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 Introduction

Here, Optical Recognition of Handwritten Digits are performed. This activity is performed using nearest neighbor algorithm and this entire algorithm and codes are implemented in Java Platform. Apart from this. Two data sets have also bee arranged for this activity. One of those two data sets is considered as the test data set whereas the other one is considered as the train data set. The model is developed for machine learning and predictive analysis.  The model is developed and trained with the train data set and then it is tested with the test data set. The codes that have been used to implement this entire project are written below.

The code

package neural;

import utils.MathUtils;

import java.util.ArrayList;

public class Nodes {

    private static final int BIAS = 1;

    private static final double LEARNING_RATIO = 0.1;

   private ArrayList input;

    private ArrayList weight;

    private double bias_weight;

    private double output;

  public Node() {

        this.inputs = new ArrayList<>();

        this.weights = new ArrayList<>();

        this.bias_weight = Math.random();

    }

 public void setInput(ArrayList inputs) {

        if (this.inputs.size() == 0) {

            this.inputs = new ArrayList<>(inputs);

            generate_Weight();

        }

             this.inputs = new ArrayList<>(inputs);

    }

  private void generateWeight() {

        for (int i = 0; i < inputs.size(); i++) {

            weight.add(Math.random());

        }

    }

public void calculateOutput() {

        double sum = 0;

        for (int i = 0; i < inputs.size(); i++) {

         sum += inputs.get(i) * weight.get(i);

        }

        sum += BIAS * bias_Weights

       output = MathUtils.sigmoidValue(sum);

      }

      public void adjustWeights(double delta) {

        for (int i = 0; i < inputs.size(); i++) {

            double d = weights.get(i);

            d += LEARNING_RATIO * delta * inputs.get(i);

            weights.set(i, d);

        }

   biasWeight += LEARNING_RATIO * delta * BIAS;

    }

  public double getOutput() {

        calculateOutput();

        return output;

    }

 }

package nodes;

import java.util.ArrayList;

public class Network {

private ArrayList nodes;

public Network() {

        nodes = new ArrayList<>();

    }

       public void addNodes(int count) {

        for (int i = 0; i < count; i++)

            nodes.add(new Node());

    }

       public void setInput(ArrayList inputs) {

        for (Node n : nodes)

            n.setInputs(inputs);

    }

        public ArrayList getOutput() {

        ArrayList outputs = new ArrayList<>();

        for (Node n : nodes)

            outputs.add(n.getOutput());

             return outputs;

    }

           public void adjust(ArrayList goodOutput) {

        for (int i = 0; i < node.size(); i++) {

            double delta = goodOutput.get(i) - node.get(i).getOutput();

            node.get(i).adjustWeights(delta);

        }

    }

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