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np.random.seed(5)
l=list(df.index)
np.random_shuffle(l)
df = df.iloc[l]

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from sklearn.model_selection import StratifiedKFold
from sklearn.base import clone

skfolds = StratifiedKFold(n_splits =3,random_state=42)

for train_index,test_index in skfolds.split(X_train,y_train_5):
  clone_clf = clone(sgd_clf)
  X_train_folds = X_train[train_index]
  y_train_folds = y_train_5[train_index]
  X_test_fold = X_train[test_index]

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#include <algorithm>  // for sort
#include <fstream>
#include <iostream>
#include <sstream>
#include <string>
#include <vector>
using namespace std;

enum class State {kEmpty, kObstacle, kClosed, kPath, kStart, kFinish};

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pragma solidity ^0.4.17;
contract Auction {
    
    // Data
    //Structure to hold details of the item
    struct Item {
        uint itemId; // id of the item
        uint[] itemTokens;  //tokens bid in favor of the item
       
    }

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pragma solidity ^0.4.0;

contract Ballot {
    struct Voter {
        uint weight;
        bool voted;
        uint8 vote;
    }
    struct Proposal{
        uint voteCount;

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class Car{
    park(){
        return "stopped";
    }
    drive(){
        return "Vroom";
    }

}
let car ;

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#!/usr/bin/env python3

# Chapter 1 -- The ``__init__()`` Method
# -----------------------------------------

# ..  sectnum::
#
# ..  contents::
#

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mport matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np

# Read in the image
image = mpimg.imread('test.jpg')

# Grab the x and y sizes and make two copies of the image
# With one copy we'll extract only the pixels that meet our selection,
# then we'll paint those pixels red in the original image to see our selection 

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"""
Mini Project:
Developed by:
Vidit Shah
Vivek Shah
Jash Rele
Deep Thakker
"""

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"""
Project Title : Lane Detection.
Developed By :- Vidit Shah,
                Vivek Shah,
                Jash Rele,
                Deep Thakker

"""
import cv2