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3 years ago

Write a C program to input a character, then check if the input

Computers and Technology

1 answer:

rodikova [14]3 years ago

6 0

Answer:

// CPP program to find type of input character

#include <iostream>

using namespace std;

void charCheck(char input_char)

{

// CHECKING FOR ALPHABET

if ((input_char >= 65 && input_char <= 90)

|| (input_char >= 97 && input_char <= 122))

cout << " Alphabet ";

// CHECKING FOR DIGITS

else if (input_char >= 48 && input_char <= 57)

cout << " Digit ";

// OTHERWISE SPECIAL CHARACTER

else

cout << " Special Character ";

}

// Driver Code

int main()

{

char input_char = '$';

charCheck(input_char);

return 0;

}

Explanation:

// CPP program to find type of input character

#include <iostream>

using namespace std;

void charCheck(char input_char)

{

// CHECKING FOR ALPHABET

if ((input_char >= 65 && input_char <= 90)

|| (input_char >= 97 && input_char <= 122))

cout << " Alphabet ";

// CHECKING FOR DIGITS

else if (input_char >= 48 && input_char <= 57)

cout << " Digit ";

// OTHERWISE SPECIAL CHARACTER

else

cout << " Special Character ";

}

// Driver Code

int main()

{

char input_char = '$';

charCheck(input_char);

return 0;

}

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Modify the WordCount program so it outputs the wordcount for each distinct word in each file. So the output of this DocWordCount

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Answer and Explanation:

package PackageDemo;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {

public static void main(String [] args) throws Exception

{

Configuration c=new Configuration();

String[] files=new GenericOptionsParser(c,args).getRemainingArgs();

Path input=new Path(files[0]);

Path output=new Path(files[1]);

Job j=new Job(c,"wordcount");

j.setJarByClass(WordCount.class);

j.setMapperClass(MapForWordCount.class);

j.setReducerClass(ReduceForWordCount.class);

j.setOutputKeyClass(Text.class);

j.setOutputValueClass(IntWritable.class);

FileInputFormat.addInputPath(j, input);

FileOutputFormat.setOutputPath(j, output);

System.exit(j.waitForCompletion(true)?0:1);

}

public static class MapForWordCount extends Mapper<LongWritable, Text, Text, IntWritable>{

public void map(LongWritable key, Text value, Context con) throws IOException, InterruptedException

{

String line = value.toString();

String[] words=line.split(",");

for(String word: words )

{

Text outputKey = new Text(word.toUpperCase().trim());

IntWritable outputValue = new IntWritable(1);

con.write(outputKey, outputValue);

}

public static class ReduceForWordCount extends Reducer<Text, IntWritable, Text, IntWritable>

{

public void reduce(Text word, Iterable<IntWritable> values, Context con) throws IOException, InterruptedException

{

int sum = 0;

for(IntWritable value : values)

{

sum += value.get();

}

con.write(word, new IntWritable(sum));

}

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