walkermatt

from threading import Timer


def debounce(wait):
    """ Decorator that will postpone a functions
        execution until after wait seconds
        have elapsed since the last time it was invoked. """
    def decorator(fn):
        def debounced(*args, **kwargs):
            def call_it():

preshing

#include <stdio.h>
#include <stdlib.h>
#include <assert.h>

typedef unsigned int u32;
typedef unsigned long long u64;

//-------------------------------------------------------------------------
// WorkArea
//-------------------------------------------------------------------------

kmike

import marisa_trie
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer


# hack to store vocabulary in MARISA Trie
class _MarisaVocabularyMixin(object):

    def fit_transform(self, raw_documents, y=None):
        super(_MarisaVocabularyMixin, self).fit_transform(raw_documents)
        self._freeze_vocabulary()

kmike

import numpy as np
import marisa_trie
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.externals import six

class MarisaCountVectorizer(CountVectorizer):

    # ``CountVectorizer.fit`` method calls ``fit_transform`` so
    # ``fit`` is not provided
    def fit_transform(self, raw_documents, y=None):

kmike

import numpy as np
import scipy.sparse as sp
import hat_trie
from sklearn.feature_extraction.text import CountVectorizer, _make_int_array


class HatTrieCountVectorizer(CountVectorizer):

    def _count_vocab(self, raw_documents, fixed_vocab):
        """Create sparse feature matrix, and vocabulary where fixed_vocab=False

kartikkukreja

def iterativeDeepeningAlphaBeta(state, evaluationFunc):
  startTime = time()

  def alphaBetaSearch(state, alpha, beta, depth):
    def maxValue(state, alpha, beta, depth):
      val = -MaxUtility
      for successor in state.getSuccessors():
        val = max(val, alphaBetaSearch(successor, alpha, beta, depth))
        if val >= beta: return val
        alpha = max(alpha, val)

mbejda

mbejda

albertotb

if __name__ == "__main__":

  if len(sys.argv) < 3 or len(sys.argv) > 4:
    print "usage: {0} TRAIN TEST VAL".format(sys.argv[0])
    sys.exit(1)
 
  train = np.loadtxt(sys.argv[1])
  test = np.loadtxt(sys.argv[2])
  val = np.loadtxt(sys.argv[3])

Dayjo

First install all these prerequisites for compiling:

sudo apt install g++ autoconf libfontconfig1-dev pkg-config libjpeg-dev libopenjpeg-dev gnome-common libglib2.0-dev gtk-doc-tools libyelp-dev yelp-tools gobject-introspection libsecret-1-dev libnautilus-extension-dev

First download the encoding files (no need to compile these) to the current working directory

wget https://poppler.freedesktop.org/poppler-data-0.4.7.tar.gz