dan0nchik

 WebRequest request = WebRequest.Create("https://api.thecatapi.com/v1/categories");
            using (var s = request.GetResponse().GetResponseStream())
            {
                using (var sr = new StreamReader(s))
                {
                    var asJSON = sr.ReadToEnd();
                    res = JsonConvert.DeserializeObject<List<CategoriesModel>>(asJSON);
                }
            }

dan0nchik

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Runtime.CompilerServices;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;

dan0nchik

#include <iostream>
#include <vector>
using namespace std;
int main()
{
    std::vector<int> v;
    for(int i =1390; i<12567;++i ){
        if((i%3==0 or i%5==0) and i%7!=0 and i%11!=0 and i%13!=0 and i%23!=0) v.push_back(i);
    }
    int max = 1389;

dan0nchik

using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Runtime.CompilerServices;
using System.Windows.Forms;

namespace Многоугольники
{
    public partial class Form1 : Form

dan0nchik

<?php
function equation($a, $b, $c){
    if ($a != 0){
        $D = $b*$b - 4*$a*$c;
        if($D < 0){
            return "No roots";
        }
        if($D == 0){
            return -$b/(2*$a);
        }

dan0nchik

from sklearn.ensemble import ExtraTreesClassifier
import matplotlib.pyplot as plt
model = ExtraTreesClassifier()
model.fit(X,y)
f = plt.figure(figsize=(10, 10))
print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers
#plot graph of feature importances for better visualization
feat_importances = pd.Series(model.feature_importances_, index=X.columns)
feat_importances.nlargest(30).plot(kind='barh')
plt.show()

dan0nchik

//
//  ImagePicker.swift
//
//  Created by Daniel Khromov on 3/2/21.
//

import SwiftUI
import UIKit

struct ImagePicker: UIViewControllerRepresentable{

dan0nchik

FAILED        [100%]Recreating table test_when_clean with data from file
Done
Task: cls
|   iter    |  target   | algo_i... | prepro... |
-------------------------------------------------
Child Iteration accuracy: 0.77528
Child Iteration accuracy: 0.786516
|  1        |  0.7865   |  2.063    |  1.061    |
Child Iteration accuracy: 0.786516
Child Iteration accuracy: 0.808988

dan0nchik

FAILED            [100%]Recreating table test_cls with data from file
Done
Task: cls
|   iter    |  target   | algo_i... | prepro... |
-------------------------------------------------
Child Iteration accuracy: 0.884955
Child Iteration accuracy: 0.862831
|  1        |  0.885    |  2.063    |  1.061    |
Child Iteration accuracy: 0.871681
Child Iteration accuracy: 0.865044

dan0nchik

def add_pupil(name, birth_year, cs_score, math_score, physics_score, data):
    data[name] = (name, birth_year, cs_score, math_score, physics_score)  #


def show_pupil(name, data):
    print(*data[name])


def del_pupil(name, data):
    del data[name]