zkavtaskin

/***
* This was inspired by Python https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html 
* Example usage choice([1, 5, 2], 2) <- Gives 1,5 and 2 same chance of occurance i.e. 1/3
* Example usage choice([1, 5, 2], 2, [0.05, 0.8, 0.15]) <-- gives 1 5% chance, 5 80% chance and 2 15% chance to show up
*/
function choice(events, size, probability) {
    if(probability != null) {
      const pSum = probability.reduce((sum, v) => sum + v);
      if(pSum < 1 - Number.EPSILON || pSum > 1 + Number.EPSILON) {
        throw Error("Overall probability has to be 1.");

zkavtaskin

/***
* Histogram "bins" numbers in the array X in to group ranges. 
* Example usage histogram([1,5,2,4,2,5,2,3,1], 2) would return back [5,2,2] where bin ranges are [1-2, 3-4, 5-6] as bin range is 2
* Example usage histogram([1,5,2,4,2,5,2,3,1], 1) would return back [2,3,1,1,2] where bin ranges are [1,2,3,4,5] as bin range is 1
*/
function histogram(X, binRange) {
  //inclusive of the first number  
  var max = Math.max(...X);
  var min = Math.min(...X);
  var len = max - min + 1;

zkavtaskin

from sklearn.linear_model import LinearRegression
import pickle
import random
import numpy as np

"""
Sample data preparation for the partition.
--------
This example uses linear function y=mx+b where m is the tenant / company
specific bias and b is the error. LinearRegression is then used to find