An Evolutionary Approach for Efficient Prototyping of Large Time Series Datasets
BRIEF EXPLANATION
Enumerating keywords
pip ...
- numpy
- pandas
- deap
- scikit-learn
- matplotlib
Bellow there is a Quickstart Guide to EvolutionaryPrototypingTS.
Next piece of code shows a basic usage of the library. In it is shown how to import a file and x
.. code-block:: python
>>> from ga_segments.ga import GA_segments
>>> import pandas as pd
>>> series = pd.read_csv('./data/50words_TRAIN', header=None).values[:, 1:]
>>> ga = GA_segments()
>>> centroid, best_fitness, log = ga.calculate_centroids(series)
Example of use of Nearest Centroid algorithm with GA-Segments:
.. code-block:: python
>>> from ga_segments.nc import NC
>>> import pandas as pd
>>> from sklearn.model_selection import train_test_split
>>> series = pd.read_csv('./data/50words_TRAIN', header=None)
>>> x, y = series[:, 1:], series[:, 0]
>>> x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.2)
>>> nc = NC()
>>> nc.fit(x_train, y_train)
>>> nc.predict(x_val)
>>> nc.labels
If eeglib has been useful in your research, please, consider citing the next article.
This library was initialy a Final Degree Project and you can find the documentation of the development in the next link:
- Final Degree Project Documentation (Spanish)
Later it was extented as part of a Master's thesis that can be found in the next link:
There are also some papers related to this library that can be seen bellow:
Characterisation of mobile-device tasks by their associated cognitive load through EEG data processing eeglib: computational analysis of cognitive performance during the use of video games