def prepare_data(seqs_x, seqs_y, seqs_z, worddict, maxlen=None, n_words=20000):
 seqsX = []
 seqsY = []
 seqsZ = []
 for cc in seqs_x:
  seqsX.append([(worddict[w] if (worddict[w] < n_words) else 1) for w in cc.split()])
 for cc in seqs_y:
  seqsY.append([(worddict[w] if (worddict[w] < n_words) else 1) for w in cc.split()])
 for cc in seqs_z:
  seqsZ.append([(worddict[w] if (worddict[w] < n_words) else 1) for w in cc.split()])
 seqs_x = seqsX
 seqs_y = seqsY
 seqs_z = seqsZ
 lengths_x = [len(s) for s in seqs_x]
 lengths_y = [len(s) for s in seqs_y]
 lengths_z = [len(s) for s in seqs_z]
 if (maxlen != None):
  new_seqs_x = []
  new_seqs_y = []
  new_seqs_z = []
  new_lengths_x = []
  new_lengths_y = []
  new_lengths_z = []
  for (l_x, s_x, l_y, s_y, l_z, s_z) in zip(lengths_x, seqs_x, lengths_y, seqs_y, lengths_z, seqs_z):
   if ((l_x < maxlen) and (l_y < maxlen) and (l_z < maxlen)):
    new_seqs_x.append(s_x)
    new_lengths_x.append(l_x)
    new_seqs_y.append(s_y)
    new_lengths_y.append(l_y)
    new_seqs_z.append(s_z)
    new_lengths_z.append(l_z)
  lengths_x = new_lengths_x
  seqs_x = new_seqs_x
  lengths_y = new_lengths_y
  seqs_y = new_seqs_y
  lengths_z = new_lengths_z
  seqs_z = new_seqs_z
  if ((len(lengths_x) < 1) or (len(lengths_y) < 1) or (len(lengths_z) < 1)):
   return (None, None, None, None, None, None)
 n_samples = len(seqs_x)
 maxlen_x = (numpy.max(lengths_x) + 1)
 maxlen_y = (numpy.max(lengths_y) + 1)
 maxlen_z = (numpy.max(lengths_z) + 1)
 x = numpy.zeros((maxlen_x, n_samples)).astype('int64')
 y = numpy.zeros((maxlen_y, n_samples)).astype('int64')
 z = numpy.zeros((maxlen_z, n_samples)).astype('int64')
 x_mask = numpy.zeros((maxlen_x, n_samples)).astype('float32')
 y_mask = numpy.zeros((maxlen_y, n_samples)).astype('float32')
 z_mask = numpy.zeros((maxlen_z, n_samples)).astype('float32')
 for (idx, [s_x, s_y, s_z]) in enumerate(zip(seqs_x, seqs_y, seqs_z)):
  x[:lengths_x[idx], idx] = s_x
  x_mask[:(lengths_x[idx] + 1), idx] = 1.0
  y[:lengths_y[idx], idx] = s_y
  y_mask[:(lengths_y[idx] + 1), idx] = 1.0
  z[:lengths_z[idx], idx] = s_z
  z_mask[:(lengths_z[idx] + 1), idx] = 1.0
 return (x, x_mask, y, y_mask, z, z_mask)

def prepare_data ( seqs_x , seqs_y , seqs_z , worddict , maxlen = None , n_words = 20000 ) : seqsX = [ ] seqsY = [ ] seqsZ = [ ] for cc in seqs_x : seqsX . append ( [ ( worddict [ w ] if ( worddict [ w ] < n_words ) else 1 ) for w in cc . split ( ) ] ) for cc in seqs_y : seqsY . append ( [ ( worddict [ w ] if ( worddict [ w ] < n_words ) else 1 ) for w in cc . split ( ) ] ) for cc in seqs_z : seqsZ . append ( [ ( worddict [ w ] if ( worddict [ w ] < n_words ) else 1 ) for w in cc . split ( ) ] ) seqs_x = seqsX seqs_y = seqsY seqs_z = seqsZ lengths_x = [ len ( s ) for s in seqs_x ] lengths_y = [ len ( s ) for s in seqs_y ] lengths_z = [ len ( s ) for s in seqs_z ] if ( maxlen != None ) : new_seqs_x = [ ] new_seqs_y = [ ] new_seqs_z = [ ] new_lengths_x = [ ] new_lengths_y = [ ] new_lengths_z = [ ] for ( l_x , s_x , l_y , s_y , l_z , s_z ) in zip ( lengths_x , seqs_x , lengths_y , seqs_y , lengths_z , seqs_z ) : if ( ( l_x < maxlen ) and ( l_y < maxlen ) and ( l_z < maxlen ) ) : new_seqs_x . append ( s_x ) new_lengths_x . append ( l_x ) new_seqs_y . append ( s_y ) new_lengths_y . append ( l_y ) new_seqs_z . append ( s_z ) new_lengths_z . append ( l_z ) lengths_x = new_lengths_x seqs_x = new_seqs_x lengths_y = new_lengths_y seqs_y = new_seqs_y lengths_z = new_lengths_z seqs_z = new_seqs_z if ( ( len ( lengths_x ) < 1 ) or ( len ( lengths_y ) < 1 ) or ( len ( lengths_z ) < 1 ) ) : return ( None , None , None , None , None , None ) n_samples = len ( seqs_x ) maxlen_x = ( numpy . max ( lengths_x ) + 1 ) maxlen_y = ( numpy . max ( lengths_y ) + 1 ) maxlen_z = ( numpy . max ( lengths_z ) + 1 ) x = numpy . zeros ( ( maxlen_x , n_samples ) ) . astype ( 'int64' ) y = numpy . zeros ( ( maxlen_y , n_samples ) ) . astype ( 'int64' ) z = numpy . zeros ( ( maxlen_z , n_samples ) ) . astype ( 'int64' ) x_mask = numpy . zeros ( ( maxlen_x , n_samples ) ) . astype ( 'float32' ) y_mask = numpy . zeros ( ( maxlen_y , n_samples ) ) . astype ( 'float32' ) z_mask = numpy . zeros ( ( maxlen_z , n_samples ) ) . astype ( 'float32' ) for ( idx , [ s_x , s_y , s_z ] ) in enumerate ( zip ( seqs_x , seqs_y , seqs_z ) ) : x [ : lengths_x [ idx ] , idx ] = s_x x_mask [ : ( lengths_x [ idx ] + 1 ) , idx ] = 1.0 y [ : lengths_y [ idx ] , idx ] = s_y y_mask [ : ( lengths_y [ idx ] + 1 ) , idx ] = 1.0 z [ : lengths_z [ idx ] , idx ] = s_z z_mask [ : ( lengths_z [ idx ] + 1 ) , idx ] = 1.0 return ( x , x_mask , y , y_mask , z , z_mask )
stage osm data into a temporary set of tables using osm2pgsql .