def __init__(self, stdscr): profiles = Profiles() profile_data = ProfileData() stdscr.refresh() stdscr.clear() wordbook = WordBook() #iterate over every word in the dictionary forever until the user enters nothing for word in wordbook.generate_words(train=False): stdscr.addstr(0, 0, "%s" % (" " * 100)) stdscr.addstr(0, 0, "-> %s " % (word)) stdscr.refresh() start = time.time() count, user_word = self.read_char(stdscr) end = time.time() stdscr.addstr( 3, 0, "Time taken: %i, times_corrected: %i" % (end - start, count)) data_point = DataPoint(time=end - start, error_count=count, distance=Levenshtein.distance( word, user_word)) break classifier = svm.SVC(gamma=1) (features, targets) = profiles.get_classifier_data() classifier.fit(features, targets) predicted = classifier.predict( [[data_point.time, data_point.error_count, data_point.distance]]) print "\nYou're probably.. %s " % predicted[0]
def __init__(self, stdscr): profiles = Profiles() profile_data = ProfileData() stdscr.refresh() stdscr.clear() wordbook = WordBook() #iterate over every word in the dictionary forever until the user enters nothing for word in wordbook.generate_words(train=False): stdscr.addstr(0,0, "%s" % (" " * 100)) stdscr.addstr(0,0, "-> %s " % (word)) stdscr.refresh() start = time.time() count, user_word = self.read_char(stdscr) end = time.time() stdscr.addstr(3, 0, "Time taken: %i, times_corrected: %i" % (end-start, count)) data_point = DataPoint(time=end-start, error_count=count, distance=Levenshtein.distance(word, user_word)) break classifier = svm.SVC(gamma=1) (features, targets) = profiles.get_classifier_data() classifier.fit(features, targets) predicted = classifier.predict([[data_point.time, data_point.error_count, data_point.distance]]) print "\nYou're probably.. %s " % predicted[0]
def __init__(self, stdscr): wordbook = WordBook() stdscr.addstr("What is your name?") profiles = Profiles() profile_data = ProfileData() stdscr.refresh() name = stdscr.getstr(1,0, 15) profile_data.set_name(name) profiles.append_profile(profile_data) stdscr.clear() #iterate over every word in the dictionary forever until the user enters nothing for word in wordbook.generate_words(train=True): stdscr.addstr(0,0, "%s" % (" " * 100)) stdscr.addstr(0,0, "-> %s " % (word)) stdscr.refresh() start = time.time() count, user_word = self.read_char(stdscr) end = time.time() stdscr.addstr(3, 0, "Time taken: %i, times_corrected: %i" % (end-start, count)) if user_word == "": break #create a data point with the Levenshtein distance, #count of errors user made while typing, and how long the process took data_point = DataPoint(time=end-start, error_count=count, distance=Levenshtein.distance(word, user_word)) profile_data.append_point(data_point) profiles.flush()
def __init__(self, stdscr): wordbook = WordBook() stdscr.addstr("What is your name?") profiles = Profiles() profile_data = ProfileData() stdscr.refresh() name = stdscr.getstr(1, 0, 15) profile_data.set_name(name) profiles.append_profile(profile_data) stdscr.clear() #iterate over every word in the dictionary forever until the user enters nothing for word in wordbook.generate_words(train=True): stdscr.addstr(0, 0, "%s" % (" " * 100)) stdscr.addstr(0, 0, "-> %s " % (word)) stdscr.refresh() start = time.time() count, user_word = self.read_char(stdscr) end = time.time() stdscr.addstr( 3, 0, "Time taken: %i, times_corrected: %i" % (end - start, count)) if user_word == "": break #create a data point with the Levenshtein distance, #count of errors user made while typing, and how long the process took data_point = DataPoint(time=end - start, error_count=count, distance=Levenshtein.distance( word, user_word)) profile_data.append_point(data_point) profiles.flush()