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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	<title>React + D3</title>
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	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.

	"""
	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:

	"""
	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)

	#!/bin/bash
	#
	# Requires:
	# - gdal_sieve.py
	# - ogr2ogr (GDAL)
	# - topojson (node.js)

	# Grab the relative directory for source file.
	SRC_DIR=`dirname $0`

	# Superpixel segmentation approach that seems to give pretty good contiguous segments.
	# (SLIC and quickshift don't seem to guarantee contiguity). The approach is to get initial
	# segments from SLIC, use the centroid of each as a marker for watershed, then clean up.

	import os, argparse
	from skimage import segmentation
	from skimage.future import graph
	import cv2, numpy
	import tempfile
	import random

	#!/usr/bin/python

	import numpy as np
	import cv2
	import densecrf as dcrf
	from skimage.segmentation import relabel_sequential
	import sys

	# Usage:
	# python dense_inference.py image annotations output