labels=model.classes_# Yte=remove_unknown_classes(Yte, labels)# idx=np.array(map(lambda x: len(x)>0,Yte))# Yte=np.array(Yte)[idx]# Xte=Xte[idx]print'predicting...',Xte.shape, sys.stdout.flush() pd.DataFrame(columns=labels,data=model.decision_function(Xte)).to_pickle('{}deci....
assert stop_word not in item_text, 'Something wrong, stop words are expected to be excluded.' i = item_text.find(f'{FN_NAME}:') # If no function call: if i < 0: show_text = remove_incomplete_special_tokens(item_text) if show_text: new_content.append(ContentItem(text=show_text...
add_pizza_to_cart: Adds a pizza to the user's cart remove_pizza_from_cart: Removes a pizza from the user's cart get_pizza_from_cart: Returns the specific details of a pizza in the user's cart get_cart: Returns the user's current cart checkout: Checks out the user's ...
Nowadays everybody uses fast open addressing, even if the load factor needs to be ~50%, unless you use Cuckoo Hashing. I.e. the usage of SipHash for their hash table in Python 3.4, ruby, rust, systemd, OpenDNS, Haskell and OpenBSD is pure security theatre. SipHash is not secure ...
if x in a: a.remove(x) Although it is effective, it is not suitable for a wide range. Is there a more optimized approach available? Solution 1: Solution: To optimize performance, I suggest utilizing a generator expression in the following manner: ...
Method for matching to dictionary: 'none' for no matching, 'lv' for Levenshtein; 'j' for Jaro, 'jw' for JaroWinkler, 'bag' for bag of words, 'exact' for exact matching. The default matchMethod is 'lv'. keyType Transformation type in creating keys: 'all' to retain all characters, ...
# remove=remove) dataset = load_files('./TED_dataset/Topics/') train, test = train_test_split(dataset, train_size = 0.8) # categories = data_train.target_names # for case categories == None # def size_mb(docs): # return sum(len(s.encode('utf-8')) for s in docs) / 1e6 #...
main_t.cancel()# This won't actually run forever because the call to# loop.run_until_complete added a callback to the# future that will stop the loop once main_t has# finished and return control back to this function.loop.run_forever()# Try to clean up all of the tasks by waiting...
Previously, we implemented theanswer code validation programin Python to help a teacher assign and mark a lot of CWP assignments to many students in their Java programming course in a university or professional school [4]. This program automatically verifies the source codes from all the students ...
This requires the use of a semicolon, which is rarely found in Python programs: Python import pdb; pdb.set_trace() While certainly not Pythonic, it stands out as a reminder to remove it after you’re done with debugging. Since Python 3.7, you can also call the built-in breakpoint(...