Data Science, Machine Learning, Pattern Recognition, Pca Analysis, Data Reduction.
components (SVD) U, S, VT = np.linalg.svd(B/np.sqrt(nPoints),full_matrices=0) Then find the first confidence interval by, theta = 2 * np.pi * np.arange(0,1,0.01) # 1-std confidence interval Xstd = U @ np.diag(S) @ np.array([np.cos(theta),np.sin(theta)]) To find the first three confidence intervals,