1D signal data processing
I have 1D signal data which I am provided, its basically synthetic data, not realistic.
I have 1000 CSV files for the same which have the following variables.
1=depth(index), 2=signal value(1D signal), 3=event id, 4=event id as a class (target variable), 5=event and class (not useful)
0 , 3 , 0 , 0 , 0
1 , 5 , 0 , 0 , 0
2 , 2 , 0 , 0 , 0
3 , 3 , 1 , 1 , 1000
4 , 6 , 0 , 1 , 1
5 , 1 , 0 , 1 , 1
6 , 2 , 2 , 2 , 2000
7 , 6 , 0 , 2 , 2
8 , 2 , 0 , 2 , 2
9 , 6 , 0 , 2 , 2
I want to train multiple classification models using keras, pytorch, seq2seq.
I haveing various idea in my mind how to create the data so it looks better and feed them in my TCN/CNN networks.
feeding as a 1D array as feature and 1D array as label in a classification model does not make a good sense.
I need some ideas, if someone has done it before.
Thanks in Advance.
python pandas numpy signal-processing
asked Nov 21 '18 at 17:54
Shivam_hbtiShivam_hbti
416
add a comment |
I have 1D signal data which I am provided, its basically synthetic data, not realistic.
I have 1000 CSV files for the same which have the following variables.
1=depth(index), 2=signal value(1D signal), 3=event id, 4=event id as a class (target variable), 5=event and class (not useful)
0 , 3 , 0 , 0 , 0
1 , 5 , 0 , 0 , 0
2 , 2 , 0 , 0 , 0
3 , 3 , 1 , 1 , 1000
4 , 6 , 0 , 1 , 1
5 , 1 , 0 , 1 , 1
6 , 2 , 2 , 2 , 2000
7 , 6 , 0 , 2 , 2
8 , 2 , 0 , 2 , 2
9 , 6 , 0 , 2 , 2
I want to train multiple classification models using keras, pytorch, seq2seq.
I haveing various idea in my mind how to create the data so it looks better and feed them in my TCN/CNN networks.
feeding as a 1D array as feature and 1D array as label in a classification model does not make a good sense.
I need some ideas, if someone has done it before.
Thanks in Advance.
python pandas numpy signal-processing
asked Nov 21 '18 at 17:54
Shivam_hbtiShivam_hbti
416
add a comment |
I have 1D signal data which I am provided, its basically synthetic data, not realistic.
I have 1000 CSV files for the same which have the following variables.
1=depth(index), 2=signal value(1D signal), 3=event id, 4=event id as a class (target variable), 5=event and class (not useful)
0 , 3 , 0 , 0 , 0
1 , 5 , 0 , 0 , 0
2 , 2 , 0 , 0 , 0
3 , 3 , 1 , 1 , 1000
4 , 6 , 0 , 1 , 1
5 , 1 , 0 , 1 , 1
6 , 2 , 2 , 2 , 2000
7 , 6 , 0 , 2 , 2
8 , 2 , 0 , 2 , 2
9 , 6 , 0 , 2 , 2
I want to train multiple classification models using keras, pytorch, seq2seq.
I haveing various idea in my mind how to create the data so it looks better and feed them in my TCN/CNN networks.
feeding as a 1D array as feature and 1D array as label in a classification model does not make a good sense.
I need some ideas, if someone has done it before.
Thanks in Advance.
python pandas numpy signal-processing
asked Nov 21 '18 at 17:54
Shivam_hbtiShivam_hbti
416
I have 1D signal data which I am provided, its basically synthetic data, not realistic.
I have 1000 CSV files for the same which have the following variables.
1=depth(index), 2=signal value(1D signal), 3=event id, 4=event id as a class (target variable), 5=event and class (not useful)
0 , 3 , 0 , 0 , 0
1 , 5 , 0 , 0 , 0
2 , 2 , 0 , 0 , 0
3 , 3 , 1 , 1 , 1000
4 , 6 , 0 , 1 , 1
5 , 1 , 0 , 1 , 1
6 , 2 , 2 , 2 , 2000
7 , 6 , 0 , 2 , 2
8 , 2 , 0 , 2 , 2
9 , 6 , 0 , 2 , 2
I want to train multiple classification models using keras, pytorch, seq2seq.
I haveing various idea in my mind how to create the data so it looks better and feed them in my TCN/CNN networks.
feeding as a 1D array as feature and 1D array as label in a classification model does not make a good sense.
I need some ideas, if someone has done it before.
Thanks in Advance.
python pandas numpy signal-processing
python pandas numpy signal-processing
asked Nov 21 '18 at 17:54
Shivam_hbtiShivam_hbti
416
asked Nov 21 '18 at 17:54
Shivam_hbtiShivam_hbti
416
asked Nov 21 '18 at 17:54
Shivam_hbtiShivam_hbti
416
asked Nov 21 '18 at 17:54
Shivam_hbtiShivam_hbti
416
asked Nov 21 '18 at 17:54
Shivam_hbtiShivam_hbti
416
416
add a comment |
add a comment |
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