shadiakiki1986

The graph is controlled by data in the text boxes above as described, and the clock on the right. The "pause" button plays the clock through time. The "step" only makes a 1-hour step. Make sure to update the trade timestamps to be after the current time of the clock (displayed right above it). The same applies for the asset performances. If you can't see the "step" or "pause" links on the right of the clock, please use the "W" cursor to reduce the width of the graph's canvas. You can click and drag on the canvas to pan, and you can zoom in and out with the mouse scroll wheel.

The cyan discs are traders, and the blue discs are assets. The blue lines are long positions, and the pink ones are short positions. An asset disc encircled in blue indicates a positive performance for the time step, whereas a pink circle indicates a negative performance.

The text in the textareas are a json description of each box's content. The json tags I use are:

• s: security ID
• q: quantity held
• c: trader ID

shadiakiki1986

{
    "name": "root/test-phpcache-filesystem",
    "require": {
        "cache/filesystem-adapter": "^0.3.2"
    },
    "authors": [
        {   
            "name": "Shadi Akiki",
            "email": "shadiakiki1986@gmail.com"
        }

shadiakiki1986

2016-12-18 عبدو الأَشقَر

١. طاب اليَد: الأَرضُ أَصبَحَت صالِحَة لِلفِلاحَة

٢. كيف العَدّان؟: كَيفَ الطّقسُ المُتَقَلِّب؟

٣. مغَطغطَة : مغَيّمَة وَصُعوبَة في الرُّؤيَة

٤. الوجاق: صوبيا تَعمَلُ عَلى الحَطَب، أَو البابور

shadiakiki1986

"""
Reconciling results from MLJAR and sklearn.
  Random forest classifier
  X==y

Usage
  pew new --python=python2 MLJAR_TEST
  pip install mljar sklearn numpy scipy pandas
  export MLJAR_TOKEN=exampleexampleexample

shadiakiki1986

#!/bin/bash
# This is a helper for aws ec2 CLI
# https://gist.github.com/shadiakiki1986/a4bc2956eaaef7dede10

if [ $# -ne 2 ] || [ "$2" == "help" ]; then
  echo "Usage:"
  echo "       bash $0 id '*'"
  echo "       bash $0 id udacity"
  echo "       bash $0 id '*udacity*'"
  echo "       bash $0 start id"

shadiakiki1986

# modified example of
# http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA.transform

import numpy as np
from sklearn.decomposition import PCA

# Just the same PCA as the example
pca = PCA(n_components=2, svd_solver='full')

# same input as example

shadiakiki1986

In response to pymssql/pymssql#496 I just forked and deep cleaned the pymssql repository in my fork.

Note that the procedure re-writes new commit SHA hashes .. which may be a reason for not doing this in the pymssql repository

All commands were copied bluntly from http://stevelorek.com/how-to-shrink-a-git-repository.html

shadiakiki1986

These are a few scripts that help me install requirements on fresh AWS EC2 instances for deep learning

shadiakiki1986

Installation scripts for H2O driverless AI

Launch order

• install.sh (requires sudo)
  • need to exit and re-ssh into machine for "docker usage without sudo" to take effect
• for GPU, refer to init-gpu.sh
• dropbox.sh (optional)
• start.sh

shadiakiki1986

# Fiddle at
# https://pyfiddle.io/fiddle/a14865cf-38c9-48d6-b90a-f5bedc7a5b6e/?m=Saved%20fiddle
#
# reshape a matrix M x N into M x P x (N-P) while creating overlapping rows
# Useful for LSTM input

import numpy as np
import pandas as pd

def stride_group(group, lahead):