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# -*- coding: utf-8 -*-
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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# Author: marmuta <marmvta@gmail.com>
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# Calculate keystroke saving rate
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# -------------------------------
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# "EFFECTS OF NGRAM ORDER AND TRAINING TEXT SIZE ON WORD PREDICTION"
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# by Gregory W. Lesher, Ph.D., Bryan J. Moulton, M.S., and D. Jeffery
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# ksr <language model> <typed text> <prediction limit>
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# Calculates the number of keystrokes saved with a maximum number of
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# <prediction limit> predictions to choose from when typing <typed text>.
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# split_corpus moby.txt
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# train training.txt 3 moby.lm
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# ksr moby.lm testing.txt 10
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# This loads language model moby.lm, uses it to create at most 10 predicted
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# words per typed letter and simulates the typing of testing.txt.
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from __future__ import division, print_function, unicode_literals
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import sys, re, codecs, math
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from pypredict import *
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from optparse import OptionParser
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import matplotlib.pyplot as plt
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parser = OptionParser(usage="Usage: %prog [options] text")
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parser.add_option("-m", "--language-model", type="str", dest="language_model",
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help="optional filename of a language model")
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parser.add_option("-n", "--num-choices", type="int", default="10",
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help="number of virtual word choices")
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parser.add_option("-l", "--learn", action="store_true", dest="learn",
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help="learn after each sentence")
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parser.add_option("-c", "--cached-model", action="store_true", dest="cached",
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help="use a model with recency caching")
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parser.add_option("-o", "--order", type="int", default="3",
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help="order of the language model")
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parser.add_option("-p", "--plot", action="store_true", dest="plot",
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help="plot the result with matplotlib")
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options, args = parser.parse_args()
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print("Please supply a text file as input for simulated typing.")
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model = CachedDynamicModel(order)
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model = DynamicModel(order)
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if options.language_model:
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with timeit("loading model"):
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model.load(options.language_model)
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sentences, spans = split_sentences(read_corpus(args[0]))
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num_choices = options.num_choices
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learn_model = model if options.learn else None
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total_chars, pressed_keys = simulate_typing(model, learn_model, sentences,
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Progress(len(sentences),
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#print get_stat_string(total_chars, pressed_keys)
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plt.show() # blocks; allows for interaction with the chart, saving images
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def get_stat_string(total_chars, pressed_keys):
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saved_keystrokes = total_chars - pressed_keys
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ksr = saved_keystrokes * 100.0 / total_chars if total_chars else 0
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return "characters %8d, keystrokes %8d, " \
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"saved %8d, ksr %6.2f%%" \
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% (total_chars, pressed_keys, saved_keystrokes, ksr)
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def __init__(self, num_sentences, plot = False):
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self._plot_progress = PlotProgress()
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self._num_sentences = num_sentences
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def __call__(self, i, n, total_chars, pressed_keys):
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step = max(1, self._num_sentences // 100)
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if i == 0 or i == n-1 or (i+1) % step == 0:
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saved_keystrokes = total_chars - pressed_keys
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ksr = saved_keystrokes * 100.0 / total_chars if total_chars else 0
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print("sentence {:6} of {:6}: {}" \
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.format(i+1, n, get_stat_string(total_chars, pressed_keys)))
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self._plot_progress(i+1, ksr)
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def __call__(self, n, ksr):
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self.xvalues.append(n)
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self.ksrs.append(ksr)
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plt.ion() # interactive mode on
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plt.plot(self.xvalues, self.ksrs),
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plt.xlabel("sentences")
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plt.ylabel('ksr [%]')
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ymin, ymax = plt.ylim()
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plt.ylim(ymin, ymax+(ymax-ymin)*0.05)
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plt.gcf().suptitle('Keystroke savings rate',
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if __name__ == '__main__':
added
removed
Onboard/pypredict/tools/ksr
