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  • Binomial test in Python

    - by Morlock
    I need to do a binomial test in Python that allows calculation for 'n' numbers of the order of 10000. I have implemented a quick binomial_test function using scipy.misc.comb, however, it is pretty much limited around n = 1000, I guess because it reaches the biggest representable number while computing factorials or the combinatorial itself. Here is my function: from scipy.misc import comb def binomial_test(n, k): """Calculate binomial probability """ p = comb(n, k) * 0.5**k * 0.5**(n-k) return p How could I use a native python (or numpy, scipy...) function in order to calculate that binomial probability? If possible, I need scipy 0.7.2 compatible code. Many thanks!

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  • Binomial test in Python for very large numbers

    - by Morlock
    I need to do a binomial test in Python that allows calculation for 'n' numbers of the order of 10000. I have implemented a quick binomial_test function using scipy.misc.comb, however, it is pretty much limited around n = 1000, I guess because it reaches the biggest representable number while computing factorials or the combinatorial itself. Here is my function: from scipy.misc import comb def binomial_test(n, k): """Calculate binomial probability """ p = comb(n, k) * 0.5**k * 0.5**(n-k) return p How could I use a native python (or numpy, scipy...) function in order to calculate that binomial probability? If possible, I need scipy 0.7.2 compatible code. Many thanks!

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  • Cleanest way to store lists of filter coefficients in a C header

    - by Nick T
    I have many (~100 or so) filter coefficients calculated with the aid of some Matlab and Excel that I want to dump into a C header file for general use, but I'm not sure what the best way to do this would be. I was starting out as so: #define BUTTER 1 #define BESSEL 2 #define CHEBY 3 #if FILT_TYPE == BUTTER #if FILT_ROLLOFF == 0.010 #define B0 256 #define B1 512 #define B2 256 #define A1 467 #define A2 -214 #elif FILT_ROLLOFF == 0.015 #define B0 256 #define B1 512 // and so on... However, if I do that and shove them all into a header, I need to set the conditionals (FILT_TYPE, FILT_ROLLOFF) in my source before including it, which seems kinda nasty. What's more, if I have 2+ different filters that want different roll-offs/filter types it won't work. I could #undef my 5 coefficients (A1-2, B0-2) in that coefficients file, but it still seems wrong to have to insert an #include buried in code.

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  • Extracting DCT coefficients from encoded images and video

    - by misha
    Is there a way to easily extract the DCT coefficients (and quantization parameters) from encoded images and video? Any decoder software must be using them to decode block-DCT encoded images and video. So I'm pretty sure the decoder knows what they are. Is there a way to expose them to whomever is using the decoder? I'm implementing some video quality assessment algorithms that work directly in the DCT domain. Currently, the majority of my code uses OpenCV, so it would be great if anyone knows of a solution using that framework. I don't mind using other libraries (perhaps libjpeg, but that seems to be for still images only), but my primary concern is to do as little format-specific work as possible (I don't want to reinvent the wheel and write my own decoders). I want to be able to open any video/image (H.264, MPEG, JPEG, etc) that OpenCV can open, and if it's block DCT-encoded, to get the DCT coefficients. In the worst case, I know that I can write up my own block DCT code, run the decompressed frames/images through it and then I'd be back in the DCT domain. That's hardly an elegant solution, and I hope I can do better. Presently, I use the fairly common OpenCV boilerplate to open images: IplImage *image = cvLoadImage(filename); // Run quality assessment metric The code I'm using for video is equally trivial: CvCapture *capture = cvCaptureFromAVI(filename); while (cvGrabFrame(capture)) { IplImage *frame = cvRetrieveFrame(capture); // Run quality assessment metric on frame } cvReleaseCapture(&capture); In both cases, I get a 3-channel IplImage in BGR format. Is there any way I can get the DCT coefficients as well?

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  • Steganography Experiment - Trouble hiding message bits in DCT coefficients

    - by JohnHankinson
    I have an application requiring me to be able to embed loss-less data into an image. As such I've been experimenting with steganography, specifically via modification of DCT coefficients as the method I select, apart from being loss-less must also be relatively resilient against format conversion, scaling/DSP etc. From the research I've done thus far this method seems to be the best candidate. I've seen a number of papers on the subject which all seem to neglect specific details (some neglect to mention modification of 0 coefficients, or modification of AC coefficient etc). After combining the findings and making a few modifications of my own which include: 1) Using a more quantized version of the DCT matrix to ensure we only modify coefficients that would still be present should the image be JPEG'ed further or processed (I'm using this in place of simply following a zig-zag pattern). 2) I'm modifying bit 4 instead of the LSB and then based on what the original bit value was adjusting the lower bits to minimize the difference. 3) I'm only modifying the blue channel as it should be the least visible. This process must modify the actual image and not the DCT values stored in file (like jsteg) as there is no guarantee the file will be a JPEG, it may also be opened and re-saved at a later stage in a different format. For added robustness I've included the message multiple times and use the bits that occur most often, I had considered using a QR code as the message data or simply applying the reed-solomon error correction, but for this simple application and given that the "message" in question is usually going to be between 10-32 bytes I have plenty of room to repeat it which should provide sufficient redundancy to recover the true bits. No matter what I do I don't seem to be able to recover the bits at the decode stage. I've tried including / excluding various checks (even if it degrades image quality for the time being). I've tried using fixed point vs. double arithmetic, moving the bit to encode, I suspect that the message bits are being lost during the IDCT back to image. Any thoughts or suggestions on how to get this working would be hugely appreciated. (PS I am aware that the actual DCT/IDCT could be optimized from it's naive On4 operation using row column algorithm, or an FDCT like AAN, but for now it just needs to work :) ) Reference Papers: http://www.lokminglui.com/dct.pdf http://arxiv.org/ftp/arxiv/papers/1006/1006.1186.pdf Code for the Encode/Decode process in C# below: using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Drawing.Imaging; using System.Drawing; namespace ImageKey { public class Encoder { public const int HIDE_BIT_POS = 3; // use bit position 4 (1 << 3). public const int HIDE_COUNT = 16; // Number of times to repeat the message to avoid error. // JPEG Standard Quantization Matrix. // (to get higher quality multiply by (100-quality)/50 .. // for lower than 50 multiply by 50/quality. Then round to integers and clip to ensure only positive integers. public static double[] Q = {16,11,10,16,24,40,51,61, 12,12,14,19,26,58,60,55, 14,13,16,24,40,57,69,56, 14,17,22,29,51,87,80,62, 18,22,37,56,68,109,103,77, 24,35,55,64,81,104,113,92, 49,64,78,87,103,121,120,101, 72,92,95,98,112,100,103,99}; // Maximum qauality quantization matrix (if all 1's doesn't modify coefficients at all). public static double[] Q2 = {1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,1}; public static Bitmap Encode(Bitmap b, string key) { Bitmap response = new Bitmap(b.Width, b.Height, PixelFormat.Format32bppArgb); uint imgWidth = ((uint)b.Width) & ~((uint)7); // Maximum usable X resolution (divisible by 8). uint imgHeight = ((uint)b.Height) & ~((uint)7); // Maximum usable Y resolution (divisible by 8). // Start be transferring the unmodified image portions. // As we'll be using slightly less width/height for the encoding process we'll need the edges to be populated. for (int y = 0; y < b.Height; y++) for (int x = 0; x < b.Width; x++) { if( (x >= imgWidth && x < b.Width) || (y>=imgHeight && y < b.Height)) response.SetPixel(x, y, b.GetPixel(x, y)); } // Setup the counters and byte data for the message to encode. StringBuilder sb = new StringBuilder(); for(int i=0;i<HIDE_COUNT;i++) sb.Append(key); byte[] codeBytes = System.Text.Encoding.ASCII.GetBytes(sb.ToString()); int bitofs = 0; // Current bit position we've encoded too. int totalBits = (codeBytes.Length * 8); // Total number of bits to encode. for (int y = 0; y < imgHeight; y += 8) { for (int x = 0; x < imgWidth; x += 8) { int[] redData = GetRedChannelData(b, x, y); int[] greenData = GetGreenChannelData(b, x, y); int[] blueData = GetBlueChannelData(b, x, y); int[] newRedData; int[] newGreenData; int[] newBlueData; if (bitofs < totalBits) { double[] redDCT = DCT(ref redData); double[] greenDCT = DCT(ref greenData); double[] blueDCT = DCT(ref blueData); int[] redDCTI = Quantize(ref redDCT, ref Q2); int[] greenDCTI = Quantize(ref greenDCT, ref Q2); int[] blueDCTI = Quantize(ref blueDCT, ref Q2); int[] blueDCTC = Quantize(ref blueDCT, ref Q); HideBits(ref blueDCTI, ref blueDCTC, ref bitofs, ref totalBits, ref codeBytes); double[] redDCT2 = DeQuantize(ref redDCTI, ref Q2); double[] greenDCT2 = DeQuantize(ref greenDCTI, ref Q2); double[] blueDCT2 = DeQuantize(ref blueDCTI, ref Q2); newRedData = IDCT(ref redDCT2); newGreenData = IDCT(ref greenDCT2); newBlueData = IDCT(ref blueDCT2); } else { newRedData = redData; newGreenData = greenData; newBlueData = blueData; } MapToRGBRange(ref newRedData); MapToRGBRange(ref newGreenData); MapToRGBRange(ref newBlueData); for(int dy=0;dy<8;dy++) { for(int dx=0;dx<8;dx++) { int col = (0xff<<24) + (newRedData[dx+(dy*8)]<<16) + (newGreenData[dx+(dy*8)]<<8) + (newBlueData[dx+(dy*8)]); response.SetPixel(x+dx,y+dy,Color.FromArgb(col)); } } } } if (bitofs < totalBits) throw new Exception("Failed to encode data - insufficient cover image coefficients"); return (response); } public static void HideBits(ref int[] DCTMatrix, ref int[] CMatrix, ref int bitofs, ref int totalBits, ref byte[] codeBytes) { int tempValue = 0; for (int u = 0; u < 8; u++) { for (int v = 0; v < 8; v++) { if ( (u != 0 || v != 0) && CMatrix[v+(u*8)] != 0 && DCTMatrix[v+(u*8)] != 0) { if (bitofs < totalBits) { tempValue = DCTMatrix[v + (u * 8)]; int bytePos = (bitofs) >> 3; int bitPos = (bitofs) % 8; byte mask = (byte)(1 << bitPos); byte value = (byte)((codeBytes[bytePos] & mask) >> bitPos); // 0 or 1. if (value == 0) { int a = DCTMatrix[v + (u * 8)] & (1 << HIDE_BIT_POS); if (a != 0) DCTMatrix[v + (u * 8)] |= (1 << HIDE_BIT_POS) - 1; DCTMatrix[v + (u * 8)] &= ~(1 << HIDE_BIT_POS); } else if (value == 1) { int a = DCTMatrix[v + (u * 8)] & (1 << HIDE_BIT_POS); if (a == 0) DCTMatrix[v + (u * 8)] &= ~((1 << HIDE_BIT_POS) - 1); DCTMatrix[v + (u * 8)] |= (1 << HIDE_BIT_POS); } if (DCTMatrix[v + (u * 8)] != 0) bitofs++; else DCTMatrix[v + (u * 8)] = tempValue; } } } } } public static void MapToRGBRange(ref int[] data) { for(int i=0;i<data.Length;i++) { data[i] += 128; if(data[i] < 0) data[i] = 0; else if(data[i] > 255) data[i] = 255; } } public static int[] GetRedChannelData(Bitmap b, int sx, int sy) { int[] data = new int[8 * 8]; for (int y = sy; y < (sy + 8); y++) { for (int x = sx; x < (sx + 8); x++) { uint col = (uint)b.GetPixel(x,y).ToArgb(); data[(x - sx) + ((y - sy) * 8)] = (int)((col >> 16) & 0xff) - 128; } } return (data); } public static int[] GetGreenChannelData(Bitmap b, int sx, int sy) { int[] data = new int[8 * 8]; for (int y = sy; y < (sy + 8); y++) { for (int x = sx; x < (sx + 8); x++) { uint col = (uint)b.GetPixel(x, y).ToArgb(); data[(x - sx) + ((y - sy) * 8)] = (int)((col >> 8) & 0xff) - 128; } } return (data); } public static int[] GetBlueChannelData(Bitmap b, int sx, int sy) { int[] data = new int[8 * 8]; for (int y = sy; y < (sy + 8); y++) { for (int x = sx; x < (sx + 8); x++) { uint col = (uint)b.GetPixel(x, y).ToArgb(); data[(x - sx) + ((y - sy) * 8)] = (int)((col >> 0) & 0xff) - 128; } } return (data); } public static int[] Quantize(ref double[] DCTMatrix, ref double[] Q) { int[] DCTMatrixOut = new int[8*8]; for (int u = 0; u < 8; u++) { for (int v = 0; v < 8; v++) { DCTMatrixOut[v + (u * 8)] = (int)Math.Round(DCTMatrix[v + (u * 8)] / Q[v + (u * 8)]); } } return(DCTMatrixOut); } public static double[] DeQuantize(ref int[] DCTMatrix, ref double[] Q) { double[] DCTMatrixOut = new double[8*8]; for (int u = 0; u < 8; u++) { for (int v = 0; v < 8; v++) { DCTMatrixOut[v + (u * 8)] = (double)DCTMatrix[v + (u * 8)] * Q[v + (u * 8)]; } } return(DCTMatrixOut); } public static double[] DCT(ref int[] data) { double[] DCTMatrix = new double[8 * 8]; for (int v = 0; v < 8; v++) { for (int u = 0; u < 8; u++) { double cu = 1; if (u == 0) cu = (1.0 / Math.Sqrt(2.0)); double cv = 1; if (v == 0) cv = (1.0 / Math.Sqrt(2.0)); double sum = 0.0; for (int y = 0; y < 8; y++) { for (int x = 0; x < 8; x++) { double s = data[x + (y * 8)]; double dctVal = Math.Cos((2 * y + 1) * v * Math.PI / 16) * Math.Cos((2 * x + 1) * u * Math.PI / 16); sum += s * dctVal; } } DCTMatrix[u + (v * 8)] = (0.25 * cu * cv * sum); } } return (DCTMatrix); } public static int[] IDCT(ref double[] DCTMatrix) { int[] Matrix = new int[8 * 8]; for (int y = 0; y < 8; y++) { for (int x = 0; x < 8; x++) { double sum = 0; for (int v = 0; v < 8; v++) { for (int u = 0; u < 8; u++) { double cu = 1; if (u == 0) cu = (1.0 / Math.Sqrt(2.0)); double cv = 1; if (v == 0) cv = (1.0 / Math.Sqrt(2.0)); double idctVal = (cu * cv) / 4.0 * Math.Cos((2 * y + 1) * v * Math.PI / 16) * Math.Cos((2 * x + 1) * u * Math.PI / 16); sum += (DCTMatrix[u + (v * 8)] * idctVal); } } Matrix[x + (y * 8)] = (int)Math.Round(sum); } } return (Matrix); } } public class Decoder { public static string Decode(Bitmap b, int expectedLength) { expectedLength *= Encoder.HIDE_COUNT; uint imgWidth = ((uint)b.Width) & ~((uint)7); // Maximum usable X resolution (divisible by 8). uint imgHeight = ((uint)b.Height) & ~((uint)7); // Maximum usable Y resolution (divisible by 8). // Setup the counters and byte data for the message to decode. byte[] codeBytes = new byte[expectedLength]; byte[] outBytes = new byte[expectedLength / Encoder.HIDE_COUNT]; int bitofs = 0; // Current bit position we've decoded too. int totalBits = (codeBytes.Length * 8); // Total number of bits to decode. for (int y = 0; y < imgHeight; y += 8) { for (int x = 0; x < imgWidth; x += 8) { int[] blueData = ImageKey.Encoder.GetBlueChannelData(b, x, y); double[] blueDCT = ImageKey.Encoder.DCT(ref blueData); int[] blueDCTI = ImageKey.Encoder.Quantize(ref blueDCT, ref Encoder.Q2); int[] blueDCTC = ImageKey.Encoder.Quantize(ref blueDCT, ref Encoder.Q); if (bitofs < totalBits) GetBits(ref blueDCTI, ref blueDCTC, ref bitofs, ref totalBits, ref codeBytes); } } bitofs = 0; for (int i = 0; i < (expectedLength / Encoder.HIDE_COUNT) * 8; i++) { int bytePos = (bitofs) >> 3; int bitPos = (bitofs) % 8; byte mask = (byte)(1 << bitPos); List<int> values = new List<int>(); int zeroCount = 0; int oneCount = 0; for (int j = 0; j < Encoder.HIDE_COUNT; j++) { int val = (codeBytes[bytePos + ((expectedLength / Encoder.HIDE_COUNT) * j)] & mask) >> bitPos; values.Add(val); if (val == 0) zeroCount++; else oneCount++; } if (oneCount >= zeroCount) outBytes[bytePos] |= mask; bitofs++; values.Clear(); } return (System.Text.Encoding.ASCII.GetString(outBytes)); } public static void GetBits(ref int[] DCTMatrix, ref int[] CMatrix, ref int bitofs, ref int totalBits, ref byte[] codeBytes) { for (int u = 0; u < 8; u++) { for (int v = 0; v < 8; v++) { if ((u != 0 || v != 0) && CMatrix[v + (u * 8)] != 0 && DCTMatrix[v + (u * 8)] != 0) { if (bitofs < totalBits) { int bytePos = (bitofs) >> 3; int bitPos = (bitofs) % 8; byte mask = (byte)(1 << bitPos); int value = DCTMatrix[v + (u * 8)] & (1 << Encoder.HIDE_BIT_POS); if (value != 0) codeBytes[bytePos] |= mask; bitofs++; } } } } } } } UPDATE: By switching to using a QR Code as the source message and swapping a pair of coefficients in each block instead of bit manipulation I've been able to get the message to survive the transform. However to get the message to come through without corruption I have to adjust both coefficients as well as swap them. For example swapping (3,4) and (4,3) in the DCT matrix and then respectively adding 8 and subtracting 8 as an arbitrary constant seems to work. This survives a re-JPEG'ing of 96 but any form of scaling/cropping destroys the message again. I was hoping that by operating on mid to low frequency values that the message would be preserved even under some light image manipulation.

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  • Random numbers from binomial distribution

    - by Sarah
    I need to generate quickly lots of random numbers from binomial distributions for dramatically different trial sizes (most, however, will be small). I was hoping not to have to code an algorithm by hand (see, e.g., this related discussion from November), because I'm a novice programmer and don't like reinventing wheels. It appears Boost does not supply a generator for binomially distributed variates, but TR1 and GSL do. Is there a good reason to choose one over the other, or is it better that I write something customized to my situation? I don't know if this makes sense, but I'll alternate between generating numbers from uniform distributions and binomial distributions throughout the program, and I'd like for them to share the same seed and to minimize overhead. I'd love some advice or examples for what I should be considering.

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  • Warning: non-integer #successes in a binomial glm! (survey packages)

    - by longrob
    I am using the twang package to create propensity scores, which are used as weigtings in a binomial glm using survey::svyglm. The code looks something like this: pscore <- ps(ppci ~ var1+var2+.........., data=dt....) dt$w <- get.weights(pscore, stop.method="es.mean") design.ps <- svydesign(ids=~1, weights=~w, data=dt,) glm1 <- svyglm(m30 ~ ppci, design=design.ps,family=binomial) This produces the following warning: Warning message: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! Does anyone know what I could be doing wrong ? I wasn't sure if this message would be better on stats.SE, but on balance I thought I would try here first.

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  • Trying to sort the coefficients of the polynomial (z-a)(z-b)(z-c)...(z-n) into a vector

    - by pajamas
    So I have a factored polynomial of the form (z-a)(z-b)(z-c)...(z-n) for n an even positive integer. Thus the coefficient of z^k for 0 <= k < n will be the sum of all distinct n-k element products taken from the set {a,b,...,n} multiplied by (-1)^k, I hope that makes sense, please ask if you need more clarification. I'm trying to put these coefficients into a row vector with the first column containing the constant coefficient (which would be abc...n) and the last column containing the coefficient for z^n (which would be 1). I imagine there is a way to brute force this with a ton of nested loops, but I'm hoping there is a more efficient way. This is being done in Matlab (which I'm not that familiar with) and I know Matlab has a ton of algorithms and functions, so maybe its got something I can use. Can anyone think of a way to do this? Example: (z-1)(z-2)(z-3) = z^3 - (1 + 2 + 3)z^2 + (1*2 + 1*3 + 2*3)z - 1*2*3 = z^3 - 6z^2 + 11z - 6. Note that this example is n=3 odd, but n=4 would have taken too long to do by hand. Edit: Let me know if you think this would be better posted at TCS or Math Stack Exchange.

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  • Confused over behavior of List.mapi in F#

    - by James Black
    I am building some equations in F#, and when working on my polynomial class I found some odd behavior using List.mapi Basically, each polynomial has an array, so 3*x^2 + 5*x + 6 would be [|6, 5, 3|] in the array, so, when adding polynomials, if one array is longer than the other, then I just need to append the extra elements to the result, and that is where I ran into a problem. Later I want to generalize it to not always use a float, but that will be after I get more working. So, the problem is that I expected List.mapi to return a List not individual elements, but, in order to put the lists together I had to put [] around my use of mapi, and I am curious why that is the case. This is more complicated than I expected, I thought I should be able to just tell it to make a new List starting at a certain index, but I can't find any function for that. type Polynomial() = let mutable coefficients:float [] = Array.empty member self.Coefficients with get() = coefficients static member (+) (v1:Polynomial, v2:Polynomial) = let ret = List.map2(fun c p -> c + p) (List.ofArray v1.Coefficients) (List.ofArray v2.Coefficients) let a = List.mapi(fun i x -> x) match v1.Coefficients.Length - v2.Coefficients.Length with | x when x < 0 -> ret :: [((List.ofArray v1.Coefficients) |> a)] | x when x > 0 -> ret :: [((List.ofArray v2.Coefficients) |> a)] | _ -> [ret]

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  • How do we calculate the filters in Mel-Frequency Cepstrum Coefficients Algorithm?

    - by André Ferreira
    After calculating the FFT and with the frequency we need to do something like this: http://instruct1.cit.cornell.edu/courses/ece576/FinalProjects/f2008/pae26%5Fjsc59/pae26%5Fjsc59/images/melfilt.png We filter the frequency spectrum with those triangles. I saw that we can use distint ways to calculcate the triangles. I will make the size of the triangles equal till 1kz and after that obtained with log function. What should we do now? With the frequency spectrum and the triangles defined.. - We should filter the frequency (frequencies limited to the triangles, if goes higher only counts till the triangle limit) and calculate the value of each triangle (and after that continue the algorithm). But when does the mel conversation happens? m = 2595 log (f/700 + 1) When do we pass from frequency to mel.. Can someone guide me in the right direction plz :d

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  • Spherical harmonics lighting interpolation

    - by TravisG
    I want to use hardware filtering to smooth out colors in texels of a texture when I'm accessing texels at coordinates that are not directly at the center of the texel, the catch being that the texels store 2 bands of spherical harmonics coefficients (=4 coefficients), not RGBA intensity values. Can I just use hardware filtering like that (GL_LINEAR with and without mip mapping) without any considerations? In other terms: If I were to first convert the coefficients back to intensity representations, than manually interpolate between two intensities, would the resulting intensity be the same as if I interpolated between the coefficient vectors directly and then converted the interpolated result to intensities?

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  • Variable length argument list - How to understand we retrieved the last argument?

    - by hkBattousai
    I have a Polynomial class which holds coefficients of a given polynomial. One of its overloaded constructors is supposed to receive these coefficients via variable argument list. template <class T> Polynomial<T>::Polynomial(T FirstCoefficient, ...) { va_list ArgumentPointer; va_start(ArgumentPointer, FirstCoefficient); T NextCoefficient = FirstCoefficient; std::vector<T> Coefficients; while (true) { Coefficients.push_back(NextCoefficient); NextCoefficient = va_arg(ArgumentPointer, T); if (/* did we retrieve all the arguments */) // How do I implement this? { break; } } m_Coefficients = Coefficients; } I know that we usually pass an extra parameter which tells the recipient method the total number of parameters, or we pass a sentimental ending parameter. But in order to keep thing short and clean, I don't prefer passing an extra parameter. Is there any way of doing this without modifying the method signature in the example?

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  • Error in glmmadmb(.....) The function maximizer failed (couldn't find STD file)

    - by Joe King
    This works fine: fit.mc1 <-MCMCglmm(bull~1,random=~school,data=dt1,family="categorical", prior=list(R=list(V=1, fix=1), G=list(G1=list(V=1, nu=0))), slice=T) So does this: fit.glmer <- glmer(bull~(1|school),data=dt1,family=binomial) But now I am trying to work with the package glmmadmb and this does not work: fit.mc12 <- glmmadmb(bull~1+(1|school), data=dt1, family="binomial", mcmc=TRUE, mcmc.opts=mcmcControl(mcmc=50000)) It generates the error: Error in glmmadmb(bull~ 1 + (1 | school), data = dt1, family = "binomial", : The function maximizer failed (couldn't find STD file) In addition: Warning message: running command '<snip>\cmd.exe <snip>\glmmadmb.exe" -maxfn 500 -maxph 5 -noinit -shess -mcmc 5000 -mcsave 5 -mcmult 1' had status 1

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  • Filtering spectrum using FIR filters

    - by Alex Hoppus
    If i have signal values x[T] and filter coefficients b[i], i can perform filtering using convolution. Suppose i have spectrum of x (after FFT) and i need to perform filtering using filters coefficients, how can i perform this? I heard that in frequency domain it will be multiplying, rather than convolution (time domain). But i can't find an equation to use it. I have 614000 values in y = fft(x[T]) vector and 119 filter coefficients (generated using fdatool), i can't multiply them directly ... Thanks.

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  • Can one extract model fit parameters after a ggplot stat_smooth call?

    - by Alex Holcombe
    Using stat_smooth, I can fit models to data. E.g. g=ggplot(tips,aes(x=tip,y=as.numeric(unclass(factor(tips$sex))-1))) +facet_grid(time~.) g=g+ stat_summary(fun.y=mean,geom="point") g=g+ stat_smooth(method="glm", family="binomial") I would like to know the coefficients of the glm binomial fits. I could re-do the fit with dlply and get the coefficients with ldply, but I'd like to avoid such duplication. Calling str(g) reveals the hierarchy of objects that ggplot creates, perhaps there's some way to get to the coefficients through that?

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  • What is the optimal way to run a set of regressions in R.

    - by stevejb
    Assume that I have sources of data X and Y that are indexable, say matrices. And I want to run a set of independent regressions and store the result. My initial approach would be results = matrix(nrow=nrow(X), ncol=(2)) for(i in 1:ncol(X)) { matrix[i,] = coefficients(lm(Y[i,] ~ X[i,]) } But, loops are bad, so I could do it with lapply as out <- lapply(1:nrow(X), function(i) { coefficients(lm(Y[i,] ~ X[i,])) } ) Is there a better way to do this?

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  • What is the best way to run a loop of regressions in R?

    - by stevejb
    Assume that I have sources of data X and Y that are indexable, say matrices. And I want to run a set of independent regressions and store the result. My initial approach would be results = matrix(nrow=nrow(X), ncol=(2)) for(i in 1:ncol(X)) { matrix[i,] = coefficients(lm(Y[i,] ~ X[i,]) } But, loops are bad, so I could do it with lapply as out <- lapply(1:nrow(X), function(i) { coefficients(lm(Y[i,] ~ X[i,])) } ) Is there a better way to do this?

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  • How to change x-axis min/max of Column chart in Excel?

    - by Ian Boyd
    Here i have a column chart of binomial distribution, showing how many times you can expect to roll a six in 235 dice rolls: Note: You could also call it a binomial mass distribution for p=1/6, n=235 Now that graph is kinda squooshed. i'd like to change the Minimum and Maximum on the horizontal axis. i'd like to change them to: Minimum: 22 Maximum: 57 Meaning i want to zoom in on this section of the graph: Bonus points to the reader who can say how the numbers 22 and 57 were arrived at If this were a Scatter graph in Excel, i could adjust the horizintal axis minimum and maximum as i desired: Unfortunately, this is a Column chart, where there are no options to adjust the minimum and maximum limits of the ordinate axis: i can do a pretty horrible thing to the graph in Photoshop, but it's not very useful afterwards: Question: how to a change the x-axis minimum and maximum of a Column chart in Excel (2007)?

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  • Math/numerical formula every computer programmer should know

    - by aaa
    This is a follow-up question to What should every programmer know and Is mathematics necessary. So the question is, as a computer programmer, what is the most important/useful mathematical or numerical formula that you use? By Formula I mean anything that involves less obvious manipulations, whenever binomial coefficients or bit hacks. I work with multidimensional arrays and various matrix representations. So for me most commonly used formulas are: A(i,j,k,..) = a[i + j*Dim0 + k*Dim0*Dim1 + ... to map indexes to one dimension ( which is basic address calculation which many people do not seem to know). And triangular number T(i) = (i*i + i)/2 which is related to binomial coefficients, used to calculate address in triangular matrixes and many other things. What is your workhorse formula that you think programmer should know?

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  • How can I output the fitted values?

    - by zenbomb
    Sorry if this is a novice question, but I don't understand it. I am fitting a sigmoid curve to my data with glm(). This works, I can plot the output and I see a nice sigmoid curve. However, how do I get R to return the final values it has fit? As I understand it, R fits the data to logit(y) = b0 + b1x, but when I do > summary(glm.out) I only get Call: glm(formula = e$V2 ~ e$V1, family = binomial(logit), data = e) Deviance Residuals: 1 2 3 4 5 6 7 -0.00001 -0.06612 -0.15118 -0.34237 0.20874 0.08724 -0.19557 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -24.784 20.509 -1.208 0.227 e$V1 2.073 1.725 1.202 0.229 (Dispersion parameter for binomial family taken to be 1) Null deviance: 4.60338 on 6 degrees of freedom Residual deviance: 0.23388 on 5 degrees of freedom AIC: 5.8525 Number of Fisher Scoring iterations: 8 How do I get b0 and b1?

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  • Monster's AI in an Action-RPG

    - by Andrea Tucci
    I'm developing an action rpg with some University colleagues. We've gotton to the monsters' AI design and we would like to implement a sort of "utility-based AI" so we have a "thinker" that assigns a numeric value on all the monster's decisions and we choose the highest (or the most appropriate, depending on monster's iq) and assign it in the monster's collection of decisions (like a goal-driven design pattern) . One solution we found is to write a mathematical formula for each decision, with all the important parameters for evaluation (so for a spell-decision we might have mp,distance from player, player's hp etc). This formula also has coefficients representing some of monster's behaviour (in this way we can alterate formulas by changing coefficients). I've also read how "fuzzy logic" works; I was fascinated by it and by the many ways of expansion it has. I was wondering how we could use this technique to give our AI more semplicity, as in create evaluations with fuzzy rules such as IF player_far AND mp_high AND hp_high THEN very_Desiderable (for a spell having an high casting-time and consume high mp) and then 'defuzz' it. In this way it's also simple to create a monster behaviour by creating ad-hoc rules for every monster's IQ category. But is it correct using fuzzy logic in a game with many parameters like an rpg? Is there a way of merging these two techniques? Are there better AI design techniques for evaluating monster's chooses?

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  • Illumination and Shading for computer graphics class

    - by Sam I Am
    I am preparing for my test tomorrow and this is one of the practice questions. I solved it partially but I am confused with the rest. Here is the problem: Consider a gray world with no ambient and specular lighting ( only diffuse lighting). The screen coordinates of a triangle P1,P2,P3, are P1=(100,100), P2= (300,150), P3 = (200, 200). The gray values at P!,P2,P3 are 1/2, 3/4, and 1/4 respectively. The light is at infinity and its direction and gray color are (1,1,1) and 1.0 respectively. The coefficients of diffused reflection is 1/2. The normals of P1,P2,P3 are N1= (0,0,1), N2 = (1,0,0), and N3 = (0,1,0) respectively. Consider the coordinates of three points P1,P2,P3 to be 0. Do not normalize the normals. I have computed that the illumination at the 3 vertices P1,P2,P3 is (1/4,3/8,1/8). Also I computed that interpolation coefficients of a point P inside the triangle whose coordinates are (220, 160) are given by (1/5,2/5,2/5). Now I have 4 more questions regarding this problem. 1) The illumination at P using Gouraud Shading is: i) 1/2 The answer is 1/2, but I have no idea how to compute it.. 2) The interpolated normal at P is given by i) (2/5, 2/5,1/5) ii) (1/2, 1/4, 1/4) iii) (3/5, 1/5, 1/5) 3) The interpolated color at P is given by: i) 1/2 Again, I know the correct answer but no idea how to solve it 4) The illumination at P using Phong Shading is i) 1/4 ii) 9/40 iii) 1/2

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  • R glm standard error estimate differences to SAS PROC GENMOD

    - by Michelle
    I am converting a SAS PROC GENMOD example into R, using glm in R. The SAS code was: proc genmod data=data0 namelen=30; model boxcoxy=boxcoxxy ~ AGEGRP4 + AGEGRP5 + AGEGRP6 + AGEGRP7 + AGEGRP8 + RACE1 + RACE3 + WEEKEND + SEQ/dist=normal; FREQ REPLICATE_VAR; run; My R code is: parmsg2 <- glm(boxcoxxy ~ AGEGRP4 + AGEGRP5 + AGEGRP6 + AGEGRP7 + AGEGRP8 + RACE1 + RACE3 + WEEKEND + SEQ , data=data0, family=gaussian, weights = REPLICATE_VAR) When I use summary(parmsg2) I get the same coefficient estimates as in SAS, but my standard errors are wildly different. The summary output from SAS is: Name df Estimate StdErr LowerWaldCL UpperWaldCL ChiSq ProbChiSq Intercept 1 6.5007436 .00078884 6.4991975 6.5022897 67911982 0 agegrp4 1 .64607262 .00105425 .64400633 .64813891 375556.79 0 agegrp5 1 .4191395 .00089722 .41738099 .42089802 218233.76 0 agegrp6 1 -.22518765 .00083118 -.22681672 -.22355857 73401.113 0 agegrp7 1 -1.7445189 .00087569 -1.7462352 -1.7428026 3968762.2 0 agegrp8 1 -2.2908855 .00109766 -2.2930369 -2.2887342 4355849.4 0 race1 1 -.13454883 .00080672 -.13612997 -.13296769 27817.29 0 race3 1 -.20607036 .00070966 -.20746127 -.20467944 84319.131 0 weekend 1 .0327884 .00044731 .0319117 .03366511 5373.1931 0 seq2 1 -.47509583 .00047337 -.47602363 -.47416804 1007291.3 0 Scale 1 2.9328613 .00015586 2.9325559 2.9331668 -127 The summary output from R is: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.50074 0.10354 62.785 < 2e-16 AGEGRP4 0.64607 0.13838 4.669 3.07e-06 AGEGRP5 0.41914 0.11776 3.559 0.000374 AGEGRP6 -0.22519 0.10910 -2.064 0.039031 AGEGRP7 -1.74452 0.11494 -15.178 < 2e-16 AGEGRP8 -2.29089 0.14407 -15.901 < 2e-16 RACE1 -0.13455 0.10589 -1.271 0.203865 RACE3 -0.20607 0.09315 -2.212 0.026967 WEEKEND 0.03279 0.05871 0.558 0.576535 SEQ -0.47510 0.06213 -7.646 2.25e-14 The importance of the difference in the standard errors is that the SAS coefficients are all statistically significant, but the RACE1 and WEEKEND coefficients in the R output are not. I have found a formula to calculate the Wald confidence intervals in R, but this is pointless given the difference in the standard errors, as I will not get the same results. Apparently SAS uses a ridge-stabilized Newton-Raphson algorithm for its estimates, which are ML. The information I read about the glm function in R is that the results should be equivalent to ML. What can I do to change my estimation procedure in R so that I get the equivalent coefficents and standard error estimates that were produced in SAS? To update, thanks to Spacedman's answer, I used weights because the data are from individuals in a dietary survey, and REPLICATE_VAR is a balanced repeated replication weight, that is an integer (and quite large, in the order of 1000s or 10000s). The website that describes the weight is here. I don't know why the FREQ rather than the WEIGHT command was used in SAS. I will now test by expanding the number of observations using REPLICATE_VAR and rerunning the analysis.

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  • Subband decomposition using Daubechies filter

    - by misha
    I have the following two 8-tap filters: h0 ['-0.010597', '0.032883', '0.030841', '-0.187035', '-0.027984', '0.630881', '0.714847', '0.230378'] h1 ['-0.230378', '0.714847', '-0.630881', '-0.027984', '0.187035', '0.030841', '-0.032883', '-0.010597'] Here they are on a graph: I'm using it to obtain the approximation (lower subband of an image). This is a(m,n) in the following diagram: I got the coefficients and diagram from the book Digital Image Processing, 3rd Edition, so I trust that they are correct. The star symbol denotes one dimensional convolution (either over rows or over columns). The down arrow denotes downsampling in one dimension (either over rows, or columns). My problem is that the filter coefficients for h0 and h1 sum to greater than 1 (approximately 1.4 or sqrt(2) to be exact). Naturally, if I convolve any image with the filter, the image will get brighter. Indeed, here's what I get (expected result on right): Can somebody suggest what the problem is here? Why should it work if the convolution filter coefficients sum to greater than 1? I have the source code, but it's quite long so I'm hoping to avoid posting it here. If it's absolutely necessary, I'll put it up later. EDIT What I'm doing is: Decompose into subbands Filter one of the subbands Recompose subbands into original image Note that the point isn't just to have a displayable subband-decomposed image -- I have to be able to perfectly reconstruct the original image from the subbands as well. So if I scale the filtered image in order to compensate for my decomposition filter making the image brighter, this is what I will have to do: Decompose into subbands Apply intensity scaling Filter one of the subbands Apply inverse intensity scaling Recompose subbands into original image Step 2 performs the scaling. This is what @Benjamin is suggesting. The problem is that then step 4 becomes necessary, or the original image will not be properly reconstructed. This longer method will work. However, the textbook explicitly says that no scaling is performed on the approximation subband. Of course, it's possible that the textbook is wrong. However, what's more possible is I'm misunderstanding something about the way this all works -- this is why I'm asking this question.

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  • is Microsoft LC random generator patented?

    - by user396672
    I need a very simple pseudo random generator (no any specific quality requirements) and I found Microsoft's variant of LCG algorithm used for rand() C runtime library function fit my needs (gcc's one seems too complex). I found the algorithm here: http://rosettacode.org/wiki/Linear_congruential_generator#C However, I worry the algorithm (including its "magic numbers" i.e coefficients) may by patented or restricted for use in some another way. Is it allowed to use this algorithm without any licence or patent restrictions or not? I can't use library rand() because I need my results to be exactly reproducible on different platforms

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