Elvis Saravia omarsar

## ml-ruby.md

      
              1 file
            
          
              58 forks
            
          
              11 comments
            
          
              322 stars
            
          
                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


liblinear-ruby: Ruby interface to LIBLINEAR using SWIG


## example.html
<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>

## GitHub-Forking.md

      
              1 file
            
          
              778 forks
            
          
              110 comments
            
          
              2872 stars
            
          
                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

  
## application.html.erb
// layout file

<body>
    <div class="container">

      <%= flash_messages %>

      <%= yield %>
    </div><!-- /container -->
</body>

## Algorithm.py
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

## tfidf.py
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):

## example1.py
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)
	// layout file

	<body>
	<div class="container">

	<%= flash_messages %>

	<%= yield %>
	</div><!-- /container -->
	</body>
	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
	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):
	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)