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import sys
from time import sleep
from selenium import webdriver
from selenium.common.exceptions import TimeoutException
from selenium.common.exceptions import WebDriverException
from eliot import start_action, to_file, log_call
# from selenium.webdriver.common.keys import Keys
# from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
# from selenium.webdriver.common.by import By
# from selenium.webdriver.support import expected_conditions as EC

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GLPK Installation process on Windows 7 SP1

Prerequists:

• Visual C++ (for GLPK v4.65 you'll need VC v2010 Express)

• Download the latest GLPK zip package: https://sourceforge.net/projects/winglpk/files/winglpk/ then extract its content to the C:\glpk.

• Create the environment variable %GLPK_HOME% pointing to the C:\glpk path you've created, and add to the system path the following value: SET PATH=%PATH%;%GLPK_HOME%\w64

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// reference http://www.geocities.jp/tomtomf/JScience/JScience.htm
//
@GrabResolver (name='jcurl-repository', root='http://jcurl.berlios.de/m2/repo/')
@Grab(group = 'org.jscience', module='jscience', version='4.3.1')

import org.jscience.mathematics.number.Complex
import org.jscience.mathematics.vector.ComplexMatrix

//計算結果の出力様関数
def show(cm) {

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def text = "Going to convert this to Base64 encoding!"

// Encode
def encoded = text.bytes.encodeBase64().toString()
println encoded


// Decode
byte[] decoded = encoded.decodeBase64()
println new String(decoded)

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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 
import math

class UpperConfidenceBound:
  def __init__(self, dataframe,  N, m):
    self.__dataset = dataframe
    self.__N = N
    self.__m = m

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"""
Update: (07.29.2022): Fix: train_test_split import
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================

For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten digit recognition, the
Bernoulli Restricted Boltzmann machine model (:class:`BernoulliRBM
<sklearn.neural_network.BernoulliRBM>`) can perform effective non-linear

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package demo

import javax.script.*
import org.renjin.script.*

class App {
  static void main(String[] args){
    // init values
    int[] values = [1,2,3]
    // Create a Renjin engine:

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Transfer a gist to a GitHub repository

clone the gist

git clone https://gist.github.com/ishu3101/6fb35afd237e42ef25f9

rename the directory

mv 6fb35afd237e42ef25f9 ConvertTo-Markdown

change the working directory to the newly renamed directory

cd ConvertTo-Markdown

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<template>
  <div id="app">
    <!-- <img src="./assets/logo.png">
    <HelloWorld msg="Welcome to Your Vue.js App"/> -->
    <div class="reveal">
      <div class="slides">
        <section>Single Horizontal Slide</section>
        <section>
          <section>Vertical Slide 1</section>
          <section>Vertical Slide 2</section>

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# Simplest linear model with keras 2.1.3 (using tensorflow backed) it worked with python 3.5
import numpy as np
import keras

model = keras.Sequential( [keras.layers.Dense(units=1, input_shape=[1])] )
model.compile(optimizer='sgd', loss='mean_squared_error')

# y = 2x - 1
xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0])