MATLAB R2007a plotpv函数存在一个BUG，Classify using the least-squares algorithm
MATLAB R2007a plotpv函数存在一个BUG，导致做模式分类绘制决策线的时候出现错误
以 Classify using the least-squares algorithm 为例介绍修正后的实例
1 function plotpv(p,t,v)
2 %PLOTPV Plot perceptron input/target vectors.
3 %
4 % Syntax
5 %
6 % plotpv(p,t)
7 % plotpv(p,t,v)
8 %
9 % Description
10 %
11 % PLOTPV(P,T) take these inputs,
12 % P - RxQ matrix of input vectors (R must be 3 or less).
13 % T - SxQ matrix of binary target vectors (S must be 3 or less).
14 % and plots column vectors in P with markers based on T.
15 %
16 % PLOTPV(P,T,V) takes an additional input,
17 % V - Graph limits = [x_min x_max y_min y_max]
18 % and plots the column vectors with limits set by V.
19 %
20 % Example
21 %
22 % The code below defines and plots the inputs and targets
23 % for a perceptron:
24 %
25 % p = [0 0 1 1; 0 1 0 1];
26 % t = [0 0 0 1];
27 % plotpv(p,t)
28 %
29 % The following code creates a perceptron with inputs ranging
30 % over the values in P, assigns values to its weights
31 % and biases, and plots the resulting classification line.
32 %
33 % net = newp(minmax(p),1);
34 % net.iw{1,1} = [-1.2 -0.5];
35 % net.b{1} = 1;
36 % plotpc(net.iw{1,1},net.b{1})
37 %
38 % See also PLOTPC.
39
40 % Mark Beale, 1-31-92
41 % Revised 12-15-93, MB
42 % Copyright 1992-2005 The MathWorks, Inc.
43 % $Revision: 1.1.6.2 $ $Date: 2005/12/22 18:20:31 $
44
45 % ERROR CHECKING
46 % ==============
47
48 if nargin < 2, error('Not enough arguments.'),end
49
50 [pr,pc] = size(p);
51 [tr,tc] = size(t);
52
53 if (pr > 3), error('P must 1, 2, or 3 rows.'), end
54 if tr > 3, error('T must have 1, 2, or 3 rows.'), end
55
56 % DEFAULTS
57 % ========
58
59 if max(pr,tr) <= 2
60 plotdim = 2;
61 else
62 plotdim = 3;
63 end
64
65 p = [p; zeros(3-pr,pc)];
66 t = [t; zeros(3-tr,tc)];
67
68 if nargin == 2
69 minx = min(p(1,:));
70 maxx = max(p(1,:));
71 miny = min(p(2,:));
72 maxy = max(p(2,:));
73 edgx = (maxx-minx)*0.4+0.1;
74 edgy = (maxy-miny)*0.4+0.1;
75 minz = min(p(3,:));
76 maxz = max(p(3,:));
77 edgz = (maxz-minz)*0.4;
78 if plotdim == 2
79 v = [minx-edgx maxx+edgx miny-edgy maxy+edgy];
80 else
81 v = [minx-edgx maxx+edgx miny-edgy maxy+edgy minz-edgz maxz+edgz];
82 end
83 end
84
85 % MARKERS
86 % =======
87
88 marker = ['ob';'or';'*b';'*r';'+b';'+r';'xb';'xr'];
89
90 % PLOTTING
91 % ========
92
93 for i=1:pc
94 x= [4 2 1]*t(:,i)+1;
95 if (x<1)
96 x=1;
97 end
98
99 if (x>size(marker,1))
100 x=size(marker,1);
101 end
102
103 m = marker(x,:);
104 plot3(p(1,i),p(2,i),p(3,i),m)
105 hold on
106 end
107
108 % PLOT SET UP
109 % ===========
110
111 set(gca,'box','on')
112 title('Vectors to be Classified')
113 xlabel('P(1)');
114 ylabel('P(2)');
115
116 if plotdim <= 2
117 view(2)
118 else
119 view(3)
120 zlabel('P(3)')
121 end
122 axis(v)
123 hold off
124
125
2 %PLOTPV Plot perceptron input/target vectors.
3 %
4 % Syntax
5 %
6 % plotpv(p,t)
7 % plotpv(p,t,v)
8 %
9 % Description
10 %
11 % PLOTPV(P,T) take these inputs,
12 % P - RxQ matrix of input vectors (R must be 3 or less).
13 % T - SxQ matrix of binary target vectors (S must be 3 or less).
14 % and plots column vectors in P with markers based on T.
15 %
16 % PLOTPV(P,T,V) takes an additional input,
17 % V - Graph limits = [x_min x_max y_min y_max]
18 % and plots the column vectors with limits set by V.
19 %
20 % Example
21 %
22 % The code below defines and plots the inputs and targets
23 % for a perceptron:
24 %
25 % p = [0 0 1 1; 0 1 0 1];
26 % t = [0 0 0 1];
27 % plotpv(p,t)
28 %
29 % The following code creates a perceptron with inputs ranging
30 % over the values in P, assigns values to its weights
31 % and biases, and plots the resulting classification line.
32 %
33 % net = newp(minmax(p),1);
34 % net.iw{1,1} = [-1.2 -0.5];
35 % net.b{1} = 1;
36 % plotpc(net.iw{1,1},net.b{1})
37 %
38 % See also PLOTPC.
39
40 % Mark Beale, 1-31-92
41 % Revised 12-15-93, MB
42 % Copyright 1992-2005 The MathWorks, Inc.
43 % $Revision: 1.1.6.2 $ $Date: 2005/12/22 18:20:31 $
44
45 % ERROR CHECKING
46 % ==============
47
48 if nargin < 2, error('Not enough arguments.'),end
49
50 [pr,pc] = size(p);
51 [tr,tc] = size(t);
52
53 if (pr > 3), error('P must 1, 2, or 3 rows.'), end
54 if tr > 3, error('T must have 1, 2, or 3 rows.'), end
55
56 % DEFAULTS
57 % ========
58
59 if max(pr,tr) <= 2
60 plotdim = 2;
61 else
62 plotdim = 3;
63 end
64
65 p = [p; zeros(3-pr,pc)];
66 t = [t; zeros(3-tr,tc)];
67
68 if nargin == 2
69 minx = min(p(1,:));
70 maxx = max(p(1,:));
71 miny = min(p(2,:));
72 maxy = max(p(2,:));
73 edgx = (maxx-minx)*0.4+0.1;
74 edgy = (maxy-miny)*0.4+0.1;
75 minz = min(p(3,:));
76 maxz = max(p(3,:));
77 edgz = (maxz-minz)*0.4;
78 if plotdim == 2
79 v = [minx-edgx maxx+edgx miny-edgy maxy+edgy];
80 else
81 v = [minx-edgx maxx+edgx miny-edgy maxy+edgy minz-edgz maxz+edgz];
82 end
83 end
84
85 % MARKERS
86 % =======
87
88 marker = ['ob';'or';'*b';'*r';'+b';'+r';'xb';'xr'];
89
90 % PLOTTING
91 % ========
92
93 for i=1:pc
94 x= [4 2 1]*t(:,i)+1;
95 if (x<1)
96 x=1;
97 end
98
99 if (x>size(marker,1))
100 x=size(marker,1);
101 end
102
103 m = marker(x,:);
104 plot3(p(1,i),p(2,i),p(3,i),m)
105 hold on
106 end
107
108 % PLOT SET UP
109 % ===========
110
111 set(gca,'box','on')
112 title('Vectors to be Classified')
113 xlabel('P(1)');
114 ylabel('P(2)');
115
116 if plotdim <= 2
117 view(2)
118 else
119 view(3)
120 zlabel('P(3)')
121 end
122 axis(v)
123 hold off
124
125
Code
1 %function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights)
2
3 train_patterns=[0.2 0.3 0.4 0.6 0.1 0.4 0.6 0.7 0.8 0.7;
4 0.7 0.3 0.5 0.5 0.4 0.6 0.2 0.4 0.6 0.5]
5 %train_targets=[ 0 0 0 0 0 1 1 1 1 1]
6 train_targets=[1 1 1 1 1 -1 -1 -1 -1 -1]
7 test_patterns=[0.7 0.7]'
8 weights=[];%[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]
9 [test_targets, a] = LS(train_patterns, train_targets, test_patterns, weights)
10
11 plotpv2(train_patterns,train_targets); %绘点，绘制分类模式
12 plotpc(a(1:end-1),a(end:end)); %绘分割线；绘制决策面
13
14
15
2
3 train_patterns=[0.2 0.3 0.4 0.6 0.1 0.4 0.6 0.7 0.8 0.7;
4 0.7 0.3 0.5 0.5 0.4 0.6 0.2 0.4 0.6 0.5]
5 %train_targets=[ 0 0 0 0 0 1 1 1 1 1]
6 train_targets=[1 1 1 1 1 -1 -1 -1 -1 -1]
7 test_patterns=[0.7 0.7]'
8 weights=[];%[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]
9 [test_targets, a] = LS(train_patterns, train_targets, test_patterns, weights)
10
11 plotpv2(train_patterns,train_targets); %绘点，绘制分类模式
12 plotpc(a(1:end-1),a(end:end)); %绘分割线；绘制决策面
13
14
15
