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')
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')
