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  • Using Markov models to convert all caps to mixed case and related problems

    - by hippietrail
    I've been thinking about using Markov techniques to restore missing information to natural language text. Restore mixed case to text in all caps Restore accents / diacritics to languages which should have them but have been converted to plain ASCII Convert rough phonetic transcriptions back into native alphabets That seems to be in order of least difficult to most difficult. Basically the problem is resolving ambiguities based on context. I can use Wiktionary as a dictionary and Wikipedia as a corpus using n-grams and Markov chains to resolve the ambiguities. Am I on the right track? Are there already some services, libraries, or tools for this sort of thing? Examples GEORGE LOST HIS SIM CARD IN THE BUSH - George lost his SIM card in the bush tantot il rit a gorge deployee - tantôt il rit à gorge déployée

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  • Programming With Markov Algorithms.

    - by Bubba88
    Hello! I Wonder if someone has used Markov Algorithm-based programming system or embedded facility in production or for scientific purpose. I know about 'REFAL' programming language invented a thousand years ago, but it all seems to be dead, so.. Ref: http://en.wikipedia.org/wiki/Markov_algorithm

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  • Using Hidden Markov Model for designing AI mp3 player

    - by Casper Slynge
    Hey guys. Im working on an assignment, where I want to design an AI for a mp3 player. The AI must be trained and designed with the use of a HMM method. The mp3 player shall have the functionality of adapting to its user, by analyzing incoming biological sensor data, and from this data the mp3 player will choose a genre for the next song. Given in the assignment is 14 samples of data: One sample consist of Heart Rate, Respiration, Skin Conductivity, Activity and finally the output genre. Below is the 14 samples of data, just for you to get an impression of what im talking about. Sample HR RSP SC Activity Genre S1 Medium Low High Low Rock S2 High Low Medium High Rock S3 High High Medium Low Classic S4 High Medium Low Medium Classic S5 Medium Medium Low Low Classic S6 Medium Low High High Rock S7 Medium High Medium Low Classic S8 High Medium High Low Rock S9 High High Low Low Classic S10 Medium Medium Medium Low Classic S11 Medium Medium High High Rock S12 Low Medium Medium High Classic S13 Medium High Low Low Classic S14 High Low Medium High Rock My time of work regarding HMM is quite low, so my question to you is if I got the right angle on the assignment. I have three different states for each sensor: Low, Medium, High. Two observations/output symbols: Rock, Classic In my own opinion I see my start probabilities as the weightened factors for either a Low, Medium or High state in the Heart Rate. So the ideal solution for the AI is that it will learn these 14 sets of samples. And when a users sensor input is received, the AI will compare the combination of states for all four sensors, with the already memorized samples. If there exist a matching combination, the AI will choose the genre, and if not it will choose a genre according to the weightened transition probabilities, while simultaniously updating the transition probabilities with the new data. Is this a right approach to take, or am I missing something ? Is there another way to determine the output probability (read about Maximum likelihood estimation by EM, but dont understand the concept)? Best regards, Casper

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  • Creating a smart text generator

    - by royrules22
    I'm doing this for fun (or as 4chan says "for teh lolz") and if I learn something on the way all the better. I took an AI course almost 2 years ago now and I really enjoyed it but I managed to forget everything so this is a way to refresh that. Anyway I want to be able to generate text given a set of inputs. Basically this will read forum inputs (or maybe Twitter tweets) and then generate a comment based on the learning. Now the simplest way would be to use a Markov Chain Text Generator but I want something a little bit more complex than that as the MKC basically only learns by word order (which word is more likely to appear after word x given the input text). I'm trying to see if there's something I can do to make it a little bit more smarter. For example I want it to do something like this: Learn from a large selection of posts in a message board but don't weight it too much For each post: Learn from the other comments in that post and weigh these inputs higher Generate comment and post See what other users' reaction to your post was. If good weigh it positively so you make more posts that are similar to the one made, and vice versa if negative. It's the weighing and learning from mistakes part that I'm not sure how to implement. I thought about Artificial Neural Networks (mainly because I remember enjoying that chapter) but as far as I can tell that's mainly used to classify things (i.e. given a finite set of choices [x1...xn] which x is this given input) not really generate anything. I'm not even sure if this is possible or if it is what should I go about learning/figuring out. What algorithm is best suited for this? To those worried that I will use this as a bot to spam or provide bad answers to SO, I promise that I will not use this to provide (bad) advice or to spam for profit. I definitely will not post it's nonsensical thoughts on SO. I plan to use it for my own amusement. Thanks!

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  • R library for discrete Markov chain simulation

    - by stevejb
    Hello, I am looking for something like the 'msm' package, but for discrete Markov chains. For example, if I had a transition matrix defined as such Pi <- matrix(c(1/3,1/3,1/3, 0,2/3,1/6, 2/3,0,1/2)) for states A,B,C. How can I simulate a Markov chain according to that transition matrix? Thanks,

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  • using R to estimate finite mixture model with underlying Markov process

    - by stevejb
    Hello, My apologies if this is more of a statistics question than an R question. I am trying to estimate the following model in R. y_t = mu0 (1 - S_t) + mu1 S_t + e_t e_t ~ N(0, sigma_t^2) sigma_t^2 = sigma_0^2 (1 - S_t) + sigma_1^2 S_t where mu_t = mu0 if S_t = 0, mu_t = mu1 if S_t = 1, and S_t is a Markov process, either 0 or 1, with transition probabilities P(S_t = 1 | S_t-1 = 1 ) = p and P(S_t = 0 | S_t-1 = 0 ) = q. Would 'flexmix' be a good library to use for this? I am new to this kind of statistics so any pointer to the right library would be appreciated. Thanks,

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  • Map large integer to a phrase

    - by Alexander Gladysh
    I have a large and "unique" integer (actually a SHA1 hash). I want (for no other reason than to have fun) to find an algorithm to convert that SHA1 hash to a (pseudo-)English phrase. The conversion should be reversible (i.e., knowing the algorithm, one must be able to convert the phrase back to SHA1 hash.) The possible usage of the generated phrase: the human readable version of Git commit ID, like a motto for a given program version (which is built from that commit). (As I said, this is "for fun". I don't claim that this is very practical — or be much more readable than the SHA1 itself.) A better algorithm would produce shorter, more natural-looking, more unique phrases. The phrase need not make sense. I would even settle for a whole paragraph of nonsense. (Though quality — englishness — of a paragraph should probably be better than for a mere phrase.) A variation: it is OK if I will be able to work only with a part of hash. Say, first six digits is OK. Possible approach: In the past I've attempted to build a probability table (of words), and generate phrases as Markov chains, seeding the generator (picking branches from probability tree), according to the bits I read from the SHA. This was not very successful, the resulting phrases were too long and ugly. I'm not sure if this was a bug, or the general flaw in the algorithm, since I had to abandon it early enough. Now I'm thinking about attempting to solve the problem once again. Any advice on how to approach this? Do you think Markov chain approach can work here? Something else?

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  • Reinforcement learning And POMDP

    - by Betamoo
    I am trying to use Multi-Layer NN to implement probability function in Partially Observable Markov Process.. I thought inputs to the NN would be: current state, selected action, result state; The output is a probability in [0,1] (prob. that performing selected action on current state will lead to result state) In training, I fed the inputs stated before, into the NN, and I taught it the output=1.0 for each case that already occurred. The problem : For nearly all test case the output probability is near 0.95.. no output was under 0.9 ! Even for nearly impossible results, it gave that high prob. PS:I think this is because I taught it happened cases only, but not un-happened ones.. But I can not at each step in the episode teach it the output=0.0 for every un-happened action! Any suggestions how to over come this problem? Or may be another way to use NN or to implement prob function? Thanks

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  • Reinforcement learning toy project

    - by Betamoo
    My toy project to learn & apply Reinforcement Learning is: - An agent tries to reach a goal state "safely" & "quickly".... - But there are projectiles and rockets that are launched upon the agent in the way. - The agent can determine rockets position -with some noise- only if they are "near" - The agent then must learn to avoid crashing into these rockets.. - The agent has -rechargable with time- fuel which is consumed in agent motion - Continuous Actions: Accelerating forward - Turning with angle I need some hints and names of RL algorithms that suit that case.. - I think it is POMDP , but can I model it as MDP and just ignore noise? - In case POMDP, What is the recommended way for evaluating probability? - Which is better to use in this case: Value functions or Policy Iterations? - Can I use NN to model environment dynamics instead of using explicit equations? - If yes, Is there a specific type/model of NN to be recommended? - I think Actions must be discretized, right? I know it will take time and effort to learn such a topic, but I am eager to.. You may answer some of the questions if you can not answer all... Thanks

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  • Reinforcement learning with neural networks

    - by Betamoo
    I am working on a project with RL & NN I need to determine the action vector structure which will be fed to a neural network.. I have 3 different actions (A & B & Nothing) each with different powers (e.g A100 A50 B100 B50) I wonder what is the best way to feed these actions to a NN in order to yield best results? 1- feed A/B to input 1, while action power 100/50/Nothing to input 2 2- feed A100/A50/Nothing to input 1, while B100/B50/Nothing to input 2 3- feed A100/A50 to input 1, while B100/B50 to input 2, while Nothing flag to input 3 4- Also to feed 100 & 50 or normalize them to 2 & 1 ? I need reasons why to choose one method Any suggestions are recommended Thanks

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  • Update Rule in Temporal difference

    - by Betamoo
    The update rule TD(0) Q-Learning: Q(t-1) = (1-alpha) * Q(t-1) + (alpha) * (Reward(t-1) + gamma* Max( Q(t) ) ) Then take either the current best action (to optimize) or a random action (to explorer) Where MaxNextQ is the maximum Q that can be got in the next state... But in TD(1) I think update rule will be: Q(t-2) = (1-alpha) * Q(t-2) + (alpha) * (Reward(t-2) + gamma * Reward(t-1) + gamma * gamma * Max( Q(t) ) ) My question: The term gamma * Reward(t-1) means that I will always take my best action at t-1 .. which I think will prevent exploring.. Can someone give me a hint? Thanks

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  • "Anagram solver" based on statistics rather than a dictionary/table?

    - by James M.
    My problem is conceptually similar to solving anagrams, except I can't just use a dictionary lookup. I am trying to find plausible words rather than real words. I have created an N-gram model (for now, N=2) based on the letters in a bunch of text. Now, given a random sequence of letters, I would like to permute them into the most likely sequence according to the transition probabilities. I thought I would need the Viterbi algorithm when I started this, but as I look deeper, the Viterbi algorithm optimizes a sequence of hidden random variables based on the observed output. I am trying to optimize the output sequence. Is there a well-known algorithm for this that I can read about? Or am I on the right track with Viterbi and I'm just not seeing how to apply it?

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  • Simplifying for-if messes with better structure?

    - by HH
    # Description: you are given a bitwise pattern and a string # you need to find the number of times the pattern matches in the string # any one liner or simple pythonic solution? import random def matchIt(yourString, yourPattern): """find the number of times yourPattern occurs in yourString""" count = 0 matchTimes = 0 # How can you simplify the for-if structures? for coin in yourString: #return to base if count == len(pattern): matchTimes = matchTimes + 1 count = 0 #special case to return to 2, there could be more this type of conditions #so this type of if-conditionals are screaming for a havoc if count == 2 and pattern[count] == 1: count = count - 1 #the work horse #it could be simpler by breaking the intial string of lenght 'l' #to blocks of pattern-length, the number of them is 'l - len(pattern)-1' if coin == pattern[count]: count=count+1 average = len(yourString)/matchTimes return [average, matchTimes] # Generates the list myString =[] for x in range(10000): myString= myString + [int(random.random()*2)] pattern = [1,0,0] result = matchIt(myString, pattern) print("The sample had "+str(result[1])+" matches and its size was "+str(len(myString))+".\n" + "So it took "+str(result[0])+" steps in average.\n" + "RESULT: "+str([a for a in "FAILURE" if result[0] != 8])) # Sample Output # # The sample had 1656 matches and its size was 10000. # So it took 6 steps in average. # RESULT: ['F', 'A', 'I', 'L', 'U', 'R', 'E']

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  • Static code analysis tools for VB6

    - by Maksym Markov
    Right now we are maintaining some old project written in VB6 we are planning to implement continues integration sefver for it. We would like to implement some code analysis as well to track that maintanability at least not getting worse. Basically there is only one requirement - the tool should be command line so we can call it from constinues integration server and it should work with VB6 projects. I will really a;preciate any recommendations regards tools to try. Thank you, Maksym

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  • Optimizing Haskell code

    - by Masse
    I'm trying to learn Haskell and after an article in reddit about Markov text chains, I decided to implement Markov text generation first in Python and now in Haskell. However I noticed that my python implementation is way faster than the Haskell version, even Haskell is compiled to native code. I am wondering what I should do to make the Haskell code run faster and for now I believe it's so much slower because of using Data.Map instead of hashmaps, but I'm not sure I'll post the Python code and Haskell as well. With the same data, Python takes around 3 seconds and Haskell is closer to 16 seconds. It comes without saying that I'll take any constructive criticism :). import random import re import cPickle class Markov: def __init__(self, filenames): self.filenames = filenames self.cache = self.train(self.readfiles()) picklefd = open("dump", "w") cPickle.dump(self.cache, picklefd) picklefd.close() def train(self, text): splitted = re.findall(r"(\w+|[.!?',])", text) print "Total of %d splitted words" % (len(splitted)) cache = {} for i in xrange(len(splitted)-2): pair = (splitted[i], splitted[i+1]) followup = splitted[i+2] if pair in cache: if followup not in cache[pair]: cache[pair][followup] = 1 else: cache[pair][followup] += 1 else: cache[pair] = {followup: 1} return cache def readfiles(self): data = "" for filename in self.filenames: fd = open(filename) data += fd.read() fd.close() return data def concat(self, words): sentence = "" for word in words: if word in "'\",?!:;.": sentence = sentence[0:-1] + word + " " else: sentence += word + " " return sentence def pickword(self, words): temp = [(k, words[k]) for k in words] results = [] for (word, n) in temp: results.append(word) if n > 1: for i in xrange(n-1): results.append(word) return random.choice(results) def gentext(self, words): allwords = [k for k in self.cache] (first, second) = random.choice(filter(lambda (a,b): a.istitle(), [k for k in self.cache])) sentence = [first, second] while len(sentence) < words or sentence[-1] is not ".": current = (sentence[-2], sentence[-1]) if current in self.cache: followup = self.pickword(self.cache[current]) sentence.append(followup) else: print "Wasn't able to. Breaking" break print self.concat(sentence) Markov(["76.txt"]) -- module Markov ( train , fox ) where import Debug.Trace import qualified Data.Map as M import qualified System.Random as R import qualified Data.ByteString.Char8 as B type Database = M.Map (B.ByteString, B.ByteString) (M.Map B.ByteString Int) train :: [B.ByteString] -> Database train (x:y:[]) = M.empty train (x:y:z:xs) = let l = train (y:z:xs) in M.insertWith' (\new old -> M.insertWith' (+) z 1 old) (x, y) (M.singleton z 1) `seq` l main = do contents <- B.readFile "76.txt" print $ train $ B.words contents fox="The quick brown fox jumps over the brown fox who is slow jumps over the brown fox who is dead."

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  • Windows is very slow with my new SSD

    - by Maksym H.
    I have a laptop HP probook 4520s with Core i5 M480 @ 2.67Ghz, 4Gb RAM, 640 GB HDD Radeon HD 6370m 1GB video card. It would seem like a good stack for work, right? But My HDD has crashed after everyday walking with laptop about 1 year. After buying my new SSD (Patriot memory - Torqx II 128 Gb SATA II) and installing new Windows 8 from scratch - it was amazing fast. But I had only install windows updates, and I feel that the speed become the same as my old HDD, after install other software for my work, it becomes so slow, so when I use my PC with old lower configuration and it really works better than my awesome laptop... I checked that TRIM and AHCI mode are turned on. So why's that? I asked for help in Patriot Memory support, they suggested to send them ATTO test results, done, sent. Here is the response: "Thank you very much for the attached results. Looking at the results, I can see that your SSD speed is a lot lower than it should be. Can you tell me your system specs?" Until they checked my email, I re-installed Windows 8 to Windows 7 and it was again perfect, but the story repeats it becomes slower and slower after every installing new software. Check out some screenshots.. (sorry for the screenshot with russian TaskManager, I hope you will recognize those parameters accordingly with your english or other lang TaskManager) So the main issue that something everytime loads the disc on 100% and the response time is jumping around 1000-3000 ms. Why am I asking about Windows? Because I tried to install Linux Mint (x86) and It just flies. So great performance independent on how many programs I have installed. Only Windows (any 7 or 8) has this problem. So guys, I appreciate any ideas about how to fix that and may be answers of main question - "why is it so.?" Thanks!

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  • OCS presence in SharePoint AJAX-based web part

    - by Maksym Ozerov
    I'm currently implementing AJAX-based web part which displays search result. This search result has user names and opposite to each name I'm rendering the OCS presence indicator. This indicator works fine in IE6 but I can't get it to work in IE7/8. Basically the problem in IE7/8 is that OCS is rendered but when you mouse over it nothing is shown. If you try to scroll page down then mouse over the OCS icon you will see the OCS actions menu in the bottom of the page instead of seeing it on the opposite to the user name. My AJAX-based web part uses jQuery post method to make a request to the server and receives json which is then rendered to the div. My HTML for the user name looks like this: <nobr> <span> <a target='_blank' href='/ViewExpert.aspx?uid=4'>Some Expert</a> <img height='1' width='3' border='0' alt='' src='/_layouts/images/blank.gif'><a class='ms-imnlink' onclick='IMNImageOnClick();return false;' href='javascript:'> <img height='12' width='12' border='0' id='3' ShowOfflinePawn='1' type='smtp' sip='[email protected]' src='/_layouts/images/blank.gif' valign='middle' name='imnmark' alt='No presence information' title=''> </a> </span> </nobr> After the HTML above is rendered on the page I call the following two lines of code: //have to reset this value, otherwise ProcessImn() fails after next AJAX request imnCount = 0; ProcessImn(); Any ideas why it doesn't work in IE7/8?

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  • HMM for perspective estimation in document image, can't understand the algorithm

    - by maximus
    Hello! Here is a paper, it is about estimating the perspective of binary image containing text and some noise or non text objects. PDF document The algorithm uses the Hidden Markov Model: actually two conditions T - text B - backgrouond (i.e. noise) It is hard to understand the algorithm itself. The question is that I've read about Hidden Markov Models and I know that it uses probabilities that must be known. But in this algorithm I can't understand, if they use HMM, how do they get those probabilities (probability of changing the state from S1 to another state for example S2)? I didn't find anything about training there also in that paper. So, if somebody understands it, please tell me. Also is it possible to use HMM without knowing the state change probabilities?

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  • No drop packets using the error models for wirelesss scenario ?

    I am trying to use the error model in ns2 with wireless links, I am using ns2.33. I tried the uniform error model and the markov chain model. No errors from ns but when I am trying to find the dropped packets due to corruption I can not find any of them using either the uniform or markov models. So please help me if I made any error in using the error model. to search for dropped packets I am using eid@eid-laptop:~/code/ns2/noisy$ cat mixed.tr | grep d My code is available here : http://pastebin.com/f68749435

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  • What algorithm(s) can be used to achieve reasonably good next word prediction?

    - by yati sagade
    What is a good way of implementing "next-word prediction"? For example, the user types "I am" and the system suggests "a" and "not" (or possibly others) as the next word. I am aware of a method that uses Markov Chains and some training text(obviously) to more or less achieve this. But I read somewhere that this method is very restrictive and applies to very simple cases. I understand basics of neural networks and genetic algorithms(though have never used them in a serious project) and maybe they could be of some help. I wonder if there are any algorithms that, given appropriate training text(e.g., newspaper articles, and the user's own typing) can come up with reasonably appropriate suggestions for the next word. If not (links to)algorithms, general high-level methods to attack this problem are welcome.

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  • Which prediction model for web page recommendation?

    - by Nilesh
    I am trying to implement a web page recommendation wherein registered users will be given a recommendation of which page to visit depending upon the previous data.So with initial study I decided to go on with clustering the data with rough sets and then will move forward to find out the sequential patters with the use of prefix span algorithm.So now I want to have a better prediction model in place which can predict the access frequency of pages.I have figured out with Markov model but still some more suggestions will be valuable.Also please help me with some references of the models too.Is it possible to directly predict the next page access with the result of PrefixSpan.If so how?

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  • Developing an AI opponent for Monopoly

    - by Bernhard Zürn
    i want to develop an AI opponent for the Board Game Monopoly. I want to implement the whole Game with Prolog (XPCE). The probability for a field on the Board being hit, can be computed with Markov Chains. I already know some "best practices" like "after 50% of the playing time it does not make sense to buy out of jail because in jail you get renting fees for your fields but you don't have to pay for other fields as long as you stay in prison". The interesting question always is: buy a streetfield ? buy houses / hotels ? how much ? so i think i would have to compute some kind of future liquidity .. does anyone know how to pack that into an algorithm or how to translate it to prolog ?

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  • Classification of relationships in words?

    - by C.
    Hi, I'm not sure whats the best algorithm to use for the classification of relationships in words. For example in the case of a sentence such as "The yellow sun" there is a relationship between yellow and sun. THe machine learning techniques I have considered so far are Baynesian Statistics, Rough Sets, Fuzzy Logic, Hidden markov model and Artificial Neural Networks. Any suggestions please? thank you :)

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