def least_factorization( x, basis, domain, basis_indices = None ): assert x.ndim == 2, 'x must be a 2d array (num_dims x num_pts)' num_dims, num_pts = x.shape numpy.set_printoptions(precision=17) basis_indices_list = [] l = numpy.eye( num_pts ) u = numpy.eye( num_pts ) p = numpy.eye( num_pts ) # This is just a guess: this vector could be much larger, or much smaller v = numpy.zeros( ( 1000, 1 ) ) v_index = 0; # Current polynomial degree k_counter = 0 # k(q) gives the degree used to eliminate the q'th point k = numpy.zeros( ( num_pts , 1 ) ) # The current LU row to factor out: lu_row = 0 index_generator = IndexGenerator() # Current degree is k_counter, and we iterate on this while ( lu_row < num_pts ): # We are going to generate the appropriate columns of W -- # these are polynomial indices for degree k of the basis. # Get the current size of k-vectors if basis_indices is None: poly_indices = index_generator.get_isotropic_level_indices(num_dims, k_counter, 1. ) n = len( basis_indices_list ) for i in xrange( len ( poly_indices ) ): poly_indices[i].set_array_index( n + i ) else: n = len( basis_indices_list ) poly_indices = [] for index in basis_indices: if index.level_sum() == k_counter: poly_indices.append( index ) index.set_array_index( n ) n += 1 basis_indices_list += poly_indices current_dim = len( poly_indices ) W = numpy.empty( ( current_dim, num_pts ), numpy.double ) for i, index in enumerate( poly_indices ): W[i,:] = basis.value( x, index, domain ) #tmp to match akils polynomials #W[i,:] /= numpy.sqrt(basis.l2_norm(index))*2 W = dot( p, W.T ) #print '##############' #print lu_row #print W[lu_row:num_pts,:] #print 'p',numpy.nonzero(p)[1] # Row-reduce W according to previous elimination steps end = W.shape[0] for q in range( lu_row ): W[q,:] = W[q,:] / l[q,q]; W[q+1:end,:] -= dot( l[q+1:end,q].reshape( ( end-q-1, 1) ) , W[q,:].reshape( (1, W.shape[1] ) ) ); #print 'W',W #print 'l', l # The mass matrix defining the inner product for this degree M = numpy.eye( current_dim ) #M = numpy.zeros( ( poly_indices.shape[0], poly_indices.shape[0] ), # numpy.double ); #for i, index in enumerate( poly_indices ): # M[i,i] = basis.l2_norm( index ) # Get upper triangular factorization of mass matrix # M = numpy.linalg.cholesky( M ) # lapack function for qr DGEQP3 wm = dot(W[lu_row:num_pts,:] , M ).T Q, R, evec = qr( wm, pivoting = True, mode = 'economic' ) #print 'wm', wm #print 'Q',Q #print 'R',R #print 'e', evec #rnk = matrix_rank( R ) rnk = 0 for i in xrange( R.shape[0] ): # If RHS is too large then if basis is fixed on entry # then an error may be thrown by Python if abs( R[i,i] ) < 0.001 * abs( R[0,0] ): break rnk += 1 #print 'rnk',rnk NN = num_pts - lu_row e = numpy.zeros( ( NN, NN ), numpy.double ) for qq in xrange( NN ): e[evec[qq],qq] = 1.; # Now first we must permute the rows by e #print numpy.nonzero(p[lu_row:num_pts,:])[1] p[lu_row:num_pts,:] = dot( e.T, p[lu_row:num_pts,:] ); # And correct by permuting l as well: #print 'l_sub', l[lu_row:num_pts,:lu_row] #print 'l_sub', l[lu_row:num_pts,:lu_row].shape l[lu_row:num_pts,:lu_row] = dot( e.T, l[lu_row:num_pts,:lu_row] ); #print 'p', numpy.nonzero(p)[1] #print 'l', l # The matrix r gives us inner product information for all rows below # these in W l[lu_row:num_pts,lu_row:lu_row+rnk] = R[:rnk,:].T; #print 'l', l #print 'usub', u[:lu_row,lu_row:lu_row+rnk] #print 'wusb', W[:lu_row,:] #print 'qsub', Q[:,:rnk] # Now we must find inner products of all the other rows above these in W u[:lu_row,lu_row:lu_row+rnk] = dot( dot( W[:lu_row,:], M ), Q[:,:rnk] ); #print 'u', u #print Q if ( v_index+(current_dim*rnk) > v.shape[0] ): v.resize( (v.shape[0] + max(1000,current_dim*rnk), 1 ) ) # The matrix q must be saved in order to characterize basis # order = 'F' is used to reshape using column major # order (used in fortran/matlab) this makes code consisten with matlab v[v_index:v_index+(current_dim*rnk)] = numpy.reshape( Q[:,:rnk], (rnk*current_dim,1),order='F').copy(); v_index = v_index+(current_dim*rnk); #print Q[:,:rnk] #print 'v',v[:v_index] # Update degree markers, and node and degree count k[lu_row:(lu_row+rnk)] = k_counter; lu_row = lu_row + rnk; k_counter = k_counter + 1; # Chop off parts of unnecessarily allocated vector v v = numpy.resize( v, ( v_index ) ); # Make matrix H: H = get_least_polynomial_coefficients( v, num_dims, num_pts, k, #basis_indices ) basis_indices_list ) return l,u,p,H,v,k,basis_indices_list
def sequential_least_factorization( x, basis, domain, N = None, basis_indices = None ): assert x.ndim == 2, 'x must be a 2d array (num_dims x num_pts)' num_dims, num_pts = x.shape if N is None: N = num_pts else: assert N <= num_pts basis_indices_list = [] l = numpy.eye( N ) u = numpy.eye( N ) p = numpy.eye( N ) # This is just a guess: this vector could be much larger, or much smaller v = numpy.zeros( ( 1000, 1 ) ) v_index = 0; # Current polynomial degree k_counter = 0 # k(q) gives the degree used to eliminate the q'th point k = numpy.zeros( ( N , 1 ) ) # The current LU row to factor out: lu_row = 0 # Every time the basis degree is increased compute the rank # of the matrix formed using the new basis terms find_rank = True index_generator = IndexGenerator() while ( lu_row < N ): # Get the current size of k-vectors if find_rank: if basis_indices is None: poly_indices = \ index_generator.get_isotropic_level_indices(num_dims, k_counter, 1. ) n = len( basis_indices_list ) for i in xrange( len ( poly_indices ) ): poly_indices[i].set_array_index( n + i ) else: print '###############', k_counter poly_indices = [] for index in basis_indices: if index.level_sum() == k_counter: poly_indices.append( index ) basis_indices_list += poly_indices current_dim = len( poly_indices ) W = numpy.empty( ( current_dim, num_pts ), numpy.double ) for i, index in enumerate( poly_indices ): W[i,:] = basis.value( x, index, domain ) #tmp to match akils polynomials #W[i,:] /= numpy.sqrt(basis.l2_norm(index))*2 W = dot( p, W.T ) # Row-reduce W according to previous elimination steps end = W.shape[0] for q in range( lu_row ): W[q,:] = W[q,:] / l[q,q]; W[q+1:end,:] -= dot( l[q+1:end,q].reshape( ( end-q-1, 1) ) , W[q,:].reshape( (1, W.shape[1] ) ) ); #wm = dot(W[lu_row:N,:] , M ).T wm = W[lu_row:N,:].T #print 'W', numpy.sqrt( numpy.sum( wm**2, axis = 0 ) ) if find_rank: #rnk = numpy.linalg.matrix_rank( wm ) Q, R, evec = qr( wm, pivoting = True, mode = 'economic' ) rnk = 0 for i in range(R.shape[0] ): # If RHS is too large then if basis is fixed on entry # then an error may be thrown by Python if abs( R[i,i] ) < 0.001 * abs( R[0,0] ): break rnk += 1 find_rank = False # Find the column with the largest inner norm and compute inner products column_norms = numpy.sqrt( numpy.sum( wm**2, axis = 0 ) ) next_index = column_norms.argmax() row = wm[:,next_index] / column_norms[next_index] tmp = numpy.dot( row, wm ) inner_products = numpy.empty( ( tmp.shape[0] ), numpy.double ) inner_products[0] = column_norms[next_index] if ( next_index > 0 ): inner_products[1:next_index+1] = tmp[:next_index] if ( next_index < tmp.shape[0]-1 ): inner_products[next_index+1:] = tmp[next_index+1:] # Determine LU permutations. # Generate permutation indices I I = numpy.empty( ( inner_products.shape[0] ), numpy.int32 ) j = 0; I[j] = next_index; j += 1; for i in xrange( I.shape[0] ): if ( i != next_index ): I[j] = i j += 1 p[lu_row:N,:] = permute_matrix_rows( p[lu_row:N,:], I ) # Permute rows of l l[lu_row:N,:lu_row] = \ permute_matrix_rows( l[lu_row:N,:lu_row], I ) # Update l with inner product information l[lu_row:N,lu_row] = inner_products; # Compute inner products with rows above u[:lu_row,lu_row] = dot( W[:lu_row,:], row ); # allocate enough memory to store new information if v.shape[0] < v_index+current_dim: v.resize( v.shape[0]+1000, 1 ) # Save current information v[v_index:v_index+current_dim,0] = row # Update counters v_index = v_index + current_dim; k[lu_row] = k_counter lu_row += 1 if rnk < 2: k_counter += 1 find_rank = True else: rnk -= 1 # Chop off parts of unnecessarily allocated vector v v = numpy.resize( v, ( v_index ) ); # Make matrix H: H = get_least_polynomial_coefficients( v, num_dims, num_pts, k, basis_indices ) return l,u,p,H,v,k,basis_indices_list