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  • Fastest way to generate delimited string from 1d numpy array

    - by Abiel
    I have a program which needs to turn many large one-dimensional numpy arrays of floats into delimited strings. I am finding this operation quite slow relative to the mathematical operations in my program and am wondering if there is a way to speed it up. For example, consider the following loop, which takes 100,000 random numbers in a numpy array and joins each array into a comma-delimited string. import numpy as np x = np.random.randn(100000) for i in range(100): ",".join(map(str, x)) This loop takes about 20 seconds to complete (total, not each cycle). In contrast, consider that 100 cycles of something like elementwise multiplication (x*x) would take than one 1/10 of a second to complete. Clearly the string join operation creates a large performance bottleneck; in my actual application it will dominate total runtime. This makes me wonder, is there a faster way than ",".join(map(str, x))? Since map() is where almost all the processing time occurs, this comes down to the question of whether there a faster to way convert a very large number of numbers to strings.

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  • Time complexity with bit cost

    - by Keyser
    I think I might have completely misunderstood bit cost analysis. I'm trying to wrap my head around the concept of studying an algorithm's time complexity with respect to bit cost (instead of unit cost) and it seems to be impossible to find anything on the subject. Is this considered to be so trivial that no one ever needs to have it explained to them? Well I do. (Also, there doesn't even seem to be anything on wikipedia which is very unusual). Here's what I have so far: The bit cost of multiplication and division of two numbers with n bits is O(n^2) (in general?) So, for example: int number = 2; for(int i = 0; i < n; i++ ){ number = i*i; } has a time complexity with respect to bit cost of O(n^3), because it does n multiplications (right?) But in a regular scenario we want the time complexity with respect to the input. So, how does that scenario work? The number of bits in i could be considered a constant. Which would make the time complexity the same as with unit cost except with a bigger constant (and both would be linear). Also, I'm guessing addition and subtraction can be done in constant time, O(1). Couldn't find any info on it but it seems reasonable since it's one assembler operation.

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  • Matlab - Propagate points orthogonally on to the edge of shape boundaries

    - by Graham
    Hi I have a set of points which I want to propagate on to the edge of shape boundary defined by a binary image. The shape boundary is defined by a 1px wide white edge. I also have the coordinates of these points stored in a 2 row by n column matrix. The shape forms a concave boundary with no holes within itself made of around 2500 points. I want to cast a ray from each point from the set of points in an orthogonal direction and detect at which point it intersects the shape boundary at. What would be the best method to do this? Are there some sort of ray tracing algorithms that could be used? Or would it be a case of taking orthogonal unit vector and multiplying it by a scalar and testing after multiplication if the end point of the vector is outside the shape boundary. When the end point of the unit vector is outside the shape, just find the point of intersection? Thank you very much in advance for any help!

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  • Is there a Boost (or other common lib) type for matrices with string keys?

    - by mohawkjohn
    I have a dense matrix where the indices correspond to genes. While gene identifiers are often integers, they are not contiguous integers. They could be strings instead, too. I suppose I could use a boost sparse matrix of some sort with integer keys, and it wouldn't matter if they're contiguous. Or would this still occupy a great deal of space, particularly if some genes have identifiers that are nine digits? Further, I am concerned that sparse storage is not appropriate, since this is an all-by-all matrix (there will be a distance in each and every cell, provided the gene exists). I'm unlikely to need to perform any matrix operations (e.g., matrix multiplication). I will need to pull vectors out of the matrix (slices). It seems like the best type of matrix would be keyed by a Boost unordered_map (a hash map), or perhaps even simply an STL map. Am I looking at this the wrong way? Do I really need to roll my own? I thought I saw such a class somewhere before. Thanks!

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  • How to Use Calculated Color Values with ColorMatrix?

    - by Otaku
    I am changing color values of each pixel in an image based on a calculation. The problem is that this takes over 5 seconds on my machine with a 1000x1333 image and I'm looking for a way to optimize it to be much faster. I think ColorMatrix may be an option, but I'm having a difficult time figure out how I would get a set of pixel RGB values, use that to calculate and then set the new pixel value. I can see how this can be done if I was just modifying (multiplying, subtracting, etc.) the original value with ColorMatrix, but now how I can use the pixels returned value to use it to calculate and new value. For example: Sub DarkenPicture() Dim clrTestFolderPath = "C:\Users\Me\Desktop\ColorTest\" Dim originalPicture = "original.jpg" Dim Luminance As Single Dim bitmapOriginal As Bitmap = Image.FromFile(clrTestFolderPath + originalPicture) Dim Clr As Color Dim newR As Byte Dim newG As Byte Dim newB As Byte For x = 0 To bitmapOriginal.Width - 1 For y = 0 To bitmapOriginal.Height - 1 Clr = bitmapOriginal.GetPixel(x, y) Luminance = ((0.21 * (Clr.R) + (0.72 * (Clr.G)) + (0.07 * (Clr.B))/ 255 newR = Clr.R * Luminance newG = Clr.G * Luminance newB = Clr.B * Luminance bitmapOriginal.SetPixel(x, y, Color.FromArgb(newR, newG, newB)) Next Next bitmapOriginal.Save(clrTestFolderPath + "colorized.jpg", ImageFormat.Jpeg) End Sub The Luminance value is the calculated one. I know I can set ColorMatrix's M00, M11, M22 to 0, 0, 0 respectively and then put a new value in M40, M41, M42, but that new value is calculated based of a value multiplication and addition of that pixel's components (((0.21 * (Clr.R) + (0.72 * (Clr.G)) + (0.07 * (Clr.B)) and the result of that - Luminance - is multiplied by the color component). Is this even possible with ColorMatrix?

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  • RFC: Whitespace's Assembly Mnemonics

    - by Noctis Skytower
    Request For Comment regarding Whitespace's Assembly Mnemonics What follows in a first generation attempt at creating mnemonics for a whitespace assembly language. STACK ===== push number copy copy number swap away away number MATH ==== add sub mul div mod HEAP ==== set get FLOW ==== part label call label goto label zero label less label back exit I/O === ochr oint ichr iint In the interest of making improvements to this small and simple instruction set, this is a second attempt. hold N Push the number onto the stack copy Duplicate the top item on the stack copy N Copy the nth item on the stack (given by the argument) onto the top of the stack swap Swap the top two items on the stack drop Discard the top item on the stack drop N Slide n items off the stack, keeping the top item add Addition sub Subtraction mul Multiplication div Integer Division mod Modulo save Store load Retrieve L: Mark a location in the program call L Call a subroutine goto L Jump unconditionally to a label if=0 L Jump to a label if the top of the stack is zero if<0 L Jump to a label if the top of the stack is negative return End a subroutine and transfer control back to the caller exit End the program print chr Output the character at the top of the stack print int Output the number at the top of the stack input chr Read a character and place it in the location given by the top of the stack input int Read a number and place it in the location given by the top of the stack What is the general consensus on the following revised list for Whitespace's assembly instructions? They definitely come from thinking outside of the box and trying to come up with a better mnemonic set than last time. When the previous python interpreter was written, it was completed over two contiguous, rushed evenings. This rewrite deserves significantly more time now that it is the summer. Of course, the next version of Whitespace (0.4) may have its instructions revised even more, but this is just a redesign of what originally was done in a few hours. Hopefully, the instructions make more sense to those new to programming jargon.

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  • Pointer Implementation Details in C

    - by Will Bickford
    I would like to know architectures which violate the assumptions I've listed below. Also I would like to know if any of the assumptions are false for all architectures (i.e. if any of them are just completely wrong). sizeof(int *) == sizeof(char *) == sizeof(void *) == sizeof(func_ptr *) The in-memory representation of all pointers for a given architecture is the same regardless of the data type pointed to. The in-memory representation of a pointer is the same as an integer of the same bit length as the architecture. Multiplication and division of pointer data types are only forbidden by the compiler. NOTE: Yes I know this is nonsensical. What I mean is - is there hardware support to forbid this incorrect usage? All pointer values can be casted to a single integer. In other words, what architectures still make use of segments and offsets? Incrementing a pointer is equivalent to adding sizeof(the pointed data type) to the memory address stored by the pointer. If p is an int32* then p+1 is equal to the memory address 4 bytes after p. I'm most used to pointers being used in a contiguous, virtual memory space. For that usage, I can generally get by thinking of them as addresses on a number line. See (http://stackoverflow.com/questions/1350471/pointer-comparison/1350488#1350488).

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  • C: 8x8 -> 16 bit multiply precision guaranteed by integer promotions?

    - by craig-blome
    I'm trying to figure out if the C Standard (C90, though I'm working off Derek Jones' annotated C99 book) guarantees that I will not lose precision multiplying two unsigned 8-bit values and storing to a 16-bit result. An example statement is as follows: unsigned char foo; unsigned int foo_u16 = foo * 10; Our Keil 8051 compiler (v7.50 at present) will generate a MUL AB instruction which stores the MSB in the B register and the LSB in the accumulator. If I cast foo to a unsigned int first: unsigned int foo_u16 = (unsigned int)foo * 10; then the compiler correctly decides I want a unsigned int there and generates an expensive call to a 16x16 bit integer multiply routine. I would like to argue beyond reasonable doubt that this defensive measure is not necessary. As I read the integer promotions described in 6.3.1.1, the effect of the first line shall be as if foo and 10 were promoted to unsigned int, the multiplication performed, and the result stored as unsigned int in foo_u16. If the compiler knows an instruction that does 8x8-16 bit multiplications without loss of precision, so much the better; but the precision is guaranteed. Am I reading this correctly? Best regards, Craig Blome

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  • help understanding differences between #define, const and enum in C and C++ on assembly level.

    - by martin
    recently, i am looking into assembly codes for #define, const and enum: C codes(#define): 3 #define pi 3 4 int main(void) 5 { 6 int a,r=1; 7 a=2*pi*r; 8 return 0; 9 } assembly codes(for line 6 and 7 in c codes) generated by GCC: 6 mov $0x1, -0x4(%ebp) 7 mov -0x4(%ebp), %edx 7 mov %edx, %eax 7 add %eax, %eax 7 add %edx, %eax 7 add %eax, %eax 7 mov %eax, -0x8(%ebp) C codes(enum): 2 int main(void) 3 { 4 int a,r=1; 5 enum{pi=3}; 6 a=2*pi*r; 7 return 0; 8 } assembly codes(for line 4 and 6 in c codes) generated by GCC: 6 mov $0x1, -0x4(%ebp) 7 mov -0x4(%ebp), %edx 7 mov %edx, %eax 7 add %eax, %eax 7 add %edx, %eax 7 add %eax, %eax 7 mov %eax, -0x8(%ebp) C codes(const): 4 int main(void) 5 { 6 int a,r=1; 7 const int pi=3; 8 a=2*pi*r; 9 return 0; 10 } assembly codes(for line 7 and 8 in c codes) generated by GCC: 6 movl $0x3, -0x8(%ebp) 7 movl $0x3, -0x4(%ebp) 8 mov -0x4(%ebp), %eax 8 add %eax, %eax 8 imul -0x8(%ebp), %eax 8 mov %eax, 0xc(%ebp) i found that use #define and enum, the assembly codes are the same. The compiler use 3 add instructions to perform multiplication. However, when use const, imul instruction is used. Anyone knows the reason behind that?

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  • Hashtable is that fast

    - by Costa
    Hi s[0]*31^(n-1) + s[1]*31^(n-2) + ... + s[n-1]. Is the hash function of the java string, I assume the rest of languages is similar or close to this implementation. If we have hash-Table and a list of 50 elements. each element is 7 chars ABCDEF1, ABCDEF2, ABCDEF3..... ABCDEFn If each bucket of hashtable contains 5 strings (I think this function will make it one string per bucket, but let us assume it is 5). If we call col.Contains("ABCDEFn"); // will do 6 comparisons and discover the difference on the 7th. The hash-table will take around 70 operations (multiplication and additions) to get the hashcode and to compare with 5 strings in bucket. and BANG it found. For list it will take around 300 comparisons to find it. for the case that there is only 10 elements, the list will take around 70 operations but the Hashtable will take around 50 operations. and note that hashtable operations are more time consuming (it is multiplications). I conclude that HybirdDictionary in .Net probably is the best choice for that most cases that require Hashtable with unknown size, because it will let me use a list till the list becomes more than 10 elements. still need something like HashSet rather than a Dictionary of keys and values, I wonder why there is no HybirdSet!! So what do u think? Thanks

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  • How to insert zeros between bits in a bitmap?

    - by anatolyg
    I have some performance-heavy code that performs bit manipulations. It can be reduced to the following well-defined problem: Given a 13-bit bitmap, construct a 26-bit bitmap that contains the original bits spaced at even positions. To illustrate: 0000000000000000000abcdefghijklm (input, 32 bits) 0000000a0b0c0d0e0f0g0h0i0j0k0l0m (output, 32 bits) I currently have it implemented in the following way in C: if (input & (1 << 12)) output |= 1 << 24; if (input & (1 << 11)) output |= 1 << 22; if (input & (1 << 10)) output |= 1 << 20; ... My compiler (MS Visual Studio) turned this into the following: test eax,1000h jne 0064F5EC or edx,1000000h ... (repeated 13 times with minor differences in constants) I wonder whether i can make it any faster. I would like to have my code written in C, but switching to assembly language is possible. Can i use some MMX/SSE instructions to process all bits at once? Maybe i can use multiplication? (multiply by 0x11111111 or some other magical constant) Would it be better to use condition-set instruction (SETcc) instead of conditional-jump instruction? If yes, how can i make the compiler produce such code for me? Any other idea how to make it faster? Any idea how to do the inverse bitmap transformation (i have to implement it too, bit it's less critical)?

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  • C++ Matrix class hierachy

    - by bpw1621
    Should a matrix software library have a root class (e.g., MatrixBase) from which more specialized (or more constrained) matrix classes (e.g., SparseMatrix, UpperTriangluarMatrix, etc.) derive? If so, should the derived classes be derived publicly/protectively/privately? If not, should they be composed with a implementation class encapsulating common functionality and be otherwise unrelated? Something else? I was having a conversation about this with a software developer colleague (I am not per se) who mentioned that it is a common programming design mistake to derive a more restricted class from a more general one (e.g., he used the example of how it was not a good idea to derive a Circle class from an Ellipse class as similar to the matrix design issue) even when it is true that a SparseMatrix "IS A" MatrixBase. The interface presented by both the base and derived classes should be the same for basic operations; for specialized operations, a derived class would have additional functionality that might not be possible to implement for an arbitrary MatrixBase object. For example, we can compute the cholesky decomposition only for a PositiveDefiniteMatrix class object; however, multiplication by a scalar should work the same way for both the base and derived classes. Also, even if the underlying data storage implementation differs the operator()(int,int) should work as expected for any type of matrix class. I have started looking at a few open-source matrix libraries and it appears like this is kind of a mixed bag (or maybe I'm looking at a mixed bag of libraries). I am planning on helping out with a refactoring of a math library where this has been a point of contention and I'd like to have opinions (that is unless there really is an objective right answer to this question) as to what design philosophy would be best and what are the pros and cons to any reasonable approach.

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  • How to write a simple Lexer/Parser with antlr 2.7?

    - by Burkhard
    Hello, I have a complex grammar (in antlr 2.7) which I need to extend. Having never used antlr before, I wanted to write a very simple Lexer and Parser first. I found a very good explanation for antlr3 and tried to adapt it: header{ #include <iostream> using namespace std; } options { language="Cpp"; } class P2 extends Parser; /* This will be the entry point of our parser. */ eval : additionExp ; /* Addition and subtraction have the lowest precedence. */ additionExp : multiplyExp ( "+" multiplyExp | "-" multiplyExp )* ; /* Multiplication and addition have a higher precedence. */ multiplyExp : atomExp ( "*" atomExp | "/" atomExp )* ; /* An expression atom is the smallest part of an expression: a number. Or when we encounter parenthesis, we're making a recursive call back to the rule 'additionExp'. As you can see, an 'atomExp' has the highest precedence. */ atomExp : Number | "(" additionExp ")" ; /* A number: can be an integer value, or a decimal value */ number : ("0".."9")+ ("." ("0".."9")+)? ; /* We're going to ignore all white space characters */ protected ws : (" " | "\t" | "\r" | "\n") { newline(); } ; It does generate four files without errors: P2.cpp, P2.hpp, P2TokenTypes.hpp and P2TokenTypes.txt. But now what? How do I create a working programm with that? I tried to add these files to a VS2005-WinConsole-Project but it does not compile: p2.cpp(277) : fatal error C1010: unexpected end of file while looking for precompiled header. Did you forget to add '#include "stdafx.h"' to your source?

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  • [Python] Tips for making a fraction calculator code more optimized (faster and using less memory)

    - by Logic Named Joe
    Hello Everyone, Basicly, what I need for the program to do is to act a as simple fraction calculator (for addition, subtraction, multiplication and division) for the a single line of input, for example: -input: 1/7 + 3/5 -output: 26/35 My initial code: import sys def euclid(numA, numB): while numB != 0: numRem = numA % numB numA = numB numB = numRem return numA for wejscie in sys.stdin: wyjscie = wejscie.split(' ') a, b = [int(x) for x in wyjscie[0].split("/")] c, d = [int(x) for x in wyjscie[2].split("/")] if wyjscie[1] == '+': licz = a * d + b * c mian= b * d nwd = euclid(licz, mian) konA = licz/nwd konB = mian/nwd wynik = str(konA) + '/' + str(konB) print(wynik) elif wyjscie[1] == '-': licz= a * d - b * c mian= b * d nwd = euclid(licz, mian) konA = licz/nwd konB = mian/nwd wynik = str(konA) + '/' + str(konB) print(wynik) elif wyjscie[1] == '*': licz= a * c mian= b * d nwd = euclid(licz, mian) konA = licz/nwd konB = mian/nwd wynik = str(konA) + '/' + str(konB) print(wynik) else: licz= a * d mian= b * c nwd = euclid(licz, mian) konA = licz/nwd konB = mian/nwd wynik = str(konA) + '/' + str(konB) print(wynik) Which I reduced to: import sys def euclid(numA, numB): while numB != 0: numRem = numA % numB numA = numB numB = numRem return numA for wejscie in sys.stdin: wyjscie = wejscie.split(' ') a, b = [int(x) for x in wyjscie[0].split("/")] c, d = [int(x) for x in wyjscie[2].split("/")] if wyjscie[1] == '+': print("/".join([str((a * d + b * c)/euclid(a * d + b * c, b * d)),str((b * d)/euclid(a * d + b * c, b * d))])) elif wyjscie[1] == '-': print("/".join([str((a * d - b * c)/euclid(a * d - b * c, b * d)),str((b * d)/euclid(a * d - b * c, b * d))])) elif wyjscie[1] == '*': print("/".join([str((a * c)/euclid(a * c, b * d)),str((b * d)/euclid(a * c, b * d))])) else: print("/".join([str((a * d)/euclid(a * d, b * c)),str((b * c)/euclid(a * d, b * c))])) Any advice on how to improve this futher is welcome. Edit: one more thing that I forgot to mention - the code can not make use of any libraries apart from sys.

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  • Basic FreeMat/MATLAB syntax - dimension error

    - by 0x90
    I am using FreeMat, and I have an RGB picture which is a 3D matrix contains the columns and rows of the pictures and the RGB values for each pixel. Since there is not an intrinsic function to convert RGB picture to YIQ, I have implement one. I came up with this code: Assume I have a 3D array, image_rgb: matrix = [0.299 0.587 0.114; 0.596 -0.274 -0.322; 0.211 -0.523 0.312]; row = 1:length(image_rgb(:,1,1)); col = 1:length(image_rgb(1,:,1)); p = image_rgb(row,col,:); %Here I have the problem mage_yiq(row,col,:) = matrix*image_rgb(row,col,:); max_y = max (max(image_yiq(:,:,1))); max_i = max (max(image_yiq(:,:,2))); max_q = max (max(image_yiq(:,:,3))); %Renormalize the image again after the multipication % to [0,1]. image_yiq(:,:,1) = image_yiq(:,:,1)/max_y; image_yiq(:,:,2) = image_yiq(:,:,2)/max_i; image_yiq(:,:,3) = image_yiq(:,:,3)/max_q; I can't understand why the matrix multiplication fails. I want the code to be nice and not just to, multiply the matrix by hand...

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  • Computation overhead in C# - Using getters/setters vs. modifying arrays directly and casting speeds

    - by Jeffrey Kern
    I was going to write a long-winded post, but I'll boil it down here: I'm trying to emulate the graphical old-school style of the NES via XNA. However, my FPS is SLOW, trying to modify 65K pixels per frame. If I just loop through all 65K pixels and set them to some arbitrary color, I get 64FPS. The code I made to look-up what colors should be placed where, I get 1FPS. I think it is because of my object-orented code. Right now, I have things divided into about six classes, with getters/setters. I'm guessing that I'm at least calling 360K getters per frame, which I think is a lot of overhead. Each class contains either/and-or 1D or 2D arrays containing custom enumerations, int, Color, or Vector2D, bytes. What if I combined all of the classes into just one, and accessed the contents of each array directly? The code would look a mess, and ditch the concepts of object-oriented coding, but the speed might be much faster. I'm also not concerned about access violations, as any attempts to get/set the data in the arrays will done in blocks. E.g., all writing to arrays will take place before any data is accessed from them. As for casting, I stated that I'm using custom enumerations, int, Color, and Vector2D, bytes. Which data types are fastest to use and access in the .net Framework, XNA, XBox, C#? I think that constant casting might be a cause of slowdown here. Also, instead of using math to figure out which indexes data should be placed in, I've used precomputed lookup tables so I don't have to use constant multiplication, addition, subtraction, division per frame. :)

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  • negative values in integer programming model

    - by Lucia
    I'm new at using the glpk tool, and after writing a model for certain integer problem and running the solver (glpsol) i get negative values in some constraint that shouldn't be negative at all: No.Row name Activity Lower bound Upper bound 8 act[1] 0 -0 9 act[2] -3 -0 10 act[2] -2 -0 That constraint is defined like this: act{j in J}: sum{i in I} d[i,j] <= y[j]*m; where the sets and variables used are like this: param m, integer, 0; param n, integer, 0; set I := 1..m; set J := 1..n; var y{j in J}, binary; As the upper bound is negative, i think the problem may be in the y[j]*m parte, of the right side of the inequality.. perhaps something with the multiplication of binarys? or that the j in that side of the constrait is undefined? i dont know... i would be greatly grateful if someone can help me with this! :) and excuse for my bad english thanks in advance!

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  • Parallelize or vectorize all-against-all operation on a large number of matrices?

    - by reve_etrange
    I have approximately 5,000 matrices with the same number of rows and varying numbers of columns (20 x ~200). Each of these matrices must be compared against every other in a dynamic programming algorithm. In this question, I asked how to perform the comparison quickly and was given an excellent answer involving a 2D convolution. Serially, iteratively applying that method, like so list = who('data_matrix_prefix*') H = cell(numel(list),numel(list)); for i=1:numel(list) for j=1:numel(list) if i ~= j eval([ 'H{i,j} = compare(' char(list(i)) ',' char(list(j)) ');']); end end end is fast for small subsets of the data (e.g. for 9 matrices, 9*9 - 9 = 72 calls are made in ~1 s). However, operating on all the data requires almost 25 million calls. I have also tried using deal() to make a cell array composed entirely of the next element in data, so I could use cellfun() in a single loop: # who(), load() and struct2cell() calls place k data matrices in a 1D cell array called data. nextData = cell(k,1); for i=1:k [nextData{:}] = deal(data{i}); H{:,i} = cellfun(@compare,data,nextData,'UniformOutput',false); end Unfortunately, this is not really any faster, because all the time is in compare(). Both of these code examples seem ill-suited for parallelization. I'm having trouble figuring out how to make my variables sliced. compare() is totally vectorized; it uses matrix multiplication and conv2() exclusively (I am under the impression that all of these operations, including the cellfun(), should be multithreaded in MATLAB?). Does anyone see a (explicitly) parallelized solution or better vectorization of the problem?

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  • When to use CTEs to encapsulate sub-results, and when to let the RDBMS worry about massive joins.

    - by IanC
    This is a SQL theory question. I can provide an example, but I don't think it's needed to make my point. Anyone experienced with SQL will immediately know what I'm talking about. Usually we use joins to minimize the number of records due to matching the left and right rows. However, under certain conditions, joining tables cause a multiplication of results where the result is all permutations of the left and right records. I have a database which has 3 or 4 such joins. This turns what would be a few records into a multitude. My concern is that the tables will be large in production, so the number of these joined rows will be immense. Further, heavy math is performed on each row, and the idea of performing math on duplicate rows is enough to make anyone shudder. I have two questions. The first is, is this something I should care about, or will SQL Server intelligently realize these rows are all duplicates and optimize all processing accordingly? The second is, is there any advantage to grouping each part of the query so as to get only the distinct values going into the next part of the query, using something like: WITH t1 AS ( SELECT DISTINCT... [or GROUP BY] ), t2 AS ( SELECT DISTINCT... ), t3 AS ( SELECT DISTINCT... ) SELECT... I have often seen the use of DISTINCT applied to subqueries. There is obviously a reason for doing this. However, I'm talking about something a little different and perhaps more subtle and tricky.

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  • c++ float subtraction rounding error

    - by Volkan Ozyilmaz
    I have a float value between 0 and 1. I need to convert it with -120 to 80. To do this, first I multiply with 200 after 120 subtract. When subtract is made I had rounding error. Let's look my example. float val = 0.6050f; val *= 200.f; Now val is 121.0 as I expected. val -= 120.0f; Now val is 0.99999992 I thought maybe I can avoid this problem with multiplication and division. float val = 0.6050f; val *= 200.f; val *= 100.f; val -= 12000.0f; val /= 100.f; But it didn't help. I have still 0.99 on my hand. Is there a solution for it? Edit: After with detailed logging, I understand there is no problem with this part of code. Before my log shows me "0.605", after I had detailed log and I saw "0.60499995946884155273437500000000000000000000000000" the problem is in different place.

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  • C# 4.0: Dynamic Programming

    - by Paulo Morgado
    The major feature of C# 4.0 is dynamic programming. Not just dynamic typing, but dynamic in broader sense, which means talking to anything that is not statically typed to be a .NET object. Dynamic Language Runtime The Dynamic Language Runtime (DLR) is piece of technology that unifies dynamic programming on the .NET platform, the same way the Common Language Runtime (CLR) has been a common platform for statically typed languages. The CLR always had dynamic capabilities. You could always use reflection, but its main goal was never to be a dynamic programming environment and there were some features missing. The DLR is built on top of the CLR and adds those missing features to the .NET platform. The Dynamic Language Runtime is the core infrastructure that consists of: Expression Trees The same expression trees used in LINQ, now improved to support statements. Dynamic Dispatch Dispatches invocations to the appropriate binder. Call Site Caching For improved efficiency. Dynamic languages and languages with dynamic capabilities are built on top of the DLR. IronPython and IronRuby were already built on top of the DLR, and now, the support for using the DLR is being added to C# and Visual Basic. Other languages built on top of the CLR are expected to also use the DLR in the future. Underneath the DLR there are binders that talk to a variety of different technologies: .NET Binder Allows to talk to .NET objects. JavaScript Binder Allows to talk to JavaScript in SilverLight. IronPython Binder Allows to talk to IronPython. IronRuby Binder Allows to talk to IronRuby. COM Binder Allows to talk to COM. Whit all these binders it is possible to have a single programming experience to talk to all these environments that are not statically typed .NET objects. The dynamic Static Type Let’s take this traditional statically typed code: Calculator calculator = GetCalculator(); int sum = calculator.Sum(10, 20); Because the variable that receives the return value of the GetCalulator method is statically typed to be of type Calculator and, because the Calculator type has an Add method that receives two integers and returns an integer, it is possible to call that Sum method and assign its return value to a variable statically typed as integer. Now lets suppose the calculator was not a statically typed .NET class, but, instead, a COM object or some .NET code we don’t know he type of. All of the sudden it gets very painful to call the Add method: object calculator = GetCalculator(); Type calculatorType = calculator.GetType(); object res = calculatorType.InvokeMember("Add", BindingFlags.InvokeMethod, null, calculator, new object[] { 10, 20 }); int sum = Convert.ToInt32(res); And what if the calculator was a JavaScript object? ScriptObject calculator = GetCalculator(); object res = calculator.Invoke("Add", 10, 20); int sum = Convert.ToInt32(res); For each dynamic domain we have a different programming experience and that makes it very hard to unify the code. With C# 4.0 it becomes possible to write code this way: dynamic calculator = GetCalculator(); int sum = calculator.Add(10, 20); You simply declare a variable who’s static type is dynamic. dynamic is a pseudo-keyword (like var) that indicates to the compiler that operations on the calculator object will be done dynamically. The way you should look at dynamic is that it’s just like object (System.Object) with dynamic semantics associated. Anything can be assigned to a dynamic. dynamic x = 1; dynamic y = "Hello"; dynamic z = new List<int> { 1, 2, 3 }; At run-time, all object will have a type. In the above example x is of type System.Int32. When one or more operands in an operation are typed dynamic, member selection is deferred to run-time instead of compile-time. Then the run-time type is substituted in all variables and normal overload resolution is done, just like it would happen at compile-time. The result of any dynamic operation is always dynamic and, when a dynamic object is assigned to something else, a dynamic conversion will occur. Code Resolution Method double x = 1.75; double y = Math.Abs(x); compile-time double Abs(double x) dynamic x = 1.75; dynamic y = Math.Abs(x); run-time double Abs(double x) dynamic x = 2; dynamic y = Math.Abs(x); run-time int Abs(int x) The above code will always be strongly typed. The difference is that, in the first case the method resolution is done at compile-time, and the others it’s done ate run-time. IDynamicMetaObjectObject The DLR is pre-wired to know .NET objects, COM objects and so forth but any dynamic language can implement their own objects or you can implement your own objects in C# through the implementation of the IDynamicMetaObjectProvider interface. When an object implements IDynamicMetaObjectProvider, it can participate in the resolution of how method calls and property access is done. The .NET Framework already provides two implementations of IDynamicMetaObjectProvider: DynamicObject : IDynamicMetaObjectProvider The DynamicObject class enables you to define which operations can be performed on dynamic objects and how to perform those operations. For example, you can define what happens when you try to get or set an object property, call a method, or perform standard mathematical operations such as addition and multiplication. ExpandoObject : IDynamicMetaObjectProvider The ExpandoObject class enables you to add and delete members of its instances at run time and also to set and get values of these members. This class supports dynamic binding, which enables you to use standard syntax like sampleObject.sampleMember, instead of more complex syntax like sampleObject.GetAttribute("sampleMember").

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  • Simple POST random number issue in PHP

    - by MrEnder
    Ok I am trying to make something to ask you random multiplication questions. Now it asks the questions fine. Generates the random questions fine. But when it reloads the page the random numbers are different... how can I fix this? <?php $rndnum1 = rand(1, 12); $rndnum2 = rand(1, 12); echo "<h3>". $rndnum1 . " x "; echo $rndnum2 . "</h3>"; if($_SERVER["REQUEST_METHOD"] == "GET") { $answer=0; } else if($_SERVER["REQUEST_METHOD"] == "POST") { $answer=trim($_POST["answerInput"]); $check=$rndnum1*$rndnum2; if($answer==$check) { echo "Correct!"; } else { echo "Wrong!"; } } ?> <form action="<?php echo $_SERVER['PHP_SELF']; ?>" method="post" > <table> <tr> <td> First Name:&nbsp; </td> <td> <input type="text" name="answerInput" value="<?php echo $answer; ?>" size="20"/> </td> <td> <?php echo $answerError; ?> </td> </tr> <tr> <td class="signupTd" colspan="2"> <input type="submit" name="submit" value="Submit"/> </td> </tr> </table> </form>

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  • spoj: runlength

    - by user285825
    For RLM problem of SPOJ: This is the problem: "Run-length encoding of a number replaces a run of digits (that is, a sequence of consecutive equivalent digits) with the number of digits followed by the digit itself. For example, 44455 would become 3425 (three fours, two fives). Note that run-length encoding does not necessarily shorten the length of the data: 11 becomes 21, and 42 becomes 1412. If a number has more than nine consecutive digits of the same type, the encoding is done greedily: each run grabs as many digits as it can, so 111111111111111 is encoded as 9161. Implement an integer arithmetic calculator that takes operands and gives results in run-length format. You should support addition, subtraction, multiplication, and division. You won't have to divide by zero or deal with negative numbers. Input/Output The input will consist of several test cases, one per line. For each test case, compute the run-length mathematics expression and output the original expression and the result, as shown in the examples. The (decimal) representation of all operands and results will fit in signed 64-bit integers." These are my testcases: input: 11 + 11 988726 - 978625 12 * 41 1124 / 1112 13 * 33 15 / 16 19222317121013161815142715181017 + 10 10 + 19222317121013161815142715181017 19222317121013161815142715181017 / 19222317121013161815142715181017 19222317121013161815142715181017 / 11 11 / 19222317121013161815142715181017 19222317121013161815142715181017 / 12 12 / 19222317121013161815142715181017 19222317121013161815142715181017 / 141621161816101118141217131817191014 141621161816101118141217131817191014 / 19222317121013161815142715181017 19222317121013161815142715181017 / 141621161816101118141217131817191013 141621161816101118141217131817191013 / 19222317121013161815142715181017 19222317121013161815142715181017 * 11 11 * 19222317121013161815142715181017 19222317121013161815142715181017 * 10 10 * 19222317121013161815142715181017 19222317121013161815142715181017 - 10 19222317121013161815142715181017 - 19222317121013161815142715181017 19222317121013161815142715181017 - 141621161816101118141217131817191014 19222317121013161815142715181017 - 141621161816101118141217131817191013 141621161816101118141217131817191013 + 141621161816101118141217131817191013 141621161816101118141217131817191013 + 141621161816101118141217131817191014 141621161816101118141217131817191014 + 141621161816101118141217131817191013 141621161816101118141217131817191014 + 10 10 + 141621161816101118141217131817191013 141621161816101118141217131817191013 + 11 11 + 141621161816101118141217131817191013 141621161816101118141217131817191013 * 12 12 * 141621161816101118141217131817191013 141621161816101118141217131817191014 - 141621161816101118141217131817191014 141621161816101118141217131817191013 - 141621161816101118141217131817191013 141621161816101118141217131817191013 - 10 141621161816101118141217131817191014 - 11 141621161816101118141217131817191014 - 141621161816101118141217131817191013 141621161816101118141217131817191014 / 141621161816101118141217131817191014 141621161816101118141217131817191014 / 141621161816101118141217131817191013 141621161816101118141217131817191013 / 141621161816101118141217131817191014 141621161816101118141217131817191013 / 141621161816101118141217131817191013 141621161816101118141217131817191014 * 11 11 * 141621161816101118141217131817191014 141621161816101118141217131817191014 / 11 11 / 141621161816101118141217131817191014 10 + 10 10 + 11 10 + 15 15 + 10 11 + 10 11 + 10 10 - 10 15 - 10 10 * 10 10 * 15 15 * 10 10 / 111213 output: 11 + 11 = 12 988726 - 978625 = 919111 12 * 41 = 42 1124 / 1112 = 1112 13 * 33 = 39 15 / 16 = 10 19222317121013161815142715181017 + 10 = 19222317121013161815142715181017 10 + 19222317121013161815142715181017 = 19222317121013161815142715181017 19222317121013161815142715181017 / 19222317121013161815142715181017 = 11 19222317121013161815142715181017 / 11 = 19222317121013161815142715181017 11 / 19222317121013161815142715181017 = 10 19222317121013161815142715181017 / 12 = 141621161816101118141217131817191013 12 / 19222317121013161815142715181017 = 10 19222317121013161815142715181017 / 141621161816101118141217131817191014 = 11 141621161816101118141217131817191014 / 19222317121013161815142715181017 = 10 19222317121013161815142715181017 / 141621161816101118141217131817191013 = 12 141621161816101118141217131817191013 / 19222317121013161815142715181017 = 10 19222317121013161815142715181017 * 11 = 19222317121013161815142715181017 11 * 19222317121013161815142715181017 = 19222317121013161815142715181017 19222317121013161815142715181017 * 10 = 10 10 * 19222317121013161815142715181017 = 10 19222317121013161815142715181017 - 10 = 19222317121013161815142715181017 19222317121013161815142715181017 - 19222317121013161815142715181017 = 10 19222317121013161815142715181017 - 141621161816101118141217131817191014 = 141621161816101118141217131817191013 19222317121013161815142715181017 - 141621161816101118141217131817191013 = 141621161816101118141217131817191014 141621161816101118141217131817191013 + 141621161816101118141217131817191013 = 19222317121013161815142715181016 141621161816101118141217131817191013 + 141621161816101118141217131817191014 = 19222317121013161815142715181017 141621161816101118141217131817191014 + 141621161816101118141217131817191013 = 19222317121013161815142715181017 141621161816101118141217131817191014 + 10 = 141621161816101118141217131817191014 10 + 141621161816101118141217131817191013 = 141621161816101118141217131817191013 141621161816101118141217131817191013 + 11 = 141621161816101118141217131817191014 11 + 141621161816101118141217131817191013 = 141621161816101118141217131817191014 141621161816101118141217131817191013 * 12 = 19222317121013161815142715181016 12 * 141621161816101118141217131817191013 = 19222317121013161815142715181016 141621161816101118141217131817191014 - 141621161816101118141217131817191014 = 10 141621161816101118141217131817191013 - 141621161816101118141217131817191013 = 10 141621161816101118141217131817191013 - 10 = 141621161816101118141217131817191013 141621161816101118141217131817191014 - 11 = 141621161816101118141217131817191013 141621161816101118141217131817191014 - 141621161816101118141217131817191013 = 11 141621161816101118141217131817191014 / 141621161816101118141217131817191014 = 11 141621161816101118141217131817191014 / 141621161816101118141217131817191013 = 11 141621161816101118141217131817191013 / 141621161816101118141217131817191014 = 10 141621161816101118141217131817191013 / 141621161816101118141217131817191013 = 11 141621161816101118141217131817191014 * 11 = 141621161816101118141217131817191014 11 * 141621161816101118141217131817191014 = 141621161816101118141217131817191014 141621161816101118141217131817191014 / 11 = 141621161816101118141217131817191014 11 / 141621161816101118141217131817191014 = 10 10 + 10 = 10 10 + 11 = 11 10 + 15 = 15 15 + 10 = 15 11 + 10 = 11 11 + 10 = 11 10 - 10 = 10 15 - 10 = 15 10 * 10 = 10 10 * 15 = 10 15 * 10 = 10 10 / 111213 = 10 I am getting consistently wrong answer. I generated the above testcases trying to make them as representative as possible (boundary conditions, etc). I am not sure how to test it further. Some guidline would be really appreciated.

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  • [Android] For-Loop Performance Oddity

    - by Jack Holt
    I just noticed something concerning for-loop performance that seems to fly in the face of the recommendations given by the Google Android team. Look at the following code: package com.jackcholt; import android.app.Activity; import android.os.Bundle; import android.util.Log; public class Main extends Activity { @Override public void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.main); loopTest(); finish(); } private void loopTest() { final long loopCount = 1228800; final int[] image = new int[8 * 320 * 480]; long start = System.currentTimeMillis(); for (int i = 0; i < (8 * 320 * 480); i++) { image[i] = i; } for (int i = 0; i < (8 * 320 * 480); i++) { image[i] = i; } Log.i("loopTest", "Elapsed time (recompute loop limit): " + (System.currentTimeMillis() - start)); start = System.currentTimeMillis(); for (int i = 0; i < 1228800; i++) { image[i] = i; } for (int i = 0; i < 1228800; i++) { image[i] = i; } Log.i("loopTest", "Elapsed time (literal loop limit): " + (System.currentTimeMillis() - start)); start = System.currentTimeMillis(); for (int i = 0; i < loopCount; i++) { image[i] = i; } for (int i = 0; i < loopCount; i++) { image[i] = i; } Log.i("loopTest", "Elapsed time (precompute loop limit): " + (System.currentTimeMillis() - start)); } } When I run this code I get the following output in logcat: I/loopTest( 726): Elapsed time (recompute loop limit): 759 I/loopTest( 726): Elapsed time (literal loop limit): 755 I/loopTest( 726): Elapsed time (precompute loop limit): 1317 As you can see the code that seems to recompute the loop limit value on every iteration of the loop compares very well to the code that uses a literal value for the loop limit. However, the code that uses a variable which contains the precomputed value for the loop limit is significantly slower than either of the others. I'm not surprised that accessing a variable should be slower that using a literal but why does code that looks like it should be using two multiply instructions on every iteration of the loop so comparable in performance to a literal? Could it be that because literals are the only thing being multiplied, the Java compiler is optimizing out the multiplication and using a precomputed literal?

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  • Would someone mind giving suggestions for this new assembly language?

    - by Noctis Skytower
    Greetings! Last semester in college, my teacher in the Computer Languages class taught us the esoteric language named Whitespace. In the interest of learning the language better with a very busy schedule (midterms), I wrote an interpreter and assembler in Python. An assembly language was designed to facilitate writing programs easily, and a sample program was written with the given assembly mnemonics. Now that it is summer, a new project has begun with the objective being to rewrite the interpreter and assembler for Whitespace 0.3, with further developments coming afterwards. Since there is so much extra time than before to work on its design, you are presented here with an outline that provides a revised set of mnemonics for the assembly language. This post is marked as a wiki for their discussion. Have you ever had any experience with assembly languages in the past? Were there some instructions that you thought should have been renamed to something different? Did you find yourself thinking outside the box and with a different paradigm than in which the mnemonics were named? If you can answer yes to any of those questions, you are most welcome here. Subjective answers are appreciated! hold N Push the number onto the stack copy Duplicate the top item on the stack copy N Copy the nth item on the stack (given by the argument) onto the top of the stack swap Swap the top two items on the stack drop Discard the top item on the stack drop N Slide n items off the stack, keeping the top item add Addition sub Subtraction mul Multiplication div Integer Division mod Modulo save Store load Retrieve L: Mark a location in the program call L Call a subroutine goto L Jump unconditionally to a label if=0 L Jump to a label if the top of the stack is zero if<0 L Jump to a label if the top of the stack is negative return End a subroutine and transfer control back to the caller exit End the program print chr Output the character at the top of the stack print int Output the number at the top of the stack input chr Read a character and place it in the location given by the top of the stack input int Read a number and place it in the location given by the top of the stack Question: How would you redesign, rewrite, or rename the previous mnemonics and for what reasons?

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