def main(path_file_in,
path_file_out,
N=800,
sr=3,
iss=0.1,
leak=0.1,
ridge=10**-1,
plot=False,
feedback=False,
return_result=False,
verbose=False):
def write_list_in_file(l, file=None, file_path=None):
"""
Write a list in a file with with one item per line (like a one column csv).
If file is given, then it assumes the file is already open for writing.
If file_path is given, then it opens the file for writing, write the list, and then close the file.
"""
if file_path is not None:
if file is not None:
raise Exception, "Too much arguments. You must choose between file and file_path."
else:
file = open(file_path, 'w')
if file is None:
raise Exception, "No file given in input."
for item in l:
file.write("%s\n" % item)
if file_path is not None:
file.close()
import io_language_coding as CtIolangcod
sentence_to_meaning = False
# Definning parameters of stimulus (in a dictionary)
d = {}
d['act_time'] = 5
d['pause'] = True
d['suppl_pause_at_the_end'] = 1 * d['act_time']
d['initial_pause'] = True
d['offset'] = False
## Random parameters
import time
millis = int(round(time.time()))
seed = millis #2#4#2
if seed is not None:
mdp.numx.random.seed(seed)
np.random.seed(seed)
[train_data_txt, test_data_txt, sent_form_info_train,
sent_form_info_test] = extract_data_io(path_file=path_file_in)
train_corpus, train_meaning = txt2corpus_and_meaning(
train_txt=train_data_txt)
if sentence_to_meaning:
test_corpus = test_data_txt
else:
test_meaning = test_data_txt
# making the list of constructions (refering to "construction grammar"), a construction is a sentence without its open class words (Nouns and Verbs)
(l_construction_train,
construction_words) = get_and_remove_ocw_in_corpus(corpus=train_corpus,
_OCW='X')
l_ocw_array_train = generate_l_ocw_array(sent_form_info_train,
train_meaning)
l_ocw_array_test = generate_l_ocw_array(sent_form_info_test, test_meaning)
#print "**************************"
#print "l_construction_train", l_construction_train
#print "construction words", construction_words
if sentence_to_meaning:
(l_construction_test,
construction_words_test) = get_and_remove_ocw_in_corpus(
corpus=test_corpus, _OCW='X')
#print "l_construction_test", l_construction_test
if construction_words != construction_words_test:
raise Exception, "The construction words are not the same for the train constructions and the test constructions. So the coding of sentences will be different and should provoque a future problem."
## Generating all the sentence stimulus (in order to have the same length for each sentence)
if sentence_to_meaning:
## Generate the stimulus input for train and test data
l_full_const = l_construction_train + l_construction_test
slice_test = slice(
len(l_construction_train),
len(l_construction_train) + len(l_construction_test))
else:
l_full_const = l_construction_train
slice_train = slice(0, len(l_construction_train))
(stim_full_data, l_full_offset) = CtIolangcod.generate_stim_input_nodic(
l_data=l_full_const,
# act_time=d['act_time'], subset=None, l_input=None,
act_time=d['act_time'],
subset=None,
l_input=construction_words,
l_nr_word=None,
mult=None,
full_time=None,
with_offset=d['offset'],
pause=d['pause'],
initial_pause=d['initial_pause'],
suppl_pause_at_the_end=d['suppl_pause_at_the_end'],
verbose=False)
stim_sent_train = stim_full_data[slice_train]
if sentence_to_meaning:
stim_sent_test = stim_full_data[slice_test]
l_m_elt = get_meaning_coding()
(stim_mean_train, l_meaning_code_train) = generate_meaning_stim(
l_structure=sent_form_info_train,
full_time=stim_sent_train[0].shape[0],
l_m_elt=l_m_elt)
if not sentence_to_meaning:
#print "*** Generating meaning for test set ... ***"
(stim_mean_test, l_meaning_code_test) = generate_meaning_stim(
l_structure=sent_form_info_test,
full_time=stim_sent_train[0].shape[0],
l_m_elt=l_m_elt)
other_corpus_used = False
# Reservoir and Read-out definitions
res = reservoir.Reservoir(N, sr, iss, leak)
#classic working of the reservoir
if feedback == False:
## test set = train set
states_out_train, internal_states_train = res.train(
stim_mean_train, stim_sent_train)
## test set not train set
states_out_test, internal_states_test = res.test(stim_mean_test)
#feedback working of the reservoir. !! Should be implemented directly in the reservoir class !!
else:
delay = 1
nb_epoch_max = 4
dim_input = stim_mean_train[0].shape[1]
dim_output = len(stim_sent_train[0][0])
input_train = []
for (x, y) in zip(np.copy(stim_mean_train), np.copy(stim_sent_train)):
for time_step_delay in range(delay):
y = np.concatenate(([[0.] * len(y[0])], y), axis=0)
input_train.append(
np.array(np.concatenate((x, y[:-delay]), axis=1)))
nb_train = 0
while nb_train < nb_epoch_max:
## test set = train set
states_out_train, internal_states_train = res.train(
input_train, stim_sent_train)
tab_feedback = []
for num_phrase in range(len(states_out_train)):
#signal tresholded
states_out_train[num_phrase] = np.array([
treshold_signal(signal_t, 1.5, -0.5)
for signal_t in states_out_train[num_phrase]
])
if nb_train == 0: #feedback kept only for the first train
#feedback assignation
feedback = np.array(states_out_train[num_phrase])
#signal delayed
for time_step_delay in range(delay):
feedback = np.concatenate(
([[0.] * len(feedback[0])], feedback), axis=0)
tab_feedback.append(feedback)
input_train[num_phrase] = input_train[num_phrase].T
input_train[num_phrase][dim_input:] = feedback[:-delay].T
input_train[num_phrase] = input_train[num_phrase].T
nb_train += 1
## test set not train set
for t in range(0, stim_mean_test[0].shape[0], 1):
input_test = []
if t == 0: #A REMODIFIER
for n_phrase in range(len(stim_mean_test)):
input_test.append(
np.concatenate((stim_mean_test[n_phrase][t:t + 1, :],
[[0.] * len(stim_sent_train[0][0])]),
axis=1))
states_out_test, internal_states_test = res.test(input_test)
import copy
states_out_test_def = copy.deepcopy(states_out_test)
else:
for n_phrase in range(len(stim_mean_test)):
#feedback assignation
feedback = np.array(states_out_test[n_phrase])
input_test.append(
np.concatenate(
(stim_mean_test[n_phrase][t:t + 1, :], feedback),
axis=1))
states_out_test, internal_states_test = res.test(input_test)
for n_phrase in range(len(stim_mean_test)):
states_out_test_def[n_phrase] = np.concatenate(
(states_out_test_def[n_phrase],
states_out_test[n_phrase]),
axis=0)
states_out_test = states_out_test_def
# Ecriture de la phrase de réponse
if other_corpus_used:
var_inutile = 0
else:
l_recovered_construction_train = convert_l_output_activity_in_construction(
l_out_act=states_out_train,
construction_words=construction_words,
min_nr_of_val_upper_thres=1)
l_recovered_sentences_train = attribute_ocw_to_constructions(
l_constructions=l_recovered_construction_train,
l_ocw_array=l_ocw_array_train,
_OCW='X')
l_recovered_construction_test = convert_l_output_activity_in_construction(
l_out_act=states_out_test,
construction_words=construction_words,
min_nr_of_val_upper_thres=2)
l_recovered_sentences_test = attribute_ocw_to_constructions(
l_constructions=l_recovered_construction_test,
l_ocw_array=l_ocw_array_test,
_OCW='X')
## Writting sentences to output file
#print " *** Writting to output file ... *** "
l_final_sent_test = []
for list_words in l_recovered_sentences_test:
l_final_sent_test.append(" ".join(list_words))
#print " *** ... Writting done ***"
#print "**********************************************"
print "********************************************** "
print " *** RECOGNIZED SENTENCES *** "
print l_final_sent_test[0]
write_list_in_file(l=l_final_sent_test, file_path=path_file_out)
if return_result:
return l_final_sent_test
## Plot inputs
if plot:
import plotting as plotting
plotting.plot_array_in_file(
root_file_name="../Results/states_out_train",
array_=states_out_train,
titles_subset=l_construction_train,
legend_=construction_words,
plot_slice=None,
title="",
subtitle="")
plotting.plot_array_in_file(
root_file_name="../Results/states_out_test",
array_=states_out_test,
titles_subset=l_recovered_sentences_test,
legend_=construction_words,
plot_slice=None,
title="",
subtitle="")
print ""
def main(path_file_in,
path_file_out,
plot=False,
fast=False,
keep_internal_states=False,
verbose=False):
import os
#sys.path.append(os.path.dirname(os.path.abspath(__file__))+"/..")
#print "path ", os.path.dirname(os.path.abspath(__file__))+"/.."
current_directory = os.path.dirname(os.path.abspath(__file__))
parent_directory = os.path.dirname(current_directory)
sys.path.append(parent_directory)
import io_language_coding as CtIolangcod
# Definning parameters of stimulus (in a dictionary)
d = {}
d['act_time'] = 5 #2#1#5#10#2
d['pause'] = True #False
d['suppl_pause_at_the_end'] = 1 * d['act_time']
d['initial_pause'] = False #True#False#False
d['offset'] = True #False#True
# Parameters for reservoir
N = 500 #500#500#1000 #100
sr = 1 #3#3#2#1
iss = 0.25 #0.01#1
leak = 0.25 / float(
d['act_time']) #0.75/float(d['act_time'])#0.75/2.#0.5#0.05
## Random parameters
seed = 5
if seed is not None:
mdp.numx.random.seed(seed)
np.random.seed(seed)
[train_data_txt, test_data_txt,
sent_form_info_test] = common.extract_data_io(path_file=path_file_in)
train_corpus, train_meaning = common.txt2corpus_and_meaning(
train_txt=train_data_txt)
test_corpus = test_data_txt
# making the list of constructions (refering to "construction grammar"), a construction is a sentence without its open class words (Nouns and Verbs)
(l_construction_train, l_ocw_array_train,
construction_words) = common.get_and_remove_ocw_in_corpus(
corpus=train_corpus,
_OCW='X',
l_closed_class=get_closed_class_words())
(l_construction_test, l_ocw_array_test,
construction_words_test) = common.get_and_remove_ocw_in_corpus(
corpus=test_corpus, _OCW='X', l_closed_class=get_closed_class_words())
if construction_words != construction_words_test:
raise Exception, "The construction words are not the same for the train constructions and the test constructions. So the coding of sentences will be different and should provoque a future problem."
#################################################
## Generating all the sentence stimulus (in order to have the same length for each sentence)
l_full_const = l_construction_train + l_construction_test
slice_test = slice(len(l_construction_train),
len(l_construction_train) + len(l_construction_test))
slice_train = slice(0, len(l_construction_train))
(stim_full_data, l_full_offset) = CtIolangcod.generate_stim_input_nodic(
l_data=l_full_const,
# act_time=d['act_time'], subset=None, l_input=None,
act_time=d['act_time'],
subset=None,
l_input=construction_words,
l_nr_word=None,
mult=None,
full_time=None,
with_offset=d['offset'],
pause=d['pause'],
initial_pause=d['initial_pause'],
suppl_pause_at_the_end=d['suppl_pause_at_the_end'],
verbose=False)
stim_sent_train = stim_full_data[slice_train]
stim_sent_test = stim_full_data[slice_test]
#################################################
## Generating all the meaning stimulus
#################################################
l_m_elt = common.get_meaning_coding(
max_nr_ocw=max_nr_ocw,
max_nr_actionrelation=max_nr_actionrelation,
elt_pred=elt_pred)
(stim_mean_train, l_meaning_code_train) = common.generate_meaning_stim(
l_data=train_meaning,
l_ocw_array=l_ocw_array_train,
full_time=stim_sent_train[0].shape[0],
l_m_elt=l_m_elt,
l_offset=l_full_offset[slice_train],
verbose=False,
initial_pause=d['initial_pause'],
pause=d['pause'],
act_time=d['act_time'])
## Defining reservoir, readout and flow
reservoir = Oger.nodes.LeakyReservoirNode(output_dim=N,
spectral_radius=sr,
input_scaling=iss,
nonlin_func=np.tanh,
leak_rate=leak)
read_out = mdp.nodes.LinearRegressionNode(use_pinv=True, with_bias=True)
flow = mdp.Flow([reservoir, read_out])
if keep_internal_states:
Oger.utils.make_inspectable(mdp.Flow)
## Trainning and testing
print "Train and test"
if not fast:
(states_out_train, internal_states_train, internal_outputs_train, neuron_states_train) = \
common._teach_and_test_flow(inputs_train_set=stim_sent_train, teacher_outputs_train_set=stim_mean_train, inputs_test_set=stim_sent_train, _flow=flow, _reservoir=reservoir, keep_internal_states=keep_internal_states)
else:
raise Exception, "have to define what to do for fast mode"
## test set not train set
(states_out_test, internal_states_test, internal_outputs_test, neuron_states_test) = \
common._test_flow(inputs_test_set=stim_sent_test, _flow=flow, _reservoir=reservoir, keep_internal_states=keep_internal_states)
if verbose:
for i in range(len(stim_mean_train)):
print "len(stim_mean_train)", len(stim_mean_train)
print "len(l_meaning_code_train)", len(l_meaning_code_train)
print l_meaning_code_train[i]
print(stim_mean_train[0] == stim_mean_train[i])
print(l_meaning_code_train[0] == l_meaning_code_train[i])
## Writting output meaning
l_recovered_meaning_test = convert_l_output_activity_in_meaning(
l_out_act=states_out_test,
l_ocw_array=l_ocw_array_test,
l_m_elt=l_m_elt)
if verbose:
print "l_recovered_meaning_test", l_recovered_meaning_test
l_final_mean_test = []
for meanings in l_recovered_meaning_test:
current_meanings = ""
if verbose:
print "meanings", meanings
for i_m in range(len(meanings)):
# if verbose:
# print " i_m:",i_m
# print " meanings[i_m]:",meanings[i_m]
if i_m > 0:
current_meanings += ','
current_meanings += " ".join(meanings[i_m])
l_final_mean_test.append(current_meanings)
print ""
print "**********************************************"
print " *** RECOGNIZED MEANINGS *** "
for elt in l_final_mean_test:
print str(elt)
print "**********************************************"
## Writting sentences to output file
print " *** Writting to output file ... *** "
#ecrire une seule ligne simple dans un fichier la phrase attendue en mode test
common.write_list_in_file(l=l_final_mean_test, file_path=path_file_out)
print " *** ... Writting done ***"
print "**********************************************"
## Plot
if plot:
print " *** Plotting to output file ... *** "
# import oct2011.plotting as plotting
import plotting as plotting
plotting.plot_array_in_file(
root_file_name="../RES_TEMP/states_out_train",
array_=states_out_train,
titles_subset=l_meaning_code_train,
# legend_=l_m_elt, plot_slice=None, title="", subtitle="")
legend_=None,
plot_slice=None,
title="",
subtitle="")
plotting.plot_array_in_file(
root_file_name="../RES_TEMP/states_out_train_sent",
array_=states_out_train,
titles_subset=train_meaning,
# legend_=l_m_elt, plot_slice=None, title="", subtitle="")
legend_=None,
plot_slice=None,
title="",
subtitle="")
plotting.plot_array_in_file(
root_file_name="../RES_TEMP/states_out_test",
array_=states_out_test,
titles_subset=l_final_mean_test,
# legend_=l_m_elt, plot_slice=None, title="", subtitle="")
legend_=None,
plot_slice=None,
title="",
subtitle="")
plotting.plot_array_in_file(
root_file_name="../RES_TEMP/intern_states_test",
array_=internal_states_test,
titles_subset=None,
plot_slice=None,
title="",
subtitle="")
print " *** ... Plotting to output file done *** "
print "**********************************************"
return l_final_mean_test
def main(path_file_in, path_file_out,N=1200, sr=3, iss=0.1, leak=0.1, ridge=10**-1, plot=False, feedback=False, return_result=False, verbose=False):
def write_list_in_file(l, file=None, file_path=None):
"""
Write a list in a file with with one item per line (like a one column csv).
If file is given, then it assumes the file is already open for writing.
If file_path is given, then it opens the file for writing, write the list, and then close the file.
"""
if file_path is not None:
if file is not None:
raise Exception, "Too much arguments. You must choose between file and file_path."
else:
file = open(file_path, 'w')
if file is None:
raise Exception, "No file given in input."
for item in l:
file.write("%s\n" % item)
if file_path is not None:
file.close()
import io_language_coding as CtIolangcod
sentence_to_meaning = False
# Definning parameters of stimulus (in a dictionary)
d = {}
d['act_time'] = 5
d['pause'] = True
d['suppl_pause_at_the_end'] = 1*d['act_time']
d['initial_pause'] = True
d['offset'] = False
## Random parameters
import time
millis = int(round(time.time() ))
seed = millis#2#4#2
if seed is not None:
mdp.numx.random.seed(seed)
np.random.seed(seed)
[train_data_txt, test_data_txt, sent_form_info_train, sent_form_info_test] = extract_data_io(path_file=path_file_in)
train_corpus, train_meaning = txt2corpus_and_meaning(train_txt=train_data_txt)
if sentence_to_meaning:
test_corpus = test_data_txt
else:
test_meaning = test_data_txt
# making the list of constructions (refering to "construction grammar"), a construction is a sentence without its open class words (Nouns and Verbs)
(l_construction_train, construction_words) = get_and_remove_ocw_in_corpus(corpus=train_corpus, _OCW='X')
l_ocw_array_train=generate_l_ocw_array(sent_form_info_train, train_meaning)
l_ocw_array_test=generate_l_ocw_array(sent_form_info_test, test_meaning)
#print "**************************"
#print "l_construction_train", l_construction_train
#print "construction words", construction_words
if sentence_to_meaning:
(l_construction_test, construction_words_test) = get_and_remove_ocw_in_corpus(corpus=test_corpus, _OCW='X')
#print "l_construction_test", l_construction_test
if construction_words!=construction_words_test:
raise Exception, "The construction words are not the same for the train constructions and the test constructions. So the coding of sentences will be different and should provoque a future problem."
## Generating all the sentence stimulus (in order to have the same length for each sentence)
if sentence_to_meaning:
## Generate the stimulus input for train and test data
l_full_const = l_construction_train + l_construction_test
slice_test = slice(len(l_construction_train),len(l_construction_train)+len(l_construction_test))
else:
l_full_const = l_construction_train
slice_train = slice(0,len(l_construction_train))
(stim_full_data, l_full_offset) = CtIolangcod.generate_stim_input_nodic(l_data=l_full_const,
# act_time=d['act_time'], subset=None, l_input=None,
act_time=d['act_time'], subset=None, l_input=construction_words,
l_nr_word=None, mult=None, full_time=None,
with_offset=d['offset'], pause=d['pause'], initial_pause=d['initial_pause'],
suppl_pause_at_the_end=d['suppl_pause_at_the_end'], verbose=False)
stim_sent_train = stim_full_data[slice_train]
if sentence_to_meaning:
stim_sent_test = stim_full_data[slice_test]
l_m_elt = get_meaning_coding()
(stim_mean_train, l_meaning_code_train) = generate_meaning_stim(l_structure=sent_form_info_train, full_time=stim_sent_train[0].shape[0], l_m_elt=l_m_elt)
if not sentence_to_meaning:
#print "*** Generating meaning for test set ... ***"
(stim_mean_test, l_meaning_code_test) = generate_meaning_stim(l_structure=sent_form_info_test, full_time=stim_sent_train[0].shape[0], l_m_elt=l_m_elt)
other_corpus_used = False
# Reservoir and Read-out definitions
res = reservoir.Reservoir(N, sr, iss, leak)
#classic working of the reservoir
if feedback==False:
## test set = train set
states_out_train, internal_states_train = res.train (stim_mean_train, stim_sent_train)
## test set not train set
states_out_test, internal_states_test = res.test(stim_mean_test)
#feedback working of the reservoir. !! Should be implemented directly in the reservoir class !!
else:
delay=1
nb_epoch_max=4
dim_input = stim_mean_train[0].shape[1]
dim_output = len(stim_sent_train[0][0])
input_train=[]
for (x,y) in zip( np.copy(stim_mean_train), np.copy(stim_sent_train)):
for time_step_delay in range(delay):
y=np.concatenate( ([[0.]*len(y[0])] , y), axis=0)
input_train.append(np.array( np.concatenate( (x, y[:-delay]), axis=1 ) ))
nb_train=0
while nb_train < nb_epoch_max:
## test set = train set
states_out_train, internal_states_train = res.train (input_train, stim_sent_train)
tab_feedback=[]
for num_phrase in range(len(states_out_train)):
#signal tresholded
states_out_train[num_phrase]=np.array([treshold_signal(signal_t,1.5,-0.5) for signal_t in states_out_train[num_phrase]])
if nb_train==0: #feedback kept only for the first train
#feedback assignation
feedback=np.array(states_out_train[num_phrase])
#signal delayed
for time_step_delay in range(delay):
feedback=np.concatenate( ([[0.]*len(feedback[0])] , feedback), axis=0)
tab_feedback.append(feedback)
input_train[num_phrase]=input_train[num_phrase].T
input_train[num_phrase][dim_input:] = feedback[:-delay].T
input_train[num_phrase]=input_train[num_phrase].T
nb_train+=1
## test set not train set
for t in range(0,stim_mean_test[0].shape[0],1):
input_test=[]
if t==0: #A REMODIFIER
for n_phrase in range(len(stim_mean_test)):
input_test.append(np.concatenate( (stim_mean_test[n_phrase][t:t+1,:] , [[0.]*len(stim_sent_train[0][0])] ) , axis=1 ) )
states_out_test, internal_states_test = res.test(input_test)
import copy
states_out_test_def=copy.deepcopy(states_out_test)
else:
for n_phrase in range(len(stim_mean_test)):
#feedback assignation
feedback=np.array(states_out_test[n_phrase])
input_test.append(np.concatenate( (stim_mean_test[n_phrase][t:t+1,:] , feedback ) , axis=1 ) )
states_out_test, internal_states_test = res.test(input_test)
for n_phrase in range(len(stim_mean_test)):
states_out_test_def[ n_phrase ]=np.concatenate( (states_out_test_def[n_phrase] , states_out_test[n_phrase]), axis=0 )
states_out_test=states_out_test_def
# Ecriture de la phrase de réponse
if other_corpus_used:
var_inutile=0
else:
l_recovered_construction_train = convert_l_output_activity_in_construction(l_out_act=states_out_train,
construction_words=construction_words,
min_nr_of_val_upper_thres=1)
l_recovered_sentences_train = attribute_ocw_to_constructions(l_constructions=l_recovered_construction_train,
l_ocw_array=l_ocw_array_train, _OCW='X')
l_recovered_construction_test = convert_l_output_activity_in_construction(l_out_act=states_out_test,
construction_words=construction_words,
min_nr_of_val_upper_thres=2)
l_recovered_sentences_test = attribute_ocw_to_constructions(l_constructions=l_recovered_construction_test,
l_ocw_array=l_ocw_array_test, _OCW='X')
## Writting sentences to output file
#print " *** Writting to output file ... *** "
l_final_sent_test = []
for list_words in l_recovered_sentences_test:
l_final_sent_test.append(" ".join(list_words))
#print " *** ... Writting done ***"
#print "**********************************************"
print "********************************************** "
print " *** RECOGNIZED SENTENCES *** "
print l_final_sent_test[0]
write_list_in_file(l=l_final_sent_test, file_path=path_file_out)
if return_result:
return l_final_sent_test
## Plot inputs
if plot:
import plotting as plotting
plotting.plot_array_in_file(root_file_name="../Results/states_out_train", array_=states_out_train, titles_subset=l_construction_train, legend_=construction_words, plot_slice=None, title="", subtitle="")
plotting.plot_array_in_file(root_file_name="../Results/states_out_test", array_=states_out_test, titles_subset=l_recovered_sentences_test, legend_=construction_words, plot_slice=None, title="", subtitle="")
print ""
def main(path_file_in, path_file_out, plot=False, fast=False, keep_internal_states=False, verbose=False):
import os
#sys.path.append(os.path.dirname(os.path.abspath(__file__))+"/..")
#print "path ", os.path.dirname(os.path.abspath(__file__))+"/.."
current_directory = os.path.dirname(os.path.abspath(__file__))
parent_directory = os.path.dirname(current_directory)
sys.path.append(parent_directory)
import io_language_coding as CtIolangcod
# Definning parameters of stimulus (in a dictionary)
d = {}
d['act_time'] = 5#2#1#5#10#2
d['pause'] = True#False
d['suppl_pause_at_the_end'] = 1*d['act_time']
d['initial_pause'] = False#True#False#False
d['offset'] = True#False#True
# Parameters for reservoir
N = 500#500#500#1000 #100
sr = 1#3#3#2#1
iss = 0.25#0.01#1
leak = 0.25/float(d['act_time'])#0.75/float(d['act_time'])#0.75/2.#0.5#0.05
## Random parameters
seed = 5
if seed is not None:
mdp.numx.random.seed(seed)
np.random.seed(seed)
[train_data_txt, test_data_txt, sent_form_info_test] = common.extract_data_io(path_file=path_file_in)
train_corpus, train_meaning = common.txt2corpus_and_meaning(train_txt=train_data_txt)
test_corpus = test_data_txt
# making the list of constructions (refering to "construction grammar"), a construction is a sentence without its open class words (Nouns and Verbs)
(l_construction_train, l_ocw_array_train, construction_words) = common.get_and_remove_ocw_in_corpus(corpus=train_corpus, _OCW='X', l_closed_class=get_closed_class_words())
(l_construction_test, l_ocw_array_test, construction_words_test) = common.get_and_remove_ocw_in_corpus(corpus=test_corpus, _OCW='X', l_closed_class=get_closed_class_words())
if construction_words!=construction_words_test:
raise Exception, "The construction words are not the same for the train constructions and the test constructions. So the coding of sentences will be different and should provoque a future problem."
#################################################
## Generating all the sentence stimulus (in order to have the same length for each sentence)
l_full_const = l_construction_train + l_construction_test
slice_test = slice(len(l_construction_train),len(l_construction_train)+len(l_construction_test))
slice_train = slice(0,len(l_construction_train))
(stim_full_data, l_full_offset) = CtIolangcod.generate_stim_input_nodic(l_data=l_full_const,
# act_time=d['act_time'], subset=None, l_input=None,
act_time=d['act_time'], subset=None, l_input=construction_words,
l_nr_word=None, mult=None, full_time=None,
with_offset=d['offset'], pause=d['pause'], initial_pause=d['initial_pause'],
suppl_pause_at_the_end=d['suppl_pause_at_the_end'], verbose=False)
stim_sent_train = stim_full_data[slice_train]
stim_sent_test = stim_full_data[slice_test]
#################################################
## Generating all the meaning stimulus
#################################################
l_m_elt = common.get_meaning_coding(max_nr_ocw=max_nr_ocw, max_nr_actionrelation=max_nr_actionrelation, elt_pred=elt_pred)
(stim_mean_train, l_meaning_code_train) = common.generate_meaning_stim(l_data=train_meaning,
l_ocw_array=l_ocw_array_train, full_time=stim_sent_train[0].shape[0],
l_m_elt=l_m_elt, l_offset=l_full_offset[slice_train], verbose=False,
initial_pause=d['initial_pause'], pause=d['pause'], act_time=d['act_time'])
## Defining reservoir, readout and flow
reservoir = Oger.nodes.LeakyReservoirNode(output_dim = N, spectral_radius = sr, input_scaling =iss, nonlin_func = np.tanh, leak_rate = leak)
read_out = mdp.nodes.LinearRegressionNode(use_pinv=True, with_bias=True)
flow = mdp.Flow([reservoir, read_out])
if keep_internal_states:
Oger.utils.make_inspectable(mdp.Flow)
## Trainning and testing
print "Train and test"
if not fast:
(states_out_train, internal_states_train, internal_outputs_train, neuron_states_train) = \
common._teach_and_test_flow(inputs_train_set=stim_sent_train, teacher_outputs_train_set=stim_mean_train, inputs_test_set=stim_sent_train, _flow=flow, _reservoir=reservoir, keep_internal_states=keep_internal_states)
else:
raise Exception, "have to define what to do for fast mode"
## test set not train set
(states_out_test, internal_states_test, internal_outputs_test, neuron_states_test) = \
common._test_flow(inputs_test_set=stim_sent_test, _flow=flow, _reservoir=reservoir, keep_internal_states=keep_internal_states)
if verbose:
for i in range(len(stim_mean_train)):
print "len(stim_mean_train)", len(stim_mean_train)
print "len(l_meaning_code_train)", len(l_meaning_code_train)
print l_meaning_code_train[i]
print (stim_mean_train[0]==stim_mean_train[i])
print (l_meaning_code_train[0]==l_meaning_code_train[i])
## Writting output meaning
l_recovered_meaning_test = convert_l_output_activity_in_meaning(l_out_act=states_out_test, l_ocw_array=l_ocw_array_test, l_m_elt=l_m_elt)
if verbose:
print "l_recovered_meaning_test", l_recovered_meaning_test
l_final_mean_test = []
for meanings in l_recovered_meaning_test:
current_meanings = ""
if verbose:
print "meanings", meanings
for i_m in range(len(meanings)):
# if verbose:
# print " i_m:",i_m
# print " meanings[i_m]:",meanings[i_m]
if i_m>0:
current_meanings+=','
current_meanings+=" ".join(meanings[i_m])
l_final_mean_test.append(current_meanings)
print ""
print "**********************************************"
print " *** RECOGNIZED MEANINGS *** "
for elt in l_final_mean_test:
print str(elt)
print "**********************************************"
## Writting sentences to output file
print " *** Writting to output file ... *** "
#ecrire une seule ligne simple dans un fichier la phrase attendue en mode test
common.write_list_in_file(l=l_final_mean_test, file_path=path_file_out)
print " *** ... Writting done ***"
print "**********************************************"
## Plot
if plot:
print " *** Plotting to output file ... *** "
# import oct2011.plotting as plotting
import plotting as plotting
plotting.plot_array_in_file(root_file_name="../RES_TEMP/states_out_train",
array_=states_out_train, titles_subset=l_meaning_code_train,
# legend_=l_m_elt, plot_slice=None, title="", subtitle="")
legend_=None, plot_slice=None, title="", subtitle="")
plotting.plot_array_in_file(root_file_name="../RES_TEMP/states_out_train_sent",
array_=states_out_train, titles_subset=train_meaning,
# legend_=l_m_elt, plot_slice=None, title="", subtitle="")
legend_=None, plot_slice=None, title="", subtitle="")
plotting.plot_array_in_file(root_file_name="../RES_TEMP/states_out_test",
array_=states_out_test, titles_subset=l_final_mean_test,
# legend_=l_m_elt, plot_slice=None, title="", subtitle="")
legend_=None, plot_slice=None, title="", subtitle="")
plotting.plot_array_in_file(root_file_name="../RES_TEMP/intern_states_test", array_=internal_states_test, titles_subset=None, plot_slice=None, title="", subtitle="")
print " *** ... Plotting to output file done *** "
print "**********************************************"
return l_final_mean_test
def test(self,
test_corpus,
sent_form_info_test,
shelf,
plot=False,
feedback=False):
l_ocw_array_test = self.generate_l_ocw_array(sent_form_info_test,
test_corpus)
(stim_mean_test, l_meaning_code_test) = self.generate_meaning_stim(
l_structure=sent_form_info_test,
full_time=shelf["stim_sent_train"][0].shape[0],
l_m_elt=shelf["l_m_elt"])
#classic working of the reservoir
if feedback == False:
## test set not train set
states_out_test, internal_states_test = shelf["res"].test(
stim_mean_test)
#feedback working of the reservoir. !! Should be implemented directly in the reservoir class !!
else:
delay = 1
nb_epoch_max = 4
dim_input = shelf["stim_sent_train"][0].shape[1]
#dim_output = len(stim_sent_train[0][0])
input_train = []
for (x, y) in zip(np.copy(shelf["stim_sent_train"]),
np.copy(shelf["stim_sent_train"])):
for time_step_delay in range(delay):
y = np.concatenate(([[0.] * len(y[0])], y), axis=0)
input_train.append(
np.array(np.concatenate((x, y[:-delay]), axis=1)))
nb_train = 0
while nb_train < nb_epoch_max:
## test set = train set
states_out_train, internal_states_train = shelf["res"].train(
input_train, shelf["stim_sent_train"])
tab_feedback = []
for num_phrase in range(len(states_out_train)):
#signal tresholded
states_out_train[num_phrase] = np.array([
self.treshold_signal(signal_t, 1.5, -0.5)
for signal_t in states_out_train[num_phrase]
])
if nb_train == 0: #feedback kept only for the first train
#feedback assignation
feedback = np.array(states_out_train[num_phrase])
#signal delayed
for time_step_delay in range(delay):
feedback = np.concatenate(
([[0.] * len(feedback[0])], feedback), axis=0)
tab_feedback.append(feedback)
input_train[num_phrase] = input_train[num_phrase].T
input_train[num_phrase][dim_input:] = feedback[:-delay].T
input_train[num_phrase] = input_train[num_phrase].T
nb_train += 1
## test set not train set
for t in range(0, stim_mean_test[0].shape[0], 1):
input_test = []
if t == 0: #A REMODIFIER
for n_phrase in range(len(stim_mean_test)):
input_test.append(
np.concatenate(
(stim_mean_test[n_phrase][t:t + 1, :],
[[0.] * len(shelf["stim_sent_train"][0][0])]),
axis=1))
states_out_test, internal_states_test = shelf["res"].test(
input_test)
import copy
states_out_test_def = copy.deepcopy(states_out_test)
else:
for n_phrase in range(len(stim_mean_test)):
#feedback assignation
feedback = np.array(states_out_test[n_phrase])
input_test.append(
np.concatenate(
(stim_mean_test[n_phrase][t:t + 1, :],
feedback),
axis=1))
states_out_test, internal_states_test = shelf["res"].test(
input_test)
for n_phrase in range(len(stim_mean_test)):
states_out_test_def[n_phrase] = np.concatenate(
(states_out_test_def[n_phrase],
states_out_test[n_phrase]),
axis=0)
states_out_test = states_out_test_def
# l_recovered_construction_train = self.convert_l_output_activity_in_construction(l_out_act=states_out_train,
# construction_words=construction_words,
# min_nr_of_val_upper_thres=1)
# l_recovered_sentences_train = self.attribute_ocw_to_constructions(l_constructions=l_recovered_construction_train,
# l_ocw_array=l_ocw_array_train, _OCW='X')
l_recovered_construction_test = self.convert_l_output_activity_in_construction(
l_out_act=states_out_test,
construction_words=shelf["construction_words"],
min_nr_of_val_upper_thres=2)
l_recovered_sentences_test = self.attribute_ocw_to_constructions(
l_constructions=l_recovered_construction_test,
l_ocw_array=l_ocw_array_test,
_OCW='X')
## Plot inputs
if plot:
import plotting as plotting
plotting.plot_array_in_file(
root_file_name="../Results/states_out_train",
array_=shelf["states_out_train"],
titles_subset=shelf["l_construction_train"],
legend_=shelf["construction_words"],
plot_slice=None,
title="",
subtitle="")
plotting.plot_array_in_file(
root_file_name="../Results/states_out_test",
array_=states_out_test,
titles_subset=l_recovered_sentences_test,
legend_=shelf["construction_words"],
plot_slice=None,
title="",
subtitle="")
return l_recovered_sentences_test
def test(self, test_corpus, sent_form_info_test, shelf, plot=False, feedback=False ):
l_ocw_array_test = self.generate_l_ocw_array(sent_form_info_test, test_corpus)
(stim_mean_test, l_meaning_code_test) = self.generate_meaning_stim(l_structure=sent_form_info_test, full_time=shelf["stim_sent_train"][0].shape[0], l_m_elt=shelf["l_m_elt"])
#classic working of the reservoir
if feedback==False:
## test set not train set
states_out_test, internal_states_test = shelf["res"].test(stim_mean_test)
#feedback working of the reservoir. !! Should be implemented directly in the reservoir class !!
else:
delay=1
nb_epoch_max=4
dim_input = shelf["stim_sent_train"][0].shape[1]
#dim_output = len(stim_sent_train[0][0])
input_train=[]
for (x,y) in zip( np.copy(shelf["stim_sent_train"]), np.copy(shelf["stim_sent_train"])):
for time_step_delay in range(delay):
y=np.concatenate( ([[0.]*len(y[0])] , y), axis=0)
input_train.append(np.array( np.concatenate( (x, y[:-delay]), axis=1 ) ))
nb_train=0
while nb_train < nb_epoch_max:
## test set = train set
states_out_train, internal_states_train = shelf["res"].train (input_train, shelf["stim_sent_train"])
tab_feedback=[]
for num_phrase in range(len(states_out_train)):
#signal tresholded
states_out_train[num_phrase]=np.array([self.treshold_signal(signal_t,1.5,-0.5) for signal_t in states_out_train[num_phrase]])
if nb_train==0: #feedback kept only for the first train
#feedback assignation
feedback=np.array(states_out_train[num_phrase])
#signal delayed
for time_step_delay in range(delay):
feedback=np.concatenate( ([[0.]*len(feedback[0])] , feedback), axis=0)
tab_feedback.append(feedback)
input_train[num_phrase]=input_train[num_phrase].T
input_train[num_phrase][dim_input:] = feedback[:-delay].T
input_train[num_phrase]=input_train[num_phrase].T
nb_train+=1
## test set not train set
for t in range(0,stim_mean_test[0].shape[0],1):
input_test=[]
if t==0: #A REMODIFIER
for n_phrase in range(len(stim_mean_test)):
input_test.append(np.concatenate( (stim_mean_test[n_phrase][t:t+1,:] , [[0.]*len(shelf["stim_sent_train"][0][0])] ) , axis=1 ) )
states_out_test, internal_states_test = shelf["res"].test(input_test)
import copy
states_out_test_def=copy.deepcopy(states_out_test)
else:
for n_phrase in range(len(stim_mean_test)):
#feedback assignation
feedback=np.array(states_out_test[n_phrase])
input_test.append(np.concatenate( (stim_mean_test[n_phrase][t:t+1,:] , feedback ) , axis=1 ) )
states_out_test, internal_states_test = shelf["res"].test(input_test)
for n_phrase in range(len(stim_mean_test)):
states_out_test_def[ n_phrase ]=np.concatenate( (states_out_test_def[n_phrase] , states_out_test[n_phrase]), axis=0 )
states_out_test=states_out_test_def
# l_recovered_construction_train = self.convert_l_output_activity_in_construction(l_out_act=states_out_train,
# construction_words=construction_words,
# min_nr_of_val_upper_thres=1)
# l_recovered_sentences_train = self.attribute_ocw_to_constructions(l_constructions=l_recovered_construction_train,
# l_ocw_array=l_ocw_array_train, _OCW='X')
l_recovered_construction_test = self.convert_l_output_activity_in_construction(l_out_act=states_out_test,
construction_words=shelf["construction_words"],
min_nr_of_val_upper_thres=2)
l_recovered_sentences_test = self.attribute_ocw_to_constructions(l_constructions=l_recovered_construction_test,
l_ocw_array=l_ocw_array_test, _OCW='X')
## Plot inputs
if plot:
import plotting as plotting
plotting.plot_array_in_file(root_file_name="../Results/states_out_train", array_=shelf["states_out_train"], titles_subset=shelf["l_construction_train"], legend_=shelf["construction_words"], plot_slice=None, title="", subtitle="")
plotting.plot_array_in_file(root_file_name="../Results/states_out_test", array_=states_out_test, titles_subset=l_recovered_sentences_test, legend_=shelf["construction_words"], plot_slice=None, title="", subtitle="")
return l_recovered_sentences_test
