def train(X, y, set_id):
EM_result = {}
#Utils.initPlot( len(no_of_expert_groups), set_id)
for ex in xrange(len(no_of_expert_groups)):
#generate expert #self.Training_instances = self.N - self.Testing_instanceswrong percentage
#60% good:40 % bad
EM_result[ex] = {}
for missing_percent in pmissing:
print "For group ", ex
expert_wrong_percentage = []
for i in xrange(int(no_of_expert_groups[ex]*expert_bads)):
#bads
num = 0.50 #((random()%0.5) + 0.5) % 1.0
expert_wrong_percentage.append(num)
for i in xrange(int(no_of_expert_groups[ex]*expert_goods)):
#goods
num = 0.50 #random()%0.5
expert_wrong_percentage.append(num)
crowds_EM = None
failed = 0
iterations = 0
total_iter = 10
while iterations < total_iter :
try:
crowds_EM = Crowds_EM( X, y, min_class_label, max_class_label, expert_wrong_percentage, verbose= verbose_output, synthetic=synthetic_data, missing=missing_data, percent_missing=missing_percent)
crowds_EM.run_EM_missing()
except Exception,e:
#Rerunning ...
import traceback
print traceback.print_exc()
failed+=1
try:
crowds_EM = Crowds_EM( X, y, min_class_label, max_class_label, expert_wrong_percentage, verbose= verbose_output, synthetic=synthetic_data, missing=missing_data, percent_missing=missing_percent)
crowds_EM.run_EM_missing()
except Exception,e:
failed+=1
pass
else :
EM_result[ex][missing_percent] = crowds_EM.results
break
else :
EM_result[ex][missing_percent] = crowds_EM.results
break
#print "iteration :" , iterations
iterations+=1
def train(X, y, set_id):
EM_result = {}
for ex in xrange(len(no_of_expert_groups)):
EM_result[ex] = {}
for ex_good in expert_good_bad:
print "For group ", ex
expert_wrong_percentage = []
expert_bads = 1-ex_good
for i in xrange(int(no_of_expert_groups[ex]*expert_bads)):
#bads
num = 0.90 #((random()%0.5) + 0.5) % 1.0 #0.9
expert_wrong_percentage.append(num)
expert_goods = ex_good
for i in xrange(int(no_of_expert_groups[ex]*expert_goods)):
#goods
num = 0.00 #random()%0.5 # 0.30
expert_wrong_percentage.append(num)
crowds_EM = None
failed = 0
iterations = 0
total_iter = 10
while iterations < total_iter :
try:
crowds_EM = Crowds_EM( X, y, min_class_label, max_class_label, expert_wrong_percentage, verbose= verbose_output)
crowds_EM.run()
except Exception,e:
#Rerunning ...
import traceback
print traceback.print_exc()
failed+=1
try:
crowds_EM = Crowds_EM( X, y, min_class_label, max_class_label, expert_wrong_percentage, verbose= verbose_output)
crowds_EM.run()
except Exception,e:
failed+=1
pass
else :
crowds_EM.results['']
EM_result[ex][ex_good] = crowds_EM.results
break
else :
EM_result[ex][ex_good] = crowds_EM.results
break
iterations+=1
