Turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Options

- RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Mute
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Posted 08-31-2011 05:01 PM
(2330 views)

hey,

I have a piece of matlab code to generate normally distributed cluster data. Since I don't have matlab installed in my PC, I am thinking to translate it to SAS IML. I just know a little with matlab. could anyone help with this? The matlab code is as below. Thanks.

%

rand('state',91225);

randn('state',19481);

lowBound = -50;

highBound =50;

nCenters = 20;

nCols = 32;

nRows = 20000;

nTestRows = 0.01 * nRows;

nBufferPoints = 100000;

nExpandFactor = 10; % How much to stretch the covariance matrix

sTrainFile = 'outtrain.txt';

sTestFile = 'outtest.txt';

% Generate the centers according to a uniform distibution.

mCenters = round(lowBound + rand(nCenters,nCols)*(highBound-lowBound));

% Generate the variances and covariances randomly to create a matrix for

% each center

mCovariance = zeros(nCols,nCols);

cCovariance = cell(nCenters,1);

for i = 1:nCenters,

mRootCovariance = nExpandFactor * ...

rand(nCols,nCols)*(highBound-lowBound) / 50;

cCovariance{i} = mRootCovariance' * mRootCovariance;

end;

% Determine what proportion of points will come from each center, then

% create a cdf to use in deciding which to generate.

vPointFraction = rand(nCenters,1);

vPointFraction = vPointFraction / sum(vPointFraction);

vPointCdf = zeros(1,nCenters);

for i = 1:nCenters,

vPointCdf(i) = sum(vPointFraction(1:i));

end;

% Create a random separating plane.

w = -2 + rand(nCols,1)*4;

gamma = lowBound / 10 + rand * (highBound-lowBound)/10;

% Now choose which classes to which each center belongs

vCenterClasses = sign(mCenters * w - gamma * ones(nCenters,1));

vZeroSpots = find(vCenterClasses==0);

vCenterClasses(vZeroSpots) = ones(length(vZeroSpots),1);

% Prepare output file

flatfile([],sTrainFile,0);

flatfile([],sTestFile,0);

% Now go through and begin generating random points.

% Do it twice: once for testing, once for training.

for nDataset = 1:2,

if (nDataset==1)

nRowsLeft = nRows;

sOutputFile = sTrainFile;

nTotRows = nRows;

else

nRowsLeft = nTestRows;

sOutputFile = sTestFile;

nTotRows = nTestRows;

end;

nMisclass = 0;

nTrainingClass1 = 0;

nTrainingClassm1 = 0;

while (nRowsLeft > 0)

disp(sprintf('Rows left = %d',nRowsLeft));

nRowsNow = min(nBufferPoints,nRowsLeft);

nRowsLeft = nRowsLeft - nRowsNow;

mNewPoints = zeros(nRowsNow,nCols);

vPointCenters = zeros(nRowsNow,1);

% Determine which center each point should belong to

vRandomNumbers = rand(nRowsNow,1);

for i = nCenters:-1:1,

vCenterMatch = (vRandomNumbers <= vPointCdf(i));

vPointCenters([vCenterMatch]) = i;

end;

% Create a vector of training classes for each point

vTrainingClasses = zeros(nRowsNow,1);

% Within each class, generate an appropriate number of random points.

for i = 1:nCenters,

vIndices = (vPointCenters==i);

nPoints = sum(vIndices);

vTrainingClasses(vIndices) = vCenterClasses(i);

mNewPoints(vIndices,:) = round( ...

mvnrnd(mCenters(i,:),cCovariance{i},nPoints));

% Count how many points are incorrectly classified

vFitClass = sign(mNewPoints(vIndices,:) * w - gamma * ...

ones(nPoints,1));

vZeroSpots = find(vFitClass==0);

vFitClass(vZeroSpots) = ones(length(vZeroSpots),1);

nMisclass = nMisclass + sum(vFitClass~=vCenterClasses(i));

end; %for

% Output the data points to disk

flatfile([mNewPoints vTrainingClasses],sOutputFile,1);

nTrainingClass1 = nTrainingClass1 + sum(vTrainingClasses==1);

nTrainingClassm1 = nTrainingClassm1 + sum(vTrainingClasses==-1);

end; %while

disp(sprintf('Percent separable estimate = %4.2f%%\n',100*(1-nMisclass/nTotRows)));

disp(sprintf('Number class 1 points = %d\n',nTrainingClass1));

disp(sprintf('Number class -1 points = %d\n',nTrainingClassm1));

end; %for-nDataset

1 REPLY 1

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

You don't say what kind of help you're looking for. For general pointers, see http://blogs.sas.com/content/iml/2011/03/09/translating-a-matlab-program-into-the-sasiml-language-a-...

You can look up the various MATLAB functions at http://www.mathworks.com/help/techdoc/ to see what they do. I assume that you know what the code is supposed to be doing? If so, and assuming that you know SAS/IML, I suggest you try to translate as much as possible into SAS/IML and then ask specific SAS/IML questions. It looks like you'll need to use the following SAS/IML functions: J (for ones() and zeros(), RANDGEN (for rand()), LOC (for find()?), RandNormal (for mvnrnd()),...

For help with generating random uniform numbers, see http://blogs.sas.com/content/iml/2011/08/24/how-to-generate-random-numbers-in-sas/

For help on sampling from multivariate normal with a given covariance, see http://blogs.sas.com/content/iml/2011/01/12/sampling-from-the-multivariate-normal-distribution/

Are you ready for the spotlight? We're accepting content ideas for **SAS Innovate 2025** to be held May 6-9 in Orlando, FL. The call is **open **until September 25. Read more here about **why** you should contribute and **what is in it** for you!

Multiple Linear Regression in SAS

Learn how to run multiple linear regression models with and without interactions, presented by SAS user Alex Chaplin.

Find more tutorials on the SAS Users YouTube channel.