Example #1
0
def perform_kde(data, params):
    grid = GridSearchCV(KernelDensity(), params)
    grid.fit(data)
    kde = grid.best_estimator_
    new_data = kde.sample(1, random_state=0)
    quasar_plots.plot_spectrum(wavelengths, new_data[0])
    plt.title('KDE of qso spectrum')
Example #2
0
def perform_kde_with_pca(data, params, num_comps):
    pca = PCA(n_components=num_comps, whiten=False)
    data = pca.fit_transform(data)
    grid = GridSearchCV(KernelDensity(), params)
    grid.fit(data)
    kde = grid.best_estimator_
    new_data = kde.sample(1, random_state=0)
    new_data = pca.inverse_transform(new_data)
    quasar_plots.plot_spectrum(wavelengths, new_data[0])
    plt.title('KDE with PCA')