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comparison m-toolbox/classes/+utils/@math/stopfit.m @ 0:f0afece42f48

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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1 % STOPFIT verify fit accuracy checking for specified condition.
2 %
3 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4 % DESCRIPTION:
5 %
6 % Verify fit accuracy checking for a specified condition. Available
7 % conditions are:
8 %
9 % Log Residuals difference
10 % Check if the minimum of the logarithmic difference between data and
11 % residuals is larger than a specified value. ie. if the conditioning
12 % value is 2, the function ensures that the difference between data and
13 % residuals is at lest 2 order of magnitude lower than data itsleves.
14 % Checking algorithm is:
15 % lsr = log10(abs(y))-log10(abs(rdl));
16 % min(lsr) > lrscond;
17 %
18 % Log Residuals difference and Root Mean Squared Error
19 % Check if the log difference between data and residuals is in
20 % larger than the value indicated in lsrcond and that the variation of
21 % the root mean squared error is lower than 10^(-1*msevar).
22 % Checking algorithm is:
23 % lsr = log10(abs(y))-log10(abs(rdl));
24 % (lsr > lrscond) && (mse < 10^(-1*lrsvarcond));
25 %
26 % CALL:
27 %
28 % [ext,msg] = stopfit(y,rdl,mse,ctp,lrscond,msevar)
29 %
30 % INPUTS:
31 %
32 % - y are the fitting data (in case of 'lrs' and 'lrsmse') or the
33 % fitted model (in case of 'rft' and 'rftmse')
34 % - rdl are the fit residuals
35 % - mse is a vector storing the values of root mean squared errors
36 % difference for the present and previuos iterations
37 % - order is the model order
38 % - ctp defines the conditioning type. Admitted values are:
39 % 1) 'chival' check if the value of the Mean Squared Error is lower
40 % than 10^(-1*lsrcond).
41 % 2) 'chivar' check if the value of the Mean Squared Error is lower
42 % than 10^(-1*lsrcond) and if the relative variation of mean squared error is
43 % lower than 10^(-1*msevar).
44 % 3) 'lrs' check if the log difference between data and residuals is
45 % point by point larger than the value indicated in lsrcond. This
46 % mean that residuals are lsrcond order of magnitudes lower than
47 % data.
48 % 4) 'lrsmse' check if the log difference between data and
49 % residuals is larger than the value indicated in lsrcond and if the
50 % relative variation of root mean squared error is lower than
51 % 10^(-1*msevar).
52 % 5) 'rft' check if the spectral flatness coefficient for the
53 % rersiduals is larger than the value passed in lrscond. In this case
54 % only values 0 < lrscond < 1 are allowed.
55 % 6) 'rftmse' check if the spectral flatness coefficient for the
56 % rersiduals is larger than the value passed in lrscond and if the
57 % relative variation of root mean squared error is lower than 10^(-1*msevar).
58 % In this case only values 0 < lrscond < 1 are allowed.
59 %
60 % OUTPUT:
61 %
62 % - ext is 1 if the specified condition is satisfied or 0 if not
63 % - msg is a string containing a messagge
64 %
65 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
66 % VERSION: $Id: stopfit.m,v 1.8 2009/04/20 14:25:50 luigi Exp $
67 %
68 % HISTORY: 09-10-2008 L Ferraioli
69 % Creation
70 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
71
72 function [ext,msg] = stopfit(y,rdl,mse,ctp,lrscond,msevar)
73
74 % switching between conditions
75 switch ctp
76 case 'chival'
77 if isempty(lrscond)
78 lrscond = 2; % Default value
79 end
80
81 hh = length(mse);
82 lsr = -1*log10(mse(hh));
83 if lsr > lrscond
84 msg = 'Reached tolerance for Mean Squared Error Value';
85 ext = true;
86 else
87 ext = false;
88 msg = '';
89 end
90
91 case 'chivar'
92
93 if isempty(lrscond)
94 a = 2; % Default value
95 else
96 a = lrscond;
97 end
98 if isempty(msevar)
99 b = 2; % Default value
100 else
101 b = msevar;
102 end
103
104 hh = length(mse);
105 tlsr = -1*log10(mse(hh));
106 if hh == 1
107 stc = 1;
108 else
109 stc = diff(mse(hh-1:hh))/mse(hh-1);
110 end
111 if all(tlsr > a) && (abs(stc) < 10^(-1*b))
112 msg = 'Reached tolerance for Mean Squared Error Value and Variation';
113 ext = true;
114 else
115 ext = false;
116 msg = '';
117 end
118
119 case 'lrs'
120 if isempty(lrscond)
121 lrscond = 2; % Default value
122 end
123
124 lsr = log10(abs(y))-log10(abs(rdl));
125 if min(lsr) > lrscond
126 msg = 'Reached tolerance for Log residuals';
127 ext = true;
128 else
129 ext = false;
130 msg = '';
131 end
132
133 case 'lrsmse'
134
135 if isempty(lrscond)
136 a = 2; % Default value
137 else
138 a = lrscond;
139 end
140 if isempty(msevar)
141 b = 2; % Default value
142 else
143 b = msevar;
144 end
145
146 tlsr = log10(abs(y))-log10(abs(rdl));
147 hh = length(mse);
148 if hh == 1
149 stc = 1;
150 else
151 stc = diff(mse(hh-1:hh))/mse(hh-1);
152 end
153 if all(tlsr > a) && (abs(stc) < 10^(-1*b))
154 msg = 'Reached tolerance for Log Residuals and Mean Squared Error variation';
155 ext = true;
156 else
157 ext = false;
158 msg = '';
159 end
160
161 case 'rft' % Check that residuals flatness is larger than a certain value
162 % Calculate residual flatness
163 rf = utils.math.spflat(abs(rdl));
164
165 % Checking that lrscond has the correct value
166 a = lrscond;
167 if (a >= 1) || (a < 0)
168 a = 0.5;
169 end
170 if rf > a
171 msg = 'Reached tolerance for residuals spectral flatness';
172 ext = true;
173 else
174 ext = false;
175 msg = '';
176 end
177
178 case 'rftmse'
179
180 % Calculate residual flatness
181 rf = utils.math.spflat(abs(rdl));
182
183 % Checking that lrscond has the correct value
184 a = lrscond;
185 if (a >= 1) || (a < 0)
186 a = 0.5;
187 end
188
189 if isempty(msevar)
190 b = 2; % Default value
191 else
192 b = msevar;
193 end
194
195 hh = length(mse);
196 if hh == 1
197 stc = 1;
198 else
199 stc = diff(mse(hh-1:hh))/mse(hh-1);
200 end
201 if (rf > a) && (abs(stc) < 10^(-1*b))
202 msg = 'Reached tolerance for residuals spectral flatness and Mean Squared Error variation';
203 ext = true;
204 else
205 ext = false;
206 msg = '';
207 end
208
209
210
211 end
212