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/* |
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* Copyright 2010, |
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* François Bleibel, |
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* Olivier Stasse, |
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* |
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* CNRS/AIST |
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* |
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*/ |
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#ifndef __SOT_GAIN_ADAPTATIVE_HH__ |
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#define __SOT_GAIN_ADAPTATIVE_HH__ |
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/* --------------------------------------------------------------------- */ |
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/* --- INCLUDE --------------------------------------------------------- */ |
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/* --------------------------------------------------------------------- */ |
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/* Matrix */ |
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#include <dynamic-graph/linear-algebra.h> |
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/* SOT */ |
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#include <dynamic-graph/all-signals.h> |
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#include <dynamic-graph/entity.h> |
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/* --------------------------------------------------------------------- */ |
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/* --- API ------------------------------------------------------------- */ |
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/* --------------------------------------------------------------------- */ |
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#if defined(WIN32) |
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#if defined(gain_adaptive_EXPORTS) |
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#define SOTGAINADAPTATIVE_EXPORT __declspec(dllexport) |
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#else |
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#define SOTGAINADAPTATIVE_EXPORT __declspec(dllimport) |
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#endif |
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#else |
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#define SOTGAINADAPTATIVE_EXPORT |
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#endif |
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/* --------------------------------------------------------------------- */ |
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/* --- CLASS ----------------------------------------------------------- */ |
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/* --------------------------------------------------------------------- */ |
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namespace dynamicgraph { |
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namespace sot { |
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/** Exponentially decreasing gain. |
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* It follows the law \f[ g(e) = a \exp (-b ||e||) + c \f]. |
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* |
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* The default values for |
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* - \f$ a = 0 \f$, |
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* - \f$ b = 0 \f$, |
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* - \f$ c = 0.1 \f$. |
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*/ |
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class SOTGAINADAPTATIVE_EXPORT GainAdaptive : public dynamicgraph::Entity { |
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public: /* --- CONSTANTS --- */ |
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/* Default values. */ |
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static const double ZERO_DEFAULT; // = 0.1 |
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static const double INFTY_DEFAULT; // = 0.1 |
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static const double TAN_DEFAULT; // = 1. |
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public: /* --- ENTITY INHERITANCE --- */ |
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static const std::string CLASS_NAME; |
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virtual void display(std::ostream &os) const; |
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virtual const std::string &getClassName(void) const { return CLASS_NAME; } |
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protected: |
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/* Parameters of the adaptative-gain function: |
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* lambda (x) = a * exp (-b*x) + c. */ |
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double coeff_a; |
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double coeff_b; |
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double coeff_c; |
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public: /* --- CONSTRUCTORS ---- */ |
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GainAdaptive(const std::string &name); |
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GainAdaptive(const std::string &name, const double &lambda); |
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GainAdaptive(const std::string &name, const double &valueAt0, |
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const double &valueAtInfty, const double &tanAt0); |
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public: /* --- INIT --- */ |
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inline void init(void) { init(ZERO_DEFAULT, INFTY_DEFAULT, TAN_DEFAULT); } |
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inline void init(const double &lambda) { init(lambda, lambda, 1.); } |
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void init(const double &valueAt0, const double &valueAtInfty, |
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const double &tanAt0); |
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/** \brief Set the gain |
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* by providing the value at 0, at \f$ \infty \f$ and the percentage of |
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* accomplishment between both to be reached when the error is |
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* \c errorReference. |
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* |
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* To visualize the curve of the gain versus the error, use |
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* \code{.py} |
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* from dynamic_graph.sot.core.gain_adaptive import GainAdaptive |
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* import numpy, matplotlib.pyplot as plt |
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* g = GainAdaptive('g') |
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* g.setByPoint(4.9, 0.001, 0.01, 0.1) |
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* |
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* errors = numpy.linspace(0, 0.1, 1000) |
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* def compute(e): |
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* t = g.error.time + 1 |
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* g.error.value = (e,) |
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* g.error.time = t |
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* g.gain.recompute(t) |
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* return g.gain.value |
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* |
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* gains = [ compute(e) for e in errors ] |
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* |
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* lg = plt.plot(errors, gains, 'r', label="Gain") |
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* ld = plt.twinx().plot(errors, [ g*e for e,g in zip(errors,gains) ], 'b', |
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* label="Derivative") |
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* lines = lg + ld |
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* plt.legend(lines, [l.get_label() for l in lines]) |
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* plt.show() |
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* \endcode |
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*/ |
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void initFromPassingPoint(const double &valueAt0, const double &valueAtInfty, |
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const double &errorReference, |
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const double &percentage); |
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void forceConstant(void); |
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public: /* --- SIGNALS --- */ |
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dynamicgraph::SignalPtr<dynamicgraph::Vector, int> errorSIN; |
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dynamicgraph::SignalTimeDependent<double, int> gainSOUT; |
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protected: |
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double &computeGain(double &res, int t); |
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private: |
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void addCommands(); |
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}; |
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} /* namespace sot */ |
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} /* namespace dynamicgraph */ |
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#endif // #ifndef __SOT_GAIN_ADAPTATIVE_HH__ |
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