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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])

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// We know we can apply type checking and compile static on all groovy classes, the good news you can do this on gradle build, so it'll be applied in every class in your project, this works in android project.

/*/ In gradle project:
apply plugin: 'groovy'
compileGroovy.groovyOptions.configurationScript = file('gradle/config/groovyc.groovy')

// In Android project (app/build.gradle)
androidGroovy {
    options {
        configure(groovyOptions) {