emrepun

import SwiftUI

struct ContentView: View {

    @State private var shareableImage: UIImage?
    @State private var didCallOnAppearForTheFirstTime = false

    var body: some View {
        VStack(spacing: 24.0) {
            makeBody()

emrepun

import SwiftUI

struct ContentView: View {

    @State var startDate: Date = .now
    @State var countDownEndDate: Date?

    var body: some View {
        VStack(spacing: 16.0) {
            Text("Duration:")

emrepun

import SwiftUI

struct ContentView: View {

    @State var startDate: Date?

    var body: some View {
        TimelineView(MetricsTimelineSchedule(from: .now)) { _ in
            VStack(spacing: 16.0) {
                ElapsedTimeView(elapsedTime: elapsedTime())

emrepun

What are the consequences for a person driving a motor vehicle under the influence of drugs (e.g. hashish, heroin, cocaine)?

[x] Confiscation of driving licence or driving ban
[x] Compulsory medical/psychological examination
[x] Fine and/or imprisonment

In which instances do you have to approach a pedestrian crossing with particular care?

[x] If pedestrians want to cross the road

[x] If the view of the pedestrian crossing is restricted

emrepun

//
//  JSONData.swift
//  CompareCoreDataFileManager
//
//  Created by Emre Havan on 09.10.20.
//  Copyright © 2020 Emre Havan. All rights reserved.
//

import Foundation

emrepun

import Foundation

struct GeometricShapes: Decodable {
    let items: [Object]
}

enum Object: Decodable {
    case square(Square)
    case sphere(Sphere)
    case cylinder(Cylinder)

emrepun

import numpy as np
import pandas as pd
import json
import pickle
import re
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer

class ReviewClassifierService:

emrepun

# Version-4
    def get_recommendations_include_rating_count_threshold_positive_negative_reviews(keywords):
        df = pd.read_csv('city_data_cleared.csv')

        score_dict = {}

        for index, row in df.iterrows():
            cs_score = CosineSimilarity.cosine_similarity_of(row['description'], keywords)

            rating = row['rating']

emrepun

# Version-3
    def get_recommendations_include_rating_count_threshold(keywords):
        df = pd.read_csv('city_data_cleared.csv')

        score_dict = {}

        for index, row in df.iterrows():
            cs_score = CosineSimilarity.cosine_similarity_of(row['description'], keywords)

            rating = row['rating']

emrepun

import numpy as np
import pandas as pd
from cosine_similarity import CosineSimilarity
from rating_extractor import RatingExtractor
import operator
import json

class RecommenderEngine:
    def __init__(self):
        print("engine initialized")