import java.util.*;
import java.io.*;

/**
 * The model for the Markov text generation assignment. See methods for details.
 * This model should use a brute-force algorithm for generating text, i.e., the entire
 * training text is rescanned each time a new character is generated.
 * 
 * @author ola
 * @author forbes
 * 
 */
public class MarkovModel extends AbstractModel {
	protected String myString;
	protected Random myRandom;
	public static final int DEFAULT_COUNT = 100; // default # random letters generated
    private static final int RANDOM_SEED = 1234; 
												

	public MarkovModel() {

		myRandom = new Random(RANDOM_SEED);
	}

	/**
	 * Create a new training text for this model based on the information read
	 * from the scanner.
	 * 
	 * @param s
	 *            is the source of information
	 */
	public void initialize(Scanner s) {
		double start = System.currentTimeMillis();
		int count = readChars(s);
		double end = System.currentTimeMillis();
		double time = (end - start) / 1000.0;
		super.messageViews("#read: " + count + " chars in: " + time + " secs");
	}

	/**
	 * Read characters from entire file into myString
	 * @param s non-null Scanner at the beginning of a file
	 * @return number of characters read
	 */
	protected int readChars(Scanner s) {
		myString = s.useDelimiter("\\Z").next();
		s.close();
		return myString.length();
	}

	/**
	 * Generate N letters using an order-K markov process where the parameter is
	 * a String containing K and N separated by whitespace with K first. If N is
	 * missing it defaults to some value.
	 */
	public void process(Object o) {
		String temp = (String) o;
		String[] nums = temp.split("\\s+");
		int k = Integer.parseInt(nums[0]);
		int numLetters = DEFAULT_COUNT;
		if (nums.length > 1) {
			numLetters = Integer.parseInt(nums[1]);
		}

		double stime = System.currentTimeMillis();
		String text = makeNGram(k, numLetters);
		double etime = System.currentTimeMillis();
		double time = (etime - stime) / 1000.0;
		this.messageViews("time to generate: " + time);
		this.notifyViews(text);

	}

	/**
	 * Generates random text that is similar to the reference text (myString).
	 * 
	 * @param k order of n-gram       
	 * @requires k > 0
	 * @param numLetters number of characters to generate      
	 * @return numLetters of randomly selected characters based on picking
	 *         representative characters that follow each k characters
	 */
	protected String makeNGram(int k, int numLetters) {
		// TODO complete makeNGram
		// Appending to StringBuilder is faster than appending to String
		StringBuilder build = new StringBuilder();

		return build.toString();
	}

}