first post here because I've got a problem thats got me stumped. I am creating a calculation tool for a project at uni, now I'm not in an it degree but for this project I havent had problem too difficult until now.
Basically I am doing design work for a building, where each floor of the building can be allocated one of 9 possible designs. By then allocating solutions to each of these then calculating costs I am trying to find the most efficient combination.
Normally I would just use some nested loops to find the best design, no problems, but for the calculation tool I am required to change the number of floors, and therefore the number of nested loops, which I am unfamiliar with how to do.
The General structure is this
1- X Number of Floors.
9 Possible designs for each floor.
Based on each combination a cost must be calculated, and if it is within the best 5 results it will be stored.
There is a total of 9^x total solutions.
So
Floor = 1
For Solution = 1 to 9
Floor = 2
For Solution = 1 to 9
CalculateCost()
if CalculateCost < Best Then
Write, Floor1 Solution Value, Floor2 Solution Value to Output
Etc...
Now I am using Vb.net, and do not really know how to do recursion. If someone could simply point me in the way of a resource that may help me on this issue I would be very grateful.
Edit - Whilst I have tried to simplify, the cost of design implementation changes based on various other factors, so I can't simply just take the cheapest design for all. I have tried to solve this through practical theory and so far found so solution, therefore the brute force method is required
You're already using For loops to try each solution for each given floor, but you're manually iterating through your Floor variable. It seems you have 9 independently declared variables with the schema FloorX Solution Value. Given all of these things, I think what you really need is a dynamic array. Here's some rough code using this approach:
Dim FloorSolutionValues() As Byte ' I'm assuming values of 1-9
Dim NumberOfFloors As Integer ' Get this from the user
ReDim FloorSolutionValues(NumberOfFloors - 1)
For CurrentFloor As Integer = 0 To NumberOfFloors - 1
For CurrentSolution As Byte = 1 To 9
If CalculateCost() < FloorSolutionValues(CurrentFloor) Then
FloorSolutionValues(CurrentFloor) = CurrentSolution
End If
Next
Next
I'm making some assumptions that may or may not be true, but this should get you on the right path.
Related
and thanks for reading my thread.
I have read some of the previous posts on formatting/normalising input data for a Neural Network, but cannot find something that addresses my queries specifically. I apologise for the long post.
I am attempting to build a radial basis function network for analysing horse racing data. I realise that this has been done before, but the data that I have is "special" and I have a keen interest in racing/sportsbetting/programming so would like to give it a shot!
Whilst I think I understand the principles for the RBFN itself, I am having some trouble understanding the normalisation/formatting/scaling of the input data so that it is presented in a "sensible manner" for the network, and I am not sure how I should formulate the output target values.
For example, in my data I look at the "Class change", which compares the class of race that the horse is running in now compared to the race before, and can have a value between -5 and +5. I expect that I need to rescale these to between -1 and +1 (right?!), but I have noticed that many more runners have a class change of 1, 0 or -1 than any other value, so I am worried about "over-representation". It is not possible to gather more data for the higher/lower class changes because thats just 'the way the data comes'. Would it be best to use the data as-is after scaling, or should I trim extreme values, or something else?
Similarly, there are "continuous" inputs - like the "Days Since Last Run". It can have a value between 1 and about 1000, but values in the range of 10-40 vastly dominate. I was going to scale these values to be between 0 and 1, but even if I trim the most extreme values before scaling, I am still going to have a huge representation of a certain range - is this going to cause me an issue? How are problems like this usually dealt with?
Finally, I am having trouble understanding how to present the "target" values for training to the network. My existing results data has the "win/lose" (0 or 1?) and the odds at which the runner won or lost. If I just use the "win/lose", it treats all wins and loses the same when really they're not - I would be quite happy with a network that ignored all the small winners but was highly profitable from picking 10-1 shots. Similarly, a network could be forgiven for "losing" on a 20-1 shot but losing a bet at 2/5 would be a bad loss. I considered making the results (+1 * odds) for a winner and (-1 / odds) for a loser to capture the issue above, but this will mean that my results are not a continuous function as there will be a "discontinuity" between short price winners and short price losers.
Should I have two outputs to cover this - one for bet/no bet, and another for "stake"?
I am sorry for the flood of questions and the long post, but this would really help me set off on the right track.
Thank you for any help anyone can offer me!
Kind regards,
Paul
The documentation that came with your RBFN is a good starting point to answer some of these questions.
Trimming data aka "clamping" or "winsorizing" is something I use for similar data. For example "days since last run" for a horse could be anything from just one day to several years but tends to centre in the region of 20 to 30 days. Some experts use a figure of say 63 days to indicate a "spell" so you could have an indicator variable like "> 63 =1 else 0" for example. One clue is to look at outliers say the upper or lower 5% of any variable and clamp these.
If you use odds/dividends anywhere make sure you use the probabilities ie 1/(odds+1) and a useful idea is to normalize these to 100%.
The odds or parimutual prices tend to swamp other predictors so one technique is to develop separate models, one for the market variables (the market model) and another for the non-market variables (often called the "fundamental" model).
Is this possible and/or recommended? Currently, the issue I'm having is that the processing time of this code I have checks for a value in an array of ~40 for a value, once it finds it we set a boolean. This same for loop is called up to 20 times so I was wondering if there was a way I could optimize this code in a better way to where I don't need to have several for loops checking for a single answer.
Here's an example of the code
For i = 0 to iCount 'iCount up to 40
If name = UCase((m_Array(i, 1))) Then
<logic>
End If
Next
Above is an example of what I"m looking at, this little chunk of code checks the array which is prepopulated prior to running this function and is usually around 30-40 items in the array. With this being called up to 20 times I feel I could reduce the amount of time it takes to run this if I could find another way to do it without the use of so many for loops.
LINQ provides a Contains extension method, which returns a Boolean, but that won't work for multidimensional arrays. Even if it did work, if performance is the concern, then Contains wouldn't help much since, internally, all the Contains method does is loop through the items until it finds the matching item.
One way to make it faster, is to use an Exit For statement to exit the loop once the first matching item is found. At least then it won't continue searching through the rest of the items after it finds the one for which it was looking:
For i = 0 to iCount 'iCount up to 40
If name = UCase((m_Array(i, 1))) Then
' logic...
Exit For
End If
Next
If you don't want it to have to search through the array at all, you would need to index your data. The simplest way to index your data is with a hash table. The Dictionary class is an easy to use implementation of a hash table. However, in the end, a hash table (just like any other indexing method) will only help performance if the situation is right. In this situation, where the array only contains 40 or so items, it's quite possible that a hash table will be slower. The only way to know for sure is to test it both ways and see if it makes any difference.
You're currently searching the list repeatedly and doing something if a member has certain properties. Instead you could check the properties of an item once, when you add an item to the list, and perform the logic then. Instead of repeating the tests it's then only done once. No searching at all is better than even the fastest search.
I have a model implemented in OPL. I want to use this model to implement a local search in java. I want to initialize solutions with some heuristics and give these initial solutions to cplex find a better solution based on the model, but also I want to limit the search to a specific neighborhood. Any idea about how to do it?
Also, how can I limit the range of all variables? And what's the best: implement these heuristics and local search in own opl or in java or even C++?
Thanks in advance!
Just to add some related observations:
Re Ram's point 3: We have had a lot of success with approach b. In particular it is simple to add constraints to fix the some of the variables to values from a known solution, and then re-solve for the rest of the variables in the problem. More generally, you can add constraints to limit the values to be similar to a previous solution, like:
var >= previousValue - 1
var <= previousValue + 2
This is no use for binary variables of course, but for general integer or continuous variables can work well. This approach can be generalised for collections of variables:
sum(i in indexSet) var[i] >= (sum(i in indexSet) value[i])) - 2
sum(i in indexSet) var[i] <= (sum(i in indexSet) value[i])) + 2
This can work well for sets of binary variables. For an array of 100 binary variables of which maybe 10 had the value 1, we would be looking for a solution where at least 8 have the value 1, but not more than 12. Another variant is to limit something like the Hamming distance (assume that the vars are all binary here):
dvar int changed[indexSet] in 0..1;
forall(i in indexSet)
if (previousValue[i] <= 0.5)
changed[i] == (var[i] >= 0.5) // was zero before
else
changed[i] == (var[i] <= 0.5) // was one before
sum(i in indexSet) changed[i] <= 2;
Here we would be saying that out of an array of e.g. 100 binary variables, only a maximum of two would be allowed to have a different value from the previous solution.
Of course you can combine these ideas. For example, add simple constraints to fix a large part of the problem to previous values, while leaving some other variables to be re-solved, and then add constraints on some of the remaining free variables to limit the new solution to be near to the previous one. You will notice of course that these schemes get more complex to implement and maintain as we try to be more clever.
To make the local search work well you will need to think carefully about how you construct your local neighbourhoods - too small and there will be too little opportunity to make the improvements you seek, while if they are too large they take too long to solve, so you don't get to make so many improvement steps.
A related point is that each neighbourhood needs to be reasonably internally connected. We have done some experiments where we fixed the values of maybe 99% of the variables in a model and solved for the remaining 1%. When the 1% was clustered together in the model (e.g. all the allocation variables for a subset of resources) we got good results, while in comparison we got nowhere by just choosing 1% of the variables at random from anywhere in the model.
An often overlooked idea is to invert these same limits on the model, as a way of forcing some changes into the solution to achieve a degree of diversification. So you could add a constraint to force a specific value to be different from a previous solution, or ensure that at least two out of an array of 100 binary variables have a different value from the previous solution. We have used this approach to get a sort-of tabu search with a hybrid matheuristic model.
Finally, we have mainly done this in C++ and C#, but it would work perfectly well from Java. Not tried it much from OPL, but it should be fine too. The key for us was being able to traverse the problem structure and use problem knowledge to choose the sets of variables we freeze or relax - we just found that easier and faster to code in a language like C#, but then the modelling stuff is more difficult to write and maintain. We are maybe a bit "old-school" and like to have detailed fine-grained control of what we are doing, and find we need to create many more arrays and index sets in OPL to achieve what we want, while we can achieve the same effect with more intelligent loops etc without creating so many data structures in a language like C#.
Those are several questions. So here are some pointers and suggestions:
In Cplex, you give your model an initial solution with the use of IloOplCplexVectors()
Here's a good example in IBM's documentation of how to alter CPLEX's solution.
Within OPL, you can do the same. You basically set a series of values for your variables, and hand those over to CPLEX. (See this example.)
Limiting the search to a specific neighborhood: There is no easy way to respond without knowing the details. But there are two ways that people do this:
a. change the objective to favor that 'neighborhood' and make other areas unattractive.
b. Add constraints that weed out other neighborhoods from the search space.
Regarding limiting the range of variables in OPL, you can do it directly:
dvar int supply in minQty..maxQty;
Or for a whole array of decision variables, you can do something along the lines of:
range CreditsAllowed = 3..12;
dvar int credits[student] in CreditsAllowed;
Hope this helps you move forward.
Y
I have 6 parameters for which I know maxi and mini values. I have a complex function that includes the 6 parameters and return a 7th value (say Y). I say complex because Y is not directly related to the 6 parameters; there are many embeded functions in between.
I would like to find the combination of the 6 parameters which returns the highest Y value. I first tried to calculate Y for every combination by constructing an hypercube but I have not enough memory in my computer. So I am looking for kinds of markov chains which progress in the delimited parameter space, and are able to overpass local peaks.
when I give one combination of the 6 parameters, I would like to know the highest local Y value. I tried to write a code with an iterative chain like a markov's one, but I am not sure how to process when the chain reach an edge of the parameter space. Obviously, some algorythms should already exist for this.
Question: Does anybody know what are the best functions in R to do these two things? I read that optim() could be appropriate to find the global peak but I am not sure that it can deal with complex functions (I prefer asking before engaging in a long (for me) process of code writing). And fot he local peaks? optim() should not be able to do this
In advance, thank you for any lead
Julien from France
Take a look at the Optimization and Mathematical Programming Task View on CRAN. I've personally found the differential evolution algorithm to be very fast and robust. It's implemented in the DEoptim package. The rgenoud package is another good candidate.
I like to use the Metropolis-Hastings algorithm. Since you are limiting each parameter to a range, the simple thing to do is let your proposal distribution simply be uniform over the range. That way, you won't run off the edges. It won't be fast, but if you let it run long enough, it will do a good job of sampling your space. The samples will congregate at each peak, and will spread out around them in a way that reflects the local curvature.
I found this on an "interview questions" site and have been pondering it for a couple of days. I will keep churning, but am interested what you guys think
"10 Gbytes of 32-bit numbers on a magnetic tape, all there from 0 to 10G in random order. You have 64 32 bit words of memory available: design an algorithm to check that each number from 0 to 10G occurs once and only once on the tape, with minimum passes of the tape by a read head connected to your algorithm."
32-bit numbers can take 4G = 2^32 different values. There are 2.5*2^32 numbers on tape total. So after 2^32 count one of numbers will repeat 100%. If there were <= 2^32 numbers on tape then it was possible that there are two different cases – when all numbers are different or when at least one repeats.
It's a trick question, as Michael Anderson and I have figured out. You can't store 10G 32b numbers on a 10G tape. The interviewer (a) is messing with you and (b) is trying to find out how much you think about a problem before you start solving it.
The utterly naive algorithm, which takes as many passes as there are numbers to check, would be to walk through and verify that the lowest number is there. Then do it again checking that the next lowest is there. And so on.
This requires one word of storage to keep track of where you are - you could cut down the number of passes by a factor of 64 by using all 64 words to keep track of where you're up to in several different locations in the search space - checking all of your current ones on each pass. Still O(n) passes, of course.
You could probably cut it down even more by using portions of the words - given that your search space for each segment is smaller, you won't need to keep track of the full 32-bit range.
Perform an in-place mergesort or quicksort, using tape for storage? Then iterate through the numbers in sequence, tracking to see that each number = previous+1.
Requires cleverly implemented sort, and is fairly slow, but achieves the goal I believe.
Edit: oh bugger, it's never specified you can write.
Here's a second approach: scan through trying to build up to 30-ish ranges of contiginous numbers. IE 1,2,3,4,5 would be one range, 8,9,10,11,12 would be another, etc. If ranges overlap with existing, then they are merged. I think you only need to make a limited number of passes to either get the complete range or prove there are gaps... much less than just scanning through in blocks of a couple thousand to see if all digits are present.
It'll take me a bit to prove or disprove the limits for this though.
Do 2 reduces on the numbers, a sum and a bitwise XOR.
The sum should be (10G + 1) * 10G / 2
The XOR should be ... something
It looks like there is a catch in the question that no one has talked about so far; the interviewer has only asked the interviewee to write a program that CHECKS
(i) if each number that makes up the 10G is present once and only once--- what should the interviewee do if the numbers in the given list are present multple times? should he assume that he should stop execting the programme and throw exception or should he assume that he should correct the mistake by removing the repeating number and replace it with another (this may actually be a costly excercise as this involves complete reshuffle of the number set)? correcting this is required to perform the second step in the question, i.e. to verify that the data is stored in the best possible way that it requires least possible passes.
(ii) When the interviewee was asked to only check if the 10G weight data set of numbers are stored in such a way that they require least paases to access any of those numbers;
what should the interviewee do? should he stop and throw exception the moment he finds an issue in the algorithm they were stored in, or correct the mistake and continue till all the elements are sorted in the order of least possible passes?
If the intension of the interviewer is to ask the interviewee to write an algorithm that finds the best combinaton of numbers that can be stored in 10GB, given 64 32 Bit registers; and also to write an algorithm to save these chosen set of numbers in the best possible way that require least number of passes to access each; he should have asked this directly, woudn't he?
I suppose the intension of the interviewer may be to only see how the interviewee is approaching the problem rather than to actually extract a working solution from the interviewee; wold any buy this notion?
Regards,
Samba