halfelf

https://web.archive.org/web/20110219163448/http://howtohft.wordpress.com/2011/02/15/how-to-build-a-fast-limit-order-book/

The response to my first few posts has been much larger than I’d imagined and I’d like to thank everyone for the encouragement.

If you’re interested in building a trading system I recommend first reading my previous post on general ideas to keep in mind.

My first really technical post will be on how to build a limit order book, probably the single most important component of a trading system. Because the data structure chosen to represent the limit order book will be the primary source of market information for trading models, it is important to make it both absolutely correct and extremely fast.

To give some idea of the data volumes, the Nasdaq TotalView ITCH feed, which is every event in every instrument traded on the Nasdaq, can have data rates of 20+ gigabytes/day with spikes of 3 megabytes/second or more. The individual messages average about 20 bytes each so this means handling

EliasHasle

#Inspired by 
#https://github.com/Kautenja/nes-py/blob/master/nes_py/wrappers/binary_to_discrete_space_env.py
#and
#https://github.com/openai/retro-baselines/blob/master/agents/sonic_util.py
#but copying directly from neither.

#Requires retro.
#Requires SuperMarioBros NES rom (USA/Japan). Once you have it, can be imported to retro:
#> python -m retro.import directory_name
#(identifies the ROM if it is there and has the .nes extension.)

zhanwenchen

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

wassname

"""
A weighted version of categorical_crossentropy for keras (2.0.6). This lets you apply a weight to unbalanced classes.
@url: https://gist.github.com/wassname/ce364fddfc8a025bfab4348cf5de852d
@author: wassname
"""
from keras import backend as K
def weighted_categorical_crossentropy(weights):
    """
    A weighted version of keras.objectives.categorical_crossentropy
    

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

ygotthilf

ssh-keygen -t rsa -b 4096 -m PEM -f jwtRS256.key
# Don't add passphrase
openssl rsa -in jwtRS256.key -pubout -outform PEM -out jwtRS256.key.pub
cat jwtRS256.key
cat jwtRS256.key.pub

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

craffel

import matplotlib.pyplot as plt

def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])
    

niw

/*
 * A simple libpng example program
 * http://zarb.org/~gc/html/libpng.html
 *
 * Modified by Yoshimasa Niwa to make it much simpler
 * and support all defined color_type.
 *
 * To build, use the next instruction on OS X.
 * $ brew install libpng
 * $ clang -lz -lpng16 libpng_test.c

jamiees2

# Enter your code here. Read input from STDIN. Print output to STDOUT
class Node:
    def __init__(self,value,point):
        self.value = value
        self.point = point
        self.parent = None
        self.H = 0
        self.G = 0
    def move_cost(self,other):
        return 0 if self.value == '.' else 1