''' Created on Feb 18, 2013 @author: tjhunter ''' import numpy as np from mm.arterial_hkt.gmrf_learning.utils import test_data from mm.arterial_hkt.gmrf_learning.cvx import * R = np.ones(5) U = np.ones(3) cols = np.array([2,3,4]) rows = np.array([0,0,0]) test_data(R, U, rows, cols) edge_count = np.array([20,20,20]) min_variance=1e-2 min_edge_count = 0 gmrf_learn_cov_cvx(R, U, rows, cols, edge_count,min_variance,min_edge_count)
P = np.arange(m)/float(m)+1 rows = np.zeros((n-1,),dtype=np.int) cols = np.arange(1,n,dtype=np.int) return (D,P,rows,cols) (D,P,rows,cols) = star(5,4) X = build_dense(D, P, rows, cols) Xs = build_sparse(D, P, rows, cols) l1 = logdet_dense(D, P, rows, cols) l2 = logdet_dense_chol(D, P, rows, cols) l3 = logdet_cholmod(D, P, rows, cols) (M,Dn,Pn) = normalized_problem(D, P, rows, cols) test_data(D, P, rows, cols) W = la.inv(X) #Q = random_projection_cholmod(D, U, rows, cols, k, factor) Q = random_projection_cholmod_csc(Xs, k=1000) A = Q.T print A.shape R = np.sum(A*A,axis=1) U = np.sum(A[rows]*A[cols],axis=1) R_ = W.diagonal() U_ = W[rows,cols] #X = build_sparse(D, P, rows, cols) #(eis,_)=eigsh(X, k=1, sigma=-1, which='LM') #ei = eis[0] #is_psd_dense(R, U, rows, cols)
