ax_eig.plot(xticks, eigenvalues)
ax_var = fig.add_subplot(132)
xticks = np.arange(0, n_eigenvalues, 1)
ax_var.xaxis.set_ticks(xticks)
ax_var.set_title("Explained variance")
ax_var.set_xlabel("Number of Principal Components")
ax_var.plot(xticks, explained_variances)
After Change
mean = METRIC.mean(data)
tpca = TangentPCA(metric=METRIC, n_components=N_COMPONENTS)
tpca = tpca.fit(data, base_point=mean)
tangent_projected_data = tpca.transform(data)
print(
"Coordinates of the Log of the first 5 data points at the mean, "
"projected on the principal components:")
