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Performance.h
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Performance.h
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/*
* Recursive Neural Networks: neural networks for data structures
*
* Copyright (C) 2018 Alessandro Vullo
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef _PERFORMANCE_H_
#define _PERFORMANCE_H_
#include "StructuredDomain.h"
#include "Instance.h"
#include <string>
#include <iostream>
class Performance {
public:
Performance();
virtual ~Performance() {}
virtual void reset() = 0;
virtual void update(Instance*) = 0;
virtual void print(std::ostream& = std::cout) = 0;
class BadPerformanceCreation: public std::logic_error {
public:
BadPerformanceCreation(std::string msg): logic_error(msg) {}
};
// factory methods
static Performance* factory(const Problem&)
throw(BadPerformanceCreation);
friend std::ostream& operator<<(std::ostream& os, Performance* p) {
p->print(os);
return os;
}
protected:
Problem _problem;
bool _ss_tr, _ios_tr;
};
class ClassificationPerformance: public Performance {
public:
~ClassificationPerformance();
void reset();
void update(Instance*);
void print(std::ostream&);
private:
ClassificationPerformance(int);
friend class Performance;
int _nc; // number of classes
int** _cm; // confusion matrix
int num_class_instances(int) const;
int num_class_errors(int) const;
int num_total_errors() const;
};
// TODO
// report error, sum of squared residuals, correlation
class RegressionPerformance: public Performance {
public:
void reset() {}
void update(Instance*) {}
void print(std::ostream&) {}
private:
RegressionPerformance() {}
friend class Performance;
};
#endif // _PERFORMANCE_H_