Millsky

var curryAdd = (x) => (y) => return x + y;

Millsky

import numpy as np 

inputs  =  np.array([[1],[100],[3],[30],[40]])
testSet =  np.array([[10],[13],[19],[33],[1]])
outputs =  np.array([[5],[203],[9],[63],[83]])
bias    =  1
biasWeight = 1
#initWeights 
weights = np.random.random()

Millsky

function convertToIntArray(hex){
var bytes = [],
    str;

for(var i=0; i<hex.length-1;i+=2){
	bytes.push(parseInt(hex.substr(i, 2), 16))
}

Millsky

import tensorflow as tf
import numpy as np

def sigmoid(x):
	return 1 / (1 + np.exp(-x))

x_learn = np.array([[1.000,2.000],
					[10.000,2.0000],
					[9.000,3.0000],
					[5.000,8.000],

Millsky

'use strict';

class example_component extends HTMLElement {
    /* THIS RETURNS THE NAME OF OUR WEB COMPONENT */
    static get is() {
        return 'v-graph'
    }

  
    /* WE CAN SET UP GETS TO RETRIEVE THE ELEMENT NAMES BAM, EASY !  */

Millsky

class node {
    value: any
    left: any
    right: any
    height: number
    balanceFactor(){
        var bf = (this.right.height) - (this.left.height);

    }
    constructor(v,h) {

Millsky

    function sortTable(table,col){
        if(table.nodeName != "TABLE"){
            throw  new Error("Node is not of type TABLE");
            return;
        }
        /* DEEP CLONE WILL BREAK THE REF */
        //table = table.cloneNode(true);
        var rows = [].slice.call(table.tBodies[0].rows,1);
        var tBody = table.tBodies[0];
        /* SORT MAP AND REPLACE */

Millsky

function observer() {
    const data = {};
    const callbacksObj = {};
    const set = (property, value) => {
        data[property] = value;
        notify(property);
    };
    const notify = (property) => {
        if(!property) {
            throw new TypeError('Property "property" is not type string')

Millsky

import R from 'ramda';
import { task, of } from 'folktale/concurrency/task';
import hash from 'object-hash';

function processService(p) {
    return p.data;
}

const getDataFailure = data => ({
    type: 'FAILURE_TYPE',

Millsky

/* Build an adjacency matrix for a super simple tree a -> b,c */
const m = [
  [0, 1, 0, 0, 0],
  [0, 0, 1, 1, 0],
  [0, 0, 0, 0, 1],
  [0, 0, 0, 0, 0],
  [0, 0, 0, 0, 0]
];

/* Data at each node */