seabre

(ns three.demo)
(def THREE js/THREE)
(def camera (THREE.PerspectiveCamera. 75 (/ (.-innerWidth js/window)
                                 (.-innerHeight js/window)) 1 10000))
(set! (.-z (.-position camera)) 1000)
(def scene (THREE.Scene.))
(def geometry (THREE.CubeGeometry. 200 200 200))
(def obj (js/Object.))
(set! (.-color obj) 0xff0000)
(set! (.-wireframe obj) true)

grugq

Operational PGP

This is a guide on how to email securely.

There are many guides on how to install and use PGP to encrypt email. This is not one of them. This is a guide on secure communication using email with PGP encryption. If you are not familiar with PGP, please read another guide first. If you are comfortable using PGP to encrypt and decrypt emails, this guide will raise your security to the next level.

karpathy

"""
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):

glamp

import pandas as pd
# http://blog.yhathq.com/static/misc/data/WineKMC.xlsx
df_offers = pd.read_excel("./WineKMC.xlsx", sheetname=0)
df_offers.columns = ["offer_id", "campaign", "varietal", "min_qty", "discount", "origin", "past_peak"]
df_offers.head()

df_transactions = pd.read_excel("./WineKMC.xlsx", sheetname=1)
df_transactions.columns = ["customer_name", "offer_id"]
df_transactions['n'] = 1
df_transactions.head()

carlossless

from scipy.optimize import leastsq
import matplotlib.pyplot as plt
import numpy as np

# d=A*(r/t)^B+C

d = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.2,1.4,1.6,1.8,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,12.0,14.0,16.0,18.0,20.0,25]
r = [-20.64,-24.09,-27.55,-31.73,-35.27,-33.91,-31.36,-28.09,-32,-49.64,-52,-54.64,-55.18,-57.18,-58.64,-59.27,-72.55,-67.73,-66.65,-70,-68,-71,-74,-76,-83,-77,-83,-80,-80,-76]
t = -52.5
x = map(lambda r: r / t, r)

andrewgodwin

Benchmark results:

  Date: 2016/03/20
  Version: asgi_redis 0.8.3 / daphne master / channels master / cpython 2.7.11
  Environment: AWS, 3x m3.large (1 daphne, 1 worker, 1 redis)
  Source: 1x m3.medium, different AZ, ab for HTTP, channels benchmarker for WS

  HTTP GET, Django View (concurrency 10, 10,000 total)
    Failures: 0.0%
    Rate: 160 req/s

craigbeck

  query IntrospectionQuery {
    __schema {
      queryType { name }
      mutationType { name }
      subscriptionType { name }
      types {
        ...FullType
      }
      directives {

Arachnid

/**
 * Base contract that all upgradeable contracts should use.
 * 
 * Contracts implementing this interface are all called using delegatecall from
 * a dispatcher. As a result, the _sizes and _dest variables are shared with the
 * dispatcher contract, which allows the called contract to update these at will.
 * 
 * _sizes is a map of function signatures to return value sizes. Due to EVM
 * limitations, these need to be populated by the target contract, so the
 * dispatcher knows how many bytes of data to return from called functions.

karpathy

""" 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

odewahn

I really liked @tjvantoll article Handling Failed HTTP Responses With fetch(). The one thing I found annoying with it, though, is that response.statusText always returns the generic error message associated with the error code. Most APIs, however, will generally return some kind of useful, more human friendly message in the body.

Here's a modification that will capture this message. The key is that rather than throwing an error, you just throw the response and then process it in the catch block to extract the message in the body:

fetch("/api/foo")
  .then( response => {
    if (!response.ok) { throw response }
    return response.json()  //we only get here if there is no error
 })