dburner

Angular2 + JSPM cheat sheet

First time setup

• install jspm beta: npm install -g jspm@beta
• set up your project: jspm init
• install dependencies: jspm install angular2 reflect-metadata zone.js es6-shim

This will create a jspm_packages folder, and a config.js file.

Open the config.js file - this file manages options for the System.js loader - tweak it as appropriate

dburner

namespace Analogy
{
    /// <summary>
    /// This example shows that a library that needs access to target .NET Standard 1.3
    /// can only access APIs available in that .NET Standard. Even though similar the APIs exist on .NET 
    /// Framework 4.5, it implements a version of .NET Standard that isn't compatible with the library.
    /// </summary>INetCoreApp10
    class Example1
    {
        public void Net45Application(INetFramework45 platform)

dburner

# vocabulary = OrderedDict()
input_length = None
vocabulary_size = max(vocabulary.values()) + 1
weights_w2v = list(map(Word2Vec.__getitem__, vocabulary.keys()))
embedding_size len(weights_w2v[0])
nb_classes = 5

# CNN hyperparms
nb_filter = 64
filter_length = 5

dburner

"""
A keras attention layer that wraps RNN layers.

Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506) 
and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449).

date: 20161101
author: wassname
url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a
"""

dburner

using System;
using System.Collections.Generic;
using System.ComponentModel.DataAnnotations.Schema;
using System.Data.Entity;
using System.Linq;
using System.Linq.Expressions;
using System.Reflection;
using System.Text;

namespace EFExtensions

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class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

dburner

Client-side SSL

For excessively paranoid client authentication.

Using self-signed certificate.

Create a Certificate Authority root (which represents this server)

Organization & Common Name: Some human identifier for this server CA.

openssl genrsa -des3 -out ca.key 4096
openssl req -new -x509 -days 365 -key ca.key -out ca.crt

dburner

Ikea Bekant standing desk with Gerton table top

dburner

# Procedure is for Ubuntu 14.04 LTS.

# Using these guides:
# http://datacenteroverlords.com/2012/03/01/creating-your-own-ssl-certificate-authority/
# https://turboflash.wordpress.com/2009/06/23/curl-adding-installing-trusting-new-self-signed-certificate/
# https://jamielinux.com/articles/2013/08/act-as-your-own-certificate-authority/

# Generate the root (GIVE IT A PASSWORD IF YOU'RE NOT AUTOMATING SIGNING!):
openssl genrsa -aes256 -out ca.key 2048
openssl req -new -x509 -days 7300 -key ca.key -sha256 -extensions v3_ca -out ca.crt

dburner

public static class EvilStringHelper
{
    private static readonly Action<string, int, char> _setChar;
    private static readonly Action<string, int> _setLength;

    static EvilStringHelper()
    {
        if (Environment.Version.Major < 4)
        {
            MethodInfo setCharMethod = typeof(string).GetMethod(