Control Random Number Generation “rng”

rng function in Matlab can be used to generate a predictable sequence of numbers. This function provides the achieving the same results after the each training period in the context of the machine learning techniques.

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Using PCA on Three Dimensional Dataset

In this work, We use PCA three dimensional data. Matlab Code % PCA Model clear all, clc , close all hold on axis equal axis([-2 2 -2 2 -2 2]) % Step 1: Get some data X = [1 2 -1 -2 0; 0.2 0 0.1 0.2 -0.4; 1.2 0.3 -1 -0.1 -0.4]'; % Step 2: Substract the mean plot3(X(:,1),X(:,2),X(:,3),'ko'); XAdjust = X-repmat(mean(X),size(X,1),1); plot3(XAdjust(:,1),XAdjust(:,2),XAdjust(:,3),'ro'); % Step 3: Calculate the covariance matrix CM = cov(X); % Step 4: Eigenvalue and Eigenvector [V D]= eig(CM); % Step 5: Choosing component f1 = V(:,1)'; f2 = V(:,2)'; f3 = V(:,3)'; F=[f1; f2; f3];

Use PCA in Machine Learning

Matlab Code % Source % http://www.dcs.gla.ac.uk/~srogers/firstcourseml/matlab/chapter7/pcaexample.html#2 clear all;close all; % Generate the data Y = [randn(20,2);randn(20,2)+5;randn(20,2)-5]; % Add 5 random dimensions N = size(Y,1); Y = [Y randn(N,5)];

Plot a Sinusoidal Wave in Matlab

In this example, we use sinusoidal wave and plot data several different figure.

Matlab

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