Java Puzzler, double chars

A teaser which uses char and double

The following compiles and produces an output. Can you explain why? 

char ch = 'A';
ch *= 1.5;
System.out.println(ch); // prints 'a'

Here is a hint for a simpler example. 

byte b = 100;
b /= 2.5;
System.out.println(b); // prints 40

Comments

  1. Aleksandr3 January 2012 at 11:34

    Well... Duh!
    'A' is 65.
    'a' is 97

    65 * 1.5 = 97.5

    And since char is an integer...

    ReplyDelete
  2. Vladimir Dolzhenko3 January 2012 at 11:48

    @Aleksandr

    char isn't an integer - it's unsigned integer

    ReplyDelete
  3. Peter Lawrey3 January 2012 at 11:52

    Or being pedantic, char is an unsigned short. ;)

    ReplyDelete
  4. Vladimir Dolzhenko3 January 2012 at 12:49

    That's another java puzzler.

    JLS the 3rd edition:
    The Unicode standard was originally designed as a fixed-width 16-bit character encoding. It has since been changed to allow for characters whose representation requires more than 16 bits. The range of legal code points is now U+0000 to U+10FFFF, using the hexadecimal U+n notation.

    So, it is not a 32-bit integer, in the same time it definitely more than short.

    ReplyDelete
  5. Peter Lawrey3 January 2012 at 13:12

    @Vladimir The String and Character classes now support "code points" which assume a String is UTF-16 encoded for characters. http://en.wikipedia.org/wiki/UTF-16

    In the case of Character there are pairs of methods.

    public static boolean isLowerCase(char ch)

    public static boolean isLowerCase(int codePoint)

    http://docs.oracle.com/javase/7/docs/api/java/lang/Character.html


    Character.MAX_VALUE is still 65535, and char is still 16-bit, but code points use int values though as you point out, not the full range of 32-bit values. ;)

    ReplyDelete

Post a Comment

Popular posts from this blog

Why You Should Tune Code Before Your Garbage Collector

Image
Optimising your memory allocations in Java could make far more difference than your choice of Garbage Collector and may even change which is the best garbage collector. In this post I look at a simple event to response latency benchmark, MarketDataSnapshot to NewOrderSingle at 50K/s for 30 minutes using JLBH to test Chronicle-FIX. The goal is to compare a system which is doing redundant work (in this case logging each message using SLF4J), compared with not logging (Chronicle-FIX records every message internally using Chronicle Queue) and how this changes the choice of Garbage Collector For the p99 (worst 1 in 100) the choice of Garbage Collector makes a different on par with optimising how loggin is done However, for the p99.99 (worst 1 in 10,000) optimsing how the logging is done is orders of magnitude more signifciant than the choice of Garbage Collector Unoptimised Benchmark This takes the optimised benchmark and adds one SLF4J log line of just ...
Read more

Asking multiple AI to optimise the same code

As different AIs are implemented differently, they don't all provide the same answer, nor do they consistently outperform one another. The best approach is to use multiple AI and pick the one you like best. My goal here is not to declare a winner based on one example, but instead to show the variety of answers you can get with different AI. I asked each AI to Suggest how to implement this more optimally private static String formatOffset(int millis) { String sign = millis < 0 ? "-" : "+"; int saveSecs = Math.abs(millis) / 1000; int hours = saveSecs / 3600; int mins = ((saveSecs / 60) % 60); int secs = (saveSecs % 60); if (secs == 0) { if (mins == 0) { return sign + twoDigitString(hours); } return sign + twoDigitString(hours) + twoDigitString(mins); } return sign + twoDigitString(hours) + twoDigitString(mins) + twoDigitString(secs); } private static String twoDigitString(int value) { ...
Read more

Which Doc Format is Best for AI Specifications?

I have a Spec Driven Project with 674 documents incl 181 specs, 429 tasks, and 40 project docs. About 1/3 of these are an acceptance sub-project to show the root project meets fit-for-purpose requirements. I used Fable to convert these to and from AsciiDoc, Markdown, and HTML. From this, I tried to draw some conclusions about which format is better than the others for AI specifications. TL;DR: Markdown for AI working documents, AsciiDoc for curated human-reviewed specs, HTML only as a publishing target. Objective Metrics Corpus Tokens (Approx) Markdown: 594k, AsciiDoc: 598K, HTML: 661k Worst Single-File Ratio AsciiDoc: 1.023x, HTML: 1.26x (due to tables and code blocks) Markup share of file content Markdown 0.9%, AsciiDoc 1.3%, HTML 9.9% - a 10x overhead for HTML Read/write Assessment In terms of readability, both Markdown and AsciiDoc are good. HTML has far more tags, which add noise. One of the most common tags is <code> some code </c...
Read more