forked from xinlongj/FS-RVFL
-
Notifications
You must be signed in to change notification settings - Fork 0
/
FSRVFL.m
159 lines (147 loc) · 6.11 KB
/
FSRVFL.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy, EstimatedOutputs] = FSRVFL(TrainingData, TestingData, nOutput, nFeatureA, nLabeledData, LamdaA, LamdaB, LaplacianOptionsA, LaplacianOptionsB, nHiddenNeurons, ActivationFunction)
% Input:
% TrainingData - Training data set
% TestingData - Testing data set
% nOutput - Number of output dimension e.g. (x y) is 2 outputs
% nFeatureA - Number of features A (A and B are two kinds of features)
% nLabeledData - Number of labeled samples, means the real position is given
% LamdaA - The weight value of A's manifold constraint
% LamdaB - The weight value of B's manifold constraint
% LaplacianOptionsA - The parameters of Laplacian A
% LaplacianOptionsB - The parameters of Laplacian B
% .NN - The number of nearest neighbors
% .GraphDistanceFunction - The draph distance function
% * 'euclidean'
% .GraphWeights - Weight type;
% * 'distance' for distance weight
% * 'binary' for binary weight
% * 'heat' for heat kernel sigma
% .GraphNormalize - 0 for normalized laplacian, 1 for not normalized laplacian
% .GraphWeightParam - The standard deviation when use 'heat' as GraphWeight
% nHiddenNeurons - Number of hidden neurons assigned to the FSRVFL
% ActivationFunction - Type of activation function:
% 'rbf' for radial basis function, G(a,b,x) = exp(-b||x-a||^2)
% 'sig' for sigmoidal function, G(a,b,x) = 1/(1+exp(-(ax+b)))
% 'sin' for sine function, G(a,b,x) = sin(ax+b)
% 'hardlim' for hardlim function, G(a,b,x) = hardlim(ax+b)
%
% Output£º
% TrainingTime - Time (seconds) spent on training model
% TestingTime - Time (seconds) spent on predicting all testing data
% TrainingAccuracy - Training accuracy:
% RMSE for EstimatedPositions of training data
% TestingAccuracy - Testing accuracy:
% RMSE for EstimatedPositions of testing data
% EstimatedPosition - The estimated postion by FSRVFL
%
%
% ------------------------------------------------------------------------
% Samples;
%
% LaplacianOptionsA.NN = 20;
% LaplacianOptionsA.GraphDistanceFunction='euclidean';
% LaplacianOptionsA.GraphWeights='binary';
% LaplacianOptionsA.GraphNormalize=1;
% LaplacianOptionsA.GraphWeightParam=1;
%
% LaplacianOptionsB.NN = 20;
% LaplacianOptionsB.GraphDistanceFunction='euclidean';
% LaplacianOptionsB.GraphWeights='binary';
% LaplacianOptionsB.GraphNormalize=1;
% LaplacianOptionsB.GraphWeightParam=1;
%
% FSRVFL(TrainingData, TestingData, 2, 5, 1000, 0.6, 0.4, LaplacianOptionsA, LaplacianOptionsB, 1000, 'sig')
% ------------------------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Authors: Xinlon Jiang
% Affiliate: Institute of Computing Technology, CAS
% EMAIL: jiangxinlong@ict.ac.cn
% Paper: Not published yet
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%% Load dataset
train_data = TrainingData;
test_data = TestingData;
T = train_data(:,1:nOutput);
P = train_data(:,nOutput + 1:end);
P_A = train_data(:,nOutput + 1:nOutput + nFeatureA);
P_B = train_data(:,nOutput + nFeatureA + 1:end);
TV.T = test_data(:,1:nOutput);
TV.P = test_data(:,nOutput + 1:end);
clear train_data test_data;
nTrainingData = size(P,1);
nTestingData = size(TV.P,1);
nInputNeurons = size(P,2);
%%%%%%%%%%% Calculate graph laplacian
LaplacianA = laplacian(P_A,'nn',LaplacianOptionsA);
LaplacianB = laplacian(P_B,'nn',LaplacianOptionsB);
J = zeros(nTrainingData,nTrainingData);
for i = 1:nLabeledData
J(i,i) = 1;
end
%%%%%%%%%%% Step 1 Initialization Phase
P0 = P(1:nLabeledData,:);
T0 = T(1:nLabeledData,:);
%=====================================================================
% TRAINING
%=====================================================================
start_time = cputime;
IW = rand(nHiddenNeurons, nInputNeurons) * 2 - 1;
switch lower(ActivationFunction)
case{'rbf'}
Bias = rand(1, nHiddenNeurons);
H = RBFun(P, IW, Bias);
HTrain = RBFun(P0, IW, Bias);
case{'sig'}
Bias = rand(1, nHiddenNeurons) * 2 - 1;
H = SigActFun(P, IW, Bias);
HTrain = SigActFun(P0, IW, Bias);
case{'sin'}
Bias = rand(1, nHiddenNeurons) * 2 - 1;
H = SinActFun(P, IW, Bias);
HTrain = SinActFun(P0, IW, Bias);
case{'hardlim'}
Bias = rand(1, nHiddenNeurons) * 2 - 1;
H = HardlimActFun(P, IW, Bias);
H = double(H);
HTrain = HardlimActFun(P0, IW, Bias);
end
H0 = (J + LamdaA * LaplacianA + LamdaB * LaplacianB) * [P H];
beta = pinv(H0) * (J * T);
end_time = cputime;
TrainingTime = end_time - start_time;
Y = [P0 HTrain] * beta;
%=====================================================================
% TESTING
%=====================================================================
start_time = cputime;
switch lower(ActivationFunction)
case{'rbf'}
Bias = rand(1,nHiddenNeurons);
HTest = RBFun(TV.P, IW, Bias);
case{'sig'}
Bias = rand(1,nHiddenNeurons) * 2 - 1;
HTest = SigActFun(TV.P, IW, Bias);
case{'sin'}
Bias = rand(1,nHiddenNeurons) * 2 - 1;
HTest = SinActFun(TV.P, IW, Bias);
case{'hardlim'}
Bias = rand(1,nHiddenNeurons) * 2 - 1;
HTest = HardlimActFun(TV.P, IW, Bias);
end
TY = [TV.P HTest] * beta;
EstimatedOutputs = TY;
end_time = cputime;
TestingTime = end_time - start_time;
%%%%%%%%%%%%%% Calculate RMSE in the case of REGRESSION
[m n] = size(T0);
dis = zeros(m,1);
for i=1:1:m
dis(i,1) = norm(T0(i,:) - Y(i,:));
end
TrainingAccuracy = sqrt(mse(dis))
[m n] = size(TV.T);
dis = zeros(m,1);
for i=1:1:m
dis(i,1) = norm(TV.T(i,:) - TY(i,:));
end
TestingAccuracy = sqrt(mse(dis))