Sorry I know the question isnt as specific as it could be. I am currently working on a replenishment forecasting system for a clothing company (dont ask why it's in VBA). The module I am currently working on is distribution forecasts down to a size level. The idea is that the planners can forecast the number to sell, then can specify a ratio between the sizes.
In order to make the interface a bit nicer I was going to give them 4 options; Assess trend, manual entry, Poisson and Normal. The last two is where I am having an issue. Given a mean and SD I'd like to drop in a ratio (preferably as %s) between the different sizes. The number of the sizes can vary from 1 to ~30 so its going to need to be a calculation.
If anyone could point me towards a method I'd be etenaly greatfull - likewise if you have suggestions for a better method.
Cheers
For the sake of anyone searching this, whilst only a temporary solution I used probability mass functions to get ratios this allowed the user to modify the mean and SD and thus skew the curve as they wished. I could then use the ratios for my calculations. Poisson also worked with this method but turned out to be a slightly stupid idea in terms of choice.
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How to stabilize the RSSI (Received Signal Strength Indicator) of low energy Bluetooth beacons (BLE) for more accurate distance calculation?
We are trying to develop an indoor navigation system and came across this problem where the RSSI is fluctuating so much that, the distance estimation is nowhere near the correct value. We tried using an advance average calculator but to no use,
The device is constantly getting RSSI values, how to filter them, how to get the mean value, I am completely lost, please help.
Can anyone suggest any npm library or point in the right direction, I have been searching for many days but have not gotten anywhere.
FRONT END: ReactNative BACKEND: NODEJS
In addition to the answer of #davidgyoung, we would like to point out that any filtering method is a compromise between quality of noise level reduction and the time-lag introduced by this filtration (depending on the characteristic filtering time you use in your method). As was pointed by #davidgyoung, if you take characteristic filtering period T you will get an average time-lag of about T/2.
Thus, I think the best approach to solve your problem is not to try to find the best filtering method but to make changes on the transmitter’s end itself.
First you can increase the number of signals, transmitter per second (most of the modern beacon allow to do so by using manufacturer applications and API).
Secondly, you can increase beacon's power (which is also usually one of the beacon’s settings), which usually reduces signal-to-noise ratio.
Finally, you can compare beacons from different vendors. At Navigine company we experimented and tested lots of different beacons from multiple manufacturers, and it appears that signal-to-noise ratio can significantly vary among existing manufacturers. From our side, we recommend taking a look at kontakt.io beacons (https://kontakt.io/) as an one of the recognized leaders with 5+ years experience in the area.
It is unlikely that you will find a pre-built package that will do what you want as your needs are pretty specific. You will most likely have to wtite your own filtering code.
A key challenge is to decide the parameters of your filtering, as an indoor nav use case often is impacted by time lag. If you average RSSI over 30 seconds, for example, the output of your filter will effectively give you the RSSI of where a moving object was on average 15 seconds ago. This may be inappropriate for your use case if dealing with moving objects. Reducing the averaging interval to 5 seconds might help, but still introduces time lag while reducing smoothing of noise. A filter called an Auto-Regressive Moving Average Filter might be a good choice, but I only have an implementation in Java so you would need to translate to JavaScript.
Finally, do not expect a filter to solve all your problems. Even if you smooth out the noise on the RSSI you may find that the distance estimates are not accurate enough for your use case. Make sure you understand the limits of what is possible with this technology. I wrote a deep dive on this topic here.
So, I've done a fair bit of reading into generation of a Sudoku puzzle. From what I can tell, the standard way to have a Sudoku puzzle of a desired difficulty is to generate a puzzle, and then grade it afterwards, and repeat until you have one of an acceptable rating. This can be refined by generating via backtracing using some of the more complex solving patterns (XY-wing, swordfish, etc.), but that's not quite what I'm wanting to do here.
What I want to do, but have been unable to find any real resource on, is generate a puzzle from a "difficulty value" (0-1.0 value, 0 being the easiest, and 1.0 being the hardest).
For example, I want create a moderately difficult puzzle, so the value .675 is selected. Now using that value I want to be able to generate a moderately difficult puzzle.
Anyone know of something like this? Or perhaps something with a similar methodology?
Adding another answer for generating a sudoku of desired difficulty on-the-fly.
This means that unlike other approaches the algorithm runs only once and returns a sudoku configuration matching the desired difficulty (with high probability within a range or with probability=1)
Various solutions for generating (and rating) a sudoku difficulty have to do with human-based techniques and approaches, which can be easily rated.
Then one (after having generated a sudoku configuration) re-solves the sudoku with the human-like solver and depending on the techniques the solver used (e.g pairs, x-wing, swordfish etc.) a difficulty rate is also assigned.
Problems with this approach
(and requirements for the use case i had)
In order to generate a sudoku with given difficulty, with previous method one needs to solve a sudoku twice (once with the basic algorithm and once with the human-like solver).
One has to (pre-)generate many sudokus which can only be rated as to difficulty after being solved by the human-like solver. So one cannot generate a desired sudoku on-the-fly once.
The human-like solver can be complicated and in most cases (if not all) is tightly coupled to 9x9 sudoku grids. So no easy generalisation to other sudokus (e.g 4x4, 16x16, 6x6 etc.)
The difficulty rating of the human-like techniques is very subjective. For example why x-wing is taken to be more difficult than hidden singles? (personaly have solved many difficult published sudoku puzzles manualy and never used such techniques)
Another approach was used which has the following benefits:
Generalises well to arbitrary sudokus (9x9, 4x4, 6x6, 16x16 etc..)
The sudoku configuration, with desired difficulty, is generated once and on-the-fly
The difficulty rating is objective.
How it works?
First of all, the simple fact that the more difficult the puzzle, the more time it needs to be solved.
But time to be solved is intimately correlated to both number of clues (givens) and average alternatives to be investigated per empty cell.
Extending my previous answer, it was mentioned that for any sudoku puzzle the minimum number of clues is an objective property of the puzzle (for example for 9x9 grids the minimum number of clues for having a valid sudoku is 17)
One can start from there and compute minimum number of clues per difficulty level (linear correlation).
Furthermore at each step of the sudoku generation process, one can make sure the average alternatives (to be investigated) per empty cell is within given bounds (as a function of desired difficulty)
Depending on whether the algorithm uses backtrack or not (for the use case discussed the algorithm does no backtracking) the desired difficulty can be reached either with probability=1 or with high probability within bounds (respectively).
Tests of the sudokus generated with this algorithm and difficulty rating based on the previous approaches (human-like solver), show a correlation of desired and estimated difficulty rates, plus a greater ability for generalisation to arbitrary sudoku configurations.
(have used this online sudoku solver (and also this one) to correlate the difficulty rates of the test sudokus)
The code is available free on github sudoku.js (along with sample demo application), a scaled-down version of CrossWord.js a professional crossword builder in JavaScript, by same author
The sudoku difficulty is related in an interesting way to the (minimum) amount of information needed to specify a unique solution for a given grid.
Sounds like information theory, yes it has applications here too.
Sudoku puzzles should have a unique solution. Furthermore sudoku puzzles have certain symmetries, i.e by row, by column and by sub-square.
These symmetries specify the minimum number of clues (and their position more or less) needed so that the solution would be unique (i.e using a sudoku compiler or an algorithm like backtrack-search).
This would be the most difficult/hard sudoku puzzle level (i.e minimum needed number of clues). Then all other difficulty levels from less hard to easy are generated by allowing more clues than the minimum amount needed.
It should be noted that sudoku difficulty levels are not standard, as explained above, one can have as many or as few difficulty levels as one wants. What is standard is the minimum number (and position) of clues (which is the hardest level and which is relatd to the sudoku symmetries), then one can generate as many difficulty levels as one wants simply by allowing extra/redundant clues to be visible as well.
It's not as elegant as what you ask, but you can simulate this behavior with caching:
Decide how many "buckets" you want for puzzles. For example, let's say you choose 20. Thus, your buckets will contain puzzles of different difficulty ranges: 0-.05, .05-.1, .1-.15, .. , .9-.95, .95-1
Generate a puzzle
Grade the puzzle
Put it in the appropriate bucket (or throw it away when the bucket is full)
Repeat till your buckets are "filled". The size of the buckets and where they are stored will be based on the needs of your application.
Then when a user requests a certain difficulty puzzle, give them a cached one from the bucket they choose. You might also want to consider swapping numbers and changing orientation of puzzles with known difficulties to generate similar puzzles with the same level of difficulty. Then repeat the above as needed when you need to refill your buckets with new puzzles.
Well, you can't know how complicated it is, before you know how to solve it. And Sudoku solving (and therefore also the difficulty rating) belongs to the NP-C complexity class, that means it's (most likely) logically impossible to find an algorithm that is (asymptotically) faster than the proposed randomly-guess-and-check.
However, if you can find one, you have solved the P versus NP problem and should clear a cupboard for the Fields Medal... :)
I have an OS X app which uses the NSSpeechSynthesizer class to read some text. I need to be able to set the speed of the reading, which is done using the rate property of NSSpeechSynthesizer. However, different voices have different baseline reading rates and different ranges as well. The problem is that I cannot find a way to get this range from the voice so that I know what values are acceptable for the voice. This is important since I want to be able to abstract from the difference in ranges and just let the user choose something like slow/normal/fast. But for a voice with a baseline of 180 words for a minute a value of 100 would be considered slow, which clearly is not the case for a voice with a baseline of 100 words per minute.
Is there a way of getting those ranges? I didn't find anything useful in the class reference for NSSpeechSynthesizer.
You can get the rate of a voice before you change it, and you could estimate that the acceptable range is the default rate ± 50 WPM or something.
That said, if you're using this to set up a slider, I'd caution you against limiting the user to only “natural”-sounding ranges. Some people, including many blind people, deliberately crank it up as fast as they can stand in order to save listening time.
Well, I know this isn't the answer you may have been looking for, but as far as I can tell, Apple hasn't made available to the public a way to change the baseline speed of an instance of NSSpeechSynthesizer object (which, as you said, has a 'rate' instance, but it cannot make constant rates for each distinct voice). For this, and I know you won't like it, you'll have to make the slower ones faster and the faster ones slower until they about match up. Sorry again, but as far as I know, Apple doesn't offer a way to change the baseline rate. Happy coding!
~Monkeyanator
This may be a simple question for those know-how guys. But I cannot figure it out by myself.
Suppose there are a large number of objects that I need to select some from. Each object has two known variables: cost and benefit. I have a budget, say $1000. How could I find out which objects I should buy to maximize the total benefit within the given budget? I want a numeric optimization solution. Thanks!
Your problem is called the "knapsack problem". You can read more on the wikipedia page. Translating the nomenclature from your original question into that of the wikipedia article, your problem's "cost" is the knapsack problem's "weight". Your problem's "benefit" is the knapsack problem's "value".
Finding an exact solution is an NP-complete problem, so be prepared for slow results if you have a lot of objects to choose from!
You might also look into Linear Programming. From MathWorld:
Simplistically, linear programming is
the optimization of an outcome based
on some set of constraints using a
linear mathematical model.
Yes, as stated before, this is the knapsack problem and I would choose to use linear programming.
The key to this problem is storing data so that you do not need to recompute things more than once (if enough memory is available). There are two general ways to go about linear programming: top-down, and bottom - up. This one is a bottom up problem.
(in general) Find base case values, what is the most optimal object to select for a small case. Then build on this. If we allow ourselves to spend more money what is the best combination of objects for that small increment in money. Possibilities could be taking more of what you previously had, taking one new object and replacing the old one, taking another small object that will still keep you under your budget etc.
Like I said, the main idea is to not recompute values. If you follow this pattern, you will get to a high number and find that in order to buy X amount of dollars worth of goods, the best solution is combining what you had for two smaller cases.
Tried doing a bit of research on the following with no luck. Thought I'd ask here in case someone has come across it before.
I help a volunteer-run radio station with their technology needs. One of the main things that have come up is they would like to schedule their advertising programmatically.
There are a lot of neat and complex rule engines out there for advertising, but all we need is something pretty simple (along with any experience that's worth thinking about).
I would like to write something in SQL if possible to deal with these entities. Ideally if someone has written something like this for other advertising mediums (web, etc.,) it would be really helpful.
Entities:
Ads (consisting of a category, # of plays per day, start date, end date or permanent play)
Ad Category (Restaurant, Health, Food store, etc.)
To over-simplify the problem, this will be a elegant sql statement. Getting there... :)
I would like to be able to generate a playlist per day using the above two entities where:
No two ads in the same category are played within x number of ads of each other.
(nice to have) high promotion ads can be pushed
At this time, there are no "ad slots" to fill. There is no "time of day" considerations.
We queue up the ads for the day and go through them between songs/shows, etc. We know how many per hour we have to fill, etc.
Any thoughts/ideas/links/examples? I'm going to keep on looking and hopefully come across something instead of learning it the long way.
Very interesting question, SMO. Right now it looks like a constraint programming problem because you aren't looking for an optimal solution, just one that satisfies all the constraints you have specified. In response to those who wanted to close the question, I'd say they need to check out constraint programming a bit. It's far closer to stackoverflow that any operations research sites.
Look into constraint programming and scheduling - I'll bet you'll find an analogous problem toot sweet !
Keep us posted on your progress, please.
Ignoring the T-SQL request for the moment since that's unlikely to be the best language to write this in ...
One of my favorites approaches to tough 'layout' problems like this is Simulated Annealing. It's a good approach because you don't need to think HOW to solve the actual problem: all you define is a measure of how good the current layout is (a score if you will) and then you allow random changes that either increase or decrease that score. Over many iterations you gradually reduce the probability of moving to a worse score. This 'simulated annealing' approach reduces the probability of getting stuck in a local minimum.
So in your case the scoring function for a given layout might be based on the distance to the next advert in the same category and the distance to another advert of the same series. If you later have time of day considerations you can easily add them to the score function.
Initially you allocate the adverts sequentially, evenly or randomly within their time window (doesn't really matter which). Now you pick two slots and consider what happens to the score when you switch the contents of those two slots. If either advert moves out of its allowed range you can reject the change immediately. If both are still in range, does it move you to a better overall score? Initially you take changes randomly even if they make it worse but over time you reduce the probability of that happening so that by the end you are moving monotonically towards a better score.
Easy to implement, easy to add new 'rules' that affect score, can easily adjust run-time to accept a 'good enough' answer, ...
Another approach would be to use a genetic algorithm, see this similar question: Best Fit Scheduling Algorithm this is likely harder to program but will probably converge more quickly on a good answer.