JT5D JT5D

## umap_sparse.py
### JHW 2018
import numpy as np
import umap


# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module

import random

## circle_detection.py
import cv2
import time
import math
import numpy as np

capture = cv2.VideoCapture(0)
print capture.get(cv2.CAP_PROP_FPS)

t = 100
w = 640.0

## GDocService.cs
// To prepare a Google Spreadsheet for this:
// make sure the spreadsheet has been published to web (FILE >> PUBLISH TO WEB), which is NOT the same as sharing to web

// To use in a different script:
// 1) include "using Google.GData.Client;"
// 2) include "using Google.GData.Spreadsheets;"
// 3) call "GDocService.GetSpreadsheet( string spreadsheetKey );", see DebugTest section for sample usage

using UnityEngine;
using System.Collections;

## VRUtility.cs
using UnityEngine;
using System.Collections;

using UnityEngine.VR; // you always need this to use special VR functions

public class VRUtility : MonoBehaviour {

	// Use this for initialization
	public void Start () {
		// set render quality to 50%, sacrificing visual quality for higher FPS

## Shield_on_off_hello.ino
//*****************************************************************************
/// @file
/// @brief
///   Arduino SmartThings Shield LED Example
//*****************************************************************************
#include <SoftwareSerial.h>   //TODO need to set due to some weird wire language linker, should we absorb this whole library into smartthings
#include <SmartThings.h>


#define PIN_THING_RX    3

## bayesian_neural_network.ipynb

      
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                JT5D
                / bayesian_neural_network.ipynb
            
            
              Created
              July 29, 2016 08:17
                — forked from anonymous/bayesian_neural_network.ipynb
            
          
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## gist:5a77da302da4f1a1e12245cdb6c4cafc
"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:

  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

## pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

## index.html
<!DOCTYPE html>
<meta charset="utf-8">

<script src="http://d3js.org/d3.v2.min.js?2.8.1"></script>
<body>
<script>

var margin = {top: 100, right: 100, bottom: 100, left: 100},
    width = 384,
    height = 256;

## gist:8d170b353010495dd91c3957eee32356
bigram  count score
external_links  1823239 11.0846952043
united_states   1201240 9.51993073859
references_external     1073067 4.49092215503
new_york        1041129 4.9935587872
th_century      617252  5.71497454564
did_not 526168  4.30135948012
los_angeles     259057  63.2530378731
new_zealand     257352  5.34529759482
does_not        239292  4.39354755795
	### JHW 2018
	import numpy as np
	import umap



	# This code from the excellent module at:
	# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module

	import random
	import cv2
	import time
	import math
	import numpy as np

	capture = cv2.VideoCapture(0)
	print capture.get(cv2.CAP_PROP_FPS)

	t = 100
	w = 640.0
	// To prepare a Google Spreadsheet for this:
	// make sure the spreadsheet has been published to web (FILE >> PUBLISH TO WEB), which is NOT the same as sharing to web

	// To use in a different script:
	// 1) include "using Google.GData.Client;"
	// 2) include "using Google.GData.Spreadsheets;"
	// 3) call "GDocService.GetSpreadsheet( string spreadsheetKey );", see DebugTest section for sample usage

	using UnityEngine;
	using System.Collections;
	using UnityEngine;
	using System.Collections;

	using UnityEngine.VR; // you always need this to use special VR functions

	public class VRUtility : MonoBehaviour {

	// Use this for initialization
	public void Start () {
	// set render quality to 50%, sacrificing visual quality for higher FPS
	//*****************************************************************************
	/// @file
	/// @brief
	/// Arduino SmartThings Shield LED Example
	//*****************************************************************************
	#include <SoftwareSerial.h> //TODO need to set due to some weird wire language linker, should we absorb this whole library into smartthings
	#include <SmartThings.h>


	#define PIN_THING_RX 3
	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):
	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward
	<!DOCTYPE html>
	<meta charset="utf-8">

	<script src="http://d3js.org/d3.v2.min.js?2.8.1"></script>
	<body>
	<script>

	var margin = {top: 100, right: 100, bottom: 100, left: 100},
	width = 384,
	height = 256;
	bigram count score
	external_links 1823239 11.0846952043
	united_states 1201240 9.51993073859
	references_external 1073067 4.49092215503
	new_york 1041129 4.9935587872
	th_century 617252 5.71497454564
	did_not 526168 4.30135948012
	los_angeles 259057 63.2530378731
	new_zealand 257352 5.34529759482
	does_not 239292 4.39354755795