HTC One(M8) launched in India For Rs 49,900

In the wake of proclaiming its new leader gadget just about a month back, HTC today propelled the One (M8) in India today. The One (M8) has been evaluated at Rs 49,900 and will be accessible from May 7. HTC additionally uncovered that the Google Play Edition of the One (M8) will be accessible for preorder today, and will be accessible for buy a couple of weeks after the fact on the Google Play Store. This might make it the first Google Play Edition gadget to be accessible in India.

To recap on the specifications, the One (M8) flaunts a 5-inch 1080p presentation with pixel thickness of 441ppi and Gorilla Glass 3 protection. It is fueled by a Snapdragon 801 quad-center processor timed at 2.5GHz and combined with Adreno 330 GPU and 2gb of RAM. Likewise included, is 16gb inner stockpiling that might be stretched to up to 128gb utilizing a microsd card. On the off chance that that is insufficient, purchasers will additionally get 50gb of extra Google Drive space.

The idea of the One (M8) is the Duo Camera at the back, which is basically a 4-Ultrapixel essential Polaroid with double LED blaze and an alternate module, which fills in as a profundity sensor. This sensor measures the separation between articles/subjects in the casing and later permits clients to change the center in the photograph. For selfies, the telephone dons a 5-megapixel front-confronting Polaroid with BSI sensor and f/2.0 gap for a finer photography experience. Notwithstanding that HTC has included various new altering characteristics including the fun altering mode to the telephone.

Connectivity choices incorporate 4G-LTE help on 2,300MHz band, which works in India, 3G backing, NFC, Bluetooth, Wi-Fi and GPS. Also, the gadget additionally underpins FDD-LTE systems, which will permit clients in India to get access to 4G system even while on international roaming in most of the countries. Other features include a 2,600mAh battery with a power saving mode, which extends the battery life to two weeks.

Java Code For Nearest Neighbor classifier

import java.io.*;

importjava.util.*;

importjava.math.*;

public class KNN

{

Scanner src=new Scanner(System.in);

introwcount=0,colcount;

String f[][];

intcolvar=0;

float[] newcol;

float[] requirement;

String[] req;

double[] dist;

intT,pos;

float min=0.0f,max=0.0f;

void display()

{

for(int q=0;q<rowcount;q++)

{

for(int s=0;s<colcount;s++)

System.out.print(f[q][s]+"\t");

System.out.println();

}

}//display-------------------------------------------

void newcolcal()

{

System.out.println("Enter column name");

String tempstr=src.nextLine();

for(int q=0;q<colcount;q++)

if(f[0][q].equals(tempstr))

colvar=q;

}//newcolcal-----------------------------------------

void normal()

{

int flag=0;

for(int q=1;q<rowcount;q++)

{

newcol[flag]=Float.parseFloat(f[q][colvar]);

flag++;

}

                for(int q=0;q<newcol.length;q++)

                if(max<newcol[q])

                max=newcol[q];

                min=max;

                for(int q=0;q<newcol.length;q++)

                if(min>newcol[q])

                min=newcol[q];

for(int q=0;q<newcol.length;q++)

newcol[q]=(newcol[q]-min)/(max-min);

T=(int)Math.sqrt(rowcount-1);

}//normal---------------------------------------------

void display1()

{

for(int q=0;q<newcol.length;q++)

f[q+1][colvar]=Float.toString(newcol[q]);

display();

}//display1()-----------------------------------------

void distance()

{

dist=new double[rowcount-1];

System.out.println("Enter requirement:");

requirement=new float[colcount-2];

req=new String[colcount-2];

for(int q=0;q<req.length;q++)

req[q]=src.nextLine();

for(int q=0;q<req.length;q++)

{

if(req[q].equals("F"))

{req[q]="0";}

if(req[q].equals("M"))

{req[q]="1";}

}

for(int q=0;q<requirement.length;q++)

requirement[q]=Float.parseFloat(req[q]);

requirement[1]=(requirement[1]-min)/(max-min);

float colsum=0.0f;

for(int q=1;q<rowcount;q++)

{

                for(int s=1;s<colcount-1;s++)

                {

                float colminus=requirement[s-1]-Float.parseFloat(f[q][s]);

                float colsqr=colminus*colminus;

                colsum=colsum+colsqr;

                }

dist[q-1]=Math.sqrt(colsum);

//System.out.println(dist[q-1]);

colsum=0.0f;

}

set();

}//distance()-----------------------------

void set()

{

double[] Tset=new double[T];

int[] rowset=new int[T];

for(int q=0;q<Tset.length;q++)

{

Tset[q]=dist[q];

rowset[q]=q;

}

for(int q=T;q<dist.length;q++)

if(dist[q]<largest(Tset))

{

Tset[pos]=dist[q];

rowset[pos]=q;

}

commonclass(rowset);

}//set()--------------------------------------------

double largest(double[] arr)

{

pos=0;

double largest=0.0d;

for(int q=0;q<arr.length;q++)

if(arr[q]>largest)

{

largest=arr[q];

pos=q;

}

return(largest);

}//largest()------------------------------------------

void commonclass(int[] array)

{

String[] commonclass=new String[array.length];

String cl[]={"short","med","tall"};

intclcheck[]=new int[cl.length];

int common=0,clpos=0;

for(int q=0;q<clcheck.length;q++)

clcheck[q]=0;

for(int q=0;q<array.length;q++)

{

for(int s=0;s<colcount;s++)

System.out.print(f[array[q]+1][s]+"\t");

System.out.println();

}

for(int q=0;q<array.length;q++)

commonclass[q]=f[array[q]+1][colcount-1];

for(int q=0;q<commonclass.length;q++)

for(int s=0;s<cl.length;s++)

if(cl[s].equals(commonclass[q]))

clcheck[s]++;

for(int q=0;q<clcheck.length;q++)

if(common<clcheck[q])

{

common=clcheck[q];

clpos=q;

}

System.out.println("Most Common class is "+cl[clpos]);

}//commonclass()------------------------------------------

void read()

{

String file="person.txt";

String line=null;

try

{

BufferedReaderbr=new BufferedReader(new FileReader(file));

while((line=br.readLine())!=null)

rowcount++;

br.close();

br=new BufferedReader(new FileReader(file));

line=br.readLine();

colcount=line.split("\t").length;

br.close();

f=new String[rowcount][colcount];

newcol=new float[rowcount-1];

br=new BufferedReader(new FileReader(file));

int row=0,col=0,lc=0;

while((line=br.readLine())!=null)

{

line=line.replace('F','0');

line=line.replace('M','1');

String[] temp=line.split("\t");

                for(int q=0;q<temp.length;q++)

                f[row][q]=temp[q];

                row++;

}

br.close();

}

catch(FileNotFoundException ex)

{System.out.println("error");}

catch(IOException ex)

{System.out.println("error");}

}

public static void main(String arg[])

{

KNN knn=new KNN();

knn.read();

knn.newcolcal();

knn.normal();

knn.display1();

knn.distance();

}}

Java Code For Bayesian Classifier

import java.io.BufferedReader;

import java.io.FileReader;

import java.util.*;

public class Bayesian {

    public static double getProbability(String value){

        String file = "data1.txt";

        String line = null;

        int i = 0, c = 0, tot = 0;      

        try{

            FileReader fr = new FileReader(file);

            BufferedReader br = new BufferedReader(fr);          

            while((line=br.readLine())!=null){

                i++;

                if(i == 1)

                continue;                              

                                tot++;

                String colData[] = line.split(" ");              

                if(colData[5].equals(value)){

                    c++;                  

                }            }          

            br.close();

                }

        catch(Exception e){

            System.out.println(e);

        }             

                return((double)c/tot);      

    }  

    public static int getCount(String value){

                String file = "data1.txt";

        String line = null;

        int i = 0, c = 0;      

        try{

            FileReader fr = new FileReader(file);

            BufferedReader br = new BufferedReader(fr);          

            while((line=br.readLine())!=null){

                i++;

                if(i == 1)

                continue;      

                String colData[] = line.split(" ");               

                if(colData[5].equals(value)){

                    c++;                  

                }            }

            br.close();

                }

        catch(Exception e){

            System.out.println(e);

        }

                return(c);      

    }  

     public static double getConditionalProbability(String unknown[], String value, int count, double pc){

                String file = "data1.txt";

        String line = null;

        int i = 0, c[] = new int[4];               

                double p = 1;    

        try{

            FileReader fr = new FileReader(file);

            BufferedReader br = new BufferedReader(fr);          

            while((line=br.readLine())!=null){

                i++;

                if(i == 1)

                continue;

                String colData[] = line.split(" ");              

                                for(int j =0; j<4; j++){

                                    if(colData[j+1].equals(unknown[j]) && colData[5].equals(value)){

                                            c[j]++;                  

                                    }                                }

            }

            br.close();

                }

        catch(Exception e){

            System.out.println(e);

        }

                for(int j = 0; j<4; j++){

                                p = p*((double)c[j]/count);

                }    

                return(p*pc);

    }  

    public static void main(String[] args) {      

        Scanner scan = new Scanner(System.in);

        String attributes[] = {"id", "age", "income", "student", "credit", "computer_buyer"};

        String unknown[] = new String[4];

        System.out.println("Enter unknown sample's details:");

        System.out.println("age:");

        unknown[0] = scan.next();

        System.out.println("income");

        unknown[1] = scan.next();

        System.out.println("student:");

        unknown[2] = scan.next();

        System.out.println("credit:");

        unknown[3] = scan.next();

        double pc1 = getProbability("yes");

        double pc2 = getProbability("no");

        int no_yes = getCount("yes");

        int no_no = getCount("no");

        System.out.println("pc1: "+ pc1 +" pc2:" + pc2);

        System.out.println("yes: "+ no_yes +" no" + no_no);

                double pc1x = getConditionalProbability(unknown, "yes", no_yes, pc1);

                double pc2x = getConditionalProbability(unknown, "no", no_no, pc2);

                if(pc1x > pc2x){

                                System.out.println("The given sample belong to class yes");

                }

                else{

                                System.out.println("The given sample belong to class no");

                }      

    }