demop1.m

感知器的MATLAB实现

%% Classification with a 2-input Perceptron
% A 2-input hard limit neuron is trained to classify 5 input vectors into two
% categories.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.16 $  $Date: 2002/03/29 19:36:11 $

%%
% Each of the four column vectors in P defines a 2-element input vectors and a
% row vector T defines the vector's target categories.  We can plot these
% vectors with PLOTPV.

P = [ -0.5 -0.5 +0.3 -0.1;  ...
      -0.5 +0.5 -0.5 +1.0];
T = [1 1 0 0];
plotpv(P,T);

%%
% The perceptron must properly classify the 5 input vectors in P into the two
% categories defined by T.  Perceptrons have HARDLIM neurons.  These neurons are
% capable of separating an input space with a straight line into two categories
% (0 and 1).
%
% NEWP creates a network object and configures it as a perceptron. The first
% argument specifies the expected ranges of two inputs.  The second determines
% that there is only one neuron in the layer.

net = newp([-1 1;-1 1],1);

%%
% The input vectors are replotted with the neuron's initial attempt at
% classification.
%
% The initial weights are set to zero, so any input gives the same output and
% the classification line does not even appear on the plot.  Fear not... we are
% going to train it!

plotpv(P,T);
plotpc(net.IW{1},net.b{1});

%%
% ADAPT returns a new network object that performs as a better classifier, the
% network output, and the error.

net.adaptParam.passes = 3;
net = adapt(net,P,T);
plotpc(net.IW{1},net.b{1});

%%
% Now SIM is used to classify any other input vector, like [0.7; 1.2]. A plot of
% this new point with the original training set shows how the network performs.
% To distinguish it from the training set, color it red.

p = [0.7; 1.2];
a = sim(net,p);
plotpv(p,a);
point = findobj(gca,'type','line');
set(point,'Color','red');

%%
% Turn on "hold" so the previous plot is not erased and plot the training set
% and the classification line.
%
% The perceptron correctly classified our new point (in red) as category "zero"
% (represented by a circle) and not a "one" (represented by a plus).

hold on;
plotpv(P,T);
plotpc(net.IW{1},net.b{1});
hold off;