nphausg

/*
 * Created by nphau on 09/12/2021, 00:30
 * Copyright (c) 2021 . All rights reserved.
 * Last modified 09/12/2021, 15:54
 */
 
public int solution(int[] numbers) {
        Arrays.sort(numbers);
        int min = 1;
        for (int i : numbers){

nphausg

/*
 * Created by nphau on 09/12/2021, 00:30
 * Copyright (c) 2021 . All rights reserved.
 * Last modified 09/12/2021, 15:54
 */
fun solution(numbers: IntArray): Int {
        val n = numbers.size
        val present = BooleanArray(n + 1)
        numbers.forEach { number ->

nphausg

// Use "PBKDF2WithHmacSHA512" with android O version and above
if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.O) {
  val skf = SecretKeyFactory.getInstance("PBKDF2WithHmacSHA512")
  val pwSpec = PBEKeySpec(secret.toCharArray(), salt, iterations, keyLength)
  skf.generateSecret(pwSpec).encoded
}

nphausg

nphausg

import io.ktor.features.*
// ...
fun main() {
   private val server by lazy {
        embeddedServer(Netty, PORT, watchPaths = emptyList()) {
            // configures Cross-Origin Resource Sharing. CORS is needed to make calls from arbitrary
            // JavaScript clients, and helps us prevent issues down the line.
            install(CORS) {
                anyHost()
            }

nphausg

dependencies {
    //... 
  // Coroutines
  implementation "org.jetbrains.kotlinx:kotlinx-coroutines-core:1.5.2"

  // Embedded Server
  implementation 'io.ktor:ktor-server-core:2.2.4'
  implementation 'io.ktor:ktor-server-netty:2.2.4'
  implementation 'io.ktor:ktor-serialization-kotlinx-json:2.2.4'
  implementation 'io.ktor:ktor-client-content-negotiation:2.2.4'

nphausg

companion object {
        private const val PORT = 5001
}
private val server by lazy {
        embeddedServer(Netty, PORT, watchPaths = emptyList()) {
            install(WebSockets)
            install(CallLogging)
            routing {
                get("/") {
                    call.respondText(

nphausg

>>> x_train, x_test, y_train, y_test = train_test_split(
...     x, y, test_size=4, random_state=4
... )
>>> x_train
array([[17, 18],
       [ 5,  6],
       [23, 24],
       [ 1,  2],
       [ 3,  4],
       [11, 12],

nphausg

# COSIN DISTANCES
# SOLUTION 1:
import numpy as np
vector_1 = np.array([1, 5, 1, 4, 0, 0, 0, 0, 0]) 
vector_2 = np.array([2, 4, 1, 1, 1, 1, 0, 0, 0])

def cos_sim(a, b):
    """Takes 2 vectors a, b and returns the cosine similarity 
    """
    dot_product = np.dot(a, b) # x.y

nphausg

 private_lane :bundle do |options|
    mode = (options[:mode])
    yarn(command: "android:bundle:#{mode}")
    sh("rm -rf ../android/app/src/main/res/drawable-*")
    sh("rm -rf ../android/app/src/main/res/raw")
    sh("cp -a ../app/assets/android/* ../android/app/src/main/res")
  end