ackjake

def leave_one_out(df_train, df_test, var, noise=.01, drop=True):

    new_var = 'mean_{}'.format(var)
    df_train[new_var] = np.nan
    df_test[new_var] = np.nan

    # training set
    loo = LeaveOneOut()
    for train_index, test_index in tqdm_notebook(loo.split(df_train)):
        loo_train = df_train.iloc[train_index]

ackjake

def get_target_mean(df_train, df_test, var, target, NFOLDS, NOISE):
    """ 
    Creates out of fold averages by the categorical variable passed.
    Decreasing the number of folds and increasing the noise can help 
    prevent over fitting the training set.
    """
    
    df_train['mean_{}'.format(var)] = np.nan
    df_test['mean_{}'.format(var)] = np.nan
    

ackjake

static bool CheckBad(int[] a)
        {
            for (int i = 0; i < a.Length; i++)
                if (a[i] != i)
                    return false;
            return true;
        }       

if (N >= 2)
            {

ackjake

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;

namespace Tic_Tac_Toe_Checker
{
    class Program
    {
        static void Main(string[] args) //testing method