steveway

import numpy as np
from os import listdir
from os.path import isfile, join
from PIL import Image
from sklearn.datasets.base import Bunch
from sklearn import svm
from sklearn.naive_bayes import MultinomialNB

steveway

import numpy as np

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

# This does work by using only one sample:
data = [[0,0,0,0,0,0,0,0,0,2,1]]
data = np.array(data, dtype=float)
target = [0,0,0,0,0,0,0,0,2,1,0]

steveway

from PIL import Image, ImageDraw, ImageFont, ImageOps
import numpy as np
import random
try:
    unichr
except NameError:
    unichr = chr

def render(input_arr):
    output = input_arr

steveway

from pybrain.structure import FeedForwardNetwork
from pybrain.structure import LinearLayer, SigmoidLayer
from pybrain.structure import FullConnection
from pybrain.rl.agents import LearningAgent
from PIL import Image, ImageDraw, ImageFont, ImageOps
import numpy as np
import copy

def mse(imageA, imageB):
    # the 'Mean Squared Error' between the two images is the

steveway

# -*- coding: utf-8 -*-
from PIL import Image, ImageDraw, ImageFont, ImageOps
import numpy as np
import os

width = 600
halfwidth = width/2
avatarsize = 48
height = 800
background_color = (255, 255, 255, 0)

steveway

//==================================================== 
// gmplay 2.1 - geekmaster's kindle video player 
// Copyright (C) 2012 by geekmaster, with MIT license: 
// http://www.opensource.org/licenses/mit-license.php 
// Modified by Stefan Murawski ([email protected])
//---------------------------------------------------- 
// Tested on DX,DXG,K3,K4main,K4diags,K5main,K5diags. 
//---------------------------------------------------- 
#include <sys/ioctl.h>     // ioctl 
#include <sys/mman.h>     // mmap, munmap 

steveway

import numpy as np
cimport numpy as np
cimport cython

DTYPE = np.double
ctypedef np.double_t DTYPE_t
DTYPE2 = np.uint8
ctypedef np.uint8_t DTYPE2_t

@cython.boundscheck(False) # turn off bounds-checking for entire function

steveway

import numpy as np

def apply_ordered_dither(inputarray, bayer_map, bayer_size):
    width, height = inputarray.shape
    for y in range(height):
        for x in range(width):
            inputarray[x, y] = 0 if inputarray[x, y] < bayer_map[x % bayer_size, y % bayer_size] else 255
    return inputarray

steveway

import numpy as np
import cv2
import gzip
import sys, os
from time import sleep, time


def main():
    width = 600
    height = 800

steveway

# //====================================================
# // raw2gmv 1.0a - raw to geekmaster video transcoder
# // Copyright (C) 2012 by geekmaster, with MIT license:
# // http://www.opensource.org/licenses/mit-license.php
# //----------------------------------------------------
##include <stdio.h>  // stdin,stdout
# typedef unsigned char u8; typedef unsigned int u32;
# int main(void) {
    # u8 o,to,tb,wb0[800*600]; u32 x,y,xi,yi,c=250,b=120; // c=contrast, b=brightness
    # while (fread(wb0,800*600,1,stdin)) for (y=0;y<800;y++) { xi=y; tb=0;