone shed light on how I can set up an interrupt ( I guess two will be required here) from the inbuilt timers in the Due?
Hey @kowshik1729, why don't you use a five of the touch keys(shown below), form them up in the shape of a cross and get the sequential data across two of those sensors to detect a left/right or up/down swipe?
https://robokits.co.in/sensors/touch-sensor/capacitive-touch-module-based-on-ttp223
@sreu13
fit and fit_transform are actually inbuilt functions found in the scikit-learn library. So I'd suggest you fit your model with the available data using those functions whose application can be found on scikit-learn's documentation and then proceed with the Binarizer.
I think you need to use the fit or fit_transform function before you predict the classes and use the Binarizer. Have a look at scikit's official documentation for the same.
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html