def OMP_CV_ORACLE( pts, vals, pce ):
# obtain best solver step using exact errors
out = orthogonal_matching_pursuit( A, vals.squeeze(),
0.,
numpy.iinfo(numpy.int32).max,
0 )
#solutions = out[0]
pce.set_coefficients( out[0] )
pred_vals = pce.evaluate_set( test_pts )
error_norms = numpy.sqrt( numpy.sum( errors**2, axis = 0 ) /
test_vals.shape[0] )
argmin = numpy.argmin( error_norms )
pce.set_coefficients( out[0][:,argmin].reshape( out[0].shape[0], 1 ) )
def least_squares_modified( pts, vals, pce, max_degree ):
num_pts = pts.shape[1]
num_reps = 2
num_dims = pts.shape[0]
indices = PolyIndexVector()
from indexing_cpp import get_hyperbolic_indices
all_indices = PolyIndexVector()
get_hyperbolic_indices( num_dims, max_degree, 1, all_indices )
#for index in all_indices:
# print index
num_basis_terms = min( 2*num_pts, len( all_indices ) / ( num_reps /2 ) )
#num_basis_terms = len( all_indices )
print num_basis_terms
key_sorter = lambda x: x.level_sum()
indices_dict = dict()
for n in xrange( num_reps ):
I = numpy.random.randint( 0, len( all_indices ), (num_basis_terms) )
#I = numpy.arange( len( all_indices ) )
indices = PolyIndexVector()
indices.resize( num_basis_terms )
for i in xrange( I.shape[0] ):
indices[i] = all_indices[int(I[i])]
indices[i].set_array_index( i )
indices_list = sorted( indices,
key = key_sorter )[::-1]
for i in xrange( len( indices ) ):
indices[i] = indices_list[i]
indices[i].set_array_index( i )
#for index in indices:
# print index, max_degree
pce.set_basis_indices( indices )
A = pce.build_vandermonde( pts )
#coeff = svd_solve_default( A, vals )[0]
#pce.set_coefficients( coeff.reshape( ( coeff.shape[0], 1 ) ) )
from compressed_sensing_cpp import orthogonal_matching_pursuit
sols, metrics = orthogonal_matching_pursuit( A, vals.squeeze(),
0.,
num_pts,
0 )
coeff = sols[:,-1].reshape( sols.shape[0], 1 )
#for index in indices:
for i in metrics[1,:]:
index = indices[int(i)]
if not indices_dict.has_key( index ):
indices_dict[index] = [coeff[index.get_array_index(),0]]
else:
indices_dict[index].append( coeff[index.get_array_index(),0] )
best_indices = PolyIndexVector()
best_indices.resize( len( indices_dict ) )
i = 0
for index in indices_dict:
best_indices[i] = index
i += 1
key_sorter = lambda x: ( numpy.absolute( numpy.asarray( indices_dict[x] ) ).mean() )
best_indices = sorted( best_indices,
key = key_sorter )[::-1]
i = 0
for index in best_indices:
print index, key_sorter( index )
i += 1
print i
if i >= num_pts:
print i
break
assert False