Intro/: Directory containing introductory examples.
HelloWorld.c A simple program that draws a box and writes "Hello World" in
HelloWorld.f it.
data The data file for the introductory progressive example.
Lines.c Reads the data from file "data" and plots just the curve with
Lines.f no labels, viewport or anything indicating quantity or units.
Viewport.c Restricts the graph to a viewport and frames the viewport,
Viewport.f leaving the remainder of the area for labels, etc.
CharLbls.c Adds labels for the chart title, X-axis title, and Y-axis
CharLbls.f title.
Tics.c Adds tic marks to the viewport edges, but since clipping was
Tics.f not set correctly, tics extend outside the viewport.
Clip.c Sets clipping such that tic marks are clipped at the viewport
Clip.f boundaries.
TicLabels.c Adds numeric tic labels to the graph this is the final
TicLabels.f installment of the progressive example.
標簽：
introductory
HelloWorld
containing
Directory
上傳時間：
2016-03-29
上傳用戶：exxxds
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標簽：
Rauch-Tung-Striebel
algorithm
smoother
which
上傳時間：
2016-04-15
上傳用戶：zhenyushaw
