If you're in the software business you've probably encountered the dreaded 'off-by-one' error.
An off-by-one error often occurs when an iterative loop iterates one time too many or too few. Someone might use "is less than or equal to" where "is less than" should have been used.
These problems aren't unique to programming.
At the time of the last millennium, there was a lot of discussion as to whether it should have been celebrated on 1st January 2000 or 2001.
In debates about the millennium off-by-one errors may not be too serious. In software they can be fatal.
Here are three techniques which help. I'll save the best till last.
If you've got a bit of code that is looping or indexing, do some manual tests to check it's doing the right thing. If you're using a language with a REPL, like Python, Ruby, Clojure or APL, you can do the test in no time at all.
Test the boundary conditions. What happens if the loop shouldn't be traversed at all, or if the array you're iterating through has no elements?
Even better, use an automated testing framework like junit, pytest or its equivalent for your language.
Make sure you write tests that check normal operation and operation at the boundary.
If you can, use a language construct or language that eliminates the need to loop.
In Java, Python or Ruby you can use a for-loop to operate on each element of an array. In Python or Ruby you can use generators to lazily collect the results of operating on the elements.
Many other languages now have similar features. And of course, in APL it's very rare to need explicit loops.
Want to add together two APL arrays?
and there's not a loop in sight!
If the idea of writing loop-free code appeals, take a look at my introduction to APL. It's incomplete, but free for the next few days, and if you 'buy' it you'll get free updates as further sections of the book appear.
APL is one of the most productive programming languages ever invented, and it's available free on all models of the Raspberry Pi.
I've just launched an early access version of an Introductory eBook, and it's free for the next seven days.
The fast-paced introductory text will teach you the core of the language in a few short, fun sessions.
Once you've finished the book you'll get links to free resources you can use to master the rest of APL's amazing capabilities.
The book is only 30% complete at present, but if you 'buy' the free version now you'll pay nothing for the book, and you'll get free updates as soon as they are published.
I'll start to increase the price next Sunday, and it will continue to go up as the book nears completion.
So don't wait - click here to get it now!
Morten Kromberg of Dyalog APL has just published a video of his keynote from the PLDI 2016 ARRAY Workshop.
It's titled Notation for Parallel Thoughts and it describes some exciting innovations in the field of programming for parallel processing.
A few weeks ago I mentioned the Dyalog APL annual problem-solving competition.
I've been researching previous contests, and wanted to share my findings.
It's worth entering, even if you don't know APL.
Many of the previous winners learned APL just for the competition. They spent a few days learning the language, and a few more working on the contest problems. Some won top prizes (worth $2000 this year).
It's worth re-entering, even if you entered last year.
Many of the winners had applied before. Some even won prizes in successive years.
It's worth entering wherever you live.
Winners have come from all over the world.
It's worth entering even if you don't win.
APL is concise and expressive, so you can try out new ideas very quickly.
APL is focused on arrays, so it will change the way you think about programming.
APL is challenging and fun, so it will stretch your mind.
The APL community is full of bright and helpful people, so you will expand your network of contacts.
The demand for APL developers exceeds the supply, so knowing APL can help you find a job.
You can find out more about the competition on the Dyalog website.
Help me with a book title and win a Raspberry Pi model 3!
I need a snappy title for a book.
The book is an introduction to the Dyalog implementation of theAPL programming language.
The book is aimed primarily at people learning it on the Raspberry Pi.
APL runs on all models of the Pi, including the £4/$5 Pi zero shown on the right.
You can download a copy of Dyalog APL for the Pi here.
If you submit a title as a comment, if you are the first to submit it, and if I use it, I will send you a Raspberry Pi model 3 complete with a power supply and an SD card.
No royalties, though, and you will need to find a monitor, keyboard and mouse.
If you have a Pi already I will send one to the beneficiary of your choice.
The book is not yet complete but it should be available in early access format on Leanpub in a few days time.
Have a go - post your title below.
I've just launched ANNSER on GitHub.
ANNSER stands for A Neural Network Simulator for Education and Research.
It is licensed under the MIT license, so it can be used for both Open Source and Commercial projects.
ANNSER is just a GitHub skeleton at the moment. I have some unreleased code which I will be committing over the next few days.
I'm hoping that ANNSER will eventually offer the same set of features as established ANN libraries like TensorFlow, Caffe and Torch, and I would like to see a GUI interface to the ANNSER DSL.
There's a huge amount of work to complete the project but we should have a useful Iteration 1 within a few weeks.
I have several reasons for choosing APL as the main implementation language.
It's my favourite language. I love Python, and I've used it since the last millennium, but I find APL more expressive, performant and productive.
With APL you can run serious networks on the $5 Raspberry Pi zero. This makes it very attractive for educational users.
APL was created as a language for exposition.
APL is unrivalled in its handling of arrays, and ANN algorithms are naturally expressed as operations on arrays.
Consider the following code fragments. Each creates a random input vector, creates a matrix of random weights and then calculates the output of a sigmoid neuron.
If you don't work in Software you may never have heard of Kent Beck but he's had a huge influence on the way we test and write code.
Yesterday Kent posted a fascinating list on Facebook. He shared some of the key ideas that guide his thinking.
The post is interesting, and stimulating, but it's a wall of text. I love reading, but I also like to think visually, so I started to mind map what he wrote.
The map source (made with Freeplane) and images are now on GitHub.
Seems like this could turn into a workshop pretty easily. Spend three days mapping your current ideas, figuring out the holes you want to fill, what you want to eliminate.
Three days sounds a lot, but maybe we could do a shorter version via a google hangout. Anyone interested? If so, please comment.