Home > Uncategorized > Download “Cross Wavelet and Wavelet Coherence Toolbox”

Download “Cross Wavelet and Wavelet Coherence Toolbox”

September 16th, 2013

The ”Cross Wavelet and Wavelet Coherence Toolbox” download link by Grinsted et al (http://www.pol.ac.uk/home/research/waveletcoherence/) is dead. We will send you an active download link upon your request (an email with the download link will be sent to you automatically after you fill the form below):






To learn more about wavelet coherence analysis, see http://www.alivelearn.net/?s=wavelet

Author: Xu Cui Categories: Uncategorized Tags:
Try Stork, a research tool we developed

Stork is a publication alert app developed by us at Stanford. As a researcher we often forget to follow up important publications - and it's practically impossible to search many keywords or researchers' names everyday. Stork can help us to search everyday and notifies us when there are new publications/grants. How Stork helped me?

About the author:

Xu Cui is a human brain research scientist in Stanford University. He lives in the Bay Area in the United States. He is also the founder of Stork (smart publication alert app), PaperBox and BizGenius.

 

He was born in He'nan province, China. He received education in Beijing University(BS), University of Tennessee (Knoxville) (MS), Baylor College of Medicine (PhD) and Stanford University (PostDoc). Read more ...
  1. iman
    May 22nd, 2014 at 05:53 | #1

    Dear Prof. Xu,
    I am writing to get more information about SVM classification.

    I have one matrix with the inputs of the training data (X) and matrix with the labels (e.g. Y, my labels are -1, 1). I would like to do cross validation with n-fold (for example n=10) test on my data and I am going to obtain accuracy, Sensitivity and Specificity parameters to determine the performance of classifier.
    I did my classification based on your tutorial (http://www.alivelearn.net/?s=libsvm).

    bestcv = 0;
    for log2c = -1:3,
    for log2g = -4:1,
    cmd = ['-v 5 -c ', num2str(2^log2c), ' -g ', num2str(2^log2g)];
    cv = svmtrain(heart_scale_label, heart_scale_inst, cmd);
    if (cv >= bestcv),
    bestcv = cv; bestc = 2^log2c; bestg = 2^log2g;
    end
    fprintf(’%g %g %g (best c=%g, g=%g, rate=%g)\n’, log2c, log2g, cv, bestc, bestg, bestcv);
    end
    end
    Based on this code I can obtain cross validation accuracy, but in addition to accuracy I would like to obtain “predicted_label” according to cross validation.
    Would you please guide me how can I obtain final predicted_label after cross validation.

    Many thanks for kindly considering my question.
    I look forward to hearing from you.
    Best,
    Iman

  2. October 3rd, 2014 at 00:22 | #2

    The new site for our cross wavelet and wavelet coherence toolbox is here:

    http://www.glaciology.net/wavelet-coherence

CAPTCHA Image