Ramanujan Filter Banks - Demos

# Ramanujan Filter Banks

Demos

import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
print('spkit version :', sp.__version__)
spkit version : 0.0.9.7

Example demo 1

sp.RFB_example_1(period=10, SNR=0, seed=10)
signal with repitative patterns
top 10 periods:  [10  5 11 18 17 16 15 14 13 12]

Example demo 2

sp.RFB_example_2(periods=[3, 7, 11], signal_length=100, SNR=10, seed=15)
DFT, L1 + penality, L2 +  penalty, L1 without penality
Top 10 periods :
 - using L1 regularisation with penalty    :  [ 3  7 11  2  6  5 10 15  4  8]
 - using L1 regularisation with no penalty :  [ 3  2 89 65 61 67 52 71 70 79]
 - using L2 regularisation with penalty    :  [ 3  7  2  6  5 11  4  8 10 13]
 - using no regularisation no  penalty     :  [ 3 11  7 28 50 90 34 26 27 29]

Example demo 2 with diff periods

sp.RFB_example_2(periods=[3,7,14], signal_length=100, SNR=10, seed=15)
DFT, L1 + penality, L2 +  penalty, L1 without penality
Top 10 periods :
 - using L1 regularisation with penalty    :  [ 3  2  6  7 10  5 11 13 15 16]
 - using L1 regularisation with no penalty :  [ 2  3 89 65 67 68 64 61 79 87]
 - using L2 regularisation with penalty    :  [ 3  2  6  7  5 11 10 13  9  8]
 - using no regularisation no  penalty     :  [ 7  3 14 90 34 26 27 28 29 30]

Total running time of the script: (0 minutes 5.550 seconds)

Related examples

Release Highlights for spkit 0.0.9.6

Release Highlights for spkit 0.0.9.6

Ramanujan Filter Banks Example

Ramanujan Filter Banks Example

Ramanujan Dictionary - with sparse penalty

Ramanujan Dictionary - with sparse penalty

Auditory Attention: Plot Group Data

Auditory Attention: Plot Group Data

EEG Artifact removal using ICA

EEG Artifact removal using ICA

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