43 #define SLEEP(secs) Sleep(secs * 1000)
46 #define SLEEP(secs) sleep(secs);
59 "Training Proportion",
63 "Proportion of the occurrence data to be used to train the models.",
81 "Maximum number of GARP runs to be performed.",
94 "HardOmissionThreshold",
95 "Hard Omission Threshold",
99 "Maximum acceptable omission error. Set to 100% to use only soft omission",
112 "ModelsUnderOmissionThreshold",
113 "Models Under Omission Threshold",
117 "Minimum number of models below omission threshold.",
130 "CommissionThreshold",
131 "Commission Threshold",
135 "Percentage of distribution of models to be taken regarding commission error.",
148 "CommissionSampleSize",
149 "Commission Sample Size",
153 "Number of samples used to calculate commission error.",
167 "Maximum Number of Threads",
171 "Maximum number of threads of executions to run simultaneously.",
190 "Maximum number of iterations run by the Genetic Algorithm.",
193 "Maximum number of iterations (generations) run by the Genetic\
209 "Defines the convergence value that makes the algorithm stop\
210 (before reaching MaxGenerations).",
227 "Maximum number of rules to be kept in solution.",
243 "Number of points sampled (with replacement) used to test rules.",
262 "GARP with best subsets - DesktopGARP implementation",
266 "GARP is a genetic algorithm that creates ecological niche \
267 models for species. The models describe environmental conditions \
268 under which the species should be able to maintain populations. \
269 For input, GARP uses a set of point localities where the species \
270 is known to occur and a set of geographic layers representing \
271 the environmental parameters that might limit the species' \
272 capabilities to survive.",
275 "GARP is a genetic algorithm that creates ecological niche \
276 models for species. The models describe environmental conditions \
277 under which the species should be able to maintain populations. \
278 For input, GARP uses a set of point localities where the species \
279 is known to occur and a set of geographic layers representing \
280 the environmental parameters that might limit the species' \
281 capabilities to survive.",
284 "Anderson, R. P., D. Lew, D. and A. T. Peterson.",
287 "Anderson, R. P., D. Lew, and A. T. Peterson. 2003. \
288 Evaluating predictive models of species' distributions: criteria \
289 for selecting optimal models.Ecological Modelling, v. 162, p. 211 232.",
291 "Ricardo Scachetti Pereira",
292 "rpereira [at] ku.edu",
int transferParametersToAlgorithm()
AlgParamMetadata parameters_bs[NUM_PARAM]
void setValue(std::string const val)
static Log * instance()
Returns the instance pointer, creating the object on the first call.
OM_ALG_DLL_EXPORT AlgMetadata const * algorithmMetadata()
AlgorithmImpl * getBSAlgorithm()
void error(const char *format,...)
'Error' level.
int getParameter(std::string const &name, std::string *value)
virtual ~DgGarpBestSubsets()
OM_ALG_DLL_EXPORT AlgorithmImpl * algorithmFactory()
void setId(std::string const id)
AlgParameter * _alg_params