SumanSudhir

#include <bits/stdc++.h>

using namespace std;

vector<string> split_string(string);

/*
 * Complete the waiter function below.
 */

SumanSudhir

def kappa_loss(p, y, n_classes=6, eps=1e-10):
    """
    QWK loss function as described in https://arxiv.org/pdf/1612.00775.pdf
    
    Arguments:
        p: a tensor with probability predictions, [batch_size, n_classes],
        y, a tensor with one-hot encoded class labels, [batch_size, n_classes]
    Returns:
        QWK loss
    """

SumanSudhir

class Solution {
public:;

    int sumSquare(int n) {
        int sum = 0;
        while(n !=0){
            sum += (n%10) * (n%10);
            n = n/10;
        }
        return sum;

SumanSudhir

// Every number > 9 on sum of squared expansion will reduce to number < 9
// In [0-9],  there are only two cases when number < 9 and it is happy number i.e 1 and 7

class Solution {
public:;
    
    int sumSquare(int n) {
        int sum = 0;
        while(n !=0){
            sum += (n%10) * (n%10);

SumanSudhir

func downloadMovieLensDatasetIfNotPresent() -> String{
    let localURL = URL(fileURLWithPath: FileManager.default.currentDirectoryPath)
    let dataFolder = DatasetUtilities.downloadResource(
        filename: "ml-25m",
        fileExtension: "zip",
        remoteRoot: URL(string: "http://files.grouplens.org/datasets/movielens/")!,
        localStorageDirectory: localURL.appendingPathComponent("data/", isDirectory: true)
        )

    return try! String(contentsOf: dataFolder.appendingPathComponent("ratings.csv"), encoding: .utf8)

SumanSudhir

SumanSudhir