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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 |
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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 |
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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)) |
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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) |
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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 |
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# 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 |
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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] |