Fabrice Sodogandji fsodogandji

The introduction to Reactive Programming you've been missing

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	"""
	Implementations of:

	Probabilistic Matrix Factorization (PMF) [1],
	Bayesian PMF (BPMF) [2],
	Modified BPFM (mBPMF)

	using `pymc3`. mBPMF is, to my knowledge, my own creation. It is an attempt
	to circumvent the limitations of `pymc3` w/regards to the Wishart distribution:

	import numpy as np
	import pdb

	from sklearn.datasets import make_classification
	from sklearn.mixture import GaussianMixture as GMM


	def fisher_vector(xx, gmm):
	"""Computes the Fisher vector on a set of descriptors.

	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="utf-8">
	<title>React + D3</title>
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<script src="http://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.5.2/underscore.js"></script>
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	<script src="http://cdnjs.cloudflare.com/ajax/libs/react/0.8.0/JSXTransformer.js"></script>
	<script src="http://cdnjs.cloudflare.com/ajax/libs/d3/3.3.13/d3.js"></script>

	#!/usr/bin/env python
	# Licensed to Tomaz Muraus under one or more
	# contributor license agreements. See the NOTICE file distributed with
	# this work for additional information regarding copyright ownership.
	# Tomaz muraus licenses this file to You under the Apache License, Version 2.0
	# (the "License"); you may not use this file except in compliance with
	# the License. You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#