gearzuloo.blogg.se - Install octave using macports

#Install octave using macports install
#Install octave using macports download

* Whitening and within-class covariance normalization techniques, * Covariance-based method for Principal Component Analysis, * Pseudo-inverse-based method for Linear Discriminant Analysis, * Improved multi-layer perceptrons implementation (Back-propagation can now be easily used in combination with any optimizer - i.e L-BFGS), * Conjugate gradient based-implementation for logistic regression, * Total variability (i-vector) implementation, * Histograms of Oriented Gradients (HOG) implementation, * Unified implementation of Local Binary Patterns (LBPs), The new release of Bob has brought the following features and/or improvements, such as: * image processing: Local Binary Patterns (LBPs), Gabor Jets, SIFT,Īnd trainers such as Support Vector Machines (SVMs), k-Means, Gaussian Mixture Models (GMMs), Inter-Session Variability modeling (ISV), Joint Factor Analysis (JFA), Probabilistic Linear Discriminant Analysis (PLDA), Bayesian intra/extra (personal) classifier, The previous release of Bob was providing:Īccessors such as FRGC, Labelled Face in the Wild, and many others, Signal-processing and machine learning toolbox is available .īob provides both efficient implementations of several machine learning algorithms as well as a framework to help researchers to publish reproducible research.

Reports on bugs and any other feedback are welcome.īob signal-processing and machine learning toolbox (v.1.2.0) Most importantly, the new version comes together with a manual.

Ĭompared to version 1.0, the new version brings many improvements in terms of the implemented models of the vocal tract, the vocal folds, the acoustic simulation, and articulatory control, as well as in terms of the user interface. VocalTractLab is an articulatory speech synthesizer and a tool to visualize and explore the mechanism of speech production with regard to articulation, acoustics, and control. It is my pleasure to announce the release of the new major version 2.0 of VocalTractLab.

Type exit to quit Octave from the command-line.VocalTractLab 2.0 : A tool for articulatory speech synthesis.

Which SVM kernel did the best job (use the average of the exon and intron accuracy to define "best") under the default script parameters? Which one took the longest to run? When you altered the parameters for the kernels, did it improve, reduce, or basically make no difference to the accuracy? Report each answer in your homework.

Run the script again and report your changes and the resulting values.

Run the Polynomial kernel script from Octave: run polynomial.m.

Run the gaussian.m script again and report the new parameter and result in your homework. Run the Gaussian kernel script from Octave: run gaussian.m.Re-run it and report the new parameters and values you got in your homework. Use a plain text editor (TextEdit, Vim editor, Notepad, etc.) to change the coef0 and gamma variables (try non-negative real numbers) in the sigmoid.m script.Run the Sigmoid kernel script from Octave: octave sigmoid.m.Unzip them and move to the created directory, then launch Octave:

#Install octave using macports download

Download the exercise files and move them to the working directory of your Terminal/Cygwin Shell (type pwd to find your current working directory).

Try out Shogun/Octave on your personal computer (after following step 2 for your OS of choice).

By now you should see that finding the "just right" rules to make a decision bounary is difficult to do by hand.

Can you draw a line to separate the two classes of red and blue? Open "2d 100x100" then the worksheet "2d 10x10". This represents the prediction scores from columns x1 and x2 for each data point (slightly zoomed). Can you pick a horizontal line to separate the blue and red data? Now look at the further zoomed "1d +/-10" worksheet. The two classes are shown in red and blue. This is zoomed (close up) data for the prediction scores of the x1 column. What is your conclusion? How did it change as you added more data? Was it difficult to figure out? Using the x1, what do you predict point 1 to be? Now try using x1 and x2. The class or label on the far right describes the true classification (-1 or +1 for introns and exons respectively). Positive scores are prediction to be exonic while negative scores are predicted to be intronic for the respective column's gene prediction scheme.

Download the example excel file for the SVM training data.

#Install octave using macports install

Binary versions (pre-compliled) of Octave are available from Octave-Forge, but one must still compile Shogun and install it (you are on your own if you go this route).