Add TacoPig Block images to Landing_Page.htm

Add more usage examples in the MATLAB script comments.

+tacopig/+covfn/Clamp.m

@@ -1,5 +1,12 @@
-% Clamps specific parameters of a covariance function so that they are not
-% altered during the learning phase.
+% Clamps specific parameters of a covariance function
+% These parameters are not altered during the learning phase.
+% Usage: tacopig.covfn.Clamp(covfn,indx,value);
+% 
+% Example instantiation:
+% GP.CovFn   = tacopig.covfn.Clamp(tacopig.covfn.SqExp(),[1 3],[4 0.6]);
+% Instantiates a Squared exponential covariance function (as a property of a Gaussian process instantiation called GP) 
+% with its first and third hyperparameters clamped to the value 4 and 0.6, respectively.
+
 
 classdef Clamp < tacopig.covfn.CovFunc
 

+tacopig/+covfn/CovFunc.m

@@ -1,4 +1,5 @@
 % Covariance Function Abstract Class
+% All noise function classes must inherent from this class.
 
 classdef CovFunc < tacopig.taco
 

+tacopig/+covfn/Exp.m

@@ -1,4 +1,10 @@
 % The Exponential covariance function class
+%
+% Example instantiation:
+% GP.CovFn   = tacopig.covfn.Exp();
+% Instantiates an Exponential covariance function (as a property of a Gaussian process instantiation called GP)
+%
+%
 % k(X1,X2) = Sigma_f^2*exp(-sqrt((X1-X2)'*diag(Lengthscales.^-2)*(X1-X2)))
 %
 % X1 and X2 are input matrices of dimensionality D x N and D x M, respectively. 

+tacopig/+covfn/Mat3.m

@@ -1,6 +1,13 @@
 % A special case of the Matern covariance function class with \nu set to 3/2 
+%
+% Example instantiation:
+% GP.CovFn   = tacopig.covfn.Mat3();
+% Instantiates a Matern3 covariance function (as a property of a Gaussian process instantiation called GP)
+%
+%
 % k(X1,X2) = Sigma_f^2*(1+sqrt(3*Tau'*diag(Lengthscales.^-2)*Tau))
 % *exp(-3*Tau'*diag(Lengthscales.^-2)*Tau);
+%
 % where Tau = X1-X2;
 % X1 and X2 are input matrices of dimensionality D x N and D x M, respectively. 
 % D is the dimesionality of the data. 

+tacopig/+covfn/Mat5.m

@@ -1,4 +1,10 @@
 % A special case of the Matern covariance function class with \nu set to 5/2 
+%
+% Example instantiation:
+% GP.CovFn   = tacopig.covfn.Mat5();
+% Instantiates a Matern5 covariance function as a property of a Gaussian process instantiation called GP
+%
+%
 % k(X1,X2) = Sigma_f^2*(1+sqrt(5*Tau'*diag(Lengthscales.^-2)*Tau)+(5/3)*Tau'*diag(Lengthscales.^-2)*Tau)
 % *exp(-5*Tau'*diag(Lengthscales.^-2)*Tau);
 % where Tau = X1-X2;

+tacopig/+covfn/Product.m

@@ -1,6 +1,13 @@
-% tacopig.covfn.Product(child1, child2, ...)
 % Defines a covariance function product of the children.
 % All children must inherit tacopig.covfn.CovFn
+%
+% Usage: tacopig.covfn.Product(child1, child2, ...)
+%
+% Example instantiation:
+% GP.CovFn   = tacopig.covfn.Product(tacopig.covfn.SqExp(),tacopig.covfn.Mat3());
+% Instantiates a Product covariance function (as a property of a Gaussian process instantiation called GP) 
+% that is the product of a squared exponential and Matern3 covariance functions
+
 
 classdef Product < tacopig.covfn.CovFunc
 

+tacopig/+covfn/Remap.m

@@ -1,6 +1,14 @@
 % Allows a re-ordering or repetition of hyperparameters
-% usage: Remap(covfn, index)
-% Eg. [1 1 2] would map optimisation vector [a b] into hyper vector [a a b]
+% Usage: Remap(covfn, index)
+% Eg. index = [1 1 2] would map optimisation vector [a b] into hyperparameter vector [a a b]
+%
+% Example instantiation
+% GP.CovFn   = tacopig.covfn.Remap(tacopig.covfn.SqExp(),[1 1 2]);
+% Instantiates a Remap of a Squared Exponential covariance function(as a property of a Gaussian process instantiation called GP) 
+% In this case, the first and second hyperparameters are remapped to the
+% one parameter so they will always be indentical (making it an isotropic covariance
+% function). The third hyperparameter is independent. 
+
 
 classdef Remap < tacopig.covfn.CovFunc
 

+tacopig/+covfn/SqExp.m

@@ -1,4 +1,10 @@
 % The Squared Exponential covariance function class
+%
+% Example Instantiation:
+% GP.CovFn = tacopig.covfn.SqExp();
+% Creates an instance of the squared exponential covariance function as the CovFn 
+% property of an instantiation of a Gaussian process class named GP.
+%
 % k(X1,X2) = Sigma_f^2*exp(-0.5*(X1-X2)'*diag(Lengthscales.^-2)*(X1-X2))
 %
 % X1 and X2 are input matrices of dimensionality D x N and D x M, respectively. 

+tacopig/+covfn/Sum.m

@@ -1,6 +1,14 @@
 % Defines a covariance function sum of the children.
 % tacopig.covfn.Sum(child1, child2, ...)
+%
 % All children must inherit tacopig.covfn.CovFn
+%
+% Example instantiation:
+% GP.CovFn   = tacopig.covfn.Sum(tacopig.covfn.SqExp(),tacopig.covfn.Mat3());
+% Instantiates a Sum covariance function (as a property of a Gaussian process instantiation called GP) 
+% that is the summation of a squared exponential and Matern3 covariance functions
+
+
 
 classdef Sum < tacopig.covfn.CovFunc
 

+tacopig/+demos/demo3D.m

@@ -37,7 +37,7 @@
 GP.y = y;
 
 % Plug in the components
-GP.MeanFn  = tacopig.meanfn.ConstantMean(0);
+GP.MeanFn  = tacopig.meanfn.FixedMean(0);
 GP.CovFn   = tacopig.covfn.SqExp();%SqExp();
 GP.NoiseFn = tacopig.noisefn.Stationary();
 GP.objective_function = @tacopig.objectivefn.NLML;