def _modify_problem(f_problem, f_out, tr_outfiles):
"""Modify the problem by writing it to a new file.
Return the name of the new problem file."""
f_init_states = tr_outfiles["init_states"]
f_axioms = tr_outfiles["axioms"]
# the problem
problem = get_contents(f_problem)
problem_tree = LispParser.get_tree(problem)
# the preferences
init_states = get_contents(f_init_states)
init_states_tree = LispParser.get_tree(init_states)
TreeHanger.add_init_state_prefs(problem_tree, init_states_tree)
# the axioms
axioms = get_contents(f_axioms)
axiom_tree = LispParser.get_tree(axioms)
TreeHanger.add_metric_functions(problem_tree, axiom_tree)
# write to the new problem file
fp = open(f_out, "w")
fp.write(problem_tree.to_lisp())
fp.close()
return f_out
def _modify_problem(f_problem, f_out, tr_outfiles): '''Modify the problem by writing it to a new file. Return the name of the new problem file.''' f_init_states = tr_outfiles["init_states"] f_axioms = tr_outfiles["axioms"] # the problem problem = get_contents(f_problem) problem_tree = LispParser.get_tree(problem) # the preferences init_states = get_contents(f_init_states) init_states_tree = LispParser.get_tree(init_states) TreeHanger.add_init_state_prefs(problem_tree, init_states_tree) # the axioms axioms = get_contents(f_axioms) axiom_tree = LispParser.get_tree(axioms) TreeHanger.add_metric_functions(problem_tree, axiom_tree) # write to the new problem file fp = open(f_out, "w") fp.write(problem_tree.to_lisp()) fp.close() return f_out
def _modify_domain(f_domain, f_out, tr_outfiles):
"""Create a new domain file. Return the name of the new file."""
f_add_effects = tr_outfiles["add_effects"]
f_del_effects = tr_outfiles["del_effects"]
f_axioms = tr_outfiles["axioms"]
# the domain
domain = get_contents(f_domain)
domain_tree = LispParser.get_tree(domain)
# add and del effects
add_effects_tree = LispParser.get_tree(get_contents(f_add_effects))
del_effects_tree = LispParser.get_tree(get_contents(f_del_effects))
TreeHanger.add_add_del_effects(domain_tree, add_effects_tree, del_effects_tree)
# domain prefs
axioms = get_contents(f_axioms)
axiom_tree = LispParser.get_tree(axioms)
TreeHanger.add_domain_prefs(domain_tree, axiom_tree)
# write to the new domain file
fp = open(f_out, "w")
fp.write(domain_tree.to_lisp())
fp.close()
return f_out
def _modify_domain(f_domain, f_out, tr_outfiles): '''Create a new domain file. Return the name of the new file.''' f_add_effects= tr_outfiles["add_effects"] f_del_effects= tr_outfiles["del_effects"] f_axioms = tr_outfiles["axioms"] # the domain domain = get_contents(f_domain) domain_tree = LispParser.get_tree(domain) # add and del effects add_effects_tree = LispParser.get_tree(get_contents(f_add_effects)) del_effects_tree = LispParser.get_tree(get_contents(f_del_effects)) TreeHanger.add_add_del_effects(domain_tree, add_effects_tree, del_effects_tree) # domain prefs axioms = get_contents(f_axioms) axiom_tree = LispParser.get_tree(axioms) TreeHanger.add_domain_prefs(domain_tree, axiom_tree) # write to the new domain file fp = open(f_out, "w") fp.write(domain_tree.to_lisp()) fp.close() return f_out
def get(request):
resp = Response()
contents = get_contents(request)
if contents:
acct_id = contents.get('id', None)
acct_email = contents.get('email', None)
try:
if acct_id:
AccountProfile.objects.get(id=acct_id)
resp.passed()
resp.add_param('result', True)
elif acct_email:
AccountProfile.objects.get(company_email__icontains=acct_email)
resp.passed()
resp.add_param('result', True)
except AccountProfile.DoesNotExist:
resp.passed()
resp.add_param('result', False)
except AccountProfile.MultipleObjectsReturned:
resp.passed()
resp.add_param('result', False)
else:
resp.failed()
resp.add_message('Missing parameters!')
return HttpResponse(resp.get_response(), content_type="application/json")
def get(request, qry_type):
resp = Response()
contents = get_contents(request)
if contents:
qry_id = contents.get('id', None)
qry_email = contents.get('email', None)
try:
accnt = None
if qry_type == 'id':
accnt = AccountProfile.objects.get(id=qry_id)
elif qry_type == 'email':
accnt = AccountProfile.objects.get(company_email__icontains=qry_email)
a_obj = {'id': accnt.id, 'name': accnt.name, 'email': accnt.company_email,
'mobile': str(accnt.company_phone), 'address_info': accnt.company_address.address_info,
'country': accnt.company_address.country.name, 'city': accnt.company_address.city.name}
resp.passed()
resp.add_param('result', a_obj)
except AccountProfile.DoesNotExist:
resp.failed()
resp.add_message('AccountProfile with ID/Email does not exist')
except AccountProfile.MultipleObjectsReturned:
resp.failed()
resp.add_message('Multiple AccountProfiles matches the Id/Email')
return HttpResponse(resp.get_response(), content_type="application/json")
def create(fname):
"""Create a PDDL Tree out of the given PDDL file."""
pddl_list = PDDL_Tree._get_pddl_list(get_contents(fname))
pddl_tree = PDDL_Tree._make_tree(pddl_list)
PDDL_Tree._alter_tree(pddl_tree)
return pddl_tree
def compare(f_baseline, f_generated):
'''Compare the given generated file to the baseline file.'''
baseline_tokens = LispParser().get_tokens(get_contents(f_baseline))
generated_tokens = LispParser().get_tokens(get_contents(f_generated))
i = j = 0
len_match = token_match = True
if len(baseline_tokens) != len(generated_tokens):
print "different amount of tokens"
len_match = False
while i < len(baseline_tokens) and j < len(generated_tokens):
if baseline_tokens[i] != generated_tokens[j]:
token_match = False
print "==> difference"
print "Context for baseline:"
print " ".join(baseline_tokens[i - LispDiff.CONTEXT_WINDOW : i + LispDiff.CONTEXT_WINDOW])
print "Context for generated:"
print " ".join(generated_tokens[j - LispDiff.CONTEXT_WINDOW : j + LispDiff.CONTEXT_WINDOW])
# here we need a way to try to line them up again
for ii in range(-1 - LispDiff.ALIGN_WINDOW, 1 + LispDiff.ALIGN_WINDOW):
if ii == 0: continue
if baseline_tokens[i + ii : i + ii + LispDiff.MATCH_WINDOW + 1] == generated_tokens[j : j + LispDiff.MATCH_WINDOW + 1]:
if ii > 0:
print "===> roll forward %d chars" % ii
else:
print "===> roll back %d chars" % (-1 * ii)
i += ii
break
i += 1
j += 1
if token_match and not len_match:
print "Tokens all match even though lengths are different"
print len(baseline_tokens)
print len(generated_tokens)
return len_match and token_match
def post(request):
resp = Response()
contents = get_contents(request)
validation = validate_signup_composite(contents, resp)
if validation:
# Update Account
update_client_account(contents, resp)
return HttpResponse(resp.get_response(), content_type="application/json")
def post(request, accnt_type):
resp = Response()
contents = get_contents(request)
validation = validate_signup_composite(contents, resp)
if validation:
# Create signup composite
create_signup_composite(contents, accnt_type, resp)
return HttpResponse(resp.get_response(), content_type="application/json")
def post(request):
resp = Response()
contents = get_contents(request)
enquiry_form = SiteEnquiryForm(contents)
if enquiry_form.is_valid():
enquiry_form.save()
resp.passed()
resp.add_message("Thank you. We received your enquiry and we'll get in touch soonest.")
else:
resp.failed()
resp.add_param('errors', compile_form_errors(enquiry_form))
return HttpResponse(resp.get_response(), content_type="application/json")
def get(request):
resp = Response()
contents = get_contents(request)
contents = get_contents(request)
if contents:
search_string = contents.get('pattern', None)
if search_string:
try:
results = UserProfile.objects.filter(active=True, account_manager=True,
name__icontains=search_string).order_by('id').values('id', 'name')
matches = [x for x in results]
resp.passed()
resp.add_param('matches', matches)
except Exception as err:
resp.failed()
resp.add_message(str(err))
else:
resp.failed()
resp.add_message('Missing request parameters.')
return HttpResponse(resp.get_response(), content_type="application/json")
def benchmark_seek_all():
tree = Parser().get_tree(get_contents('samples/gripper-domain.pddl'))
print "==> generator:"
start = time.time()
#for i in xrange(1):
tree.seek_all([':action'])
print(time.time() - start) #* 1000
print "==> list"
start = time.time()
#for i in xrange(1):
tree.seek_all_list([':action'])
print(time.time() - start) #* 1000
def benchmark_seek_all():
tree = Parser().get_tree(get_contents('samples/gripper-domain.pddl'))
print "==> generator:"
start = time.time()
#for i in xrange(1):
tree.seek_all([':action'])
print (time.time() - start) #* 1000
print "==> list"
start = time.time()
#for i in xrange(1):
tree.seek_all_list([':action'])
print (time.time() - start) #* 1000
def post(self, request, **kwargs):
customer_type = kwargs.get('account_type', None)
contents = get_contents(request)
resp = Response()
if contents:
_pageSize = contents.get('pageSize', 10)
_pageIndex = contents.get('pageIndex', 1)
_filter = contents.get('filters', [])
_sort = contents.get('sort', None)
kwargs = build_query_filter(_filter)
pager = build_pagination_markers(_pageIndex, _pageSize)
# Run Query
accounts = None
if customer_type == 'pod':
kwargs['account_type__name__iexact'] = 'pod'
elif customer_type == 'streamz':
kwargs['account_type__name__iexact'] = 'streamz'
result_size = AccountProfile.objects.filter(**kwargs).count()
results = AccountProfile.objects.filter(**kwargs)[pager['lm']:pager['um']]
try:
accounts = [{'id': x.id, 'name': x.name, 'account_code': x.account_code,
'company_email': x.company_email,
'account_officer': {'id': x.account_officer.id, 'label': x.account_officer.name}
if x.account_officer is not None else {'id': "",'label': ""},
'industry': {'id': x.industry.id, 'label': x.industry.name},
'company_address': {'address_info': x.company_address.address_info,
'city': {'id': x.company_address.city.id,
'label': x.company_address.city.name},
'country': {'id': x.company_address.country.id,
'label': x.company_address.country.name}
},
'company_phone': {'country_code': x.company_phone.country_code,
'phone_number': x.company_phone.phone_number},
'contact_person': {'first_name': x.contact_person.first_name,
'last_name': x.contact_person.last_name}} for x in results]
resp.passed()
resp.add_param('result', accounts)
resp.add_param('result_size', result_size)
except Exception as err:
resp.failed()
resp.add_message(str(err))
return HttpResponse(resp.get_response(), content_type="application/json")
def profile_tree(fname):
'''Run the PDDL parser on the given file.
Extract and show meaningful information.'''
contents = get_contents(fname)
parser = Parser()
tree = parser.get_tree(contents)
print "==> tree-type:",
t = tree.get_type()
print "'%s'" % t
print "==> domain: '%s'" % tree.get_domain()
if t == "problem":
print "==> problem: '%s'" % tree.get_problem()
print "==> objects:"
print tree.get_objects()
print "==> init state:"
print tree.get_init_state()
print "==> goal:"
print tree.get_goal()
else:
print "==> predicates:"
print tree.get_predicates()
print "==> actions:"
for a in tree.get_actions():
print "==> action: '%s'" % a.get_action_name()
#a.print_tree()
print "==> parameters: "
print a.get_parameters()
print "==> preconditions: "
print a.get_preconditions()
print "==> effects"
print a.get_effects()
def profile_tree(fname):
'''Run the PDDL parser on the given file.
Extract and show meaningful information.'''
contents = get_contents(fname)
parser = Parser()
tree = parser.get_tree(contents)
print "==> tree-type:",
t = tree.get_type()
print "'%s'" % t
print "==> domain: '%s'" % tree.get_domain()
if t == "problem":
print "==> problem: '%s'" % tree.get_problem()
print "==> objects:"
print tree.get_objects()
print "==> init state:"
print tree.get_init_state()
print "==> goal:"
print tree.get_goal()
else:
print "==> predicates:"
print tree.get_predicates()
print "==> actions:"
for a in tree.get_actions():
print "==> action: '%s'" % a.get_action_name()
#a.print_tree()
print "==> parameters: "
print a.get_parameters()
print "==> preconditions: "
print a.get_preconditions()
print "==> effects"
print a.get_effects()
def main():
title = sys.argv[1]
client = HttpClient()
params = {
'action': 'query',
'format': 'json',
'prop': 'revisions',
'rvprop': 'content',
'titles': title,
}
data = client.get(URL, params)
if data is not None:
contents = get_contents(data)
entities = get_entities(contents)
pprint(entities)
print('=' * 72)
print('\nカテゴリのみを出力')
for category in filter(lambda x: x.startswith('Category:'), entities):
print(category)
def synthesizeAllImages(self, image_number):
self.bg_img_list = utils.getBackgroundListFromDir(
self.args['background_image_dir'])
self.font_list = utils.getFontListFromDir(self.args['fonts_dir'])
start_index = self.restoreFromPartImageDir()
for i in tqdm.tqdm(range(start_index, image_number)):
content, content_index = utils.get_contents(
self.id_character_dict, self.args['characters_length_tuple'])
background_image_path, font_path = map(
utils.getRandomOneFromList, [self.bg_img_list, self.font_list])
image, points = self.putContent2Image(content,
background_image_path,
font_path,
self.args['add_rectangle'])
if self.args['save_full_image']:
self.saveImage(image, i)
part_images, roi_points = utils.cropImageByPoints(image, points)
self.saveImage(part_images, i, is_part=1)
self.saveAnnotation(content_index, points, i)
return
def Iop_Add64(self, left, right): return utils.mask(left + right)
def Iop_Add32(self, left, right): return utils.mask(left + right, 32)
def Iop_Add8(self, left, right): return utils.mask(left + right, 8)
def Iop_Sub64(self, left, right): return utils.mask(left - right)
def Iop_Sub32(self, left, right): return utils.mask(left - right, 32)
def Iop_Shl64(self, left, right): return utils.mask(left << right)
def Iop_Shl32(self, left, right): return utils.mask(left << right, 32)
def Iop_CmpEQ64(self, left, right): return 1 if utils.mask(left, 64) == utils.mask(right, 64) else 0
def Iop_CmpEQ32(self, left, right): return 1 if utils.mask(left, 32) == utils.mask(right, 32) else 0
def Iop_CmpNE64(self, left, right): return 1 if utils.mask(left, 64) != utils.mask(right, 64) else 0
def Iop_CmpNE32(self, left, right): return 1 if utils.mask(left, 32) != utils.mask(right, 32) else 0
if __name__ == "__main__":
import sys
if len(sys.argv) < 3:
print "Usage: classifier.py architecture filename [-v]"
sys.exit(1)
arch = archinfo.arch_from_id(sys.argv[1]).__class__
code = utils.get_contents(sys.argv[2])
classifier = GadgetClassifier(arch, log_level = logging.DEBUG if len(sys.argv) > 3 else logging.WARNING)
gadgets = classifier.create_gadgets_from_instructions(code, 0x40000)
for g in gadgets:
print g
for v_index, vocab_count in enumerate(vocab_count_list):
score += vocab_count * math.log(
vocab_count_matrix[c_index][v_index])
score_list.append(score)
class_result.append(score_list.index(max(score_list)) + 1)
return class_result
file_indices = get_file_indices('training.txt')
stopword_list = get_stopword_list('stopwords.txt')
contents_list = []
for i in range(CLASSES):
content_by_class = get_contents(file_indices[i])
contents_list.append(content_by_class) # length is 13
# further processing for contents
updated_contents_list = []
for contents in contents_list:
updated_contents = process_content(stopword_list, contents)
updated_contents_list.append(updated_contents)
vocab_list = get_vocab_list('vocabs.txt')
vocab_prob_matrix = train_multinomial_model(vocab_list, updated_contents_list)
doc_list = get_contents(list(range(1, 1096)))
updated_doc_list = process_content(stopword_list, doc_list)
class_result = test_multinomial_model(vocab_prob_matrix, vocab_list,
updated_doc_list)
# There, we only load a pair of images. one is style image, the other is content image.
content_img = image.imread(os.path.join(args.image_root, "tubingen.jpg"))
style_img = image.imread(os.path.join(args.image_root, "the_scream.jpg"))
# image.imread(): load a image, return NDArray. data format is BGR, w.t. HWC.
# imshow
'''
plt.imshow(style_img.asnumpy()) # use asnumpy() transform to numpy ndarray.
plt.show()
plt.imshow(content_img.asnumpy())
plt.show()
'''
# generate content image and features.
content_x, content_y = utils.get_contents(
net=net,
ctx=ctx,
content_img=content_img,
image_shape=args.image_shape,
content_layers=args.content_layers,
style_layers=args.style_layers)
# content_x is the content image, content_y is the features of content image(list of features).
# Our need is content_y. Features.
# content_x is NDArray.
# generate style image and features.
style_x, style_y = utils.get_styles(net=net,
ctx=ctx,
style_img=style_img,
image_shape=args.image_shape,
content_layers=args.content_layers,
style_layers=args.style_layers)
# style_x is the style image, style_y is the features of style image(list of features).
final_vocabs = []
for vocabs in vocab_list:
for vocab in vocabs:
final_vocabs.append(vocab[0])
final_vocabs = list(set(final_vocabs))[:500]
with open(vocabs_file, 'w') as f:
for vocab in final_vocabs:
f.write(vocab + '\n')
file_indices = get_file_indices('training.txt')
stopword_list = get_stopword_list('stopwords.txt')
contents_list = []
terms_list = []
for i in range(CLASSES):
content_by_class = get_contents(file_indices[i])
contents_list.append(content_by_class) # length is 13
terms_by_class = generate_terms(content_by_class, stopword_list)
terms_list.append(terms_by_class) # length is 13
# further processing for contents
updated_contents_list = []
for contents in contents_list:
updated_contents = process_content(stopword_list, contents)
updated_contents_list.append(updated_contents)
total_LLR_list = feature_selection(updated_contents_list, terms_list)
generate_vocabs(total_LLR_list, 'vocabs.txt')
return utils.mask(left >> right, 8)
def Iop_CmpEQ64(self, left, right):
return 1 if utils.mask(left, 64) == utils.mask(right, 64) else 0
def Iop_CmpEQ32(self, left, right):
return 1 if utils.mask(left, 32) == utils.mask(right, 32) else 0
def Iop_CmpNE64(self, left, right):
return 1 if utils.mask(left, 64) != utils.mask(right, 64) else 0
def Iop_CmpNE32(self, left, right):
return 1 if utils.mask(left, 32) != utils.mask(right, 32) else 0
if __name__ == "__main__":
import sys
if len(sys.argv) < 3:
print "Usage: classifier.py architecture filename [-v]"
sys.exit(1)
arch = archinfo.arch_from_id(sys.argv[1]).__class__
code = utils.get_contents(sys.argv[2])
classifier = GadgetClassifier(
arch,
log_level=logging.DEBUG if len(sys.argv) > 3 else logging.WARNING)
gadgets = classifier.create_gadgets_from_instructions(code, 0x40000)
for g in gadgets:
print g