AndreCAndersen

andre@andersen:~$ mkdir scptest
andre@andersen:~$ mkdir scptarget
andre@andersen:~$ cd scptest/
andre@andersen:~/scptest$ touch temp1
andre@andersen:~/scptest$ touch temp2
andre@andersen:~/scptest$ scp ~/scptest/* andre@localhost ~/scptarget
cp: cannot stat ‘andre@localhost’: No such file or directory
andre@andersen:~/scptest$ scp ~/scptest/* andre@localhost:~/scptarget
andre@localhost's password: REDACTED
temp1                                    100%    0     0.0KB/s   00:00

AndreCAndersen

import tensorflow as tf

f1 = lambda x: x*1
f2 = lambda x: x*2
f3 = lambda x: x*3

x = [f1, f2, f3]
y = [1, 2, 3]

result = list(map(lambda x,y: x(y), x, y))

AndreCAndersen

import inspect
import types


def execute_source(callback_imports, callback_name, callback_source, args):
    for callback_import in callback_imports:
        exec(callback_import, globals())
    exec('import time' + "\n" + callback_source)
    callback = locals()[callback_name]
    return callback(*args)

AndreCAndersen

do {
    save copy of network
    find and delete lowest weighted connection of network
    do one epoch of learning and calculate new network error
    if (new network error is above lower threshold){
        relearn lost knowledge
    }
} while (network relearning converged in time)
return saved copy of network

AndreCAndersen

# This source code is provided under the following Creative Commons license: 
#   Attribution-NonCommercial-ShareAlike 4.0 International 
#   https://creativecommons.org/licenses/by-nc-sa/4.0/
# Please contact the author for commercial use: andre@andersen.im

# Fixed parameters

exchange <- 'OSE'
instrument <- 'OSEBX'
p_level = 0.90

AndreCAndersen

using System;
using System.Collections;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.IO;
using System.Linq;
using System.Text;
 
namespace SteganographyTest

AndreCAndersen

hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") }

X_all_size = 50
n = 6 # Original number of features
k = 2 # Reduced number of features
split_ratio = 0.8

#G enerate some dummy data
X_all <- hilbert(X_all_size)[,1:n]