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import time
import re
import unicodedata

'''
listado de palabras que quiero descartar
'''
pal_comunes = ['los','las','sin','con','tan','por']
lista_categorias = ['lacteos','comestibles','auto','hogar']
lista_productos = ['leche','huevo','aceite','yerba']

sanchezg

"""Download stories of a Pivotal Tracker project.

You need the project ID (an int) and your API token. The latter can be
obtained in ``https://www.pivotaltracker.com/profile``, or using curl::

   $ curl -u username:password -X GET https://www.pivotaltracker.com/services/v3/tokens/active

"""
import getpass
import pickle

sanchezg

/* Merge sort in C */
#include<stdio.h>
#include<stdlib.h>

// Function to Merge Arrays L and R into A. 
// lefCount = number of elements in L
// rightCount = number of elements in R. 
void Merge(int *A,int *L,int leftCount,int *R,int rightCount) {
	int i,j,k;

sanchezg

#include <iostream>
using namespace std;

struct Base1
{
    virtual void hola() = 0;
};

struct Base2
{

sanchezg

def classify_elements(dataset, interest_key, classifications_number):
    """
        This function tries to classify a set of continuous features from a
        dataset into a 
    """
    keys = dataset.keys()
    if interest_key not in keys:
        print 'The feature is not in the dataset'
        return
    count_elements = len(dataset)

sanchezg

#!/usr/bin/python
"""
Random HAL9000 quotes generator. Quotes are taken from quotes.hal file.
"""

from random import randint

class bcolors:
    HEADER = '\033[95m'
    OKBLUE = '\033[94m'

sanchezg

import numpy as np

class DirectTransformer:
    """Utility for building class-like features from a single-point function, but that may need
    some general configuration first (you usually override __init__ for that)
    """

    def fit(self, X, y=None):
        return self

sanchezg

import numpy as np

class OneHotTransformer:
    def __init__(self, func):
        self.f = func

    def fit(self, X, y=None):
        unseen = object()
        seen = set()
        for x in X:

sanchezg

from sklearn.feature_extraction.text import CountVectorizer

class ProjectionCountVectorizer(CountVectorizer):

    def __init__(self, projection_path, *args, **kwargs):
        self.projection_path = projection_path.split('/')
        super().__init__(*args, **kwargs)

    def build_preprocessor(self):
        built = super().build_preprocessor()

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class Singleton(object):
  _instances = {}
  def __new__(class_, *args, **kwargs):
    if class_ not in class_._instances:
        class_._instances[class_] = super(Singleton, class_).__new__(class_, *args, **kwargs)
    return class_._instances[class_]

class MyClass(Singleton):
  pass