digorithm

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package controller;

import dao.PersonDAO;
import java.io.IOException;
import java.io.PrintWriter;

digorithm

package dao;

import java.sql.Connection;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.sql.Statement;
import java.util.ArrayList;

import model.Person;

digorithm

package dao;

import java.sql.Statement;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;

public class ConnectionClass {

digorithm

package model;

public class Person {

    public Person() {

    }

    public String name;
    public String phone;

digorithm

"""
Programa desenvolvido por Rodrigo Araujo.
Materia: Compiladores
Orientador: jose couto

programa: reconhecedor de estrutura de repeticao FOR do java

"""

import re

digorithm

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cross_validation import cross_val_score
from sklearn.linear_model import LogisticRegression

"""
Here we solve the problem of predicting if a wine is white or red
"""
reds = pd.read_csv('winequality-red.csv', sep=';')

digorithm

import javax.swing.JFrame;

import org.math.plot.*;

public class TestGradDescent {
	public static void main(String[] args ){
		InitialData id = new InitialData();
		id.plotData();
		GradientDescent gd = new GradientDescent(id);
		gd.execute();

digorithm

public void printConvergence(){

      double[] theta0plot2 = new double[iters];
      double[] theta1plot2 = new double[iters];
      double[] tplot2 = new double[iters];
      System.arraycopy(theta0plot, 0, theta0plot2, 0, iters);
      System.arraycopy(theta1plot, 0, theta1plot2, 0, iters);
      System.arraycopy(tplot, 0, tplot2, 0, iters);
 		
      // Plot the convergence of data

digorithm

public void addTrendLine(Plot2DPanel plot, boolean removePrev){
    	if (removePrev){
    		plot.removePlot(trendline);
    	}
    	double[] yEnd = new double[initial_data.x.length];
    	for (int i=0; i<initial_data.x.length; i++)
    		yEnd[i] = hypothesisFunction(initial_data.x[i]);
    	trendline = plot.addLinePlot("final", initial_data.x, yEnd);
    }

digorithm

public void execute(){
    	do {

    		this.theta1 -= alpha * deriveTheta1();
    		this.theta0 -= alpha * deriveTheta0();

    		//used for plotting
    		tplot[iters] = iters;
    		theta0plot[iters] = theta0;
    		theta1plot[iters] = theta1;