'broca',
'gestures',
'hearing',
'learning',
'motor',
'movement',
'speech',
'syntactic',
'verbal',
'visuospatial']
folds = [features[a:b] for a, b in [(0,6), (6,12), (12,18)]]
data = []
with open('times.txt', 'w+') as log:
for fold_id, fold in enumerate(folds):
for ds in range(6,11):
with Timer('DOWNSAMPLED BY {ds}\n'.format_map(locals()),
print_func=log.write) as t:
matrix = BrainMatrix('emd', downsample=ds, name='validation')
matrix.compute_distances(fold)
inconsistency = matrix.plot_dendrogram()
stress = matrix.plot_mds(metric=False)
data.append([ds, fold_id, t.elapsed, inconsistency, stress])
df = pd.DataFrame(columns=['downsample', 'fold', 'time', 'inconsistency', 'stress'], data=data)
df.to_pickle('validation.pkl')
#f, ax = sns.plt.subplots(figsize=(7, 7))
#ax.set(yscale="log")
#sns.pointplot(x='features', y='time', hue='downsample', data=df)
#sns.plt.show()
from brain_matrix import BrainMatrix, load_brainmatrix
from utils import Timer
if __name__ == '__main__':
print('\n\nRUNNING TESTS\n----------------------------')
with Timer('RUN TIME'):
# euclidaen_emd
matrix = BrainMatrix('emd', downsample=30, name='test')
matrix.compute_distances(['syntactic', 'speech', 'semantic', 'music'])
del matrix
matrix = load_brainmatrix('test')
assert isinstance(matrix['semantic']['speech'].distance, float)
# create some figure in figs/
matrix.plot_mds(clusters=2, dim=3, interactive=True)
matrix.plot_dendrogram()
matrix.write_csv()
print('TESTS PASSED')
