print 'Change G covar:'
ipdb.set_trace()
gp.covar.diff(gp.covar.setG, 1. * (sp.rand(N, f) < 0.2))
print 'Change G gp:'
ipdb.set_trace()
gp.diff(gp.covar.setG, 1. * (sp.rand(N, f) < 0.2))
ipdb.set_trace()
gp0 = GP(covar=copy.deepcopy(gp.covar), mean=copy.deepcopy(gp.mean))
t0 = time.time()
print 'GP2KronSum.LML():', gp.LML()
print 'Time elapsed:', time.time() - t0
# compare with normal gp
# assess compatibility with this GP
t0 = time.time()
print 'GP.LML():', gp0.LML()
print 'Time elapsed:', time.time() - t0
t0 = time.time()
print 'GP2KronSum.LML_grad():', gp.LML_grad()
print 'Time elapsed:', time.time() - t0
t0 = time.time()
print 'GP.LML_grad():', gp0.LML_grad()
print 'Time elapsed:', time.time() - t0
pdb.set_trace()
gp.optimize()
cov.setRandomParams()
pdb.set_trace()
print ((cov.inv_debug()-cov.inv())**2).mean()<1e-9
print (cov.logdet_debug()-cov.logdet())**2
print (cov.logdet_grad_i_debug(0)-cov.logdet_grad_i(0))**2
if 0:
t0 = time.time()
print 'GP2KronSum.LML():', gp.LML()
print 'Time elapsed:', time.time() - t0
# compare with normal gp
# assess compatibility with this GP
gp0 = GP(covar = copy.deepcopy(gp.covar), mean = copy.deepcopy(gp.mean))
t0 = time.time()
print 'GP.LML():', gp0.LML()
print 'Time elapsed:', time.time() - t0
if 0:
pdb.set_trace()
print gp.LML() - gp0.LML()
print ((gp.LML_grad()['covar'] - gp0.LML_grad()['covar'])**2).mean()
pdb.set_trace()
gp.covar.setRandomParams()
gp0.covar.setParams(gp.covar.getParams())
print gp.LML() - gp0.LML()
print ((gp.LML_grad()['covar'] - gp0.LML_grad()['covar'])**2).mean()
pdb.set_trace()
# gp.col_cov_has_changed_debug()
