一:运行源代码EnumTest.java,分析运行结果。
代码如下:
public class EnumTest { public static void main(String[] args) {
Size s=Size.SMALL;
Size t=Size.LARGE;
//s和t引用同一个对象?
System.out.println(s==t); //
//是原始数据类型吗?
System.out.println(s.getClass().isPrimitive());
//从字符串中转换
Size u=Size.valueOf("SMALL");
System.out.println(s==u); //true
//列出它的所有值
for(Size value:Size.values()){
System.out.println(value);
}
} }
enum Size{SMALL,MEDIUM,LARGE};
结果截图:
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" alt="" />
枚举类型(Enumerated Type) 很早就出现在编程语言中,它被用来将一组类似的值包含到一种类型当中。而这种枚举类型的名称则会被定义成独一无二的类型描述符,在这一点上和常量的定义相似。不过相比较常量类型,枚举类型可以为申明的变量提供更大的取值范围。
二:原码,反码,补码的区别辨析
原码就是符号位加上真值的绝对值, 即用第一位表示符号, 其余位表示值,
正数的反码是其本身
负数的反码是在其原码的基础上, 符号位不变,其余各个位取反.
补码的表示方法是:
正数的补码就是其本身
负数的补码是在其原码的基础上, 符号位不变, 其余各位取反, 最后+1. (即在反码的基础上+1)
示例程序:
public class qq { /**
* @param args
*/
public static void main(String[] args) {
// TODO Auto-generated method stub System.out.println(0xffffffff);
}
}
得到结果为-1
java中数据使用的是反码表示的。
三:通过原始类型的包装类完成类型转换
源代码:
public class qq { public static void main(String args[]) {
System.out.println("0.05 + 0.01 = " + (0.05 + 0.01));
System.out.println("1.0 - 0.42 = " + (1.0 - 0.42));
System.out.println("4.015 * 100 = " + (4.015 * 100));
System.out.println("123.3 / 100 = " + (123.3 / 100));
}
}
截图:
aaarticlea/png;base64,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" alt="" />
四:Java变量遵循“同名变量的屏蔽原则”
Java变量是有作用域的。如果两个作用域完全不同的变量同名,不会引起冲突。
举一个简单的例子:一班有一个学生叫小明,二班也有一个学生叫小明,在一班和二班分开上课的时候,点名就不会有冲突。
验证代码:
public class qq { /**
* @param args
*/
private static int code=12;
public static void main(String[] args) {
// TODO Auto-generated method stub
int code=-10;
System.out.println(code); } }
结果截图:
aaarticlea/png;base64,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" alt="" />
五:为什么double类型的数值进行运算得不到“数学上精确”的结果?
这个涉及到二进制与十进制的转换问题。
N进制可以理解为:数值×基数的幂,例如我们熟悉的十进制数123.4=1×10²+2×10+3×(10的0次幂)+4×(10的-1次幂);其它进制的也是同理,例如二进制数11.01=1×2+1×(2的0次幂)+0+1×(2的-2次幂)=十进制的3.25。
double类型的数值占用64bit,即64个二进制数,除去最高位表示正负符号的位,在最低位上一定会与实际数据存在误差(除非实际数据恰好是2的n次方)。
六:java中输出格式
源代码如下:
public class qq { /**
* @param args
*/
public static void main(String[] args) {
// TODO Auto-generated method stub
int X=100;
int Y=200;
System.out.println("X+Y="+X+Y);
System.out.println(X+Y+"=X+Y"); } }
结果截图:
aaarticlea/png;base64,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" alt="" />
因为+X+Y表示只是输出X和Y的值,X+Y表示输出X+Y的值。