Transformation en ondelettes dans l’apprentissage de la machine
import pywt import matplotlib.pyplot as plt db_wavelets = pywt.wavelist('db')[:5] print(db_wavelets) *** ['db1', 'db2', 'db3', 'db4', 'db5'] fig, axarr = plt.subplots(ncols=5, nrows=5, figsize=(20,16)) fig.suptitle('Daubechies family of wavelets', fontsize=16) for col_no, waveletname in enumerate(db_wavelets): wavelet = pywt.Wavelet(waveletname) no_moments = wavelet.vanishing_moments_psi family_name = wavelet.family_name for row_no, level in enumerate(range(1,6)): wavelet_function, scaling_function, x_values = wavelet.wavefun(level = level) axarr[row_no, col_no].set_title("{} - level {}\n{} vanishing moments\n{} samples".format( waveletname, level, no_moments, len(x_values)), loc='left') axarr[row_no, col_no].plot(x_values, wavelet_function, 'bD--') axarr[row_no, col_no].set_yticks([]) axarr[row_no, col_no].set_yticklabels([]) plt.tight_layout() plt.subplots_adjust(top=0.9) plt.show()