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License: MIT License
Implementation of my master's thesis
License: MIT License
The class label needs to be added to the measurement. The class label is written in the filename.
Good start already done is here.
Repeat LSTM
Understand how RNNs work
Understand how they or other networks can be used for sequence classification
See here.
Do this instead of #46.
The skew
and kurt
become NaN currently sometimes.
Related to #37 .
Lukas develops a fingerprint from the same data I am using. It might be a good idea to compare my approach to his and start with implementing his approach, also for practice.
read_recursive
and read
Good sources to answer this question:
It was implemented from Lukas and TU Graz using this Matlab function. Check for some known values that my function is doing something similar.
Currently all negative PD values are converted to 0. Try converting them to their corresponding positive value and see whether this will improve the accuracy.
This is necessary to do that because otherwise too much data needs to be allocated crashing the program.
I think the actual data, but I should an expert to be sure.
The values are 0 for a lot of datasets. Investigate this.
Add a classifier combining the bag of words approach with the MLP classifier over a particular duration e.g. 30 seconds.
Lukuas bins the PD values into 100 bins. Is this a good idea before doing the correlation?
Make sure that the data is deleted from the history as well.
Moritz sent me .mat
files. I have to convert these to .csv
files in order to work with them. Here is a good source for doing that.
I want to replicate the plots from the data to get a better understanding of the data.
This will speed up the processing a lot.
Thereby think whether the access to PD diff via the function pd_diff
is correct. Currently thought of solutions:
PD_DIFF
in measurementTIMEDIFF
from measurementSee this function.
Questions:
Building upon #37 I want to normalize the time series. I am not able to do that currently with the TimeSeriesMinMaxScaler from tslearn. So the goal of this issue is to either make it work or implement an adaption myself. The adaption is preferred, because I want to have a scaler which does a global scaling and not a scaling per time series.
I think it shouldn't be possible currently with the amount of data.
Related to #3.
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