For example, you could use a dependent t-test to understand whether there was a difference in smokers' daily cigarette consumption before and after a 6 week hypnotherapy programme i.
They're a lot of fun. Since there were only five puzzles in the magazine, I started thinking about how a computer program could generate nurikabe puzzles.
It shouldn't be hard to generate nurikabe "mazes" that satisfy the game constraints. However, like most logic puzzles, it seems like the hard part of generating nurikabe is guaranteeing that the ones you produce are solvable.
For that, the only way I know is to try to solve them, whether manually or by writing an auto-solver. So below is a draft of some rules for an automatic nurikabe solver to follow. This would be a fun program to write! What does this imply for writing an auto-solver? That for sufficiently large puzzles, if we wanted to do an exhaustive search for solutions, it would take much longer than we can afford to wait.
Does this mean that writing a nurikabe solver, and possibly generator, is a lost cause? Theoretically, perhaps, but practically, I don't think so. When I solve a nurikabe puzzle by hand, it doesn't take me exponential time.
I don't go six levels deep into hypotheses in order to find out what square to color black or white next. Usually there are deterministic ways to move forward, i. Of course, this may be because the puzzles I try to solve are those invented and tested by humans to be not-too-hard for humans.
My hope is that using mostly deterministic methods, an auto-solver can either solve the puzzle in a reasonable amount of time, or give up. A non-exhaustive solver won't be able to prove in every case whether there is exactly one solution to a puzzle, but maybe in most cases, or many cases, it can do a good enough job by doing this reduced task: One question that arises about the generate-and-auto-solve approach is, how efficient will this be?
What proportion of well-formed nurikabe puzzles are solvable? Will I have to generatepuzzles to find one good one? I have no idea what the answer is. If the proportion of good ones is small, would generation heuristics help much? One could look at a collection of human-designed nurikabe puzzles and try to come up with heuristics.
Maybe an approach from a different direction would yield better results. Instead of 1 generate a random, well-formed nurikabe puzzle and then 2 check how solvable it is, maybe it would be possible to generate a solvable puzzle from the beginning, something like this: I'm not sure of the details of how this would work.
Nurikabe software There is a free nurikabe-player program for download here slow connection. It has some predefined puzzles and lets you solve them assisted by some convenient "smarts"save progress, and create your own puzzles manually.
There is another freeware nurikabe program for Palm here.
I haven't played it, but it seems to do the same thing. Neither offers an auto-solver or a puzzle generator.
Online nurikabe puzzles can be found here. Nurikabe constraints A nurikabe puzzle must conform to these laws: A The black a. B There can never be a 2x2 square of black cells. C Every region "island" of white "land" cells must contain exactly one number. D Each number must be in a region of white cells whose cell count is equal to the number.
E Diagonal adjacency doesn't count as connectedness. F Two numbers cannot be connected to each other by white cells. That is, they cannot be in the same region; i. Nurikabe-solving rules OK, now some rules for solving nurikabe:What is TIO?
TIO is a family of online interpreters for an evergrowing list of practical and recreational programming languages.
To use TIO, simply click the arrow . Alphabetical List of Power Plant Projects. Information about "e-filing" and "e-commenting" at the California Energy Commission; Fact Sheet - Certifying Thermal Power Plants. Generating test cases from Dashboard recordings The Dashboard in TPT is a freely configurable, graphical user interface that is used to .
A system for quickly generating training data with weak supervision - HazyResearch/snorkel. Introduction Purpose Generating minimal test cases for effective program test is a tool for generating test cases for a ‘c’ program.
The main purpose of this tool is to automatically generate the minimum number of test cases for a given program to perform effective test on it. test cases, which is generally tedious and prone to errors.
In addition, it might not always be immediately clear to the user whether a new test case really improves the coverage.