what is recurrent complexity relations in terms of java programming?
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When a numerical expression is defined recursively, we call its set of defining equations a recurrence relation.
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well.. here is a really wonderful book for this :\n\nDesign and Analysis of algorithms by Anany Levitin.. try getting this book.\n\nAnd : \nhere's a dictionary of algorithms:\n\nhttp://www.nist.gov/dads/\nhere, each and every algorithm's details have been given. And links to other sites, where simulations of algorithms are put up as JAVA APPLETS\n\nfor eg:\nhere's a link to quick sort simultaion using java applet, which i got from NIST . gov :\n\nhttp://www.cosc.canterbury.ac.nz/people/mukundan/dsal/QSort.html\n\nN JOY ; )
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The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – known as the Viterbi path – that result in a sequence of observed events, especially in the context of hidden Markov models. The forward algorithm is a closely related algorithm for computing the probability of a sequence of observed events. These algorithms form a subset of information theory.\n\nHere's an example:\n\nAssume you have a friend who lives far away and whom you call daily to talk about what each of you did that day. Your friend has only three things he's interested in: walking in the park, shopping, and cleaning his apartment. The choice of what to do is determined exclusively by the weather on a given day. You have no definite information about the weather where your friend lives, but you know general trends. Based on what he tells you he did each day, you try to guess what the weather must have been like.\n\nYou believe that the weather operates as a discrete Markov chain. There are two states, "Rainy" and "Sunny", but you cannot observe them directly, that is, they are hidden from you. On each day, there is a certain chance that your friend will perform one of the following activities, depending on the weather: "walk", "shop", or "clean". Since your friend tells you about his activities, those are the observations. The entire system is that of a hidden Markov model (HMM).\n\nYou know the general weather trends in the area and you know what your friend likes to do on average. In other words, the parameters of the HMM are known. In fact, you can write them down in the Python programming language:\n\nstates = ('Rainy', 'Sunny')\n\nobservations = ('walk', 'shop', 'clean')\n\nstart_probability = {'Rainy': 0.6, 'Sunny': 0.4}\n\ntransition_probability = {\n   'Rainy' : {'Rainy': 0.7, 'Sunny': 0.3},\n   'Sunny' : {'Rainy': 0.4, 'Sunny': 0.6},\n   }\n\nemission_probability = {\n   'Rainy' : {'walk': 0.1, 'shop': 0.4, 'clean': 0.5},\n   'Sunny' : {'walk': 0.6, 'shop': 0.3, 'clean': 0.1},\n   }\n\nIn this fragment, start_probability refers to your uncertainty about which state the HMM is in when your friend first calls you (all you know is that it tends to be rainy on average). The particular probability distribution used here is not the equilibrium one, which is (given the transition probabilities) actually approximately {'Rainy': 0.571, 'Sunny': 0.429}. The transition_probability refers to the change of the weather in the underlying Markov chain. In this example, there is only a 30% chance that tomorrow will be sunny if today is rainy. The emission_probability tells you how likely your friend is to perform a certain activity on each day. If it's rainy, there is a 50% chance that he is cleaning his apartment; if it's sunny, there is a 60% chance that he will go outside for a walk.\n\nYou talk to your friend three days in a row and discover that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment. You have two questions: What is the overall probability of this sequence of observations? And what is the most likely sequence of rainy/sunny days that would explain these observations? The first question is answered by the forward algorithm; the second question is answered by the Viterbi algorithm. These two algorithms are structurally so similar (in fact, they are both instances of the same abstract algorithm) that they can be implemented in a single function:\n\ndef forward_viterbi(y, X, sp, tp, ep):\n   T = {}\n   for state in X:\n       ##          prob.      V. path  V. prob.\n       T[state] = (sp[state], [state], sp[state])\n   for output in y:\n       U = {}\n       for next_state in X:\n
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chigidem, armen jan. :-p\n\nThis is why I've created my own language and compiling system. Sure, it took a TON of work at first... but in the end, I ended up doing menial tasks in seconds instead of minutes or hours. :-p
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It's a recursive function that's not primitive recursive.\n\nIt grows extremely fast.  See the Wikipedia entry.
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just think in terms of 0's and 1's, programming logic, and error-correcting codes.  add to that cryptography and cryptanalysis, as well as algorithms and -- well -- flowcharts!  all of these had their roots in one mathematical idea or another, and they can be seen in almost every computer science discipline.
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The number is too small for a real "pattern" to emerge.\nThe digits could be the first 10 values of some sequence given by some ( many ) recurrence relation, could be ...\n\nIn order for a proper pattern to emerge, it should be much larger...
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Yes.\nThis is one of selling point of java
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You have to measure the approximate number or operations for each algorithm.  Say one is O(n) and another is O(2n).  \n\ncheck out some sites about algorithm complexity\n\nhttp://www.cs.ucd.ie/staff/jcarthy/home/complexity.html\nhttp://en.wikipedia.org/wiki/Big_O_notation
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Sounds like Combinatorics.  Combinations, permutations, a bit of graph theory, discrete math, I usually describe it as "Advanced Counting".
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It is a strings proccessing alg which uses a failure function to improve the run time rather than bm alg\nrun time:m+n\nm:the length of the pattern\nn: the length of the text\nwe use tis algorithm to find the pat string in the text string..\nmore details \nmail me at ziko@math.com\ni can provid a pascal source for it
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Prolog or Lisp. Machine learning\nMore detail please.\nI'm more for Java.\nBut most likely you are looking for the toolkit From MATLAB(tm) the first link.
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Neville's algorithm is an algorithm used to construct unique, interpolating polynomials. It uses n + 1 points to construct polynomials of degree n.\n\nIt is similar to Aitken's algorithm, which is nowadays not used.
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Please go here:\nhttp://www.cut-the-knot.org/recurrence/hanoi.shtml
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Irrational persistent actions
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More details pls.\nGo to the source:\nhttp://www.java.com/en
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This example illustrates recursive calls:\n\nint factorial( int num );      /* Function prototype */\n\nint main()\n{\n    int result, number;\n    .\n    .\n    .\n    result = factorial( number );\n}\n\nint factorial( int num )      /* Function definition */\n{\n    .\n    .\n    .\n    if ( ( num > 0 ) || ( num <= 10 ) )\n        return( num * factorial( num - 1 ) );\n}\n\n\n\nsorry cant find any program of that sort , and to write one i may well take more than one hour.\n\ngood luck :)