sanikamal

public class Solution {

    public static void print(String[] arr){
        for(int i = arr.length - 1; i >= 0; i--){
            System.out.print(arr[i] + " ");
        }
    }

    public static void main(String[] args) {
        Scanner in = new Scanner(System.in);

sanikamal

<?php
$handle = fopen ("php://stdin","r");
$t = fgets($handle);

for($i = 0; $i < $t; $i++) { 
    $input = trim(fgets($handle));
    $even = ''; 
    $odd = '';
    
    for($j = 0; $j < strlen($input); $j++) {

sanikamal

<?php
function solution($R, $V) {
    // write your code in PHP7.0
    $minA = 0;
    $minB = 0;
    $balA = 0;
    $balB = 0;
    for ($i = 0; $i < strlen($R); $i++) {
            if ($R[$i] == 'A') {
                $balA += $V[$i];

sanikamal

Overview

• Task - 1 : Create the configuration files
• Task - 2 : Import infrastructure
• Task - 3 : Configure a remote backend
• Task - 4 : Modify and update infrastructure
• Task - 5 : Taint and destroy resources
• Task - 6 : Use a module from the Registry
• Task - 7 : Configure a firewall

Task - 1 : Create the configuration files

sanikamal

# Text Tokenization using NLTK

from nltk.tokenize import sent_tokenize, \
    word_tokenize, WordPunctTokenizer

in_text = 'Use this option to select your font. The Show only monospaced fonts option if selected shortens the list of available fonts.'

# Sentence Tokenization
print(sent_tokenize(in_text))

sanikamal

## Task 1: Create a dataset to store your machine learning models  
### dataset name is up to you

cloudshell: bq mk austin  
or  
create the dataset from bigquery console


## Task 2: Create a forecasting BigQuery machine learning model.

sanikamal

import cv2
# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade/haarcascade_frontalface_default.xml')
# Read the input image
img = cv2.imread('images/sanikamal.jpg')
# Convert into grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.3, 4)
# Draw rectangle around the faces

sanikamal

#Import Library
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

#Load Train and Test datasets
#Identify feature and response variable(s)
x_train=input_variables_values_training_datasets
y_train=target_variables_values_training_datasets
x_test=input_variables_values_test_datasets

sanikamal

Task 1: Create a project jumphost instance

Navigation menu > Compute engine > VM Instance


Task 2: Create a Kubernetes service cluster

gcloud config set compute/zone us-east1-b

gcloud container clusters create nucleus-jumphost-webserver1

sanikamal

import math
cal_condition = input('What do you want to calculate? \ntype "n" for the number of months, \ntype "a" for the annuity monthly payment,\ntype "p" for the credit principal:\n')
 
if cal_condition=='n':

# You need 8 years and 2 months to repay this credit!
    credit_principal = int(input('Enter the credit principal:\n'))
    monthly_pay = float(input('Enter the monthly payment:\n'))
    interest = float(input('Enter the credit interest:\n'))
    i = interest/(12*100)