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C precomputed expressions


C precomputed expressions

By : user2951824
Date : November 18 2020, 03:49 PM
wish helps you "Elegant" is not really a well defined term, you would have been better off specifying something more measurable, like "speed".
If speed is indeed the goal, an the system parameter is one that doesn't change at runtime, you can have a portable solution like:
code :
#undef SQRT_LM
#if LONG_MAX == 64
    #define SQRT_LM 8
#endif
#if LONG_MAX == 256
    #define SQRT_LM 16
#endif
: : :
#ifndef SQRT_LM
    #error Weird LONG_MAX value, please adjust code above.
#endif
void doSomething(int x) {
    static long sqrt_lm = -1;
    if (sqrt_lm == -1)
        sqrt_lm = sqrt(LONG_MAX);
    // Now can use sqrt_lm freely
}


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guidance on precomputed SQL attributes

guidance on precomputed SQL attributes


By : encikwan
Date : March 29 2020, 07:55 AM
I hope this helps . I personally wouldn't denormalize until performance trade-offs force my hand (because the downside of denormalizations are too drastic IMHO), but you might also consider:
Convenience: e.g. if two different client apps want to calculate the same derived attributes, they both have to code up the queries to calculate them. Denormalization offers both client apps the derived attribute in a simpler way. Stability over time: e.g. if the formula for calculating a derived attribute is changeable, denormalization allows you to capture and store the derived value at a point in time so future calculations will never get it wrong Simpler queries: adding complexity to the DB structure can mean your Select query is simpler at the client end. Performance: Select queries on denormalized data can be quicker.
using precomputed kernels with libsvm

using precomputed kernels with libsvm


By : Anurag Datta
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , You seem to already have figured out the problem... According to the README file included in the MATLAB package:
code :
%# read dataset
[dataClass, data] = libsvmread('./heart_scale');

%# split into train/test datasets
trainData = data(1:150,:);
testData = data(151:270,:);
trainClass = dataClass(1:150,:);
testClass = dataClass(151:270,:);
numTrain = size(trainData,1);
numTest = size(testData,1);

%# radial basis function: exp(-gamma*|u-v|^2)
sigma = 2e-3;
rbfKernel = @(X,Y) exp(-sigma .* pdist2(X,Y,'euclidean').^2);

%# compute kernel matrices between every pairs of (train,train) and
%# (test,train) instances and include sample serial number as first column
K =  [ (1:numTrain)' , rbfKernel(trainData,trainData) ];
KK = [ (1:numTest)'  , rbfKernel(testData,trainData)  ];

%# train and test
model = svmtrain(trainClass, K, '-t 4');
[predClass, acc, decVals] = svmpredict(testClass, KK, model);

%# confusion matrix
C = confusionmat(testClass,predClass)
*
optimization finished, #iter = 70
nu = 0.933333
obj = -117.027620, rho = 0.183062
nSV = 140, nBSV = 140
Total nSV = 140
Accuracy = 85.8333% (103/120) (classification)

C =
    65     5
    12    38
geom_boxplot with precomputed values

geom_boxplot with precomputed values


By : Micael Gomez
Date : March 29 2020, 07:55 AM
This might help you In the past, I have been able to create boxplots using ggplot2 by providing the lower whisker, lower quantile, median, upper quantile, and upper whisker along with x-axis labels. For example: , This works using ggplot2 version 0.9.1 (and R 2.15.0)
code :
library(ggplot2)

DF <- data.frame(x=c("A","B"), min=c(1,2), low=c(2,3), mid=c(3,4), top=c(4,5), max=c(5,6))

ggplot(DF, aes(x=x, ymin = min, lower = low, middle = mid, upper = top, ymax = max)) +
  geom_boxplot(stat = "identity")
BoW in OpenCV using precomputed features

BoW in OpenCV using precomputed features


By : Mark Tiger
Date : March 29 2020, 07:55 AM
wish helps you Once you have built the vocabulary (codebook) using cv::BOWKMeansTrainer::cluster() method, you can then match a descriptor (with suitable size and type) to the codebook. You first have to choose the type of matcher you need with a norm to use. (see opencv doc)
For example, with cv::BFMatcher and L2 norm
code :
// init the matcher with you pre-trained codebook
cv::Ptr<cv::DescriptorMatcher > matcher = new cv::BFMatcher(cv::NORM_L2);
matcher->add(std::vector<cv::Mat>(1, vocabulary));
// matches
std::vector<cv::DMatch> matches;
matcher->match(new_descriptors,matches);
matches[i].trainIdx; 
Use a precomputed array or a function?

Use a precomputed array or a function?


By : Balaji Sukumar
Date : March 29 2020, 07:55 AM
I wish this help you I think he is trying to compare the speed between a polynomial computation and a lookup table.
It depends. The lookup table is usually stored in memory and the LD instruction will be involved. If the lookup table is not cached then expect long delay from memory.
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