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@meherranjan
meherranjan / post-receive
Last active Mar 16, 2018 — forked from lemiorhan/post-receive
Post-receive hook to deploy the code being pushed to production branch to a specific folder
View post-receive
#!/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
@meherranjan
meherranjan / iterative-binary-search.js
Last active Feb 4, 2019
Iterative Binary Search in JavaScript
View iterative-binary-search.js
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]
@meherranjan
meherranjan / recursive-binary-search.js
Last active Feb 4, 2019
Recursive Binary Search in JavaScript
View recursive-binary-search.js
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
View training-data.js
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]
];
View generate-weights-and-bias.js
const getRandomWeights = () => Array.from({ length: 2 }, () => Math.random() * 0.5 - 0.2)
const getRandomBias = () => Math.random() * 0.5 - 0.2
View get-input-target.js
const getInput = (data, index) => data[index];
const getTarget = input => [...input].pop();
View weighted-input-sum.js
const getWeightedInputSum = (input, weight, bias) => weight[0] * input[0] + weight[1] * input[1] + bias;
View sigmoid.js
const sigmoid = sum => 1 / (1 + Math.exp(-sum));
View train-model.js
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);
View train-model-2.js
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,
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