设计模式《JAVA与模式》之解释器模式
在阎宏博士的《JAVA与模式》一书中开头是这样描述解释器（Interpreter）模式的：
解释器模式是类的行为模式。给定一个语言之后，解释器模式可以定义出其文法的一种表示，并同时提供一个解释器。客户端可以使用这个解释器来解释这个语言中的句子。
解释器模式的结构
下面就以一个示意性的系统为例，讨论解释器模式的结构。系统的结构图如下所示：
模式所涉及的角色如下所示：
（1）抽象表达式(Expression)角色：声明一个所有的具体表达式角色都需要实现的抽象接口。这个接口主要是一个interpret()方法，称做解释操作。
（2）终结符表达式(Terminal Expression)角色：实现了抽象表达式角色所要求的接口，主要是一个interpret()方法；文法中的每一个终结符都有一个具体终结表达式与之相对应。比如有一个简单的公式R=R1+R2，在里面R1和R2就是终结符，对应的解析R1和R2的解释器就是终结符表达式。
（3）非终结符表达式(Nonterminal Expression)角色：文法中的每一条规则都需要一个具体的非终结符表达式，非终结符表达式一般是文法中的运算符或者其他关键字，比如公式R=R1+R2中，“+"就是非终结符，解析“+”的解释器就是一个非终结符表达式。
（4）环境(Context)角色：这个角色的任务一般是用来存放文法中各个终结符所对应的具体值，比如R=R1+R2，我们给R1赋值100，给R2赋值200。这些信息需要存放到环境角色中，很多情况下我们使用Map来充当环境角色就足够了。
为了说明解释器模式的实现办法，这里给出一个最简单的文法和对应的解释器模式的实现，这就是模拟Java语言中对布尔表达式进行操作和求值。
在这个语言中终结符是布尔变量，也就是常量true和false。非终结符表达式包含运算符and，or和not等布尔表达式。这个简单的文法如下：
Expression ::= Constant | Variable | Or | And | Not
And ::= Expression 'AND' Expression
Or ::= Expression 'OR' Expression
Not ::= 'NOT' Expression
Variable ::= 任何标识符
Constant ::= 'true' | 'false'
解释器模式的结构图如下所示：
源代码
抽象表达式角色
public abstract class Expression {
/**
* 以环境为准，本方法解释给定的任何一个表达式
*/
public abstract boolean interpret(Context ctx);
/**
* 检验两个表达式在结构上是否相同
*/
public abstract boolean equals(Object obj);
/**
* 返回表达式的hash code
*/
public abstract int hashCode();
/**
* 将表达式转换成字符串
*/
public abstract String toString();
}
一个Constant对象代表一个布尔常量
public class Constant extends Expression{
private boolean value;
public Constant(boolean value){
this.value = value;
}
@Override
public boolean equals(Object obj) {
if(obj != null && obj instanceof Constant){
return this.value == ((Constant)obj).value;
}
return false;
}
@Override
public int hashCode() {
return this.toString().hashCode();
}
@Override
public boolean interpret(Context ctx) {
return value;
}
@Override
public String toString() {
return new Boolean(value).toString();
}
}
一个Variable对象代表一个有名变量
public class Variable extends Expression {
private String name;
public Variable(String name){
this.name = name;
}
@Override
public boolean equals(Object obj) {
if(obj != null && obj instanceof Variable)
{
return this.name.equals(
((Variable)obj).name);
}
return false;
}
@Override
public int hashCode() {
return this.toString().hashCode();
}
@Override
public String toString() {
return name;
}
@Override
public boolean interpret(Context ctx) {
return ctx.lookup(this);
}
}
代表逻辑“与”操作的And类，表示由两个布尔表达式通过逻辑“与”操作给出一个新的布尔表达式的操作
public class And extends Expression {
private Expression left,right;
public And(Expression left , Expression right){
this.left = left;
this.right = right;
}
@Override
public boolean equals(Object obj) {
if(obj != null && obj instanceof And)
{
return left.equals(((And)obj).left) &&
right.equals(((And)obj).right);
}
return false;
}
@Override
public int hashCode() {
return this.toString().hashCode();
}
@Override
public boolean interpret(Context ctx) {
return left.interpret(ctx) && right.interpret(ctx);
}
@Override
public String toString() {
return "(" + left.toString() + " AND " + right.toString() + ")";
}
}
代表逻辑“或”操作的Or类，代表由两个布尔表达式通过逻辑“或”操作给出一个新的布尔表达式的操作
public class Or extends Expression {
private Expression left,right;
public Or(Expression left , Expression right){
this.left = left;
this.right = right;
}
@Override
public boolean equals(Object obj) {
if(obj != null && obj instanceof Or)
{
return this.left.equals(((Or)obj).left) && this.right.equals(((Or)obj).right);
}
return false;
}
@Override
public int hashCode() {
return this.toString().hashCode();
}
@Override
public boolean interpret(Context ctx) {
return left.interpret(ctx) || right.interpret(ctx);
}
@Override
public String toString() {
return "(" + left.toString() + " OR " + right.toString() + ")";
}
}
代表逻辑“非”操作的Not类，代表由一个布尔表达式通过逻辑“非”操作给出一个新的布尔表达式的操作
public class Not extends Expression {
private Expression exp;
public Not(Expression exp){
this.exp = exp;
}
@Override
public boolean equals(Object obj) {
if(obj != null && obj instanceof Not)
{
return exp.equals(
((Not)obj).exp);
}
return false;
}
@Override
public int hashCode() {
return this.toString().hashCode();
}
@Override
public boolean interpret(Context ctx) {
return !exp.interpret(ctx);
}
@Override
public String toString() {
return "(Not " + exp.toString() + ")";
}
}
环境(Context)类定义出从变量到布尔值的一个映射
public class Context {
private Map<Variable,Boolean> map = new HashMap<Variable,Boolean>();
public void assign(Variable var , boolean value){
map.put(var, new Boolean(value));
}
public boolean lookup(Variable var) throws IllegalArgumentException{
Boolean value = map.get(var);
if(value == null){
throw new IllegalArgumentException();
}
return value.booleanValue();
}
}
客户端类
public class Client {
public static void main(String[] args) {
Context ctx = new Context();
Variable x = new Variable("x");
Variable y = new Variable("y");
Constant c = new Constant(true);
ctx.assign(x, false);
ctx.assign(y, true);
Expression exp = new Or(new And(c,x) , new And(y,new Not(x)));
System.out.println("x=" + x.interpret(ctx));
System.out.println("y=" + y.interpret(ctx));
System.out.println(exp.toString() + "=" + exp.interpret(ctx));
}
}
运行结果如下：aaarticlea/png;base64,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" 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