itwasmycode

import scala.math.pow
import scala.util.Random
// KEMALEMRECOLAK CHECK! DONE!

@annotation.tailrec
def digitXorerFunc(xorer:List[Int]) : Option[Int] = xorer match{
  case Nil => None
  case xs::Nil => Some(xs)
  case xs::ys::Nil => Some(xs^ys)
  case xs::ys::zs => digitXorerFunc((xs^ys)::zs)

itwasmycode

# 4,202,105
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import time
from collections import Counter, deque, namedtuple
from dataclasses import dataclass 
from pprint import pprint

np.random.seed(105)

itwasmycode

% Students, Optional Lessons, Required Lessons and Student Grades
student(01, emre, comp).
student(02, merve, com).
student(03, eylul, elec).
oLesson(03, logicprog, 5).
oLesson(04, softengine, 6).
rLesson(01, programming, 3).
sLesson(01, 01, 40).
sLesson(01, 02, 10).
sLesson(03,02,0).

itwasmycode

def balance(chars: List[Char], tst: Int, tst1:Int) : Boolean= {
  val pht = Array('(',')')
  if ((chars==List()) && tst == tst1) true
  else if(chars==List() && tst!= tst1)false
  else balance(chars.tail, tst + bool2int(chars.head==pht(0))
               ,tst1+bool2int(chars.head==pht(1)))
}

itwasmycode

import time
import numpy as np
import collections
import math
from collections import namedtuple
from itertools import chain
from functools import wraps

def bit_producer(mx):
    """

itwasmycode

import time
import numpy as np
import collections
from collections import namedtuple
from itertools import chain
from functools import wraps

def bit_producer(mx):
    """
    DOCSTRING

itwasmycode

import collection.mutable.{Map => MM}
import scala.math.pow
import scala.collection.mutable.ListBuffer
import scala.collection.mutable.{ArrayBuffer}
//Lookup array described.
val lookupArr = Array(0xE, 0x4, 0xD, 0x1, 0x2, 0xF, 0xB, 0x8, 0x3, 0xA, 0x6, 0xC, 0x5, 0x9, 0x0, 0x7)
//There is an logical it is being applied to a 4-digit by logical XOR. 
def xorerFunc(vl1 : String) =  {
    var holderStr = 1
    for(vlr<-0.to(3))

itwasmycode

def forward_selection(dataframe, predictors, response, max_features=8):
    def process_linear_model(predictors_):
        print(predictors_, "predictors")
        X = dataframe[predictors_].values
        y = dataframe[response]
        model = LinearRegression()
        model = model.fit(X,y)
        y_pred = model.predict(X)
        RSS = mean_squared_error(y, y_pred) * len(y)
        return (model, RSS, predictors_)