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  • Python-daemon doesn't kill its kids

    - by Brian M. Hunt
    When using python-daemon, I'm creating subprocesses likeso: import multiprocessing class Worker(multiprocessing.Process): def __init__(self, queue): self.queue = queue # we wait for things from this in Worker.run() ... q = multiprocessing.Queue() with daemon.DaemonContext(): for i in xrange(3): Worker(q) while True: # let the Workers do their thing q.put(_something_we_wait_for()) When I kill the parent daemonic process (i.e. not a Worker) with a Ctrl-C or SIGTERM, etc., the children don't die. How does one kill the kids? My first thought is to use atexit to kill all the workers, likeso: with daemon.DaemonContext(): workers = list() for i in xrange(3): workers.append(Worker(q)) @atexit.register def kill_the_children(): for w in workers: w.terminate() while True: # let the Workers do their thing q.put(_something_we_wait_for()) However, the children of daemons are tricky things to handle, and I'd be obliged for thoughts and input on how this ought to be done. Thank you.

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  • a more pythonic way to express conditionally bounded loop?

    - by msw
    I've got a loop that wants to execute to exhaustion or until some user specified limit is reached. I've got a construct that looks bad yet I can't seem to find a more elegant way to express it; is there one? def ello_bruce(limit=None): for i in xrange(10**5): if predicate(i): if not limit is None: limit -= 1 if limit <= 0: break def predicate(i): # lengthy computation return True Holy nesting! There has to be a better way. For purposes of a working example, xrange is used where I normally have an iterator of finite but unknown length (and predicate sometimes returns False).

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  • Common Pitfalls in Python

    - by Anurag Uniyal
    Today I was bitten again by "Mutable default arguments" after many years. I usually don't use mutable default arguments unless needed but I think with time I forgot about that, and today in the application I added tocElements=[] in a pdf generation function's argument list and now 'Table of Content' gets longer and longer after each invocation of "generate pdf" :) My question is what other things should I add to my list of things to MUST avoid? Mutable default arguments Import modules always same way e.g. from y import x and import x are different things, they are treated as different modules. Do not use range in place of lists because range() will become an iterator anyway, the following will fail: myIndexList = [0,1,3] isListSorted = myIndexList == range(3) # will fail in 3.0 isListSorted = myIndexList == list(range(3)) # will not same thing can be mistakenly done with xrange: `myIndexList == xrange(3)`. Catching multiple exceptions try: raise KeyError("hmm bug") except KeyError,TypeError: print TypeError It prints "hmm bug", though it is not a bug, it looks like we are catching exceptions of type KeyError,TypeError but instead we are catching KeyError only as variable TypeError, use this instead: try: raise KeyError("hmm bug") except (KeyError,TypeError): print TypeError

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  • Find the closest vector

    - by Alexey Lebedev
    Hello! Recently I wrote the algorithm to quantize an RGB image. Every pixel is represented by an (R,G,B) vector, and quantization codebook is a couple of 3-dimensional vectors. Every pixel of the image needs to be mapped to (say, "replaced by") the codebook pixel closest in terms of euclidean distance (more exactly, squared euclidean). I did it as follows: class EuclideanMetric(DistanceMetric): def __call__(self, x, y): d = x - y return sqrt(sum(d * d, -1)) class Quantizer(object): def __init__(self, codebook, distanceMetric = EuclideanMetric()): self._codebook = codebook self._distMetric = distanceMetric def quantize(self, imageArray): quantizedRaster = zeros(imageArray.shape) X = quantizedRaster.shape[0] Y = quantizedRaster.shape[1] for i in xrange(0, X): print i for j in xrange(0, Y): dist = self._distMetric(imageArray[i,j], self._codebook) code = argmin(dist) quantizedRaster[i,j] = self._codebook[code] return quantizedRaster ...and it works awfully, almost 800 seconds on my Pentium Core Duo 2.2 GHz, 4 Gigs of memory and an image of 2600*2700 pixels:( Is there a way to somewhat optimize this? Maybe the other algorithm or some Python-specific optimizations.

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  • Python - List of Lists Slicing Behavior

    - by Dan Dobint
    When I define a list and try to change a single item like this: list_of_lists = [['a', 'a', 'a'], ['a', 'a', 'a'], ['a', 'a', 'a']] list_of_lists[1][1] = 'b' for row in list_of_lists: print row It works as intended. But when I try to use list comprehension to create the list: row = ['a' for range in xrange(3)] list_of_lists = [row for range in xrange(3)] list_of_lists[1][1] = 'b' for row in list_of_lists: print row It results in an entire column of items in the list being changed. Why is this? How can I achieve the desired effect with list comprehension?

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  • how to fill a part of a circle using PIL?

    - by valya
    hello. I'm trying to use PIL for a task but the result is very dirty. What I'm doing is trying to fill a part of a piece of a circle, as you can see on the image. Here is my code: def gen_image(values): side = 568 margin = 47 image = Image.open(settings.MEDIA_ROOT + "/i/promo_circle.jpg") draw = ImageDraw.Draw(image) draw.ellipse((margin, margin, side-margin, side-margin), outline="white") center = side/2 r = side/2 - margin cnt = len(values) for n in xrange(cnt): angle = n*(360.0/cnt) - 90 next_angle = (n+1)*(360.0/cnt) - 90 nr = (r * values[n] / 5) max_r = r min_r = nr for cr in xrange(min_r*10, max_r*10): cr = cr/10.0 draw.arc((side/2-cr, side/2-cr, side/2+cr, side/2+cr), angle, next_angle, fill="white") return image

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  • ValueError: too many values to unpack in a tuple

    - by falosi
    Please put some light on why am getting a too many to unpack (ValueError in my for loop).Have tried deb naislist = [('CONTROL FILE', '0', '0', '0'), ('REDO LOG', '0', '0', '0'), ('ARCHIVED LOG', '.69', '.59', '3'), ('BACKUP PIECE', '46.54', '0', '192'), ('IMAGE COPY', '0', '0', '0'), ('FLASHBACK LOG', '10.15', '6.31', '82'), ('FOREIGN ARCHIVED LOG', '0', '0', '0')] print "size of naislist is ",len((naislist)) heading = ('MAIN MENU', 'LEVELS', 'LEVEL2', 'LEVEL3') rearrange = dict(zip((0, 1, 2, 3), (len(str(x)) for x in heading))) for tu, x in naislist: rearrange.update((i, max(rearrange[i], len(str(el)))) for i, el in enumerate(tu)) rearrange[4] = max(rearrange[4], len(str(x))) forkit = '|'. join('%%-%ss' % rearrange[i] for i in xrange(0, 4)) print '\n'.join((forkit % heading, '-|-'.join(rearrange[i] * '-' for i in xrange(4)), '\n'.join(forkit % (a, b, c, d) for (a, b, c), d in naislist)))

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  • python - from matrix to dictionary in single line

    - by Sanich
    matrix is a list of lists. I've to return a dictionary of the form {i:(l1[i],l2[i],...,lm[i])} Where the key i is matched with a tuple the i'th elements from each list. Say matrix=[[1,2,3,4],[9,8,7,6],[4,8,2,6]] so the line: >>> dict([(i,tuple(matrix[k][i] for k in xrange(len(matrix)))) for i in xrange(len(matrix[0]))]) does the job pretty well and outputs: {0: (1, 9, 4), 1: (2, 8, 8), 2: (3, 7, 2), 3: (4, 6, 6)} but fails if the matrix is empty: matrix=[]. The output should be: {} How can i deal with this?

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  • Python - multi-line array

    - by Ockonal
    Hi guys, in c++ I can wrote: int someArray[8][8]; for (int i=0; i < 7; i++) for (int j=0; j < 7; j++) someArray[i][j] = 0; And how can I initialize multi-line arrays in python? I tried: array = [[],[]] for i in xrange(8): for j in xrange(8): array[i][j] = 0

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  • Python finding index in a array

    - by NIH
    I am trying to see if a company from a list of companies is in a line in a file. If it is I utilize the index of that company to increment a variable in another array. The following is my python code. I keep getting the following error: AttributeError: 'set' object has no attribute 'index'. I cannot figure out what is going wrong and think the error is the line that is surrounded by **. companies={'white house black market', 'macy','nordstrom','filene','walmart'} positives=[0 for x in xrange(len(companies))] negatives=[0 for x in xrange(len(companies))] for line in f: for company in companies: if company in line.lower(): words=tokenize.word_tokenize(line) bag=bag_of_words(words) classif=classifier.classify(bag) if classif=='pos': **indice =companies.index(company)** positives[indice]+=1 elif classif=='neg': **indice =companies.index(company)** negatives[indice]+=1

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  • Chunking a List - .NET vs Python

    - by Abhijeet Patel
    Chunking a List As I mentioned last time, I'm knee deep in python these days. I come from a statically typed background so it's definitely a mental adjustment. List comprehensions is BIG in Python and having worked with a few of them I can see why. Let's say we need to chunk a list into sublists of a specified size. Here is how we'd do it in C#  static class Extensions   {       public static IEnumerable<List<T>> Chunk<T>(this List<T> l, int chunkSize)       {           if (chunkSize <0)           {               throw new ArgumentException("chunkSize cannot be negative", "chunkSize");           }           for (int i = 0; i < l.Count; i += chunkSize)           {               yield return new List<T>(l.Skip(i).Take(chunkSize));           }       }    }    static void Main(string[] args)  {           var l = new List<string> { "a", "b", "c", "d", "e", "f","g" };             foreach (var list in l.Chunk(7))           {               string str = list.Aggregate((s1, s2) => s1 + "," + s2);               Console.WriteLine(str);           }   }   A little wordy but still pretty concise thanks to LINQ.We skip the iteration number plus chunkSize elements and yield out a new List of chunkSize elements on each iteration. The python implementation is a bit more terse. def chunkIterable(iter, chunkSize):      '''Chunks an iterable         object into a list of the specified chunkSize     '''        assert hasattr(iter, "__iter__"), "iter is not an iterable"      for i in xrange(0, len(iter), chunkSize):          yield iter[i:i + chunkSize]    if __name__ == '__main__':      l = ['a', 'b', 'c', 'd', 'e', 'f']      generator = chunkIterable(l,2)      try:          while(1):              print generator.next()      except StopIteration:          pass   xrange generates elements in the specified range taking in a seed and returning a generator. which can be used in a for loop(much like using a C# iterator in a foreach loop) Since chunkIterable has a yield statement, it turns this method into a generator as well. iter[i:i + chunkSize] essentially slices the list based on the current iteration index and chunksize and creates a new list that we yield out to the caller one at a time. A generator much like an iterator is a state machine and each subsequent call to it remembers the state at which the last call left off and resumes execution from that point. The caveat to keep in mind is that since variables are not explicitly typed we need to ensure that the object passed in is iterable using hasattr(iter, "__iter__").This way we can perform chunking on any object which is an "iterable", very similar to accepting an IEnumerable in the .NET land

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  • C# Why can't I find Sum() of this HashSet. says "Arithmetic operation resulted in an overflow."

    - by user2332665
    I was trying to solve this problem projecteuler,problem125 this is my solution in python(just for understanding the logic) import math lim=10**8 found=set() for start in xrange(1,int(math.sqrt(lim))): sos = start*start for i in xrange(start+1,int(math.sqrt(lim))): sos += (i*i) if sos >= lim: break s=str(int(sos)) if s==s[::-1]: found.add(sos) print sum(found) the same code I wrote in C# is as follows using System; using System.Collections.Generic; using System.Linq; using System.Text; namespace ConsoleApplication1 { class Program { public static bool isPalindrome(string s) { string temp = ""; for (int i=s.Length-1;i>=0;i-=1){temp+=s[i];} return (temp == s); } static void Main(string[] args) { int lim = Convert.ToInt32(Math.Pow(10,8)); var found = new HashSet<int>(); for (int start = 1; start < Math.Sqrt(lim); start += 1) { int s = start *start; for (int i = start + 1; start < Math.Sqrt(lim); i += 1) { s += i * i; if (s > lim) { break; } if (isPalindrome(s.ToString())) { found.Add(s); } } } Console.WriteLine(found.Sum()); } } } the code debugs fine until it gives an exception at Console.WriteLine(found.Sum()); (line31). Why can't I find Sum() of the set found

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  • Combinatorial optimisation of a distance metric

    - by Jose
    I have a set of trajectories, made up of points along the trajectory, and with the coordinates associated with each point. I store these in a 3d array ( trajectory, point, param). I want to find the set of r trajectories that have the maximum accumulated distance between the possible pairwise combinations of these trajectories. My first attempt, which I think is working looks like this: max_dist = 0 for h in itertools.combinations ( xrange(num_traj), r): for (m,l) in itertools.combinations (h, 2): accum = 0. for ( i, j ) in itertools.izip ( range(k), range(k) ): A = [ (my_mat[m, i, z] - my_mat[l, j, z])**2 \ for z in xrange(k) ] A = numpy.array( numpy.sqrt (A) ).sum() accum += A if max_dist < accum: selected_trajectories = h This takes forever, as num_traj can be around 500-1000, and r can be around 5-20. k is arbitrary, but can typically be up to 50. Trying to be super-clever, I have put everything into two nested list comprehensions, making heavy use of itertools: chunk = [[ numpy.sqrt((my_mat[m, i, :] - my_mat[l, j, :])**2).sum() \ for ((m,l),i,j) in \ itertools.product ( itertools.combinations(h,2), range(k), range(k)) ]\ for h in itertools.combinations(range(num_traj), r) ] Apart from being quite unreadable (!!!), it is also taking a long time. Can anyone suggest any ways to improve on this?

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  • Setting an Excel Range with an Array using Python and comtypes?

    - by technomalogical
    Using comtypes to drive Python, it seems some magic is happening behind the scenes that is not converting tuples and lists to VARIANT types: # RANGE(“C14:D21”) has values # Setting the Value on the Range with a Variant should work, but # list or tuple is not getting converted properly it seems >>>from comtypes.client import CreateObject >>>xl = CreateObject("Excel.application") >>>xl.Workbooks.Open(r'C:\temp\my_file.xlsx') >>>xl.Visible = True >>>vals=tuple([(x,y) for x,y in zip('abcdefgh',xrange(8))]) # creates: #(('a', 0), ('b', 1), ('c', 2), ('d', 3), ('e', 4), ('f', 5), ('g', 6), ('h', 7)) >>>sheet = xl.Workbooks[1].Sheets["Sheet1"] >>>sheet.Range["C14","D21"].Value() (('foo',1),('foo',2),('foo',3),('foo',4),('foo',6),('foo',6),('foo',7),('foo',8)) >>>sheet.Range["C14","D21"].Value[()] = vals # no error, this blanks out the cells in the Range According to the comtypes docs: When you pass simple sequences (lists or tuples) as VARIANT parameters, the COM server will receive a VARIANT containing a SAFEARRAY of VARIANTs with the typecode VT_ARRAY | VT_VARIANT. This seems to be inline with what MSDN says about passing an array to a Range's Value. I also found this page showing something similar in C#. Can anybody tell me what I'm doing wrong? EDIT I've come up with a simpler example that performs the same way (in that, it does not work): >>>from comtypes.client import CreateObject >>>xl = CreateObject("Excel.application") >>>xl.Workbooks.Add() >>>sheet = xl.Workbooks[1].Sheets["Sheet1"] # at this point, I manually typed into the range A1:B3 >>> sheet.Range("A1","B3").Value() ((u'AAA', 1.0), (u'BBB', 2.0), (u'CCC', 3.0)) >>>sheet.Range("A1","B3").Value[()] = [(x,y) for x,y in zip('xyz',xrange(3))] # Using a generator expression, per @Mike's comment # However, this still blanks out my range :(

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  • Python - Things one MUST avoid

    - by Anurag Uniyal
    Today I was bitten again by "Mutable default arguments" after many years. I usually don't use mutable default arguments unless needed but I think with time I forgot about that, and today in the application I added tocElements=[] in a pdf generation function's argument list and now 'Table of Content' gets longer and longer after each invocation of "generate pdf" :) My question is what other things should I add to my list of things to MUST avoid? 1 Mutable default arguments 2 import modules always same way e.g. 'from y import x' and 'import x' are totally different things actually they are treated as different modules see http://stackoverflow.com/questions/1459236/module-reimported-if-imported-from-different-path 3 Do not use range in place of lists because range() will become an iterator anyway, so things like this will fail, so wrap it by list myIndexList = [0,1,3] isListSorted = myIndexList == range(3) # will fail in 3.0 isListSorted = myIndexList == list(range(3)) # will not same thing can be mistakenly done with xrange e.g myIndexList == xrange(3). 4 Catching multiple exceptions try: raise KeyError("hmm bug") except KeyError,TypeError: print TypeError It prints "hmm bug", though it is not a bug, it looks like we are catching exceptions of type KeyError,TypeError but instead we are catching KeyError only as variable TypeError, instead use try: raise KeyError("hmm bug") except (KeyError,TypeError): print TypeError

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  • Python optimization problem?

    - by user342079
    Alright, i had this homework recently (don't worry, i've already done it, but in c++) but I got curious how i could do it in python. The problem is about 2 light sources that emit light. I won't get into details tho. Here's the code (that I've managed to optimize a bit in the latter part): import math, array import numpy as np from PIL import Image size = (800,800) width, height = size s1x = width * 1./8 s1y = height * 1./8 s2x = width * 7./8 s2y = height * 7./8 r,g,b = (255,255,255) arr = np.zeros((width,height,3)) hy = math.hypot print 'computing distances (%s by %s)'%size, for i in xrange(width): if i%(width/10)==0: print i, if i%20==0: print '.', for j in xrange(height): d1 = hy(i-s1x,j-s1y) d2 = hy(i-s2x,j-s2y) arr[i][j] = abs(d1-d2) print '' arr2 = np.zeros((width,height,3),dtype="uint8") for ld in [200,116,100,84,68,52,36,20,8,4,2]: print 'now computing image for ld = '+str(ld) arr2 *= 0 arr2 += abs(arr%ld-ld/2)*(r,g,b)/(ld/2) print 'saving image...' ar2img = Image.fromarray(arr2) ar2img.save('ld'+str(ld).rjust(4,'0')+'.png') print 'saved as ld'+str(ld).rjust(4,'0')+'.png' I have managed to optimize most of it, but there's still a huge performance gap in the part with the 2 for-s, and I can't seem to think of a way to bypass that using common array operations... I'm open to suggestions :D

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  • Scala n00b: Critique my code

    - by Peter
    G'day everyone, I'm a Scala n00b (but am experienced with other languages) and am learning the language as I find time - very much enjoying it so far! Usually when learning a new language the first thing I do is implement Conway's Game of Life, since it's just complex enough to give a good sense of the language, but small enough in scope to be able to whip up in a couple of hours (most of which is spent wrestling with syntax). Anyhoo, having gone through this exercise with Scala I was hoping the Scala gurus out there might take a look at the code I've ended up with and provide feedback on it. I'm after anything - algorithmic improvements (particularly concurrent solutions!), stylistic improvements, alternative APIs or language constructs, disgust at the length of my function names - whatever feedback you've got, I'm keen to hear it! You should be able to run the following script via "scala GameOfLife.scala" - by default it will run a 20x20 board with a single glider on it - please feel free to experiment. // CONWAY'S GAME OF LIFE (SCALA) abstract class GameOfLifeBoard(val aliveCells : Set[Tuple2[Int, Int]]) { // Executes a "time tick" - returns a new board containing the next generation def tick : GameOfLifeBoard // Is the board empty? def empty : Boolean = aliveCells.size == 0 // Is the given cell alive? protected def alive(cell : Tuple2[Int, Int]) : Boolean = aliveCells contains cell // Is the given cell dead? protected def dead(cell : Tuple2[Int, Int]) : Boolean = !alive(cell) } class InfiniteGameOfLifeBoard(aliveCells : Set[Tuple2[Int, Int]]) extends GameOfLifeBoard(aliveCells) { // Executes a "time tick" - returns a new board containing the next generation override def tick : GameOfLifeBoard = new InfiniteGameOfLifeBoard(nextGeneration) // The next generation of this board protected def nextGeneration : Set[Tuple2[Int, Int]] = aliveCells flatMap neighbours filter shouldCellLiveInNextGeneration // Should the given cell should live in the next generation? protected def shouldCellLiveInNextGeneration(cell : Tuple2[Int, Int]) : Boolean = (alive(cell) && (numberOfAliveNeighbours(cell) == 2 || numberOfAliveNeighbours(cell) == 3)) || (dead(cell) && numberOfAliveNeighbours(cell) == 3) // The number of alive neighbours for the given cell protected def numberOfAliveNeighbours(cell : Tuple2[Int, Int]) : Int = aliveNeighbours(cell) size // Returns the alive neighbours for the given cell protected def aliveNeighbours(cell : Tuple2[Int, Int]) : Set[Tuple2[Int, Int]] = aliveCells intersect neighbours(cell) // Returns all neighbours (whether dead or alive) for the given cell protected def neighbours(cell : Tuple2[Int, Int]) : Set[Tuple2[Int, Int]] = Set((cell._1-1, cell._2-1), (cell._1, cell._2-1), (cell._1+1, cell._2-1), (cell._1-1, cell._2), (cell._1+1, cell._2), (cell._1-1, cell._2+1), (cell._1, cell._2+1), (cell._1+1, cell._2+1)) // Information on where the currently live cells are protected def xVals = aliveCells map { cell => cell._1 } protected def xMin = (xVals reduceLeft (_ min _)) - 1 protected def xMax = (xVals reduceLeft (_ max _)) + 1 protected def xRange = xMin until xMax + 1 protected def yVals = aliveCells map { cell => cell._2 } protected def yMin = (yVals reduceLeft (_ min _)) - 1 protected def yMax = (yVals reduceLeft (_ max _)) + 1 protected def yRange = yMin until yMax + 1 // Returns a simple graphical representation of this board override def toString : String = { var result = "" for (y <- yRange) { for (x <- xRange) { if (alive (x,y)) result += "# " else result += ". " } result += "\n" } result } // Equality stuff override def equals(other : Any) : Boolean = { other match { case that : InfiniteGameOfLifeBoard => (that canEqual this) && that.aliveCells == this.aliveCells case _ => false } } def canEqual(other : Any) : Boolean = other.isInstanceOf[InfiniteGameOfLifeBoard] override def hashCode = aliveCells.hashCode } class FiniteGameOfLifeBoard(val boardWidth : Int, val boardHeight : Int, aliveCells : Set[Tuple2[Int, Int]]) extends InfiniteGameOfLifeBoard(aliveCells) { override def tick : GameOfLifeBoard = new FiniteGameOfLifeBoard(boardWidth, boardHeight, nextGeneration) // Determines the coordinates of all of the neighbours of the given cell override protected def neighbours(cell : Tuple2[Int, Int]) : Set[Tuple2[Int, Int]] = super.neighbours(cell) filter { cell => cell._1 >= 0 && cell._1 < boardWidth && cell._2 >= 0 && cell._2 < boardHeight } // Information on where the currently live cells are override protected def xRange = 0 until boardWidth override protected def yRange = 0 until boardHeight // Equality stuff override def equals(other : Any) : Boolean = { other match { case that : FiniteGameOfLifeBoard => (that canEqual this) && that.boardWidth == this.boardWidth && that.boardHeight == this.boardHeight && that.aliveCells == this.aliveCells case _ => false } } override def canEqual(other : Any) : Boolean = other.isInstanceOf[FiniteGameOfLifeBoard] override def hashCode : Int = { 41 * ( 41 * ( 41 + super.hashCode ) + boardHeight.hashCode ) + boardWidth.hashCode } } class GameOfLife(initialBoard: GameOfLifeBoard) { // Run the game of life until the board is empty or the exact same board is seen twice // Important note: this method does NOT necessarily terminate!! def go : Unit = { var currentBoard = initialBoard var previousBoards = List[GameOfLifeBoard]() while (!currentBoard.empty && !(previousBoards contains currentBoard)) { print(27.toChar + "[2J") // ANSI: clear screen print(27.toChar + "[;H") // ANSI: move cursor to top left corner of screen println(currentBoard.toString) Thread.sleep(75) // Warning: unbounded list concatenation can result in OutOfMemoryExceptions ####TODO: replace with LRU bounded list previousBoards = List(currentBoard) ::: previousBoards currentBoard = currentBoard tick } // Print the final board print(27.toChar + "[2J") // ANSI: clear screen print(27.toChar + "[;H") // ANSI: move cursor to top left corner of screen println(currentBoard.toString) } } // Script starts here val simple = Set((1,1)) val square = Set((4,4), (4,5), (5,4), (5,5)) val glider = Set((2,1), (3,2), (1,3), (2,3), (3,3)) val initialBoard = glider (new GameOfLife(new FiniteGameOfLifeBoard(20, 20, initialBoard))).go //(new GameOfLife(new InfiniteGameOfLifeBoard(initialBoard))).go // COPYRIGHT PETER MONKS 2010 Thanks! Peter

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  • PyML 0.7.2 - How to prevent accuracy from dropping after storing/loading a classifier?

    - by Michael Aaron Safyan
    This is a followup from "Save PyML.classifiers.multi.OneAgainstRest(SVM()) object?". The solution to that question was close, but not quite right, (the SparseDataSet is broken, so attempting to save/load with that dataset container type will fail, no matter what. Also, PyML is inconsistent in terms of whether labels should be numbers or strings... it turns out that the oneAgainstRest function is actually not good enough, because the labels need to be strings and simultaneously convertible to floats, because there are places where it is assumed to be a string and elsewhere converted to float) and so after a great deal of hacking and such I was finally able to figure out a way to save and load my multi-class classifier without it blowing up with an error.... however, although it is no longer giving me an error message, it is still not quite right as the accuracy of the classifier drops significantly when it is saved and then reloaded (so I'm still missing a piece of the puzzle). I am currently using the following custom mutli-class classifier for training, saving, and loading: class SVM(object): def __init__(self,features_or_filename,labels=None,kernel=None): if isinstance(features_or_filename,str): filename=features_or_filename; if labels!=None: raise ValueError,"Labels must be None if loading from a file."; with open(os.path.join(filename,"uniquelabels.list"),"rb") as uniquelabelsfile: self.uniquelabels=sorted(list(set(pickle.load(uniquelabelsfile)))); self.labeltoindex={}; for idx,label in enumerate(self.uniquelabels): self.labeltoindex[label]=idx; self.classifiers=[]; for classidx, classname in enumerate(self.uniquelabels): self.classifiers.append(PyML.classifiers.svm.loadSVM(os.path.join(filename,str(classname)+".pyml.svm"),datasetClass = PyML.VectorDataSet)); else: features=features_or_filename; if labels==None: raise ValueError,"Labels must not be None when training."; self.uniquelabels=sorted(list(set(labels))); self.labeltoindex={}; for idx,label in enumerate(self.uniquelabels): self.labeltoindex[label]=idx; points = [[float(xij) for xij in xi] for xi in features]; self.classifiers=[PyML.SVM(kernel) for label in self.uniquelabels]; for i in xrange(len(self.uniquelabels)): currentlabel=self.uniquelabels[i]; currentlabels=['+1' if k==currentlabel else '-1' for k in labels]; currentdataset=PyML.VectorDataSet(points,L=currentlabels,positiveClass='+1'); self.classifiers[i].train(currentdataset,saveSpace=False); def accuracy(self,pts,labels): logger=logging.getLogger("ml"); correct=0; total=0; classindexes=[self.labeltoindex[label] for label in labels]; h=self.hypotheses(pts); for idx in xrange(len(pts)): if h[idx]==classindexes[idx]: logger.info("RIGHT: Actual \"%s\" == Predicted \"%s\"" %(self.uniquelabels[ classindexes[idx] ], self.uniquelabels[ h[idx] ])); correct+=1; else: logger.info("WRONG: Actual \"%s\" != Predicted \"%s\"" %(self.uniquelabels[ classindexes[idx] ], self.uniquelabels[ h[idx] ])) total+=1; return float(correct)/float(total); def prediction(self,pt): h=self.hypothesis(pt); if h!=None: return self.uniquelabels[h]; return h; def predictions(self,pts): h=self.hypotheses(self,pts); return [self.uniquelabels[x] if x!=None else None for x in h]; def hypothesis(self,pt): bestvalue=None; bestclass=None; dataset=PyML.VectorDataSet([pt]); for classidx, classifier in enumerate(self.classifiers): val=classifier.decisionFunc(dataset,0); if (bestvalue==None) or (val>bestvalue): bestvalue=val; bestclass=classidx; return bestclass; def hypotheses(self,pts): bestvalues=[None for pt in pts]; bestclasses=[None for pt in pts]; dataset=PyML.VectorDataSet(pts); for classidx, classifier in enumerate(self.classifiers): for ptidx in xrange(len(pts)): val=classifier.decisionFunc(dataset,ptidx); if (bestvalues[ptidx]==None) or (val>bestvalues[ptidx]): bestvalues[ptidx]=val; bestclasses[ptidx]=classidx; return bestclasses; def save(self,filename): if not os.path.exists(filename): os.makedirs(filename); with open(os.path.join(filename,"uniquelabels.list"),"wb") as uniquelabelsfile: pickle.dump(self.uniquelabels,uniquelabelsfile,pickle.HIGHEST_PROTOCOL); for classidx, classname in enumerate(self.uniquelabels): self.classifiers[classidx].save(os.path.join(filename,str(classname)+".pyml.svm")); I am using the latest version of PyML (0.7.2, although PyML.__version__ is 0.7.0). When I construct the classifier with a training dataset, the reported accuracy is ~0.87. When I then save it and reload it, the accuracy is less than 0.001. So, there is something here that I am clearly not persisting correctly, although what that may be is completely non-obvious to me. Would you happen to know what that is?

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  • PyML 0.7.2 - How to prevent accuracy from dropping after stroing/loading a classifier?

    - by Michael Aaron Safyan
    This is a followup from "Save PyML.classifiers.multi.OneAgainstRest(SVM()) object?". The solution to that question was close, but not quite right, (the SparseDataSet is broken, so attempting to save/load with that dataset container type will fail, no matter what. Also, PyML is inconsistent in terms of whether labels should be numbers or strings... it turns out that the oneAgainstRest function is actually not good enough, because the labels need to be strings and simultaneously convertible to floats, because there are places where it is assumed to be a string and elsewhere converted to float) and so after a great deal of hacking and such I was finally able to figure out a way to save and load my multi-class classifier without it blowing up with an error.... however, although it is no longer giving me an error message, it is still not quite right as the accuracy of the classifier drops significantly when it is saved and then reloaded (so I'm still missing a piece of the puzzle). I am currently using the following custom mutli-class classifier for training, saving, and loading: class SVM(object): def __init__(self,features_or_filename,labels=None,kernel=None): if isinstance(features_or_filename,str): filename=features_or_filename; if labels!=None: raise ValueError,"Labels must be None if loading from a file."; with open(os.path.join(filename,"uniquelabels.list"),"rb") as uniquelabelsfile: self.uniquelabels=sorted(list(set(pickle.load(uniquelabelsfile)))); self.labeltoindex={}; for idx,label in enumerate(self.uniquelabels): self.labeltoindex[label]=idx; self.classifiers=[]; for classidx, classname in enumerate(self.uniquelabels): self.classifiers.append(PyML.classifiers.svm.loadSVM(os.path.join(filename,str(classname)+".pyml.svm"),datasetClass = PyML.VectorDataSet)); else: features=features_or_filename; if labels==None: raise ValueError,"Labels must not be None when training."; self.uniquelabels=sorted(list(set(labels))); self.labeltoindex={}; for idx,label in enumerate(self.uniquelabels): self.labeltoindex[label]=idx; points = [[float(xij) for xij in xi] for xi in features]; self.classifiers=[PyML.SVM(kernel) for label in self.uniquelabels]; for i in xrange(len(self.uniquelabels)): currentlabel=self.uniquelabels[i]; currentlabels=['+1' if k==currentlabel else '-1' for k in labels]; currentdataset=PyML.VectorDataSet(points,L=currentlabels,positiveClass='+1'); self.classifiers[i].train(currentdataset,saveSpace=False); def accuracy(self,pts,labels): logger=logging.getLogger("ml"); correct=0; total=0; classindexes=[self.labeltoindex[label] for label in labels]; h=self.hypotheses(pts); for idx in xrange(len(pts)): if h[idx]==classindexes[idx]: logger.info("RIGHT: Actual \"%s\" == Predicted \"%s\"" %(self.uniquelabels[ classindexes[idx] ], self.uniquelabels[ h[idx] ])); correct+=1; else: logger.info("WRONG: Actual \"%s\" != Predicted \"%s\"" %(self.uniquelabels[ classindexes[idx] ], self.uniquelabels[ h[idx] ])) total+=1; return float(correct)/float(total); def prediction(self,pt): h=self.hypothesis(pt); if h!=None: return self.uniquelabels[h]; return h; def predictions(self,pts): h=self.hypotheses(self,pts); return [self.uniquelabels[x] if x!=None else None for x in h]; def hypothesis(self,pt): bestvalue=None; bestclass=None; dataset=PyML.VectorDataSet([pt]); for classidx, classifier in enumerate(self.classifiers): val=classifier.decisionFunc(dataset,0); if (bestvalue==None) or (val>bestvalue): bestvalue=val; bestclass=classidx; return bestclass; def hypotheses(self,pts): bestvalues=[None for pt in pts]; bestclasses=[None for pt in pts]; dataset=PyML.VectorDataSet(pts); for classidx, classifier in enumerate(self.classifiers): for ptidx in xrange(len(pts)): val=classifier.decisionFunc(dataset,ptidx); if (bestvalues[ptidx]==None) or (val>bestvalues[ptidx]): bestvalues[ptidx]=val; bestclasses[ptidx]=classidx; return bestclasses; def save(self,filename): if not os.path.exists(filename): os.makedirs(filename); with open(os.path.join(filename,"uniquelabels.list"),"wb") as uniquelabelsfile: pickle.dump(self.uniquelabels,uniquelabelsfile,pickle.HIGHEST_PROTOCOL); for classidx, classname in enumerate(self.uniquelabels): self.classifiers[classidx].save(os.path.join(filename,str(classname)+".pyml.svm")); I am using the latest version of PyML (0.7.2, although PyML.__version__ is 0.7.0). When I construct the classifier with a training dataset, the reported accuracy is ~0.87. When I then save it and reload it, the accuracy is less than 0.001. So, there is something here that I am clearly not persisting correctly, although what that may be is completely non-obvious to me. Would you happen to know what that is?

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  • Project Euler 9: (Iron)Python

    - by Ben Griswold
    In my attempt to learn (Iron)Python out in the open, here’s my solution for Project Euler Problem 9.  As always, any feedback is welcome. # Euler 9 # http://projecteuler.net/index.php?section=problems&id=9 # A Pythagorean triplet is a set of three natural numbers, # a b c, for which, # a2 + b2 = c2 # For example, 32 + 42 = 9 + 16 = 25 = 52. # There exists exactly one Pythagorean triplet for which # a + b + c = 1000. Find the product abc. import time start = time.time() product = 0 def pythagorean_triplet(): for a in range(1,501): for b in xrange(a+1,501): c = 1000 - a - b if (a*a + b*b == c*c): return a*b*c print pythagorean_triplet() print "Elapsed Time:", (time.time() - start) * 1000, "millisecs" a=raw_input('Press return to continue')

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  • Compound assignment operators in Python's Numpy library

    - by Leonard
    The "vectorizing" of fancy indexing by Python's numpy library sometimes gives unexpected results. For example: import numpy a = numpy.zeros((1000,4), dtype='uint32') b = numpy.zeros((1000,4), dtype='uint32') i = numpy.random.random_integers(0,999,1000) j = numpy.random.random_integers(0,3,1000) a[i,j] += 1 for k in xrange(1000): b[i[k],j[k]] += 1 Gives different results in the arrays 'a' and 'b' (i.e. the appearance of tuple (i,j) appears as 1 in 'a' regardless of repeats, whereas repeats are counted in 'b'). This is easily verified as follows: numpy.sum(a) 883 numpy.sum(b) 1000 It is also notable that the fancy indexing version is almost two orders of magnitude faster than the for loop. My question is: "Is there an efficient way for numpy to compute the repeat counts as implemented using the for loop in the provided example?"

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  • Project Euler 8: (Iron)Python

    - by Ben Griswold
    In my attempt to learn (Iron)Python out in the open, here’s my solution for Project Euler Problem 8.  As always, any feedback is welcome. # Euler 8 # http://projecteuler.net/index.php?section=problems&id=8 # Find the greatest product of five consecutive digits # in the following 1000-digit number import time start = time.time() number = '\ 73167176531330624919225119674426574742355349194934\ 96983520312774506326239578318016984801869478851843\ 85861560789112949495459501737958331952853208805511\ 12540698747158523863050715693290963295227443043557\ 66896648950445244523161731856403098711121722383113\ 62229893423380308135336276614282806444486645238749\ 30358907296290491560440772390713810515859307960866\ 70172427121883998797908792274921901699720888093776\ 65727333001053367881220235421809751254540594752243\ 52584907711670556013604839586446706324415722155397\ 53697817977846174064955149290862569321978468622482\ 83972241375657056057490261407972968652414535100474\ 82166370484403199890008895243450658541227588666881\ 16427171479924442928230863465674813919123162824586\ 17866458359124566529476545682848912883142607690042\ 24219022671055626321111109370544217506941658960408\ 07198403850962455444362981230987879927244284909188\ 84580156166097919133875499200524063689912560717606\ 05886116467109405077541002256983155200055935729725\ 71636269561882670428252483600823257530420752963450' max = 0 for i in xrange(0, len(number) - 5): nums = [int(x) for x in number[i:i+5]] val = reduce(lambda agg, x: agg*x, nums) if val > max: max = val print max print "Elapsed Time:", (time.time() - start) * 1000, "millisecs" a=raw_input('Press return to continue')

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  • Project Euler 13: (Iron)Python

    - by Ben Griswold
    In my attempt to learn (Iron)Python out in the open, here’s my solution for Project Euler Problem 13.  As always, any feedback is welcome. # Euler 13 # http://projecteuler.net/index.php?section=problems&id=13 # Work out the first ten digits of the sum of the # following one-hundred 50-digit numbers. import time start = time.time() number_string = '\ 37107287533902102798797998220837590246510135740250\ 46376937677490009712648124896970078050417018260538\ 74324986199524741059474233309513058123726617309629\ 91942213363574161572522430563301811072406154908250\ 23067588207539346171171980310421047513778063246676\ 89261670696623633820136378418383684178734361726757\ 28112879812849979408065481931592621691275889832738\ 44274228917432520321923589422876796487670272189318\ 47451445736001306439091167216856844588711603153276\ 70386486105843025439939619828917593665686757934951\ 62176457141856560629502157223196586755079324193331\ 64906352462741904929101432445813822663347944758178\ 92575867718337217661963751590579239728245598838407\ 58203565325359399008402633568948830189458628227828\ 80181199384826282014278194139940567587151170094390\ 35398664372827112653829987240784473053190104293586\ 86515506006295864861532075273371959191420517255829\ 71693888707715466499115593487603532921714970056938\ 54370070576826684624621495650076471787294438377604\ 53282654108756828443191190634694037855217779295145\ 36123272525000296071075082563815656710885258350721\ 45876576172410976447339110607218265236877223636045\ 17423706905851860660448207621209813287860733969412\ 81142660418086830619328460811191061556940512689692\ 51934325451728388641918047049293215058642563049483\ 62467221648435076201727918039944693004732956340691\ 15732444386908125794514089057706229429197107928209\ 55037687525678773091862540744969844508330393682126\ 18336384825330154686196124348767681297534375946515\ 80386287592878490201521685554828717201219257766954\ 78182833757993103614740356856449095527097864797581\ 16726320100436897842553539920931837441497806860984\ 48403098129077791799088218795327364475675590848030\ 87086987551392711854517078544161852424320693150332\ 59959406895756536782107074926966537676326235447210\ 69793950679652694742597709739166693763042633987085\ 41052684708299085211399427365734116182760315001271\ 65378607361501080857009149939512557028198746004375\ 35829035317434717326932123578154982629742552737307\ 94953759765105305946966067683156574377167401875275\ 88902802571733229619176668713819931811048770190271\ 25267680276078003013678680992525463401061632866526\ 36270218540497705585629946580636237993140746255962\ 24074486908231174977792365466257246923322810917141\ 91430288197103288597806669760892938638285025333403\ 34413065578016127815921815005561868836468420090470\ 23053081172816430487623791969842487255036638784583\ 11487696932154902810424020138335124462181441773470\ 63783299490636259666498587618221225225512486764533\ 67720186971698544312419572409913959008952310058822\ 95548255300263520781532296796249481641953868218774\ 76085327132285723110424803456124867697064507995236\ 37774242535411291684276865538926205024910326572967\ 23701913275725675285653248258265463092207058596522\ 29798860272258331913126375147341994889534765745501\ 18495701454879288984856827726077713721403798879715\ 38298203783031473527721580348144513491373226651381\ 34829543829199918180278916522431027392251122869539\ 40957953066405232632538044100059654939159879593635\ 29746152185502371307642255121183693803580388584903\ 41698116222072977186158236678424689157993532961922\ 62467957194401269043877107275048102390895523597457\ 23189706772547915061505504953922979530901129967519\ 86188088225875314529584099251203829009407770775672\ 11306739708304724483816533873502340845647058077308\ 82959174767140363198008187129011875491310547126581\ 97623331044818386269515456334926366572897563400500\ 42846280183517070527831839425882145521227251250327\ 55121603546981200581762165212827652751691296897789\ 32238195734329339946437501907836945765883352399886\ 75506164965184775180738168837861091527357929701337\ 62177842752192623401942399639168044983993173312731\ 32924185707147349566916674687634660915035914677504\ 99518671430235219628894890102423325116913619626622\ 73267460800591547471830798392868535206946944540724\ 76841822524674417161514036427982273348055556214818\ 97142617910342598647204516893989422179826088076852\ 87783646182799346313767754307809363333018982642090\ 10848802521674670883215120185883543223812876952786\ 71329612474782464538636993009049310363619763878039\ 62184073572399794223406235393808339651327408011116\ 66627891981488087797941876876144230030984490851411\ 60661826293682836764744779239180335110989069790714\ 85786944089552990653640447425576083659976645795096\ 66024396409905389607120198219976047599490197230297\ 64913982680032973156037120041377903785566085089252\ 16730939319872750275468906903707539413042652315011\ 94809377245048795150954100921645863754710598436791\ 78639167021187492431995700641917969777599028300699\ 15368713711936614952811305876380278410754449733078\ 40789923115535562561142322423255033685442488917353\ 44889911501440648020369068063960672322193204149535\ 41503128880339536053299340368006977710650566631954\ 81234880673210146739058568557934581403627822703280\ 82616570773948327592232845941706525094512325230608\ 22918802058777319719839450180888072429661980811197\ 77158542502016545090413245809786882778948721859617\ 72107838435069186155435662884062257473692284509516\ 20849603980134001723930671666823555245252804609722\ 53503534226472524250874054075591789781264330331690' total = 0 for i in xrange(0, 100 * 50 - 1, 50): total += int(number_string[i:i+49]) print str(total)[:10] print "Elapsed Time:", (time.time() - start) * 1000, "millisecs" a=raw_input('Press return to continue')

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  • Python: Create a duplicate of an array

    - by Dan
    I have an double array alist[1][1]=-1 alist2=[] for x in xrange(10): alist2.append(alist[x]) alist2[1][1]=15 print alist[1][1] and I get 15. Clearly I'm passing a pointer rather than an actual variable... Is there an easy way to make a seperate double array (no shared pointers) without having to do a double for loop? Thanks, Dan

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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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