mihikadutta

#!/bin/bash

target_branch="gh-pages"
working_tree="/home/meherranjan/aerosailor.com"

while read oldrev newrev refname
do
    branch=$(git rev-parse --symbolic --abbrev-ref $refname)
    if [ -n "$branch" ] && [ "$target_branch" == "$branch" ]; then
    

mihikadutta

const sortedList = [1,2,3,4,5,6,9,11,12,13,14,15,16,17,18,19,20]

const binarySearch = (list, item) => {
	let low = 0
	let high = list.length - 1
	let guess = null
	
	while(low <= high) {
		let mid = Math.floor((high + low)/2)
		guess = list[mid]

mihikadutta

sortedList = [11, 24, 33, 64, 95, 106, 217, 228, 299, 310]

const getMid = (low, high) => {
	return Math.floor((high + low) / 2)
}

const recursiveBS = (list, item, low, high) => {
	(!low && !high ) && (low = 0, high = list.length - 1)

	let found = false

mihikadutta

const trainingData = [
    [4, 8, 0],
    [4, 2, 0],
    [5, 7, 0],
    [7, 4, 0],
    [9, 9, 1],
    [7, 10, 1],
    [10, 12, 1],
    [3, 12, 1]
];

mihikadutta

const getRandomWeights = () => Array.from({ length: 2 }, () => Math.random() * 0.5 - 0.2)
const getRandomBias = () => Math.random() * 0.5 - 0.2

mihikadutta

const getInput = (data, index) => data[index];
const getTarget = input => [...input].pop();

mihikadutta

const getWeightedInputSum = (input, weight, bias) => weight[0] * input[0] + weight[1] * input[1] + bias;

mihikadutta

const sigmoid = sum => 1 / (1 + Math.exp(-sum));

mihikadutta

const trainModel = data => {
  let weights = getRandomWeights();
  let bias = getRandomBias();

  for (let i = 0; i < 50000; i++) {
    const randomIndex = getRandomIndex(data.length);

    const input = getInput(data, randomIndex);
    const target = getTarget(input);

mihikadutta

  for (let i = 0; i < 50000; i++) {
    ...
    ...
    const weightedSum = getWeightedInputSum(weights, input, bias);
    const weightedSumWRTweight0 = input[0];
    const weightedSumWRTweight1 = input[1];
    const weightedSumWRTbias = 1;

    const prediction = compose(
      sigmoid,