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# Repository configuration file automatically generated
# by Entropy on your behalf.

[sabayonlinux.org]
enabled = true
desc = Sabayon Linux Official Repository
repo = http://pkg.sabayon.org#bz2
repo = http://pkg.repo.sabayon.org#bz2

# Main sabayonlinux.org Repository

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#!/bin/bash


ERASER=$(xinput --list | grep 'eraser' | grep -o [0-9][0-9])
STYLUS=$(($ERASER+1))
TOUCH=$(xinput --list | grep 'Wacom' | grep -o [0-9][0-9] | grep -v $ERASER | grep -v $STYLUS)

#xsetwacom set $STYLUS TopX -100
#xsetwacom set $STYLUS TopY 12
#xsetwacom set $STYLUS BottomX 26170

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/* The class IODataQueue is used in various places in the kernel. There are a couple of exploitable integer overflow issues in the ::enqueue method: */

Boolean IODataQueue::enqueue(void * data, UInt32 dataSize)
{
  const UInt32       head      = dataQueue->head;  // volatile
  const UInt32       tail      = dataQueue->tail;
  const UInt32       entrySize = dataSize + DATA_QUEUE_ENTRY_HEADER_SIZE;  <-- (a)
  IODataQueueEntry * entry;

  if ( tail >= head )

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Prerequisites:
==============
1. Raspbian
2. Raspberry Pi (2)

sudo apt-get update
sudo apt-get upgrade
cd

## Source: http://node-arm.herokuapp.com/

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{-# LANGUAGE UnicodeSyntax #-}

{-
Reads: AGATCGAGTG
Prints: sorted 3-mers:   AGA AGT ATC CGA GAG GAT GTG TCG
        unsorted 3-mers: AGA GAT ATC TCG CGA GAG AGT GTG
-}

import           Data.Function
import           Data.List

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/* C-Standard-Libraries */
#ifndef LIBRARIES
#define LIBRARIES
    #include <stdio.h>      /* stdio.h — standard buffered input/output */
    #include <stdlib.h>     /* stdlib.h — standard library definitions */
    #include <string.h>     /* string.h — string operations */
    #include <stdarg.h>     /* stdarg.h — handle variable argument list */
    #include <stdbool.h>    /* stdbool.h — boolean type and values */
    #include <stddef.h>     /* stddef.h — standard type definitions */

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  # Performs a beam search, to be run in the "Decoder"
  def Beamsearch(self,
             predict_fun, # RNN() with: new_states as a list, outputs of shape (batch_size, n_symbols)
             states0,     # RNN()'s initial layer states are equal to states0
             batch_size,  # Size derived from initial layer states0
             start_symbol # Tagging the encoded target sequence with <S> as a start symbol for example
             stop_symbol  # '                                      ' </S> as a stop symbol for example
             max_length,  # Maximum length of a given sequence
             beam_size=4  # See [simple beam search](https://github.com/SeitaroShinagawa/simple_beamsearch) for an example of probabilities
            ):

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# Make ML-Pipeline Runs Reproducible
random_state=7

# Testing our Pipeline with an ML-Ready Dataset
from sklearn.datasets import load_iris
iris = load_iris()

# Assign ML-Input and Ml-Target
Xi = iris.data   # Input
yi = iris.target # Output

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from socket import socket, error, setdefaulttimeout
import ssl
import re
import time
import sys
import os
from hashlib import md5
import json

os.chdir(os.path.split(sys.argv[0])[0])