jilm

## gist:9f9acfb30b1ff47b5198a4c00dc7aebf
HLEDEJ
  KDYŽ NENÍ ZNAČKA
    POLOŽ
    POLOŽ
    KDYŽ NENÍ ZEĎ
      KROK
      HLEDEJ
    KONEC
    VLEVO-VBOK
    KDYŽ NENÍ ZEĎ

## template.tex
\documentclass[a4paper]{book}
\usepackage[utf8]{inputenc}
\usepackage{marginnote}
\usepackage{amssymb}
%\usepackage{fontawesome}
\usepackage{color}
\usepackage{xcolor}
\usepackage{dialogue}
\usepackage{multicol}
\usepackage{adforn}

## spm_math.html
<!DOCTYPE html>
<html>

    <head>
        <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
        <meta charset="UTF-8">
        <link rel="stylesheet" href="main.css">
        <script type="text/x-mathjax-config">
            MathJax.Hub.Config({
                TeX: { equationNumbers: { autoNumber: "all" } }

## main.css
body {
    background-color: #fcf8f1;
    text-align: left;
    display: flex;
    flex-direction: column;
    width: 100%;
    flex-grow: 1;
    padding: 0;
    margin: 0;
    height: 100%;

## spm.py
import math

def poisson(lmbd, k):

    return math.exp(-lmbd + k * math.log(lmbd) - math.lgamma(k+1))

def poisson_cumulative(lmbd, s):

    return sum([poisson(lmbd, k) for k in range(s+1)])


## spm_math.md

      
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                jilm
                / spm_math.md
            
            
              Last active
              May 17, 2019 14:40
            
              
                The math remarks regarding reliability.
              
          
    Vyhodnocení skladových zásob

Pojmy

Deployed

Počet nasazených kusů dílu daného typu $d(t)$. Může být funkcí času.
Kumulovaná provozní historie


## prepare_dict.sh
cat document | cut -d ';' -f 13 | tr '[:upper:]' '[:lower:]' | tr '[:punct:]' ' ' | tr ' ' '\n' | sort | uniq | ./stemmer > stems.txt
cat document | cut ';' -f 13 | tr '[:upper:]' '[:lower:]' | tr '[:punct:]' ' ' | tr ' ' '\n' | sort | uniq  > words.txt
cat words.txt | awk '{print "["$0"]"}' | tr '[' '"' | tr ']' '"'
paste words.txt stems.txt

## grid.py
import PIL.Image
import sys

filename = sys.argv[1]
im = PIL.Image.open(filename)
im = im.convert('L')
w, h = im.size
gs = min(w, h) // 12

for r in range(h // gs):

## Dictionary prepare
cat | tr 'A-ZÁČĎĚÉÍŇŘŠŤÚÝŽ' 'a-záčďěéíňřšťúýž' | tr "[:punct:][:space:]" "\n" | sort | uniq -c

## nn.py
import numpy as np

def sigmoid(x):
    return 1.0/(1.0+np.exp(-x))

def sigmoidPrime(s):
    return s*(1-s)

def sigmoid_derivative(x):
    return np.exp(-x)/(1.0+np.exp(-x))**2
	HLEDEJ
	KDYŽ NENÍ ZNAČKA
	POLOŽ
	POLOŽ
	KDYŽ NENÍ ZEĎ
	KROK
	HLEDEJ
	KONEC
	VLEVO-VBOK
	KDYŽ NENÍ ZEĎ
	\documentclass[a4paper]{book}
	\usepackage[utf8]{inputenc}
	\usepackage{marginnote}
	\usepackage{amssymb}
	%\usepackage{fontawesome}
	\usepackage{color}
	\usepackage{xcolor}
	\usepackage{dialogue}
	\usepackage{multicol}
	\usepackage{adforn}
	<!DOCTYPE html>
	<html>

	<head>
	<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
	<meta charset="UTF-8">
	<link rel="stylesheet" href="main.css">
	<script type="text/x-mathjax-config">
	MathJax.Hub.Config({
	TeX: { equationNumbers: { autoNumber: "all" } }
	body {
	background-color: #fcf8f1;
	text-align: left;
	display: flex;
	flex-direction: column;
	width: 100%;
	flex-grow: 1;
	padding: 0;
	margin: 0;
	height: 100%;
	import math

	def poisson(lmbd, k):

	return math.exp(-lmbd + k * math.log(lmbd) - math.lgamma(k+1))

	def poisson_cumulative(lmbd, s):

	return sum([poisson(lmbd, k) for k in range(s+1)])
	cat document \| cut -d ';' -f 13 \| tr '[:upper:]' '[:lower:]' \| tr '[:punct:]' ' ' \| tr ' ' '\n' \| sort \| uniq \| ./stemmer > stems.txt
	cat document \| cut ';' -f 13 \| tr '[:upper:]' '[:lower:]' \| tr '[:punct:]' ' ' \| tr ' ' '\n' \| sort \| uniq > words.txt
	cat words.txt \| awk '{print "["$0"]"}' \| tr '[' '"' \| tr ']' '"'
	paste words.txt stems.txt
	import PIL.Image
	import sys

	filename = sys.argv[1]
	im = PIL.Image.open(filename)
	im = im.convert('L')
	w, h = im.size
	gs = min(w, h) // 12

	for r in range(h // gs):
	import numpy as np

	def sigmoid(x):
	return 1.0/(1.0+np.exp(-x))

	def sigmoidPrime(s):
	return s*(1-s)

	def sigmoid_derivative(x):
	return np.exp(-x)/(1.0+np.exp(-x))**2