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  • Why not allow mutation of the this binding?

    - by gnucom
    Hi Everyone, I'm building a interpreter/compiler for a school project (well now its turning into a hobby project) and an instructor warned me not to allow mutation of the 'this' binding (he said it was gross and made a huge deal about it) but I never learned why this is so... dangerous or bad. I'm very curious about why this is so bad. I figured this sort of feature could be useful in some way or another. I'm wondering if anyone familiar with building languages can tell me what sort of problems mutation on the 'this' binding can cause, and if they know of any cool or useful tricks that one could do if it actually was allowed. Do any languages that you're aware of allow mutation of 'this'? Thanks,

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  • Mutation Testing

    You may have a twinge of doubt when your code passes all its unit tests. They might say that the code is OK, but if the code is definitely incorrect, will the unit tests fail? Mutation Testing is a relatively simple, but ingenious, way of checking that your tests will spot the fact that your code is malfunctioning. It is definitely something that every developer should be aware of.

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  • Internal Mutation of Persistent Data Structures

    - by Greg Ros
    To clarify, when I mean use the terms persistent and immutable on a data structure, I mean that: The state of the data structure remains unchanged for its lifetime. It always holds the same data, and the same operations always produce the same results. The data structure allows Add, Remove, and similar methods that return new objects of its kind, modified as instructed, that may or may not share some of the data of the original object. However, while a data structure may seem to the user as persistent, it may do other things under the hood. To be sure, all data structures are, internally, at least somewhere, based on mutable storage. If I were to base a persistent vector on an array, and copy it whenever Add is invoked, it would still be persistent, as long as I modify only locally created arrays. However, sometimes, you can greatly increase performance by mutating a data structure under the hood. In more, say, insidious, dangerous, and destructive ways. Ways that might leave the abstraction untouched, not letting the user know anything has changed about the data structure, but being critical in the implementation level. For example, let's say that we have a class called ArrayVector implemented using an array. Whenever you invoke Add, you get a ArrayVector build on top of a newly allocated array that has an additional item. A sequence of such updates will involve n array copies and allocations. Here is an illustration: However, let's say we implement a lazy mechanism that stores all sorts of updates -- such as Add, Set, and others in a queue. In this case, each update requires constant time (adding an item to a queue), and no array allocation is involved. When a user tries to get an item in the array, all the queued modifications are applied under the hood, requiring a single array allocation and copy (since we know exactly what data the final array will hold, and how big it will be). Future get operations will be performed on an empty cache, so they will take a single operation. But in order to implement this, we need to 'switch' or mutate the internal array to the new one, and empty the cache -- a very dangerous action. However, considering that in many circumstances (most updates are going to occur in sequence, after all), this can save a lot of time and memory, it might be worth it -- you will need to ensure exclusive access to the internal state, of course. This isn't a question about the efficacy of such a data structure. It's a more general question. Is it ever acceptable to mutate the internal state of a supposedly persistent or immutable object in destructive and dangerous ways? Does performance justify it? Would you still be able to call it immutable? Oh, and could you implement this sort of laziness without mutating the data structure in the specified fashion?

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  • Mutation Problem - Clojure

    - by Silanglaya Valerio
    having trouble changing an element of my function represented as a list. code for random function: (defn makerandomtree-10 [pc maxdepth maxwidth fpx ppx] (if-let [output (if (and (< (rand) fpx) (> maxdepth 0)) (let [head (nth operations (rand-int (count operations))) children (doall (loop[function (list) width maxwidth] (if (pos? width) (recur (concat function (list (makerandomtree-10 pc (dec maxdepth) (+ 2 (rand-int (- maxwidth 1))) fpx ppx))) (dec width)) function)))] (concat (list head) children)) (if (and (< (rand) ppx) (>= pc 0)) (nth parameters (rand-int (count parameters))) (rand-int 100)))] output )) I will provide also a mutation function, which is still not good enough. I need to be able to eval my statement, so the following is still insufficient. (defn mutate-5 "chooses a node changes that" [function pc maxwidth pchange] (if (< (rand) pchange) (let [output (makerandomtree-10 pc 3 maxwidth 0.5 0.6)] (if (seq? output) output (list output))) ;mutate the children of root ;declare an empty accumulator list, with root as its head (let [head (list (first function)) children (loop [acc(list) walker (next function)] (println "----------") (println walker) (println "-----ACC-----") (println acc) (if (not walker) acc (if (or (seq? (first function)) (contains? (set operations) (first function))) (recur (concat acc (mutate-5 walker pc maxwidth pchange)) (next walker)) (if (< (rand) pchange) (if (some (set parameters) walker) (recur (concat acc (list (nth parameters (rand-int (count parameters))))) (if (seq? walker) (next walker) nil)) (recur (concat acc (list (rand-int 100))) (if (seq? walker) (next walker) nil))) (recur acc (if (seq? walker) (next walker) nil)))) ))] (concat head (list children))))) (side note: do you have any links/books for learning clojure?)

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  • mutation...........

    - by superguay
    Hey everybody, I'm kinda confused..... (define m (list 1 2 3 '(5 8))) (let ((l (cdr m))) (set! l '(28 88))) ==>(1 2 3 (5 8)) (define o (list 1 2 3 '(5 8))) (let ((l (cdr o))) (set-car! l '(28 88))) ==> (1 (28 88) 3 (5 8)) Why does (set! l '(28 88))) not update m?

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  • trying to make a scheme procedure

    - by asfejaeofijfe
    trying to make a scheme procedure called make-odd-mapper! its supposed to be a procedure that takes one input, a procedure, and produces a procedure as an output ex: (define i4 (mlist 10 2 30 4)) (i4) {10 2 30 4} ((make-odd-mapper! add-one) i4) i4 {11 2 31 4} I know the problem needs to mutate the input list and that set-mcar! and void are apart of it......could anyone give me some reasonable lines of code to solve this? It would be useful in case anyone was wondering about mutation.....and using it to create a procedure that makes a procedure as its output....

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  • problem with evolutionary algorithms degrading into simulated annealing: mutation too small?

    - by Schnalle
    i have a problem understanding evolutionary algorithms. i tried using this technique several times, but i always ran into the same problem: degeneration into simulated annealing. lets say my initial population, with fitness in brackets, is: A (7), B (9), C (14), D (19) after mating and mutation i have following children: AB (8.3), AC (12.2), AD (14.1), BC(11), BD (14.7), CD (17) after elimination of the weakest, we get A, AB, B, AC next turn, AB will mate again with a result around 8, pushing AC out. next turn, AB again, pushing B out (assuming mutation changes fitness mostly in the 1 range). now, after only a few turns the pool is populated with the originally fittest candidates (A, B) and mutations of those two (AB). this happens regardless of the size of the initial pool, it just takes a bit longer. say, with an initial population of 50 it takes 50 turns, then all others are eliminated, turning the whole setup in a more complicated simulated annealing. in the beginning i also mated canditates with themselves, worsening the problem. so, what do i miss? are my mutation rates simply too small and will it go away if i increase them? here's the project i'm using it for: http://stefan.schallerl.com/simuan-grid-grad/ yeah, the code is buggy and the interface sucks, but i'm too lazy to fix it right now - and be careful, it may lock up your browser. better use chrome, even thought firefox is not slower than chrome for once (probably the tracing for the image comparison pays off, yay!). if anyone is interested, the code can be found here. here i just dropped the ev-alg idea and went for simulated annealing. ps: i'm not even sure about simulated annealing - it is like evolutionary algorithms, just with a population size of one, right?

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  • Explain JAVA code

    - by MIW
    I need some help to explain the meaning from line 5 to line 9. Thanks String words = "Rain Rain go away"; String mutation1, mutation2, mutation3, mutation4; mutation1 = words.toUpperCase(); System.out.println ("** " + mutation1 + " Nursery Rhyme **"); mutation1 = words.concat ("\nCome again another day"); mutation2 = "Johnny Johnny wants to play"; mutation3 = mutation2.replace (mutation2.charAt(5), 'i'); mutation4 = mutation3.substring (7, 27); System.out.print ("\'" + mutation1 + "\n" + mutation4 + "\'\n"); 10.System.out.println ("Title length: " + words.length());

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  • Why does adding Crossover to my Genetic Algorithm gives me worse results?

    - by MahlerFive
    I have implemented a Genetic Algorithm to solve the Traveling Salesman Problem (TSP). When I use only mutation, I find better solutions than when I add in crossover. I know that normal crossover methods do not work for TSP, so I implemented both the Ordered Crossover and the PMX Crossover methods, and both suffer from bad results. Here are the other parameters I'm using: Mutation: Single Swap Mutation or Inverted Subsequence Mutation (as described by Tiendil here) with mutation rates tested between 1% and 25%. Selection: Roulette Wheel Selection Fitness function: 1 / distance of tour Population size: Tested 100, 200, 500, I also run the GA 5 times so that I have a variety of starting populations. Stop Condition: 2500 generations With the same dataset of 26 points, I usually get results of about 500-600 distance using purely mutation with high mutation rates. When adding crossover my results are usually in the 800 distance range. The other confusing thing is that I have also implemented a very simple Hill-Climbing algorithm to solve the problem and when I run that 1000 times (faster than running the GA 5 times) I get results around 410-450 distance, and I would expect to get better results using a GA. Any ideas as to why my GA performing worse when I add crossover? And why is it performing much worse than a simple Hill-Climb algorithm which should get stuck on local maxima as it has no way of exploring once it finds a local max?

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  • Why does adding Crossover to my Genetic Algorithm give me worse results?

    - by MahlerFive
    I have implemented a Genetic Algorithm to solve the Traveling Salesman Problem (TSP). When I use only mutation, I find better solutions than when I add in crossover. I know that normal crossover methods do not work for TSP, so I implemented both the Ordered Crossover and the PMX Crossover methods, and both suffer from bad results. Here are the other parameters I'm using: Mutation: Single Swap Mutation or Inverted Subsequence Mutation (as described by Tiendil here) with mutation rates tested between 1% and 25%. Selection: Roulette Wheel Selection Fitness function: 1 / distance of tour Population size: Tested 100, 200, 500, I also run the GA 5 times so that I have a variety of starting populations. Stop Condition: 2500 generations With the same dataset of 26 points, I usually get results of about 500-600 distance using purely mutation with high mutation rates. When adding crossover my results are usually in the 800 distance range. The other confusing thing is that I have also implemented a very simple Hill-Climbing algorithm to solve the problem and when I run that 1000 times (faster than running the GA 5 times) I get results around 410-450 distance, and I would expect to get better results using a GA. Any ideas as to why my GA performing worse when I add crossover? And why is it performing much worse than a simple Hill-Climb algorithm which should get stuck on local maxima as it has no way of exploring once it finds a local max?

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  • 2D vector value replacement using classes; genetic algorithm mutation

    - by gcolumbus
    I have a 2D vector as defined by the classes below. Note that I've used classes because I'm trying to program a genetic algorithm such that many, many 2D vectors will be created and they will all be different. class Quad: public std::vector<int> { public: Quad() : std::vector<int>(4,0) {} }; class QuadVec : public std::vector<Quad> { }; An important part of my algorithm, however, is that I need to be able to "mutate" (randomly change) particular values in a certain number of randomly chosen 2D vectors. This has me stumped. I can easily write code to randomly select the value within the 2D vector that will be selected for "mutation" but how do I actually enact that change using classes? I understand how this would be done with one 2D vector that has already been initialized but how do I do this if it hasn't? Please let me know if I haven't provided enough info or am not clear as I tend to do that. Thanks for your time and help!

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  • Java: versioned data structures?

    - by Jason S
    I have a data structure that is pretty simple (basically a structure containing some arrays and single values), but I need to record the history of the data structure so that I can efficiently get the contents of the data structure at any point in time. Is there a relatively straightforward way to do this? The best way I can think of would be to encapsulate the whole data structure with something that handles all the mutating operations by storing data in functional data structures, and then for each mutation operation caching a copy of the data structure in a Map indexed by time-ordering (e.g. a TreeMap with real time as keys, or a HashMap with a counter of mutation operations combined with one or more indexes stored in TreeMaps mapping real time / tick count / etc. to mutation operations) any suggestions?

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  • Are batch mutations atomic in Cassandra?

    - by user317459
    The Cassandra API supports batch mutations: batch_mutate(keyspace, mutation_map, consistency_level): Executes the specified mutations on the keyspace. mutation_map is a map; the outer map maps the key to the inner map, which maps the column family to the Mutation; can be read as: map. To be more specific, the outer map key is a row key, the inner map key is the column family name. A Mutation specifies either columns to insert or columns to delete. See Mutation and Deletion above for more details. Are all mutations that are executed in a batch executed atomically? So if one of the mutations fails, do the others fail too?

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  • Cassandra Batch_insert example in C#.net

    - by Sandeep
    Can any one please give me an example on how to work on Cassandra batch_insert in C# thrift client? If possible please let me know where am I going wrong in the following code. Dictionary dictionary = new Dictionary(); Dictionary subColumns = new Dictionary(); List listOfMutations = new List(); listOfMutations.Add(new Mutation() { Column_or_supercolumn = new ColumnOrSuperColumn() { Column = new Column() { Name = utf8Encoding.GetBytes("AA"), Value = utf8Encoding.GetBytes("Answer Automation"), Timestamp = timeStamp } } }); listOfMutations.Add(new Mutation() { Column_or_supercolumn = new ColumnOrSuperColumn() { Column = new Column() { Name = utf8Encoding.GetBytes("CT"), Value = utf8Encoding.GetBytes("Call Tracker"), Timestamp = timeStamp } } }); listOfMutations.Add( new Mutation() { Column_or_supercolumn = new ColumnOrSuperColumn() { Column = new Column() { Name = utf8Encoding.GetBytes("TL"), Value = utf8Encoding.GetBytes("Track That Lead"), Timestamp = timeStamp } } }); SuperColumn superColumn = new SuperColumn() { Name=utf8Encoding.GetBytes("Indatus") }; subColumns.Add("Super1", listOfMutations); dictionary.Add("Indatus", subColumns); client.batch_mutate("Keyspace1",dictionary, ConsistencyLevel.ONE); I understand that SuperColumn struct expects List but I does not have a list of Columns. Rather I have List. Thanks in Advance.

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  • Fuzzing for Security

    - by Sylvain Duloutre
    Yesterday, I attended an internal workshop about ethical hacking. Hacking skills like fuzzing can be used to quantitatively assess and measure security threats in software.  Fuzzing is a software testing technique used to discover coding errors and security loopholes in software, operating systems or networks by injecting massive amounts of random data, called fuzz, to the system in an attempt to make it crash. If the program contains a vulnerability that can leads to an exception, crash or server error (in the case of web apps), it can be determined that a vulnerability has been discovered.A fuzzer is a program that generates and injects random (and in general faulty) input to an application. Its main purpose is to make things easier and automated.There are typically two methods for producing fuzz data that is sent to a target, Generation or Mutation. Generational fuzzers are capable of building the data being sent based on a data model provided by the fuzzer creator. Sometimes this is simple and dumb as sending random bytes, swapping bytes or much smarter by knowing good values and combining them in interesting ways.Mutation on the other hand starts out with a known good "template" which is then modified. However, nothing that is not present in the "template" or "seed" will be produced.Generally fuzzers are good at finding buffer overflow, DoS, SQL Injection, Format String bugs etc. They do a poor job at finding vulnerabilites related to information disclosure, encryption flaws and any other vulnerability that does not cause the program to crash.  Fuzzing is simple and offers a high benefit-to-cost ratio but does not replace other proven testing techniques.What is your computer doing over the week-end ?

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  • Genetic Algorithm new generation exponentially increasing

    - by Rdz
    I'm programming Genetic Algorithm in C++ and after searching all kind of ways of doing GA'a operators (selection, crossover, mutation) I came up with a doubt. Let's say I have an initial population of 500. My selection will consist in getting the top 20% of 500(based on best fitness). So I get 100 individuals to mate. When I do the crossover I'll get 2 children where both together have 50% of surviving. So far so good. I start the mutation, and everything's ok.. Now when I start choosing the Next generation, I see that I have a big number of children (in this case, 4950 if you wanna know). Now the thing is, every time I run GA, if I send all the children to the next generation, the number of individuals per generation will increase exponentially. So there must be a way of choosing the children to fulfill a new generation without getting out of this range of the initial population. What I'm asking here is if there is anyway of choosing the children to fill the new generations OR should I choose somehow (and maybe reduce) the parents to mate so I don't get so many children in the end. Thanks :)

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  • CouchOne et Membase fusionnent pour créer « Couchbase » et une famille de produits NoSQL de nouvelle génération

    CouchOne et Membase fusionnent pour créer « Couchbase » Et une famille de produits NoSQL de nouvelle génération Le paysage des solutions NoSQL est en pleine mutation depuis l'annonce de la première grande fusion de ce secteur, désormais très prospère et concurrentiel. Les deux entreprises CouchOne et Membase viennent de fusionner et d'annoncer la consolidation de leurs efforts pour la création de « Couchbase », le « premier éditeur de solution exhaustive de base de données NoSQL ». Sur son site officiel, CouchOne promet des possibilités de scalabilité sans précédent pour les produits qui vont résulter de cette fusion, pouvant propulser des « datacenter de la taille ...

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  • Nester

    - by csharp-source.net
    Nester is a tool for mutation testing of your C# source code in order to assess the adequacy of your unit tests. It involves modification of programs to see if existing tests can distinguish the original program from the modified program.

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  • Genetic Algorithms applied to Curve Fitting

    - by devoured elysium
    Let's imagine I have an unknown function that I want to approximate via Genetic Algorithms. For this case, I'll assume it is y = 2x. I'd have a DNA composed of 5 elements, one y for each x, from x = 0 to x = 4, in which, after a lot of trials and computation and I'd arrive near something of the form: best_adn = [ 0, 2, 4, 6, 8 ] Keep in mind I don't know beforehand if it is a linear function, a polynomial or something way more ugly, Also, my goal is not to infer from the best_adn what is the type of function, I just want those points, so I can use them later. This was just an example problem. In my case, instead of having only 5 points in the DNA, I have something like 50 or 100. What is the best approach with GA to find the best set of points? Generating a population of 100, discard the worse 20% Recombine the remaining 80%? How? Cutting them at a random point and then putting together the first part of ADN of the father with the second part of ADN of the mother? Mutation, how should I define in this kind of problem mutation? Is it worth using Elitism? Any other simple idea worth using around? Thanks

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