def main() :
# setting up options
parser = OptionParser()
parser.add_option("--num_iter", action="store", type="int", dest="num_iterations",default=100,help="number of iterations you would like to run em+smoothed l0. Default is 50")
parser.add_option("--initial_alpha", action="store", type="float", dest="initial_alpha",default = 0.0,help="initial_alpha,the weight of the smoothed l0 penalty. Defaut is 0 ")
parser.add_option("--final_alpha", action="store", type="float", dest="final_alpha",default = 0.0,help="final_alpha,the weight of the smoothed l0 penalty. Defaut is 0 ")
parser.add_option("--beta", action="store", type="float", dest="beta",default = 0.5,help="beta, the smoothness of the l0 prior, smaller the sigma, closer the approximation to true L0. Default is 0.5")
parser.add_option("--slack", action="store_true", dest="slack_option",default = False,help="if you want to project on the simplex with slack.")
parser.add_option("--noslack", action="store_false", dest="slack_option",default = False, help="if you want to project on the simplex with no slack. This is the regular projection approach")
parser.add_option("--num_pgd_iterations", action="store", type="int", dest="num_pgd_iterations",default = 10,help="Number of Projected Gradient Descent to run. Default is 100")
parser.add_option("--eta", action="store", type="float", dest="eta",default = 0.1,help="Eta, the constant step size for PGD using armijo line search. Default is 0.1")
parser.add_option("--armijo_beta", action="store", type="float", dest="armijo_beta",default = 0.5,help="Set value for Armijo beta, the beta used in in armijo line search. Default value is 0.2")
parser.add_option("--armijo_sigma", action="store", type="float", dest="armijo_sigma",default = 0.5,help="Set value for Armijo sigma, the sigma used in in armijo line search. Lower bound is 0.0001")
parser.add_option("--lower_bound", action="store", type="float", dest="lower_bound",default = 0.000001,help="Set value for the lower bound on the probability.Default is 10E-6")
parser.add_option("--cipher_data_file", action="store", type="string", dest="cipher_data_file",default ='cipher.data',help="Cipher data file for training")
parser.add_option("--cipher_noq_file", action="store", type="string", dest="cipher_noq_file",default ='cipher.noq',help="Cipher data without quotes")
parser.add_option("--cipher_decode_file", action="store", type="string", dest="cipher_decode_file",default ='cipher.decode',help="Cipher file for decoding")
parser.add_option("--cipher_gold_file", action="store", type="string", dest="cipher_gold_file",default ='cipher.gold',help="The correct decipherment")
parser.add_option("--lm", action="store", type="string", dest="lm",default ='lm.carmel',help="The lm file")
parser.add_option("--noe_lm", action="store", type="string", dest="noe_lm",default ='lm.carmel',help="The noe lm file")
parser.add_option("--gaussian_params_file", action="store", type="string", dest="gaussian_params_file",default =None,help="The means and variances for each gaussian letter")
parser.add_option("--unigram_probs_file", action="store", type="string", dest="unigram_probs_file",default =None,help="The means and variances for each gaussian letter")
parser.add_option("--std_mult", action="store", type="float", dest="std_mult",default =1.,help="The multiplier for the std ")
parser.add_option("--u", action="store_true", dest="uniform_init",default=False)
parser.add_option("--g", action="store_true", dest="gaussian_init",default=False)
parser.add_option("--i", action="store_true", dest="identity_init",default=False)
parser.add_option("--hpc", action="store_true", dest="hpc",default=False)
parser.add_option("--full_fst", action="store_true", dest="full_fst",default=False)
parser.add_option("--posterior_decoding", action="store_true", dest="posterior_decoding",default=False)
(options, args) = parser.parse_args()
print options
print args
#getting the values from optparse
num_iterations = options.num_iterations
initial_alpha = options.initial_alpha
final_alpha = options.final_alpha
beta = options.beta
slack_option = options.slack_option
num_pgd_iterations = options.num_pgd_iterations
eta = options.eta
armijo_beta = options.armijo_beta
armijo_sigma = options.armijo_sigma
lower_bound = options.lower_bound
cipher_data_file = options.cipher_data_file
cipher_decode_file = options.cipher_decode_file
cipher_noq_file = options.cipher_noq_file
cipher_gold_file = options.cipher_gold_file
lm = options.lm
noe_lm = options.noe_lm
gaussian_params_file = options.gaussian_params_file
unigram_probs_file = options.unigram_probs_file
uniform_init = options.uniform_init
gaussian_init = options.gaussian_init
identity_init = options.identity_init
hpc = options.hpc
std_mult = options.std_mult
full_fst = options.full_fst
posterior_decoding_flag = options.posterior_decoding
#setting up paramters
fractional_counts_language = {}
fractional_counts_channel = {}
probabilities_channel = {}
probabilities_language = {}
current_probabilities = {}
current_fractional_counts = {}
#constraint_parameters = []
initial_parameter_args = {}
current_initial_parameter_args = {}
parameter_to_index = {}
parameter_counter = 0
num_constraints = 1
constraint_tags_dict = {} #this will hold the tag for the constraint. the key is the constraint id and the value is the tag corresponding to this constraint
#beta = float(0)
#alpha = float(0)
global_num_parameters = 0
init_option = ''
current_optimization_tag = '' #this will hold the of the constraint for which we are doing the optimization
#adding parser options
'''
print 'beta is ',beta
print 'alpha is ',alpha
print 'eta is ',eta
raw_input()
'''
gold_cipher = emMethods.readCipherFile(cipher_gold_file)
ciphertext = emMethods.readCipherFile(cipher_noq_file)
ciphertext_with_spaces = open(cipher_noq_file).readline().strip().split()
print gold_cipher
#dictionary = emMethods.createDictionary('complete.dict.new-formatted')#.small')
#word_lines= emMethods.readWordLines('test.words.new-formatted')
cipher_letter_dict = emMethods.getUniqCipherLetters(cipher_noq_file)
cipher_probs = defaultdict(float)
emMethods.getCipherLetterProbs(cipher_probs,cipher_noq_file)
print 'cipher probs are '
#gaussians = defaultdict(list)
#emMethods.readGaussians(gaussians,gaussian_params_file)
plain_unigram_probs = dict((line.strip().split()[0],float(line.strip().split()[1])) for line in open(unigram_probs_file))
#get cipher letter probs
del cipher_letter_dict['_']
#word_list_five = emMethods.readWordList('TEXT.3.linear')
#plaintext = map(chr, range(97, 123))
plaintext_letters = []
ciphertext_letters = []
for k in range(65, 91):
plaintext_letters.append(chr(k))
ciphertext_letters.append(chr(k).lower())
print plaintext_letters
print 'the number of unique cipher letter is %d'%len(cipher_letter_dict.keys())
print cipher_letter_dict
num_cipher_letters = len(cipher_letter_dict.keys())
num_plain_letters = 26
#gold_tag_sequence = emMethods.readWordList('test.tags.new-formatted.linear')
free_parameters_channel = defaultdict(lambda:defaultdict(float))
free_parameters_language = defaultdict(lambda:defaultdict(float))
print 'starting to create parameters'
total_language_parameters = 0
total_channel_parameters = 0
#for line in cipher_lines :
#print 'created parameters for a line'
#(language_parameters,channel_parameters) = emMethods.getFreeParametersBigram(line,dictionary,free_parameters_language,free_parameters_channel)
if full_fst == True:
emMethods.getFreeCipherParametersChannel(plaintext_letters,free_parameters_channel)
num_cipher_letters = 26
else :
emMethods.getFreeCipherParametersChannel(plaintext_letters,free_parameters_channel,cipher_letter_dict = cipher_letter_dict)
temp = {'_':0.0}
free_parameters_channel['_'] = temp
#now, we will build all the lattices, and create a special start node and end node for every sentence
start_node_end_node_list = []
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
probabilities_channel = copy.deepcopy(free_parameters_channel)
#print 'gaussians'
#print gaussians
#createRandomPoint(probabilities_channel)
if (uniform_init == True) :
print 'uniform initialization'
emMethods.initUniformProbs(probabilities_channel)
# emMethods.initUniformProbs(probabilities_language,probabilities_channel)
if (gaussian_init == True) :
print 'gaussian initialization'
#emMethods.initFromGaussians(probabilities_channel,gaussians,cipher_probs,std_mult)
emMethods.initFromGaussiansSingleStd(probabilities_channel,plain_unigram_probs,cipher_probs,std_mult)
if (identity_init == True) :
emMethods.initIdentity(probabilities_channel)
#print 'channel probabilities after weighting are '
#print probabilities_channel
#raw_input()
emMethods.writeFst('cipher.fst',probabilities_channel)
#sys.exit()
final_probabilities_channel = copy.deepcopy(free_parameters_channel)
run_training = ''
if hpc == True:
run_training = "/home/nlg-05/vaswani/graehl/carmel/bin/linux64/carmel --train-cascade -u -M 0 -m -HJ %s %s cipher.fst"%(cipher_data_file,lm)
run_posterior_decoding = "/home/nlg-05/vaswani/graehl/carmel/bin/linux64/carmel --train-cascade -u -M 0 -m -HJ %s %s posterior_decode.fst"%(cipher_data_file,lm)
else :
run_training = "/Users/avaswani/graehl/carmel/bin/macosx/carmel --train-cascade -u -M 0 -m -HJ %s %s cipher.fst"%(cipher_data_file,lm)
run_posterior_decoding = "/Users/avaswani/graehl/carmel/bin/macosx/carmel --train-cascade -u -M 0 -m -HJ %s %s posterior_decode.fst"%(cipher_data_file,lm)
#running the EM iterations
#we are creating the indexes for algencan . Notice that here, the probabilities language is already uniform and therefore none of them will be zero
index_to_parameter = {}
createParametersForScaling(probabilities_channel,parameter_to_index,index_to_parameter)
start_time = time.clock()
print 'start time was ',start_time
#fractional_counts_dump_file = open('fractional.params','w')
#probabilities_dump_file = open('probs.params','w')
#optimized_probabilities_dump_file = open('probs.optimized.params','w')
alpha_delta = (final_alpha-initial_alpha)/(num_iterations-1)
current_alpha = initial_alpha
for i in range (0,num_iterations) :
print 'the iteration number was ',i
#this will create the parameters
total_corpus_probability = 0.0
(status,output) = commands.getstatusoutput(run_training)
print 'we just ran the training'
print output
print status
prob_match = probability_re.search(output)
print 'current alpha is ',current_alpha
if prob_match == None :
print'we should have found a probability'
else :
print 'the probability is %s'%prob_match.group(1)
temp_corpus_probability = float(prob_match.group(1)[2:len(prob_match.group(1))])
total_corpus_probability = 0.693147181 * temp_corpus_probability
print 'reading language fractional counts'
emMethods.readCarmelFractionalCounts('cipher.fst.trained',fractional_counts_channel,'channel')
print 'read the fst'
print' the probability of the corpus was %f' %total_corpus_probability
print 'we are now checking the accuracies'
noe_command = 'cat tagging.fsa | sed \'s/*e*//g\' > tagging.fsa.noe'
(status,output) = commands.getstatusoutput(noe_command)
print 'we wrote the noe fsa'
if hpc == True:
viterbi_command = "cat %s | /home/nlg-05/vaswani/graehl/carmel/bin/linux64/carmel -u -srbk 1 -QEWI %s cipher.fst > decipherment_output"%(cipher_decode_file,noe_lm)
else :
viterbi_command = "cat %s | /Users/avaswani/graehl/carmel/bin/macosx/carmel -u -srbk 1 -QEWI %s cipher.fst > decipherment_output"%(cipher_decode_file,noe_lm)
(status,output) = commands.getstatusoutput(viterbi_command)
print 'status',status
print 'output',output
#tagged_sequence = emMethods.readTaggingOutput('tagging_output')
deciphered_sequence = emMethods.readCipherFile('decipherment_output')
print 'length of deciphered sequence was ',len(deciphered_sequence)
accuracy = emMethods.calcAccuracy(gold_cipher,deciphered_sequence)
print 'The accuracy was %s and the objective function value was %s'%(str(accuracy),str(evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,current_alpha,beta)))
expected_counts = zeros(shape=(num_plain_letters,num_plain_letters))
#current_fractional_counts = fractional_counts_channel
#first populating the expected counts and probabilities matrix
#for i,plain_letter in enumerate(probabilities_channel.keys()) :
emMethods.dictionaryToArray(expected_counts,fractional_counts_channel,parameter_to_index)
'''
print 'Doing posterior decoding'
# decode
m = Munkres()
#J is not a matrix. its pairs of coordinates
J = m.compute(-expected_counts)
key = dict((index_to_parameter[plaintext_letters[n]][J[n][1]],plaintext_letters[J[n][0]]) for n in range(len(ciphertext_letters)))
print 'the key was ',key
posterior_decoded_string = []
for letter in ciphertext:
if letter == '_':
posterior_decoded_string.append('_')
else :
posterior_decoded_string.append(key[letter])
posterior_decoded_accuracy = emMethods.calcAccuracy(gold_cipher,posterior_decoded_string)
print 'The posterior decoded accuracy was',posterior_decoded_accuracy
'''
pdgRowAndColumnConstraints(probabilities_channel,parameter_to_index,num_pgd_iterations,current_alpha,beta,eta,lower_bound,armijo_beta,armijo_sigma,num_plain_letters,num_cipher_letters,expected_counts=expected_counts)
#now writing the fsa back again
emMethods.writeFst('cipher.fst',probabilities_channel)
#if the user has asked for posterior decoding, then we must do it
if posterior_decoding_flag == True:
print 'running command',run_posterior_decoding
emMethods.writePosteriorDecodingFST(ciphertext_with_spaces,probabilities_channel,'posterior_decode.fst')
(status,output) = commands.getstatusoutput(run_posterior_decoding)
print 'we just ran posterior decoding'
print output
print status
prob_match = probability_re.search(output)
if prob_match == None :
print'we should have found a probability in posterior decoding'
else :
print 'the posterior decoding probability is %s'%prob_match.group(1)
temp_corpus_probability = float(prob_match.group(1)[2:len(prob_match.group(1))])
total_corpus_probability = 0.693147181 * temp_corpus_probability
print 'the posterior decoding probability in base e is',total_corpus_probability
print 'Getting the posterior decode'
posterior_decode = emMethods.getPosteriorDecode('posterior_decode.fst.trained')
print 'the posterior decode was ',posterior_decode
posterior_decode_accuracy = emMethods.calcAccuracy(gold_cipher,posterior_decode)
print 'The posterior decoded accuracy was ',posterior_decode_accuracy
#print 'checking the initial zeros in channel model'
#checkZeros(probabilities_channel)
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
final_probabilities_channel = copy.deepcopy(probabilities_channel)
print 'at the end of the iteration'
current_alpha += alpha_delta
elapsed_time = time.clock() - start_time
print 'the elapsed time was ',elapsed_time
def main() :
global fractional_counts_language ,fractional_counts_channel,probabilities_channel,probabilities_language,beta,alpha,current_fractional_counts,current_optimization_params,init_option,initial_parameter_args,constraint_tags_dict,parameter_counter,current_optimization_tag,slack_option
#adding parser options
parser = OptionParser()
parser.add_option("--num_iter", action="store", type="int", dest="num_iterations",default=100,help="number of iterations you would like to run em+smoothed l0. Default is 50")
parser.add_option("--alpha", action="store", type="float", dest="alpha",default = 0.0,help="alpha,the weight of the smoothed l0 penalty. Defaut is 0 ")
parser.add_option("--beta", action="store", type="float", dest="beta",default = 0.5,help="beta, the smoothness of the l0 prior, smaller the sigma, closer the approximation to true L0. Default is 0.5")
parser.add_option("--slack", action="store_true", dest="slack_option",default = False,help="if you want to project on the simplex with slack.")
parser.add_option("--noslack", action="store_false", dest="slack_option",default = False, help="if you want to project on the simplex with no slack. This is the regular projection approach")
parser.add_option("--num_pgd_iterations", action="store", type="int", dest="num_pgd_iterations",default = 50,help="Number of Projected Gradient Descent to run. Default is 100")
parser.add_option("--eta", action="store", type="float", dest="eta",default = 0.5,help="Eta, the constant step size for PGD using armijo line search. Default is 0.1")
parser.add_option("--armijo_beta", action="store", type="float", dest="armijo_beta",default = 0.5,help="Set value for Armijo beta, the beta used in in armijo line search. Default value is 0.2")
parser.add_option("--armijo_sigma", action="store", type="float", dest="armijo_sigma",default = 0.5,help="Set value for Armijo sigma, the sigma used in in armijo line search. Lower bound is 0.0001")
parser.add_option("--lower_bound", action="store", type="float", dest="lower_bound",default = 0.000001,help="Set value for the lower bound on the probability.Default is 10E-6")
(options, args) = parser.parse_args()
#print options
#print args
#getting the values from optparse
num_iterations = options.num_iterations
alpha = options.alpha
beta = options.beta
slack_option = options.slack_option
num_pgd_iterations = options.num_pgd_iterations
eta = options.eta
armijo_beta = options.armijo_beta
armijo_sigma = options.armijo_sigma
lower_bound = options.lower_bound
gold_cipher = emMethods.readCipherFile('cipher.gold.noq')
print gold_cipher
#dictionary = emMethods.createDictionary('complete.dict.new-formatted')#.small')
#word_lines= emMethods.readWordLines('test.words.new-formatted')
cipher_letter_dict = emMethods.getUniqCipherLetters('cipher.data.noq')
#word_list_five = emMethods.readWordList('TEXT.3.linear')
#plaintext = map(chr, range(97, 123))
plaintext = []
for k in range(65, 91):
plaintext.append(chr(k))
print plaintext
print 'the number of unique cipher letter is %d'%len(cipher_letter_dict.keys())
print cipher_letter_dict
#gold_tag_sequence = emMethods.readWordList('test.tags.new-formatted.linear')
free_parameters_channel = {}
free_parameters_language = {}
print 'starting to create parameters'
total_language_parameters = 0
total_channel_parameters = 0
#for line in cipher_lines :
#print 'created parameters for a line'
#(language_parameters,channel_parameters) = emMethods.getFreeParametersBigram(line,dictionary,free_parameters_language,free_parameters_channel)
emMethods.getFreeCipherParametersChannel(cipher_letter_dict,plaintext,free_parameters_channel)
temp = {'_':0.0}
free_parameters_channel['_'] = temp
#print free_parameters_channel
#sys.exit()
#print language_parameters
#print channel_parameters
#total_language_parameters += language_parameters
#total_channel_parameters += channel_parameters
#print 'total language parameters is %d' %(total_language_parameters)
#print 'total channel parameters is %d' %(total_channel_parameters)
#now, we will build all the lattices, and create a special start node and end node for every sentence
start_node_end_node_list = []
# print len(word_list)
# num_taggings = emMethods.getNumTaggings(word_list,dictionary)
# print 'num_taggings '
# print type(num_taggings)
#print num_taggings
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
probabilities_channel = copy.deepcopy(free_parameters_channel)
createRandomPoint(probabilities_channel)
#emMethods.initUniformProbs(probabilities_channel)
# emMethods.initUniformProbs(probabilities_language,probabilities_channel)
emMethods.writeFst('cipher.fst',probabilities_channel)
final_probabilities_channel = copy.deepcopy(free_parameters_channel)
run_training = r'./carmel --train-cascade -M 0 -m -HJ cipher.data cipher.wfsa cipher.fst'
#running the EM iterations
#we are creating the indexes for algencan . Notice that here, the probabilities language is already uniform and therefore none of them will be zero
createParametersForScaling(probabilities_channel)
start_time = time.clock()
print 'start time was ',start_time
#fractional_counts_dump_file = open('fractional.params','w')
#probabilities_dump_file = open('probs.params','w')
#optimized_probabilities_dump_file = open('probs.optimized.params','w')
for i in range (0,num_iterations) :
print 'the iteration number was ',i
'''
print 'checking the initial zeros inlanguage'
checkZeros(probabilities_language)
print 'checking the initial zeros in channel'
checkZeros(probabilities_channel)
'''
#best_tag_sequence = emMethods.viterbiSearch(start,end,probabilities_channel,probabilities_language,lattice_skeleton)
#emMethods.calcAccuracy(gold_tag_sequence,best_tag_sequence)
#raw_input()
#this will create the parameters
total_corpus_probability = 0.0
(status,output) = commands.getstatusoutput(run_training)
print 'we just ran the training'
print output
prob_match = probability_re.search(output)
if prob_match == None :
print'we should have found a probability'
else :
print 'the probability is %s'%prob_match.group(1)
temp_corpus_probability = float(prob_match.group(1)[2:len(prob_match.group(1))])
total_corpus_probability = 0.693147181 * temp_corpus_probability
print 'reading language fractional counts'
emMethods.readCarmelFractionalCounts('cipher.fst.trained',fractional_counts_channel,'channel')
print 'read the fst'
print' the probability of the corpus was %f' %total_corpus_probability
print 'we are now checking the accuracies'
noe_command = 'cat tagging.fsa | sed \'s/*e*//g\' > tagging.fsa.noe'
(status,output) = commands.getstatusoutput(noe_command)
print 'we wrote the noe fsa'
viterbi_command = r'cat cipher.data.quotes | ./carmel -srbk 1 -QEWI cipher.wfsa.noe cipher.fst > decipherment_output'
(status,output) = commands.getstatusoutput(viterbi_command)
#tagged_sequence = emMethods.readTaggingOutput('tagging_output')
deciphered_sequence = emMethods.readCipherFile('decipherment_output')
accuracy = emMethods.calcAccuracy(gold_cipher,deciphered_sequence)
print 'The accuracy was %s and the objective function value was %s'%(str(accuracy),str(evaluateObjectiveFuncValue(total_corpus_probability,probabilities_channel,probabilities_language,alpha,beta)))
#emMethods.reEstimateProbabilities(probabilities_channel,probabilities_language,fractional_counts_channel,fractional_counts_language)
#fractional_counts_language = copy.deepcopy(free_parameters_language)
#fractional_counts_channel = copy.deepcopy(free_parameters_channel)
#first optimizing the tag bigrams and doing it per parameter
for tag in initial_parameter_args.keys() :
print 'we are starting a new tag'
if len(initial_parameter_args[tag].keys()) == 1 :
continue
print 'we are currently optimizing for tag', tag
#current_initial_parameter_args = initial_parameter_args[tag]
current_optimization_tag = tag
parameter_counter = len(initial_parameter_args[tag].keys())
current_fractional_counts = fractional_counts_channel
constraint_tags_dict[1] = tag
temp_language_probs = dict(probabilities_channel)
#optimizing per constraint
init_option = 'current_prob'
current_optimization_params = 'channel'
#creating the initial vector to pass to the projected gradient descent algorithm
x=zeros(parameter_counter)
expected_counts=zeros(parameter_counter)
expected_counts_sum = 0.
for next_tag in initial_parameter_args[current_optimization_tag].keys() :
if initial_parameter_args[current_optimization_tag][next_tag] == 'ONE' :
continue
else :
parameter_number = initial_parameter_args[current_optimization_tag][next_tag]
x[parameter_number] = probabilities_channel[current_optimization_tag][next_tag]
expected_counts[parameter_number] = current_fractional_counts[current_optimization_tag][next_tag]
expected_counts_sum += current_fractional_counts[current_optimization_tag][next_tag]
print 'expected counts sum was ',expected_counts_sum
if expected_counts_sum <= 0.5 :
#set all the probabilities to 0
#for next_tag in initial_parameter_args[current_optimization_tag] :
# new_probabilities = zeros(len(initial_parameter_args[current_optimization_tag].keys()))
# assignProbs(new_probabilities,probabilities_channel)
continue
#raw_input()
#the optimization steps have to come here
#print 'we are optimizing the tag bigrams now '
#current_eta = eta_0 #/sqrt(num_iterations+1)
print 'Doing projected gradient descent'
new_probabilities = projectedGradientDescentWithArmijoRule(x = x,expected_counts = expected_counts,num_pgd_iterations = num_pgd_iterations,eta = eta,lower_bound = lower_bound,armijo_beta = armijo_beta,armijo_sigma = armijo_sigma)
print 'finished projected gradient descent'
#new_probabilities = newtonProjectedGradientDescentWithArmijoRule(x,num_pgd_iterations,eta,lower_bound,armijo_beta,armijo_sigma)
#print new_probabilities
#raw_input()
if current_optimization_params == 'tag_bigrams' :
#print 'we are replacing bigram probs'
assignProbs(new_probabilities,probabilities_language)
# checkZeros(probabilities_language)
else :
print 'we are replacing channel probs'
assignProbs(new_probabilities,probabilities_channel)
#now writing the fsa back again
# emMethods.writeFsa('tagging.fsa',probabilities_language)
# emMethods.writeFst('tagging.fst',probabilities_channel)
emMethods.writeFst('cipher.fst',probabilities_channel)
#emMethods.writeFstLnFormat('tagging.fst',probabilities_channel)
print 'checking the initial zeros in channel model'
checkZeros(probabilities_channel)
#fractional_counts_language.clear()
#fractional_counts_channel.clear()
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
final_probabilities_channel = copy.deepcopy(probabilities_channel)
print 'at the end of the iteration'
#raw_input()
elapsed_time = time.clock() - start_time
print 'the elapsed time was ',elapsed_time
def main() :
global fractional_counts_language ,fractional_counts_channel,probabilities_channel,sigma,alpha,current_fractional_counts,current_optimization_params,init_option
num_iterations = int(sys.argv[1])
alpha = float(sys.argv[2])
sigma = float(sys.argv[3])
# dictionary = emMethods.createDictionary('DICT2')#.small')
# word_list = emMethods.readWordList('TEXT.linear')
#word_list_five = emMethods.readWordList('TEXT.5.linear')
# gold_tag_sequence = emMethods.readWordList('GOLD.linear')
gold_cipher = emMethods.readCipherFile('cipher.gold.noq')
print gold_cipher
#dictionary = emMethods.createDictionary('complete.dict.new-formatted')#.small')
#word_lines= emMethods.readWordLines('test.words.new-formatted')
cipher_letter_dict = emMethods.getUniqCipherLetters('cipher.data.noq')
#word_list_five = emMethods.readWordList('TEXT.3.linear')
#plaintext = map(chr, range(97, 123))
plaintext = []
for k in range(65, 91):
plaintext.append(chr(k))
print plaintext
print 'the number of unique cipher letter is %d'%len(cipher_letter_dict.keys())
print cipher_letter_dict
#gold_tag_sequence = emMethods.readWordList('test.tags.new-formatted.linear')
free_parameters_channel = {}
free_parameters_language = {}
print 'starting to create parameters'
total_language_parameters = 0
total_channel_parameters = 0
#for line in cipher_lines :
#print 'created parameters for a line'
#(language_parameters,channel_parameters) = emMethods.getFreeParametersBigram(line,dictionary,free_parameters_language,free_parameters_channel)
emMethods.getFreeCipherParametersChannel(cipher_letter_dict,plaintext,free_parameters_channel)
temp = {'_':0.0}
free_parameters_channel['_'] = temp
#print free_parameters_channel
#sys.exit()
#print language_parameters
#print channel_parameters
#total_language_parameters += language_parameters
#total_channel_parameters += channel_parameters
#print 'total language parameters is %d' %(total_language_parameters)
#print 'total channel parameters is %d' %(total_channel_parameters)
#now, we will build all the lattices, and create a special start node and end node for every sentence
start_node_end_node_list = []
# print len(word_list)
# num_taggings = emMethods.getNumTaggings(word_list,dictionary)
# print 'num_taggings '
# print type(num_taggings)
#print num_taggings
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
probabilities_channel = copy.deepcopy(free_parameters_channel)
emMethods.initUniformProbs(probabilities_channel)
# emMethods.initUniformProbs(probabilities_language,probabilities_channel)
emMethods.writeFst('cipher.fst',probabilities_channel)
run_training = r'./carmel.static --train-cascade -M 0 -m -HJ cipher.data cipher.wfsa cipher.fst'
# skel_size += len(col)
#running the EM iterations
for i in range (0,num_iterations) :
'''
print 'checking the initial zeros inlanguage'
checkZeros(probabilities_language)
print 'checking the initial zeros in channel'
checkZeros(probabilities_channel)
'''
#best_tag_sequence = emMethods.viterbiSearch(start,end,probabilities_channel,probabilities_language,lattice_skeleton)
#emMethods.calcAccuracy(gold_tag_sequence,best_tag_sequence)
#raw_input()
#this will create the parameters
total_corpus_probability = 0.0
(status,output) = commands.getstatusoutput(run_training)
print 'we just ran the training'
prob_match = probability_re.search(output)
if prob_match == None :
print'we should have found a probability'
else :
print 'the probability is %s'%prob_match.group(1)
total_corpus_probability = float(prob_match.group(1)[2:len(prob_match.group(1))])
print 'reading channel fractional counts'
emMethods.readCarmelFractionalCounts('cipher.fst.trained',fractional_counts_channel,'channel')
print 'read the fst'
print' the probability of the corpus was %f' %total_corpus_probability
print 'we are now checking the accuracies'
noe_command = 'cat tagging.fsa | sed \'s/*e*//g\' > tagging.fsa.noe'
(status,output) = commands.getstatusoutput(noe_command)
print 'we wrote the noe fsa'
viterbi_command = r'cat cipher.data | ./carmel.static -srbk -QEWI 1 cipher.wfsa.noe cipher.fst > decipherment_output'
(status,output) = commands.getstatusoutput(viterbi_command)
#tagged_sequence = emMethods.readTaggingOutput('tagging_output')
deciphered_sequence = emMethods.readCipherFile('decipherment_output')
accuracy = emMethods.calcAccuracy(gold_cipher,deciphered_sequence)
print 'The accuracy was %s and the objective function value was %s'%(str(accuracy),str(evaluateObjectiveFuncValue(total_corpus_probability,probabilities_channel,alpha,sigma)))
#first optimizing the channel
current_fractional_counts = fractional_counts_channel
createParameters(probabilities_channel,current_fractional_counts,free_parameters_channel,alpha,sigma)
temp_channel_probs = dict(probabilities_channel)
init_option = 'current_prob'
current_optimization_params = 'channel'
algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
#I should check if the objective function is increasing
channel_probs_after_init_current_prob = copy.deepcopy(probabilities_channel)
#obj_val1 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,alpha,sigma)
total_obj_val1 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_channel,alpha,sigma)
obj_val1 = evaluateOptimizationFunction(initial_parameter_args,probabilities_channel,fractional_counts_channel,alpha,sigma)
print 'the function value was obj 1 %f'%obj_val1
#emMethods.clearAlphaBeta(lattice_skeleton)
init_option = 'zeros'
current_optimization_params = 'channel'
algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
channel_probs_after_init_zeros = copy.deepcopy(probabilities_channel)
#obj_val2 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,alpha,sigma)a
total_obj_val2 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_channel,alpha,sigma)
obj_val2 = evaluateOptimizationFunction(initial_parameter_args,probabilities_channel,fractional_counts_channel,alpha,sigma)
print 'the function value was obj 2 %f'%obj_val2
#emMethods.clearAlphaBeta(lattice_skeleton)
if (total_obj_val1 >= total_obj_val2) :
#init_option = 'current_prob'
#current_optimization_params = 'tag_bigrams'
if (obj_val1 < obj_val2) :
print 'the final objective function value was opposite'
probabilities_channel = copy.deepcopy(channel_probs_after_init_current_prob)
#algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
print 'the final objective function value was obj 1 %f'%total_obj_val1
else :
#init_option = 'zeros'
#current_optimization_params = 'tag_bigrams'
probabilities_channel = copy.deepcopy(channel_probs_after_init_zeros)
if (obj_val2 < obj_val1) :
print 'the final objective function value was opposite'
print 'the final objective function value was obj 2 %f'%total_obj_val2
#raw_input()
# emMethods.reEstimateProbabilities(probabilities_channel,probabilities_language,fractional_counts_channel,fractional_counts_language)
#emMethods.reEstimateProbabilities(probabilities_channel,probabilities_language,fractional_counts_channel,fractional_counts_language)
#print 'writing the fsa'
#now writing the fsa back again
#emMethods.writeFsa('tagging.fsa',probabilities_language)
emMethods.writeFst('cipher.fst',probabilities_channel)
#print 'checking the zeros in tag bigram model'
#checkZeros(probabilities_language)
print 'checking the zeros in channel model'
checkZeros(probabilities_channel)
#fractional_counts_language.clear()
#fractional_counts_channel.clear()
#fractional_counts_language = copy.deepcopy(free_parameters_language)
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
def main() :
# setting up options
parser = OptionParser()
parser.add_option("--num_iter", action="store", type="int", dest="num_iterations",default=100,help="number of iterations you would like to run em+smoothed l0. Default is 50")
parser.add_option("--num_noise_samples", action="store", type="int", dest="num_noise_samples",default=10,help="number of noise samples. Default is 10")
parser.add_option("--initial_alpha", action="store", type="float", dest="initial_alpha",default = 0.0,help="initial_alpha,the weight of the smoothed l0 penalty. Defaut is 0 ")
parser.add_option("--final_alpha", action="store", type="float", dest="final_alpha",default = 0.0,help="final_alpha,the weight of the smoothed l0 penalty. Defaut is 0 ")
parser.add_option("--beta", action="store", type="float", dest="beta",default = 0.5,help="beta, the smoothness of the l0 prior, smaller the sigma, closer the approximation to true L0. Default is 0.5")
parser.add_option("--slack", action="store_true", dest="slack_option",default = False,help="if you want to project on the simplex with slack.")
parser.add_option("--noslack", action="store_false", dest="slack_option",default = False, help="if you want to project on the simplex with no slack. This is the regular projection approach")
parser.add_option("--num_pgd_iterations", action="store", type="int", dest="num_pgd_iterations",default = 10,help="Number of Projected Gradient Descent to run. Default is 100")
parser.add_option("--eta", action="store", type="float", dest="eta",default = 0.1,help="Eta, the constant step size for PGD using armijo line search. Default is 0.1")
parser.add_option("--armijo_beta", action="store", type="float", dest="armijo_beta",default = 0.5,help="Set value for Armijo beta, the beta used in in armijo line search. Default value is 0.2")
parser.add_option("--armijo_sigma", action="store", type="float", dest="armijo_sigma",default = 0.5,help="Set value for Armijo sigma, the sigma used in in armijo line search. Lower bound is 0.0001")
parser.add_option("--lower_bound", action="store", type="float", dest="lower_bound",default = 0.000001,help="Set value for the lower bound on the probability.Default is 10E-6")
parser.add_option("--cipher_data_file", action="store", type="string", dest="cipher_data_file",default ='cipher.data',help="Cipher data file for training")
parser.add_option("--cipher_noq_file", action="store", type="string", dest="cipher_noq_file",default ='cipher.noq',help="Cipher data without quotes")
parser.add_option("--cipher_decode_file", action="store", type="string", dest="cipher_decode_file",default ='cipher.decode',help="Cipher file for decoding")
parser.add_option("--cipher_gold_file", action="store", type="string", dest="cipher_gold_file",default ='cipher.gold',help="The correct decipherment")
parser.add_option("--lm", action="store", type="string", dest="lm",default ='lm.carmel',help="The lm file")
parser.add_option("--noe_lm", action="store", type="string", dest="noe_lm",default ='lm.carmel',help="The noe lm file")
parser.add_option("--gaussian_params_file", action="store", type="string", dest="gaussian_params_file",default =None,help="The means and variances for each gaussian letter")
parser.add_option("--unigram_probs_file", action="store", type="string", dest="unigram_probs_file",default =None,help="The means and variances for each gaussian letter")
parser.add_option("--std_mult", action="store", type="float", dest="std_mult",default =1.,help="The multiplier for the std ")
parser.add_option("--u", action="store_true", dest="uniform_init",default=False)
parser.add_option("--g", action="store_true", dest="gaussian_init",default=False)
parser.add_option("--i", action="store_true", dest="identity_init",default=False)
parser.add_option("--hpc", action="store_true", dest="hpc",default=False)
parser.add_option("--full_fst", action="store_true", dest="full_fst",default=False)
parser.add_option("--fst_init", action="store_true", dest="fst_init",default=False)
parser.add_option("--noise_lm", action="store", type="string", dest="noise_lm",default ='noise.lm.carmel',help="The noise lm file")
parser.add_option("--noise_channel_fst", action="store", type="string", dest="noise_channel_fst",default ='noise.fst.carmel',help="The channel fst")
parser.add_option("--noise_probs_file", action="store", type="string", dest="noise_probs_file",default ='noise.probs',help="The noise probs file")
parser.add_option("--noise_samples_file", action="store", type="string", dest="noise_samples_file",default ='noise.samples',help="The noise samples file")
(options, args) = parser.parse_args()
print options
print args
#getting the values from optparse
num_iterations = options.num_iterations
initial_alpha = options.initial_alpha
final_alpha = options.final_alpha
beta = options.beta
slack_option = options.slack_option
num_pgd_iterations = options.num_pgd_iterations
eta = options.eta
armijo_beta = options.armijo_beta
armijo_sigma = options.armijo_sigma
lower_bound = options.lower_bound
cipher_data_file = options.cipher_data_file
cipher_decode_file = options.cipher_decode_file
cipher_noq_file = options.cipher_noq_file
cipher_gold_file = options.cipher_gold_file
lm = options.lm
noe_lm = options.noe_lm
gaussian_params_file = options.gaussian_params_file
unigram_probs_file = options.unigram_probs_file
uniform_init = options.uniform_init
gaussian_init = options.gaussian_init
identity_init = options.identity_init
hpc = options.hpc
std_mult = options.std_mult
full_fst = options.full_fst
num_noise_samples = options.num_noise_samples
noise_lm = options.noise_lm
noise_channel_fst = options.noise_channel_fst
fst_init = options.fst_init
noise_probs_file = options.noise_probs_file
noise_samples_file = options.noise_samples_file
noise_probs_file = options.noise_probs_file
#setting up paramters
fractional_counts_language = {}
fractional_counts_channel = {}
probabilities_channel = {}
probabilities_language = {}
current_probabilities = {}
current_fractional_counts = {}
#constraint_parameters = []
initial_parameter_args = {}
current_initial_parameter_args = {}
parameter_to_index = {}
parameter_counter = 0
num_constraints = 1
constraint_tags_dict = {} #this will hold the tag for the constraint. the key is the constraint id and the value is the tag corresponding to this constraint
#beta = float(0)
#alpha = float(0)
global_num_parameters = 0
init_option = ''
current_optimization_tag = '' #this will hold the of the constraint for which we are doing the optimization
#adding parser options
'''
print 'beta is ',beta
print 'alpha is ',alpha
print 'eta is ',eta
raw_input()
'''
gold_cipher = emMethods.readCipherFile(cipher_gold_file)
print gold_cipher
#dictionary = emMethods.createDictionary('complete.dict.new-formatted')#.small')
#word_lines= emMethods.readWordLines('test.words.new-formatted')
cipher_letter_dict = emMethods.getUniqCipherLetters(cipher_noq_file)
cipher_letters = open(cipher_noq_file).readline().split()
#sys.exit()
cipher_probs = defaultdict(float)
emMethods.getCipherLetterProbs(cipher_probs,cipher_noq_file)
print 'cipher probs are '
#gaussians = defaultdict(list)
#emMethods.readGaussians(gaussians,gaussian_params_file)
plain_unigram_probs = dict((line.strip().split()[0],float(line.strip().split()[1])) for line in open(unigram_probs_file))
#get cipher letter probs
del cipher_letter_dict['_']
#word_list_five = emMethods.readWordList('TEXT.3.linear')
#plaintext = map(chr, range(97, 123))
plaintext = []
for k in range(65, 91):
plaintext.append(chr(k))
print plaintext
print 'the number of unique cipher letter is %d'%len(cipher_letter_dict.keys())
print cipher_letter_dict
num_cipher_letters = len(cipher_letter_dict.keys())
num_plain_letters = 26
#gold_tag_sequence = emMethods.readWordList('test.tags.new-formatted.linear')
free_parameters_channel = defaultdict(lambda:defaultdict(float))
free_parameters_language = defaultdict(lambda:defaultdict(float))
print 'starting to create parameters'
total_language_parameters = 0
total_channel_parameters = 0
#for line in cipher_lines :
#print 'created parameters for a line'
#(language_parameters,channel_parameters) = emMethods.getFreeParametersBigram(line,dictionary,free_parameters_language,free_parameters_channel)
if full_fst == True:
emMethods.getFreeCipherParametersChannel(plaintext,free_parameters_channel)
num_cipher_letters = 26
else :
emMethods.getFreeCipherParametersChannel(plaintext,free_parameters_channel,cipher_letter_dict = cipher_letter_dict)
temp = {'_':0.0}
free_parameters_channel['_'] = temp
#now, we will build all the lattices, and create a special start node and end node for every sentence
start_node_end_node_list = []
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
probabilities_channel = copy.deepcopy(free_parameters_channel)
#print 'gaussians'
#print gaussians
#createRandomPoint(probabilities_channel)
if (uniform_init == True) :
print 'uniform initialization'
emMethods.initUniformProbs(probabilities_channel)
# emMethods.initUniformProbs(probabilities_language,probabilities_channel)
if (gaussian_init == True) :
print 'gaussian initialization'
#emMethods.initFromGaussians(probabilities_channel,gaussians,cipher_probs,std_mult)
emMethods.initFromGaussiansSingleStd(probabilities_channel,plain_unigram_probs,cipher_probs,std_mult)
if (identity_init == True) :
emMethods.initIdentity(probabilities_channel)
if fst_init == True:
emMethods.initFromWfst('init.fst',probabilities_channel,'channel')
#print 'channel probabilities after weighting are '
#print probabilities_channel
#raw_input()
emMethods.writeFst('cipher.fst',probabilities_channel)
#sys.exit()
final_probabilities_channel = copy.deepcopy(free_parameters_channel)
#running the EM iterations
#we are creating the indexes for algencan . Notice that here, the probabilities language is already uniform and therefore none of them will be zero
createParametersForScaling(probabilities_channel,parameter_to_index)
start_time = time.clock()
print 'start time was ',start_time
#fractional_counts_dump_file = open('fractional.params','w')
#probabilities_dump_file = open('probs.params','w')
#optimized_probabilities_dump_file = open('probs.optimized.params','w')
alpha_delta = (final_alpha-initial_alpha)/(num_iterations-1)
current_alpha = initial_alpha
###########GENERATING THE NOISE FILE LIST##########################################
noise_file_list = []
for k in range(num_noise_samples):
noise_file_list.append("%s.noise%d"%(cipher_noq_file,k))
#reading the noise probsa
noise_probs = []
for line in open(noise_probs_file):
if line.strip() == '':
continue
else:
noise_probs.append(float(line.strip()))
print 'number of noise probs is ',len(noise_probs)
#this will be needed for computations
log_num_noise_samples = math.log(num_noise_samples)
#reading noise samples
total_noise_samples = 0
noise_samples = []
for line in open(noise_samples_file):
if line.strip() == '':
continue
else:
noise_samples.append(line.strip())
total_noise_samples += 1
print 'total number of noise samples is',total_noise_samples
###############GENERATE CARMEL TRAINING AND DECODING COMMANDS##############################
print 'Generating the training and decoding commands...'
run_true_training = ''
run_noise_training = ''
if hpc == True:
run_true_training = "/home/nlg-05/vaswani/graehl/carmel/bin/linux64/carmel --train-cascade -u -M 0 -m -HJ %s %s cipher.fst"%(cipher_data_file,lm)
run_noise_training = "/home/nlg-05/vaswani/graehl/carmel/bin/linux64/carmel --train-cascade -u -M 0 -m -HJ %s %s %s"%(cipher_data_file,noise_lm,noise_channel_fst)
else :
run_true_training = "/Users/avaswani/graehl/carmel/bin/macosx/carmel --train-cascade -u -M 0 -m -HJ %s %s cipher.fst"%(cipher_data_file,lm)
run_noise_training = "/Users/avaswani/graehl/carmel/bin/macosx/carmel --train-cascade -u -M 0 -m -HJ %s %s %s"%(cipher_data_file,noise_lm,noise_channel_fst)
noise_sample_training_commands_for_model = []
#noise_sample_training_commands_for_noise = []
for k in range(num_noise_samples):
run_noise_sample_true_training = ''
#run_noise_sample_noise_training = ''
if hpc == True:
run_noise_sample_true_training = "/home/nlg-05/vaswani/graehl/carmel/bin/linux64/carmel --train-cascade -u -M 0 -m -HJ %s %s cipher.fst"%(noise_file_list[k],lm)
#run_noise_sample_noise_training = "/home/nlg-05/vaswani/graehl/carmel/bin/linux64/carmel --train-cascade -u -M 0 -m -HJ %s %s %s"%(noise_file_list[k],noise_lm,noise_channel_fst)
else :
run_noise_sample_true_training = "/Users/avaswani/graehl/carmel/bin/macosx/carmel --train-cascade -u -M 0 -m -HJ %s %s cipher.fst"%(noise_file_list[k],lm)
#run_noise_sample_noise_training = "/Users/avaswani/graehl/carmel/bin/macosx/carmel --train-cascade -u -M 0 -m -HJ %s %s %s"%(noise_file_list[k],noise_lm,noise_channel_fst)
noise_sample_training_commands_for_model.append(run_noise_sample_true_training)
#noise_sample_training_commands_for_noise.append(run_noise_sample_noise_training)
viterbi_command = ''
if hpc == True:
viterbi_command = "cat %s | /home/nlg-05/vaswani/graehl/carmel/bin/linux64/carmel -u -srbk 1 -QEWI %s cipher.fst > decipherment_output"%(cipher_decode_file,noe_lm)
else :
viterbi_command = "cat %s | /Users/avaswani/graehl/carmel/bin/macosx/carmel -u -srbk 1 -QEWI %s cipher.fst > decipherment_output"%(cipher_decode_file,noe_lm)
print 'Running Training ...'
print 'command for running noise training is ',run_noise_training
log_prob_under_noise = runCarmel(run_noise_training)
'''
log_noise_sample_probs_under_noise = []
for k in range(num_noise_samples):
#log_noise_sample_prob_under_noise = runCarmel(noise_sample_training_commands_for_noise[k])
log_noise_sample_probs_under_noise.append(log_noise_sample_prob_under_noise)
print 'the log noise sample probs under noise are ',log_noise_sample_probs_under_noise
'''
##########################RUN NCE TRAINING###############################
for i in range (0,num_iterations) :
###############GENERATING K NOISE SAMPLES#########################
print 'Generating noise samples...'
noise_ciphertext = list(cipher_letters)
for k in range(num_noise_samples):
noise_file = open(noise_file_list[k],'w')
#emMethods.generateNoiseCipher(noise_ciphertext)
noise_ciphertext = noise_samples[i*num_noise_samples+k]
#print 'noise ciphertext is ',' '.join(noise_ciphertext)
print 'noise ciphertext is ',noise_ciphertext
#noise_file.write("\n%s\n"%' '.join(["\"%s\""%item for item in noise_ciphertext]))
noise_file.write("\n%s"%noise_ciphertext)
noise_file.close()
print 'Getting Accuracy'
################RUNNING VITERBI AND GETTING ACCURCY###################
runViterbiAndGetAccuracy(gold_cipher,viterbi_command)
#dampening the learning rate
eta = eta/(1+i)
current_function_value = 0.
print 'the iteration number was ',i
print 'current alpha is ',current_alpha
print 'Computing expected counts...'
###############COMPUTING EXPECTED COUNTS ###############################
log_prob_under_model = runCarmel(run_true_training)
print 'reading language fractional counts from cipher.fst.trained'
emMethods.readCarmelFractionalCounts('cipher.fst.trained',fractional_counts_channel,'channel')
print 'Running the noise model'
log_prob_under_noise = runCarmel(run_noise_training)
positive_weight = computePositiveWeight(log_prob_under_model,log_prob_under_noise,num_noise_samples)
#compute the
p_d_equals_one = log_prob_under_model - (logaddexp(log_num_noise_samples+log_prob_under_noise,log_prob_under_model))
current_function_value += p_d_equals_one
print 'p of d equals 1 at the current point is ', p_d_equals_one
print 'The positive weight was',positive_weight
if positive_weight <= 10E-10:
positive_weight = 10E-10
expected_counts = zeros(shape=(num_plain_letters,num_plain_letters))
emMethods.dictionaryToArray(expected_counts,fractional_counts_channel,parameter_to_index)
expected_counts *= positive_weight
total_noise_expected_counts = zeros(shape=(num_plain_letters,num_plain_letters))
log_noise_sample_probs_under_noise = []
#log_noise_sample_probs_under_noise = []
for k in range(num_noise_samples):
temp_noise_fractional_counts = copy.deepcopy(free_parameters_channel)
log_noise_sample_prob_under_model = runCarmel(noise_sample_training_commands_for_model[k])
print 'reading noise language fractional counts from cipher.fst.trained'
emMethods.readCarmelFractionalCounts('cipher.fst.trained',temp_noise_fractional_counts,'channel')
#log_noise_sample_prob_under_noise = runCarmel(noise_sample_training_commands_for_noise[k])
log_noise_sample_prob_under_noise = noise_probs[i*num_noise_samples+k]
log_noise_sample_probs_under_noise.append(log_noise_sample_prob_under_noise)
negative_weight = computeNegativeWeight(log_noise_sample_prob_under_model,log_noise_sample_probs_under_noise[k],num_noise_samples)
if negative_weight <= 10E-10 :
negative_weight = 10E-10
print 'The negative weight was',negative_weight
p_d_equals_zero = log_noise_sample_probs_under_noise[k]+ log_num_noise_samples -\
(logaddexp(log_num_noise_samples+log_noise_sample_probs_under_noise[k],log_noise_sample_prob_under_model))
current_function_value += p_d_equals_zero
print 'p of d equals 0 at the current point',k,' is ', p_d_equals_zero
temp_noise_expected_counts = zeros(shape=(num_plain_letters,num_plain_letters))
emMethods.dictionaryToArray(temp_noise_expected_counts,temp_noise_fractional_counts,parameter_to_index)
#print 'temp noise expected counts are ',temp_noise_expected_counts
temp_noise_expected_counts *= negative_weight
#print 'temp noise exptected counts are ',temp_noise_expected_counts
total_noise_expected_counts += temp_noise_expected_counts
print 'the log noise sample probs under noise are ',log_noise_sample_probs_under_noise
#print 'total noise expected counts are ',total_noise_expected_counts
#getting the final exptected counts
expected_counts -= total_noise_expected_counts
print 'Current function value is ',current_function_value
###############PROJECTING THE POINT ONTO THE DOUBLY STOCHASTIC MATRIX###########################
new_feasible_point,current_point,grad = nceUpdate(eta,expected_counts,probabilities_channel,parameter_to_index,num_plain_letters)
print 'Running line search....'
#############LINE SEARCH#########################
armijo_bound = 0.0
#print 'new feasible point is ',new_feasible_point
#raw_input()
#print 'gradient is ',grad
#raw_input()
armijo_bound = -armijo_sigma * armijo_beta * (grad*(new_feasible_point-current_point)).sum()
print 'Armijo bound is ',armijo_bound
terminate_line_srch = False
num_steps = 1
#current_beta = 1.0 #armijo_beta
current_beta = armijo_beta
final_beta = 0
current_armijo_bound = armijo_bound
no_update = True
best_func_value = current_function_value
while(terminate_line_srch != True) :
#print 'num steps is ',num_steps
temp_point = zeros(shape=current_point.shape)
temp_point = current_point * (1.0 - current_beta) + current_beta * new_feasible_point
#print 'the temp point is '
#print temp_point
#evaluating the function at the current point
#first writing the fst
#assigning the probabilities and writing the fsa
temp_probabilities_channel = copy.deepcopy(free_parameters_channel)
assignProbsMatrix(temp_point,temp_probabilities_channel,parameter_to_index)
temp_probabilities_channel['_']['_'] = 1.0
emMethods.writeFst('cipher.fst',temp_probabilities_channel)
#print 'temp point is ',temp_point
#sys.exit()
func_value_at_temp_point = 0.
temp_log_prob_under_model = runCarmel(run_true_training)
temp_p_d_equals_one = temp_log_prob_under_model - (logaddexp(log_num_noise_samples+log_prob_under_noise,temp_log_prob_under_model))
print 'p of d equals 1 at the temp point is ', temp_p_d_equals_one
func_value_at_temp_point += temp_p_d_equals_one
#then go over the noise samples
for k in range(num_noise_samples):
temp_log_noise_sample_prob_under_model = runCarmel(noise_sample_training_commands_for_model[k])
temp_p_d_equals_zero = log_noise_sample_probs_under_noise[k] + log_num_noise_samples -\
(logaddexp(log_num_noise_samples+log_noise_sample_probs_under_noise[k],temp_log_noise_sample_prob_under_model))
func_value_at_temp_point += temp_p_d_equals_zero
print 'p of d equals 0 at the temp point',k,' is ', temp_p_d_equals_zero
print 'the function value at the temp point is %.16f'%func_value_at_temp_point
#raw_input()
if func_value_at_temp_point > best_func_value :
best_func_value = func_value_at_temp_point
final_beta = current_beta
no_update = False
#print 'we arrived at a better function value'
#raw_input()
# print 'we just updated thef final beta to ',final_beta
#if (func_value_at_temp_point - current_function_value >= current_armijo_bound) :
# terminate_line_srch = True
#elif current_function_value - func_value_at_temp_point < 0:
# terminate_line_srch = True
if num_steps >= 5 :
terminate_line_srch = True
current_beta = armijo_beta * current_beta
current_armijo_bound = armijo_bound * current_beta
num_steps += 1
if no_update == False :
current_point = (1.0-final_beta)*current_point + final_beta*new_feasible_point
if no_update == True :#x.all() == current_point.all() :
print 'not update was true'
break;
#print 'the current point is ',current_point
#raw_input()
#assigning the probabilities and writing the fsa
assignProbsMatrix(current_point,probabilities_channel,parameter_to_index)
emMethods.writeFst('cipher.fst',probabilities_channel)
#print 'checking the initial zeros in channel model'
#checkZeros(probabilities_channel)
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
final_probabilities_channel = copy.deepcopy(probabilities_channel)
print 'at the end of the iteration'
current_alpha += alpha_delta
elapsed_time = time.clock() - start_time
print 'the elapsed time was ',elapsed_time