Annotation of /trunk/gadget/regressionline.h

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1 :	agomez	1	#ifndef regressionline_h
2 :			#define regressionline_h
3 :
4 :			#include "doublevector.h"
5 :
6 :			enum LineType { FREE = 1, FIXEDSLOPE, FIXEDINTERCEPT, FIXED };
7 :
8 :			/**
9 :			* \class Regression
10 :			* \brief This is the base class used to fit a regression line to compare 2 vectors
11 :			* \note This will always be overridden by the derived classes that actually fit the regression line
12 :			*/
13 :			class Regression {
14 :			public:
15 :			/**
16 :			* \brief This is the default Regression constructor
17 :			*/
18 :			Regression();
19 :			/**
20 :			* \brief This is the Regression constructor for a regression line of a specified LineType
21 :			* \param ltype is the LineType of the regression line
22 :			*/
23 :			Regression(LineType ltype);
24 :			/**
25 :			* \brief This is the default Regression destructor
26 :			*/
27 :			~Regression() {};
28 :			/**
29 :			* \brief This is the function that stores 2 vectors that will be compared using a regression line
30 :			* \param modData is the DoubleVector containing the modelled data
31 :			* \param obsData is the DoubleVector containing the observed data
32 :			*/
33 :			virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData) = 0;
34 :			/**
35 :			* \brief This is the function that fits a regression line to compare the 2 vectors that have been stored, according to the LineType that has been defined
36 :			*/
37 :			void calcFit();
38 :			/**
39 :			* \brief This function will set the intercept of the regression line
40 :			* \param intercept is the intercept of the regression line
41 :			*/
42 :			void setIntercept(double intercept) { a = intercept; };
43 :			/**
44 :			* \brief This function will set the slope of the regression line
45 :			* \param slope is the slope of the regession line
46 :			*/
47 :			void setSlope(double slope) { b = slope; };
48 :			/**
49 :			* \brief This function will set the weights that can be used to fit the regression line
50 :			* \param weights is the DoubleVector of weights to be used
51 :			*/
52 :			void setWeights(const DoubleVector& weights);
53 :			/**
54 :			* \brief This function will check to see if an error has occured
55 :			* \return error
56 :			*/
57 :			int getError() { return error; };
58 :			/**
59 :			* \brief This function will return the sum of squares of errors calculated when fitting the regression line
60 :			* \return sse
61 :			*/
62 :			double getSSE();
63 :			/**
64 :			* \brief This function will return the intercept of the regression line
65 :			* \return a
66 :			*/
67 :			double getIntercept() { return a; };
68 :			/**
69 :			* \brief This function will return the slope of the regression line
70 :			* \return b
71 :			*/
72 :			double getSlope() { return b; };
73 :			/**
74 :			* \brief This function will return the fit type for the regression line
75 :			* \return fittype
76 :			*/
77 :			LineType getType() const { return linetype; };
78 :			protected:
79 :			/**
80 :			* \brief This function will calculate the sum of squares of errors for the regession line
81 :			*/
82 :			void calcSSE();
83 :			/**
84 :			* \brief This function will calculate the weighted sum of squares of errors for the regession line
85 :			*/
86 :			void calcSSEWeights();
87 :			/**
88 :			* \brief This function will calculate the slope of the regession line
89 :			*/
90 :			void calcSlope();
91 :			/**
92 :			* \brief This function will calculate the intercept of the regession line
93 :			*/
94 :			void calcIntercept();
95 :			/**
96 :			* \brief This function will calculate both the slope and the intercept of the regession line
97 :			*/
98 :			void calcSlopeIntercept();
99 :			/**
100 :			* \brief This is the flag to denote whether an error has occured
101 :			*/
102 :			int error;
103 :			/**
104 :			* \brief This is the flag to denote whether the weights should be used when calculating the fit to the regression line
105 :			*/
106 :			int useweights;
107 :			/**
108 :			* \brief This is the sum of squares of errors from the regression line
109 :			*/
110 :			double sse;
111 :			/**
112 :			* \brief This is the intercept of the regression line
113 :			*/
114 :			double a;
115 :			/**
116 :			* \brief This is the slope of the regression line
117 :			*/
118 :			double b;
119 :			/**
120 :			* \brief This is the DoubleVector of weights that can be used to fit the regression line
121 :			*/
122 :			DoubleVector w;
123 :			/**
124 :			* \brief This is the DoubleVector that will contain the the modelled data to be used to fit the regression line
125 :			*/
126 :			DoubleVector x;
127 :			/**
128 :			* \brief This is the DoubleVector that will contain the the observed data to be used to fit the regression line
129 :			*/
130 :			DoubleVector y;
131 :			/**
132 :			* \brief This denotes what type of line fit is to be used for the regression line
133 :			*/
134 :			LineType linetype;
135 :			};
136 :
137 :			/**
138 :			* \class LinearRegression
139 :			* \brief This is the class used to fit a linear regression line to compare 2 vectors
140 :			*/
141 :			class LinearRegression : public Regression {
142 :			public:
143 :			/**
144 :			* \brief This is the default LinearRegression constructor
145 :			* \param ltype is the LineType of the regression line
146 :			*/
147 :			LinearRegression(LineType ltype);
148 :			/**
149 :			* \brief This is the default LinearRegression destructor
150 :			*/
151 :			~LinearRegression() {};
152 :			/**
153 :			* \brief This is the function that stores 2 vectors that will be compared using a linear regression line
154 :			* \param modData is the DoubleVector containing the modelled data
155 :			* \param obsData is the DoubleVector containing the observed data
156 :			*/
157 :			virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData);
158 :			};
159 :
160 :			/**
161 :			* \class LogLinearRegression
162 :			* \brief This is the class used to fit a log linear regression line to compare 2 vectors
163 :			*/
164 :			class LogLinearRegression : public Regression {
165 :			public:
166 :			/**
167 :			* \brief This is the default LogLinearRegression constructor
168 :			* \param ltype is the LineType of the regression line
169 :			*/
170 :			LogLinearRegression(LineType ltype);
171 :			/**
172 :			* \brief This is the default LogLinearRegression destructor
173 :			*/
174 :			~LogLinearRegression() {};
175 :			/**
176 :			* \brief This is the function that stores 2 vectors that will be compared using a log linear regression line
177 :			* \param modData is the DoubleVector containing the modelled data
178 :			* \param obsData is the DoubleVector containing the observed data
179 :			*/
180 :			virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData);
181 :			};
182 :
183 :			/**
184 :			* \class WeightRegression
185 :			* \brief This is the class used to fit a weighted linear regression line to compare 2 vectors
186 :			*/
187 :			class WeightRegression : public LinearRegression {
188 :			public:
189 :			/**
190 :			* \brief This is the default WeightRegression constructor
191 :			* \param ltype is the LineType of the regression line
192 :			*/
193 :			WeightRegression(LineType ltype);
194 :			/**
195 :			* \brief This is the default WeightRegression destructor
196 :			*/
197 :			~WeightRegression() {};
198 :			/**
199 :			* \brief This is the function that stores 2 vectors that will be compared using a linear regression line
200 :			* \param modData is the DoubleVector containing the modelled data
201 :			* \param obsData is the DoubleVector containing the observed data
202 :			*/
203 :			virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData);
204 :			};
205 :
206 :			/**
207 :			* \class LogWeightRegression
208 :			* \brief This is the class used to fit a weighted log linear regression line to compare 2 vectors
209 :			*/
210 :			class LogWeightRegression : public LogLinearRegression {
211 :			public:
212 :			/**
213 :			* \brief This is the default LogWeightRegression constructor
214 :			* \param ltype is the LineType of the regression line
215 :			*/
216 :			LogWeightRegression(LineType ltype);
217 :			/**
218 :			* \brief This is the default LogWeightRegression destructor
219 :			*/
220 :			~LogWeightRegression() {};
221 :			/**
222 :			* \brief This is the function that stores 2 vectors that will be compared using a log linear regression line
223 :			* \param modData is the DoubleVector containing the modelled data
224 :			* \param obsData is the DoubleVector containing the observed data
225 :			*/
226 :			virtual void storeVectors(const DoubleVector& modData, const DoubleVector& obsData);
227 :			};
228 :
229 :			#endif

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