<s> from __future__ import absolute_import <EOL> import inspect <EOL> import itertools <EOL> import random <EOL> import warnings <EOL> import numpy as np <EOL> from . gd import GradientDescent <EOL> from . bfgs import Lbfgs <EOL> from . cg import NonlinearConjugateGradient <EOL> from . rprop import Rprop <EOL> from . rmsprop import RmsProp <EOL> from . adadelta import Adadelta <EOL> from . adam import Adam <EOL> try : <EOL> from sklearn . grid_search import ParameterSampler <EOL> except ImportError : <EOL> pass <EOL> def is_garray ( cand ) : <EOL> return hasattr ( cand , '<STR_LIT>' ) <EOL> def is_array ( cand ) : <EOL> return is_garray ( cand ) or isinstance ( cand , np . ndarray ) <EOL> def clear_info ( info ) : <EOL> """<STR_LIT>""" <EOL> items = info . iteritems ( ) <EOL> items = ( ( k , float ( v . reshape ( ( <NUM_LIT:1> , ) ) [ <NUM_LIT:0> ] ) if is_array ( v ) and v . size == <NUM_LIT:1> else v ) <EOL> for k , v in items ) <EOL> items = ( ( k , v ) for k , v in items if not is_array ( v ) ) <EOL> items = ( ( k , v ) for k , v in items if k not in ( '<STR_LIT:args>' , '<STR_LIT>' ) ) <EOL> return dict ( items ) <EOL> def coroutine ( f ) : <EOL> """<STR_LIT>""" <EOL> def started ( * args , ** kwargs ) : <EOL> cr = f ( * args , ** kwargs ) <EOL> next ( cr ) <EOL> return cr <EOL> return started <EOL> def aslist ( item ) : <EOL> if not isinstance ( item , ( list , tuple ) ) : <EOL> item = [ item ] <EOL> return item <EOL> def mini_slices ( n_samples , batch_size ) : <EOL> """<STR_LIT>""" <EOL> n_batches , rest = divmod ( n_samples , batch_size ) <EOL> if rest != <NUM_LIT:0> : <EOL> n_batches += <NUM_LIT:1> <EOL> return [ slice ( i * batch_size , ( i + <NUM_LIT:1> ) * batch_size ) for i in range ( n_batches ) ] <EOL> def draw_mini_slices ( n_samples , batch_size , with_replacement = False ) : <EOL> slices = mini_slices ( n_samples , batch_size ) <EOL> idxs = range ( len ( slices ) ) <EOL> if with_replacement : <EOL> yield random . choice ( slices ) <EOL> else : <EOL> while True : <EOL> random . shuffle ( idxs ) <EOL> for i in idxs : <EOL> yield slices [ i ] <EOL> def draw_mini_indices ( n_samples , batch_size ) : <EOL> assert n_samples > batch_size <EOL> idxs = range ( n_samples ) <EOL> random . shuffle ( idxs ) <EOL> pos = <NUM_LIT:0> <EOL> while True : <EOL> while pos + batch_size <= n_samples : <EOL> yield idxs [ pos : pos + batch_size ] <EOL> pos += batch_size <EOL> batch = idxs [ pos : ] <EOL> needed = batch_size - len ( batch ) <EOL> random . shuffle ( idxs ) <EOL> batch += idxs [ <NUM_LIT:0> : needed ] <EOL> yield batch <EOL> pos = needed <EOL> def optimizer ( identifier , wrt , * args , ** kwargs ) : <EOL> """<STR_LIT>""" <EOL> klass_map = { <EOL> '<STR_LIT>' : GradientDescent , <EOL> '<STR_LIT>' : Lbfgs , <EOL> '<STR_LIT>' : NonlinearConjugateGradient , <EOL> '<STR_LIT>' : Rprop , <EOL> '<STR_LIT>' : RmsProp , <EOL> '<STR_LIT>' : Adadelta , <EOL> '<STR_LIT>' : Adam , <EOL> } <EOL> klass = klass_map [ identifier ] <EOL> argspec = inspect . getargspec ( klass . __init__ ) <EOL> if argspec . keywords is None : <EOL> expected_keys = set ( argspec . args ) <EOL> given_keys = set ( kwargs . keys ( ) ) <EOL> unused_keys = given_keys - expected_keys <EOL> for i in unused_keys : <EOL> warnings . warn ( '<STR_LIT>' <EOL> % ( i , klass ) ) <EOL> used_keys = expected_keys & given_keys <EOL> kwargs = dict ( ( k , kwargs [ k ] ) for k in used_keys ) <EOL> try : <EOL> opt = klass ( wrt , * args , ** kwargs ) <EOL> except TypeError : <EOL> raise TypeError ( '<STR_LIT>' % ( klass , argspec . args ) ) <EOL> return opt <EOL> def shaped_from_flat ( flat , shapes ) : <EOL> """<STR_LIT>""" <EOL> shapes = [ ( i , ) if isinstance ( i , int ) else i for i in shapes ] <EOL> sizes = [ np . prod ( i ) for i in shapes ] <EOL> n_used = <NUM_LIT:0> <EOL> views = [ ] <EOL> for size , shape in zip ( sizes , shapes ) : <EOL> this = flat [ n_used : n_used + size ] <EOL> n_used += size <EOL> this . shape = shape <EOL> views . append ( this ) <EOL> return views <EOL> def empty_with_views ( shapes , empty_func = np . empty ) : <EOL> """<STR_LIT>""" <EOL> shapes = [ ( i , ) if isinstance ( i , int ) else i for i in shapes ] <EOL> sizes = [ np . prod ( i ) for i in shapes ] <EOL> n_pars = sum ( sizes ) <EOL> flat = empty_func ( n_pars ) <EOL> views = shaped_from_flat ( flat , shapes ) <EOL> return flat , views <EOL> def minibatches ( arr , batch_size , d = <NUM_LIT:0> ) : <EOL> """<STR_LIT>""" <EOL> if d == <NUM_LIT:0> : <EOL> n_batches , rest = divmod ( len ( arr ) , batch_size ) <EOL> else : <EOL> n_batches , rest = divmod ( arr . shape [ d ] , batch_size ) <EOL> if rest : <EOL> n_batches += <NUM_LIT:1> <EOL> slices = ( slice ( i * batch_size , ( i + <NUM_LIT:1> ) * batch_size ) <EOL> for i in range ( n_batches ) ) <EOL> if d == <NUM_LIT:0> : <EOL> res = [ arr [ i ] for i in slices ] <EOL> elif d == <NUM_LIT:1> : <EOL> res = [ arr [ : , i ] for i in slices ] <EOL> elif d == <NUM_LIT:2> : <EOL> res = [ arr [ : , : , i ] for i in slices ] <EOL> return res <EOL> def iter_minibatches ( lst , batch_size , dims , n_cycles = False , random_state = None ) : <EOL> """<STR_LIT>""" <EOL> batches = [ minibatches ( i , batch_size , d ) for i , d in zip ( lst , dims ) ] <EOL> if len ( batches ) > <NUM_LIT:1> : <EOL> if any ( len ( i ) != len ( batches [ <NUM_LIT:0> ] ) for i in batches [ <NUM_LIT:1> : ] ) : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> counter = itertools . count ( ) <EOL> if random_state is not None : <EOL> random . seed ( random_state . normal ( ) ) <EOL> while True : <EOL> indices = [ i for i , _ in enumerate ( batches [ <NUM_LIT:0> ] ) ] <EOL> while True : <EOL> random . shuffle ( indices ) <EOL> for i in indices : <EOL> yield tuple ( b [ i ] for b in batches ) <EOL> count = next ( counter ) <EOL> if n_cycles and count >= n_cycles : <EOL> raise StopIteration ( ) <EOL> class OptimizerDistribution ( object ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , ** options ) : <EOL> """<STR_LIT>""" <EOL> self . options = options <EOL> def rvs ( self ) : <EOL> opt = random . choice ( list ( self . options . keys ( ) ) ) <EOL> grid = self . options [ opt ] <EOL> sample = list ( ParameterSampler ( grid , n_iter = <NUM_LIT:1> ) ) [ <NUM_LIT:0> ] <EOL> return opt , sample </s>
