$/*$

$$ Do not edit any given code in this file.

$*/$

import java.io$.$File;

import java.io$.$FileWriter;

import java.io$.$IOException;

import java.util.Scanner;

import java.util.ArrayList;

public class Model $\{$

$/**$

$*$ The weight matrix between layer $3$ and the output layer

$*/$

$$ private double$[][]$ weights;

$$

$/**$

$*$ The bias vector of the output layer

$*/$

$$ private double$[]$ bias;

$/**$

$*$ ToDo: Write a no$-$arg constructor with empty body

$*/$

$/**$

$*$ ToDo: Write a constructor that initialize the model to be the DEEP COPY of the given arguments

$*$ @param $1$: a double$[][]$ that keeps the data of weights

$*$ @param $2$: a double$[][]$ that keeps the data of bias

$*/$

$$

$/**$

$*$ ToDo: Write a constructor that initialize the model from a given saved model file

$*$ @param $1$: A String that indicates the path of the saved model file

$*/$

$/**$

$*$ The getter of the weights field.

$*/$

$$ public double$[][]$ getWeights$()\{$

$$ return weights;

$\}$

$/**$

$*$ The getter of the bias field.

$*/$

$$ public double$[]$ getBias$()\{$

$$ return bias;

$\}$

$$

$/**$

$*$ ToDo: Write a method named "save" that saves the current model into a file

$*$ @param $1$: A String that indicates the path to the destination location for saving the file

$*/$

$/**$

$*$ ToDo: A method named "load" that loads the model from a saved model file.

$*$ @param $1$: A String that indicates the path to the destination location for model file to be loaded.

$*/$

$/**$

$*$ A method that trains the model

$*$ @param $1$: An int that indicates the number of possible output labels

$*$ @param $2$: An int that indicates the number of iteration for the training

$*$ @param $3$: A String the indicates the path of the directory that keeps the training data

$*$ @param $4$: A double that indicates the learning rate

$*/$

$$ public void trainModel$($int numLabel, int iter, String path, double learningRate$)$ throws IOException $\{$

$$ Model m $=$ Trainer.train$($numLabel$,$ iter, path, learningRate$)$;

$$ weights $=$ m$.$getWeights$()$;

$$ bias $=$ m$.$getBias$()$;

$\}$

$$

$/**$

$*$ A method that performs classification according to the current model

$*$ @param $1$: A String that indicates the location of the target data that to be classified

$*$ @return the integer that indicates the classification result $($the label$)$

$*/$

$$ public int classification$($String file$)$ throws IOException $\{$

$$ return Trainer.predict$($file$,$ weights, bias$)$;

$\}$

$$

$/**$

$*$ Prints the weight matrix for debugging purpose if needed

$*/$

$$ public void printWeights$()\{$

$$ if $($weights $!=$ null$)$

$$ ExtraMUtil.matPrint$($weights$)$;

$\}$

$$

$/**$

$*$ Prints the bias vector for debugging purpose if needed

$*/$

$$ public void printBias$()\{$

$$ if $($bias $!=$ null$)$

$$ ExtraMUtil.vecPrint$($bias$)$;

$\}$

$\}$