aidevnn

/* This file is public domain. Author: Fredrik Johansson. */

// original code at
// https://github.com/fredrik-johansson/arb/blob/master/examples/poly_roots.c

// This code assumes that libflint-arb and all its dependencies are installed
// Use the arguments "-lflint-arb -lflint" to compile it

#include <string.h>
#include <ctype.h>

aidevnn

Console.WriteLine("Hello, World!");

Random rnd = new();

string Change(string w)
{
    var w0 = w.Trim();
    if (w0.Length < 4)
        return string.Join("", w0.OrderBy(c => rnd.NextDouble()));

aidevnn

Issue-driven Development

Principle of issue-driven development is simple:

• Always associate development to GitHub issue.

Issue-driven development achieves:

• Modularity: Commits and branches are self-contained.
• Granularity: Commits and branches intend a single Issue.
• Transparency: Discussion on each edition can be seen on Issue.

aidevnn

const express = require('express')
const app = express()
const port = 3000

const templateForm = `
<title>NodeJs Simple Http Server</title>
<a href="/">Hello World, NodeJs v${process.versions.node}</a>
<br/>
<form method="post">
  <span style="float: left; width: 100px">First name:</span><input type="text" name="fname"><br>

aidevnn

var http = require('http');
var qString = require('querystring');

http.createServer(function (req, res) {
  res.writeHead(200, {'Content-Type': 'text/html'});
  res.write('<title>NodeJs Simple Http Server</title>');

  if (req.method === 'GET') {
    res.write(`
<a href="/">Hello World, NodeJs v${process.versions.node}</a>

aidevnn

<title>PHP Simple Http Server</title>
<?php
if (empty($_POST)) {
?>
<a href="">Hello World, PHP <?php echo phpversion(); ?></a>
<br/>
<form method="post">
  <span style="float: left; width: 100px">First name:</span><input type="text" name="fname"><br>
  <span style="float: left; width: 100px">Last name:</span><input type="text" name="lname"><br>
  <input type="submit" value="Submit">

aidevnn

import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from sklearn import datasets

def train_test_split(X, y, test_size=0.5):
    """ Split the data into train and test sets """
    # Split the training data from test data in the ratio specified in

aidevnn

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()

aidevnn

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.callbacks import Callback
import numpy as np
import time
from tabulate import tabulate

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])