Hi All, I have a bunch of sensor data that comes as waveforms which are converted to floating point numbers at some sampling rate. I have 6 sensors whose values come as data in 6 columns and for each waveform there are 80 sequences of floating point values. All of this represent input data related to 1 out of 120 categories of output data. So, my dataset looks like this:
columns ----> sensor1 sensor2 sensor3 sensor4 sensor5 sensor6 output
rows
|
1 x11 x21 x31 x41 x51 x61 y1
2 x21 x22 x32 x42 x52 x62 y1
. ……………………………………………………………….
. …………………………………………………………………
80 x801 x802 x803 x804 x805 x806 y1
So, for one reading of y1, x11 to x806 forms the dataset, but I need to make sure that x11, x21…x801 is a sequence, x21,x22, x32…x802 is a sequence etc. Which means each column data for rows 1 to 80 is a sequence and the 6 sequences together is a pattern for a y1. The amplitude of x values could change for a given y, but the sequence would be similar. That is, a, b, c,……… and 1.2a, 1.2b,1.2c… would generate same y output as long as a, b,c etc occurs in same/similar order across the 6 sensors
I need to quickly develop a model in fastai where given a bunch of training data, I need to have a predictor such that when the 80 rows but of sensor data is streamed in, output category is detected.
Given a labelled dataset, I have following questions:
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How do I load this data in fastai as a 6 sequences of 80 values for a given output? I could transpose 80 rows to 80 columns, but how do I link columns as sequence representation for each sensor
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Once I figure the dataset structure for the train/set for the labelled data, what model class do I choose? It looks like a RNN problem, but I would not have millions of rows of dataset, it is fairly small with a few 10s of thousands for each category. In that case would it still be RNN ?
Appreciate any guidance on getting started. Is Rossman sample kind of notebook a good way to get started?