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Elvis Saravia omarsar

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@onyxfish
onyxfish / example1.py
Created March 5, 2010 16:51
Basic example of using NLTK for name entity extraction.
import nltk
with open('sample.txt', 'r') as f:
sample = f.read()
sentences = nltk.sent_tokenize(sample)
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)
@sloria
sloria / tfidf.py
Created September 1, 2013 20:57
import math
from text.blob import TextBlob as tb
def tf(word, blob):
return blob.words.count(word) / len(blob.words)
def n_containing(word, bloblist):
return sum(1 for blob in bloblist if word in blob)
def idf(word, bloblist):
@marinhoarthur
marinhoarthur / Algorithm.py
Last active May 5, 2022 12:39
A simple genetic algorithm written in Python fully based on an article by Lee Jacobson from his blog theprojectspot.com
from Population import Population
from Individual import Individual
from random import random, randint
class Algorithm():
#Constants
Uniform_rate = 0.5
Mutation_rate = 0.015
Tournament_size = 5
@suryart
suryart / application.html.erb
Last active October 26, 2023 00:16
Rails 4 flash messages using Twitter Bootstrap(bootstrap-sass: https://github.com/thomas-mcdonald/bootstrap-sass). An improved version of https://gist.github.com/roberto/3344628
// layout file
<body>
<div class="container">
<%= flash_messages %>
<%= yield %>
</div><!-- /container -->
</body>
@Chaser324
Chaser324 / GitHub-Forking.md
Last active May 13, 2024 11:18
GitHub Standard Fork & Pull Request Workflow

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

@evansims
evansims / example.html
Last active February 5, 2024 16:52
Embedding or sharing a image or photo uploaded to Google Drive.
<a href="https://drive.google.com/uc?export=view&id=XXX"><img src="https://drive.google.com/uc?export=view&id=XXX" style="width: 500px; max-width: 100%; height: auto" title="Click for the larger version." /></a>
@gbuesing
gbuesing / ml-ruby.md
Last active February 28, 2024 15:13
Resources for Machine Learning in Ruby

UPDATE a fork of this gist has been used as a starting point for a community-maintained "awesome" list: machine-learning-with-ruby Please look here for the most up-to-date info!

Resources for Machine Learning in Ruby

Gems

@puf
puf / index.html
Last active January 15, 2023 19:41
Zero to App: Develop with Firebase (for Web - Google I/O 2016)
<html>
<head>
<script src="https://www.gstatic.com/firebasejs/3.0.0/firebase.js"></script>
<title>ZeroToApp</title>
<style>
#messages { width: 40em; border: 1px solid grey; min-height: 20em; }
#messages img { max-width: 240px; max-height: 160px; display: block; }
#header { position: fixed; top: 0; background-color: white; }
.push { margin-bottom: 2em; }
@keyframes yellow-fade { 0% {background: #f2f2b8;} 100% {background: none;} }
@yxtay
yxtay / tensorflow_word2vec_cbow_basic.py
Last active December 21, 2023 03:25
Basic implementation of CBOW word2vec with TensorFlow. Minimal modification to the skipgram word2vec implementation in the TensorFlow tutorials.
# References
# - https://www.tensorflow.org/versions/r0.10/tutorials/word2vec/index.html
# - https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import math
@omarsar
omarsar / data_mining_2017_fall_nthu.md
Last active October 14, 2017 02:51
Data Mining Lab Session ( 2017 Fall)

Computing Resources

  • Operating system: Preferably Linux or MacOS. If you have Windows, things may crash unexpectedly (try installing a virtual machine if you need to)
  • RAM: Minimum 8GB
  • Disk space: Mininium 8GB

Software Requirements

Here is a list of the required programs and libraries necessary for this lab session. (Please install them before coming to our lab session on Tuesday; this will save us a lot of time, plus these are the same libraries you may need for your first assignment).

  • Python 3+ (Note: lab and assignment will be done strictly using Python 3)
  • Install latest version of Python 3