def display(self, states_out_train, states_out_test, l_meaning_code_train,
train_meaning, l_final_mean_test, internal_states_test):
print " *** Plotting to output file ... *** "
import oct2011.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 "**********************************************"
def display(self, states_out_train, states_out_test, l_meaning_code_train, train_meaning, l_final_mean_test, internal_states_test):
print " *** Plotting to output file ... *** "
import oct2011.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 "**********************************************"
def main(path_file_in, path_file_out, sentence_to_meaning=True, plot=False, fast=False, keep_internal_states=False, verbose=False):
import sys,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)
print "sys.path.append(parent_directory)", parent_directory
import Common_Tools.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, sentence_to_meaning=sentence_to_meaning)
# print "**************************"
# print "train data_txt", train_data_txt
# print "test data_txt", test_data_txt
# print "sent_form_info_test", sent_form_info_test
train_corpus, train_meaning = common.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, 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())
# print "**************************"
# print "l_construction_train", l_construction_train
# print "l_ocw_array_train", l_ocw_array_train
if sentence_to_meaning:
(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())
# 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."
else:
# check if a special form of sentence is requested (canonical or non-canonical form)
# i.e. check if there is at least one element that is not None
print ""
print "*** Managing sentence form ... ***"
print "sent_form_info_test:", sent_form_info_test
# if all sentence information is None (not attributed)
if all(elt is None for elt in sent_form_info_test):
# generate default form of sentence
l_ocw_array_test = generate_l_ocw_array_in_canonical_order(l_meaning=test_meaning)
# if at least one element is not None
else:
# call specific method to deal with the specified order of each meanings in the list
l_ocw_array_test = generate_l_ocw_array_in_specified_order(l_meaning=test_meaning, l_sent_form = sent_form_info_test)
print "*** ... sentence form managed ***"
# print "l_ocw_array_test", l_ocw_array_test
#################################################
## 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_train = slice(0,len(l_construction_train))
slice_test = slice(len(l_construction_train),len(l_construction_train)+len(l_construction_test))
# print "slice_train", slice_train
print "slice_test", slice_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]
#################################################
## Generating all the meaning stimulus
#################################################
# l_m_elt = common.get_meaning_coding()
# elt_pred=['P','F','O','R']
l_m_elt = common.get_meaning_coding(max_nr_ocw=8, max_nr_actionrelation=2, elt_pred=elt_pred)
# print ""
# print "*** Generating meaning for train set ... ***"
# (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, verbose=False)
(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'])
# print "*** ... meaning generated for train set ***"
# print "l_m_elt", l_m_elt
# print "stim_mean_train[0].shape", stim_mean_train[0].shape
# print "l_meaning_code_train", l_meaning_code_train
# print ""
if not sentence_to_meaning:
print "*** Generating meaning for test set ... ***"
(stim_mean_test, l_meaning_code_test) = common.generate_meaning_stim(l_data=test_meaning, l_ocw_array=l_ocw_array_test, full_time=stim_sent_train[0].shape[0], l_m_elt=l_m_elt, verbose=False)
print "*** ... meaning generated for test set ***"
print ""
## 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:
## test set = train set
if sentence_to_meaning:
(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:
(states_out_train, internal_states_train, internal_outputs_train, neuron_states_train) = \
common._teach_and_test_flow(inputs_train_set=stim_mean_train, teacher_outputs_train_set=stim_sent_train, inputs_test_set=stim_mean_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
if sentence_to_meaning:
(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)
else:
(states_out_test, internal_states_test, internal_outputs_test, neuron_states_test) = \
common._test_flow(inputs_test_set=stim_mean_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
if sentence_to_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 output sentence
if not sentence_to_meaning:
if not fast and (verbose or plot):
print ""
print "**********************************************"
print "*** Processing recovery of train sentences ..."
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')
print "*** l_recovered_sentences_train: ***"
for s in l_recovered_sentences_train:
print s
print "**********************************************"
if verbose:
print ""
print "**********************************************"
print "*** Processing recovery of test sentences ..."
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')
if verbose:
print "*** l_recovered_sentences_test: ***"
for s in l_recovered_sentences_test:
print s
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
if sentence_to_meaning:
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="")
else:
plotting.plot_array_in_file(root_file_name="../../RES_TEMP/states_out_train_recov", array_=states_out_train, titles_subset=l_recovered_sentences_train, legend_=l_m_elt, 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_recovered_sentences_test, legend_=construction_words, plot_slice=None, title="", subtitle="")
## Plot internal states
if sentence_to_meaning:
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="")
else:
plotting.plot_array_in_file(root_file_name="../../RES_TEMP/intern_states_test", array_=internal_states_test, titles_subset=l_ocw_array_test, 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, plot=False, keep_internal_states=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, 'wb')
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 Common_Tools.io_language_coding as CtIolangcod
import sys
sys.path.append("../Common_Tools")
print sys.path
import io_language_coding as CtIolangcod
sys.path.append("../iCub_language")
sentence_to_meaning = False
# Definning parameters of stimulus (in a dictionary)
d = {}
d['act_time'] = 5#10#2
d['pause'] = True#False
d['suppl_pause_at_the_end'] = 1*d['act_time']
d['initial_pause'] = True#False#False
d['offset'] = False#True
# Parameters for reservoir
N = 500#1000 #100
sr = 2#3#3#2#1
iss = 0.01#1
leak = 0.75#0.5#0.05
## output dic
# d['start_teacher'] = 1#'end'
## Random parameters
seed = 5#2#4#2
# seed 2 works with 2 sentences : both with 1 relation, 1 Canonical, 1 Non-canonical
if seed is not None:
mdp.numx.random.seed(seed)
np.random.seed(seed)
# if verbose:
# print "Spectra radius of generated matrix before applying another spectral radius: "+str(Oger.utils.get_spectral_radius(w))
# if spectral_radius is not None:
# w *= d['spectral_radius'] / Oger.utils.get_spectral_radius(w)
# if verbose:
# print "Spectra radius matrix after applying another spectral radius: "+str(Oger.utils.get_spectral_radius(w))
# if randomize_seed_afterwards:
# """ redifine randomly the seed in order to not fix the seed also for other methods that are using numpy.random methods.
# """
# import time
# mdp.numx.random.seed(int(time.time()*10**6))
# [train_data_txt, test_data_txt] = extract_data_io(path_file=path_file_in, sentence_to_meaning=sentence_to_meaning)
[train_data_txt, test_data_txt, sent_form_info_test] = extract_data_io(path_file=path_file_in, sentence_to_meaning=sentence_to_meaning)
print "**************************"
print "train data_txt", train_data_txt
print "test data_txt", test_data_txt
print "sent_form_info_test", sent_form_info_test
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, l_ocw_array_train, construction_words) = get_and_remove_ocw_in_corpus(corpus=train_corpus, _OCW='X')
print "**************************"
print "l_construction_train", l_construction_train
print "l_ocw_array_train", l_ocw_array_train
if sentence_to_meaning:
(l_construction_test, l_ocw_array_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."
else:
# check if a special form of sentence is requested (canonical or non-canonical form)
# i.e. check if there is at least one element that is not None
print ""
print "*** Managing sentence form ... ***"
print "sent_form_info_test:", sent_form_info_test
# if all sentence information is None (not attributed)
if all(elt is None for elt in sent_form_info_test):
# generate default form of sentence
l_ocw_array_test = generate_l_ocw_array_in_canonical_order(l_meaning=test_meaning)
# if at least one element is not None
else:
# call specific method to deal with the specified order of each meanings in the list
l_ocw_array_test = generate_l_ocw_array_in_specified_order(l_meaning=test_meaning, l_sent_form = sent_form_info_test)
print "*** ... sentence form managed ***"
print "l_ocw_array_test", l_ocw_array_test
## 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_train = slice(0,len(l_construction_train))
slice_test = slice(len(l_construction_train),len(l_construction_train)+len(l_construction_test))
# print "slice_train", slice_train
print "slice_test", slice_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]
print "stim_sent_train[0].shape", stim_sent_train[0].shape
print "stim_sent_train[0].shape[0]", stim_sent_train[0].shape[0]
l_m_elt = get_meaning_coding()
print ""
print "*** Generating meaning for train set ... ***"
(stim_mean_train, l_meaning_code_train) = 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, verbose=False)
print "*** ... meaning generated for train set ***"
print "l_m_elt", l_m_elt
print "stim_mean_train[0].shape", stim_mean_train[0].shape
print "l_meaning_code_train", l_meaning_code_train
print ""
if not sentence_to_meaning:
print "*** Generating meaning for test set ... ***"
(stim_mean_test, l_meaning_code_test) = generate_meaning_stim(l_data=test_meaning, l_ocw_array=l_ocw_array_test, full_time=stim_sent_train[0].shape[0], l_m_elt=l_m_elt, verbose=False)
print "*** ... meaning generated for test set ***"
print ""
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)
print "Train and test"
# (states_out_test, internal_states_test, internal_outputs_test, neuron_states_test) = \
## test set = train set
(states_out_train, internal_states_train, internal_outputs_train, neuron_states_train) = \
_teach_and_test_flow(inputs_train_set=stim_mean_train, teacher_outputs_train_set=stim_sent_train, inputs_test_set=stim_mean_train, _flow=flow, _reservoir=reservoir, keep_internal_states=keep_internal_states)
## test set not train set
(states_out_test, internal_states_test, internal_outputs_test, neuron_states_test) = \
_test_flow(inputs_test_set=stim_mean_test, _flow=flow, _reservoir=reservoir, keep_internal_states=keep_internal_states)
# (states_out_test, internal_states_test, internal_outputs_test, neuron_states_test) = \
# _teach_and_test_flow(inputs_train_set=stim_mean_train, teacher_outputs_train_set=stim_sent_train, inputs_test_set=stim_mean_test, _flow=flow, _reservoir=reservoir, keep_internal_states=keep_internal_states)
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])
# Ecriture de la phrase de réponse
print ""
print "**********************************************"
print "*** Processing recovery of train sentences ..."
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')
print "*** l_recovered_sentences_train: ***"
for s in l_recovered_sentences_train:
print s
print "**********************************************"
print ""
print "**********************************************"
print "*** Processing recovery of test sentences ..."
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')
print "*** l_recovered_sentences_test: ***"
for s in l_recovered_sentences_test:
print s
print "**********************************************"
## 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))
#ecrire une seule ligne simple dans un fichier la phrase attendue en mode test
write_list_in_file(l=l_final_sent_test, file_path=path_file_out)
print " *** ... Writting done ***"
print "**********************************************"
## Plot inputs
if plot:
print " *** Plotting to output file ... *** "
import oct2011.plotting as plotting
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/test_sent_train", array_=stim_sent_train, plot_slice=None, title="", subtitle="")
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/test_sent_test", array_=stim_sent_test, plot_slice=None, title="", subtitle="")
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/test_mean_train0", array_=stim_mean_train[0].T, plot_slice=None, title="", subtitle="")
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/test_mean_train1", array_=stim_mean_train[1].T, plot_slice=None, title="", subtitle="")
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/test_mean_train_T", array_=stim_mean_train[0].T, plot_slice=None, title="", subtitle="")
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/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="../../RES_TEMP/states_out_train_recov", array_=states_out_train, titles_subset=l_recovered_sentences_train, legend_=construction_words, plot_slice=None, title="", subtitle="")
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/states_out_train_detail", array_=states_out_train[0].T, titles_subset=l_construction_train[0], 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_recovered_sentences_test, legend_=construction_words, plot_slice=None, title="", subtitle="")
## Plot internal states
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/intern_states_train", array_=internal_states_train, titles_subset=l_construction_train, plot_slice=None, title="", subtitle="")
plotting.plot_array_in_file(root_file_name="../../RES_TEMP/intern_states_test", array_=internal_states_test, titles_subset=l_ocw_array_test, plot_slice=None, title="", subtitle="")
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/test_int_states", array_=out, plot_slice=None, title="", subtitle="")
# plotting.plot_array_in_file(root_file_name="../../RES_TEMP/test_int_states_T", array_=out.T, plot_slice=None, title="", subtitle="")
print " *** ... Plotting to output file done *** "
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
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
