Hi, I am trying to install Tisean to conduct PCA on my dataset. I am unable to instal tisean in Octave. Iam getting the following error. Please help. >> pkg install tisean-0.2.3.tar.gz configure: error: in `/tmp/oct-P4jiNI/tisean-0.2.3/src': configure: error: cannot run C++ compiled programs. If you meant to cross compile, use `--host'. See `config.log' for more details checking for g++... g++ checking whether the C++ compiler works... yes checking for C++ compiler default output file name... a.exe checking for suffix of executables... .exe checking whether we are cross compiling... pkg: error running the configure script for tisean. error: called from configure_make at line 78 column 9 install at line 184 column 7 pkg at line 437 column 9 Regards, Sravanthi Kurri |
On 04.08.2018 02:23, Sai Sravanthi wrote:
> > Hi, > > I am trying to install Tisean to conduct PCA on my dataset. > I am unable to instal tisean in Octave. Iam getting the following error. > Please help. > >>> pkg install tisean-0.2.3.tar.gz > configure: error: in `/tmp/oct-P4jiNI/tisean-0.2.3/src': > configure: error: cannot run C++ compiled programs. > If you meant to cross compile, use `--host'. > See `config.log' for more details > checking for g++... g++ > checking whether the C++ compiler works... yes > checking for C++ compiler default output file name... a.exe > checking for suffix of executables... .exe it looks like you are running Octave on Windows. The tisean package is part of the official Octave for Windows installer which you can get from https://www.gnu.org/software/octave/#install It should be possible for you to simply load and then use the tisean package: >> pkg load tisean >> help pca 'pca' is a function from the file …/tisean-0.2.3/pca.m -- Function File: EIGVAL = pca (S) -- Function File: [EIGVAL, EIGVEC] = pca (S) -- Function File: [EIGVAL, EIGVEC, TS] = pca (S) -- Function File: [...] = pca (S, PARAMNAME, PARAMVALUE, ...) Performs a global principal component analysis (PCA). It gives the eigenvalues of the covariance matrix and depending on the flag W settings the eigenvectors, projections of the input time series. … Oliver signature.asc (499 bytes) Download Attachment |
Hi,
If you only want PCA you do not need tisean at all. If X is your dataset (rows: samples, cols= variables) X_ = X - mean (X); # center variables [U S V] = svd (X_, 1); PCA_basis = V; # columns are your PCA vectors P = S * U.'; # These are the scores, such that X_ = V * P lambda = diag (S).^2 / ( size(X,1) - 1); # Eigenvalues of the sample covaraince matrix cumvar = cumsum (lambda) / sum (lambda); # explained variance as function of number of components Regards, |
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I am not your personal consultant. Only if you keep it in the mialing list I might be able to help. Regards, On Wed, Aug 8, 2018 at 10:50 PM Sai Sravanthi <[hidden email]> wrote: > > Thank you Soo much. My assignment submission is on 10th. I was really worried because of the tight deadline. I'll try this now and will get back to you if I need any more help > > On Wed, Aug 8, 2018, 9:48 PM Juan Pablo Carbajal <[hidden email]> wrote: >> >> Hi, >> >> If you only want PCA you do not need tisean at all. >> If X is your dataset (rows: samples, cols= variables) >> >> X_ = X - mean (X); # center variables >> [U S V] = svd (X_, 1); >> PCA_basis = V; # columns are your PCA vectors >> P = S * U.'; # These are the scores, such that X_ = V * P >> lambda = diag (S).^2 / ( size(X,1) - 1); # Eigenvalues of the sample >> covaraince matrix >> cumvar = cumsum (lambda) / sum (lambda); # explained variance as >> function of number of components >> >> Regards, |
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