SVD example

svd.py

101 
102     eigen_vecs, eigen_vals, Vh = \
103         np.linalg.svd(X.T, full_matrices=False, compute_uv=True)
104 
105     print("svd done")
106 
107     print(eigen_vals.shape)
108     print(eigen_vecs.shape)
109 
110     NPCA = 5000
111 
112     print(eigen_vals[NPCA-1])
113 
114     cev = 100 - (np.sum(eigen_vals) - np.sum(eigen_vals[:NPCA]))  / np.sum(eigen_vals) * 100
115     print("Cumulative explained variance = %f %%" % cev )
116     
117     X  = np.einsum("jk,kl->jl", X,  eigen_vecs[:,:NPCA])
118     Xs = np.einsum("jk,kl->jl", Xs, eigen_vecs[:,:NPCA])
119 
120     print(X.shape)