void StochasticLearner::declareArguments(nor_utils::Args& args) { BaseLearner::declareArguments(args); args.declareArgument("graditer", "Declares the number of randomly drawn training size for SGD" "whereas it declares the number of iteration for the Batch Gradiend Descend" " size <num> of training set. " "Example: --graditer 50 -> Uses only 50 randomly chosen training instance", 1, "<num>"); args.declareArgument("gradmethod", "Declares the gradient method: " " (sgd) Stochastic Gradient Descent, (bgd) Batch Gradient Descent" "Example: --gradmethod sgd -> Uses stochastic gradient method", 1, "<method>"); args.declareArgument("tfunc", "Target function: " "exploss: Exponential Loss, edge: max. edge" "Example: --tfunc exploss -> Uses exponantial loss for minimizing", 1, "<function>"); args.declareArgument("initgamma", "The initial learning rate in gradient descent" "Default values is 10.0", 1, "<gamma>"); args.declareArgument("gammdivperiod", "The periodicity of decreasing the learning rate \\gamma" "Default values is 1", 1, "<period>"); }
void BanditSingleStumpLearner::declareArguments(nor_utils::Args& args) { FeaturewiseLearner::declareArguments(args); args.declareArgument("updaterule", "The update weights in the UCT can be the 1-sqrt( 1- edge^2 ) [edge]\n" " or the alpha [alphas]\n" " Default is the first one\n", 1, "<type>"); args.declareArgument("rsample", "Number of features to be considered\n" " Default is one\n", 1, "<K>"); args.declareArgument("banditalgo", "The bandit algorithm (UCBK, UCBKRandomized, EXP3 )\n" "Default is UCBK\n", 1, "<algoname>"); args.declareArgument("percent", "The percent of database will be used for estimating the payoffs(EXP3G)\n" " Default is 10%\n", 1, "<p>"); }
void ParasiteLearner::declareArguments(nor_utils::Args& args) { BaseLearner::declareArguments(args); args.declareArgument("pool", "The name of the shyp file containing the pool of\n" " weak learners, followed by the number of desired\n" " weak learners. If -1 or more than the number of \n" " weak learners, we use all of them", 2, "<fileName> <nBaseLearners>"); args.declareArgument("closed", "Include negatives of weak learners (default = false)."); }
void TreeLearnerUCT::declareArguments(nor_utils::Args& args) { BaseLearner::declareArguments(args); args.declareArgument("baselearnertype", "The name of the learner that serves as a basis for the product\n" " and the number of base learners to be multiplied\n" " Don't forget to add its parameters\n", 2, "<baseLearnerType> <numBaseLearners>"); args.declareArgument("updaterule", "The update weights in the UCT can be the 1-sqrt( 1- edge^2 ) [edge]\n" " or the alpha [alphas]\n" " or edgesquare [edgesquare]\n" " Default is the first one\n", 1, "<type>"); }
void EnumLearnerSA::declareArguments(nor_utils::Args& args) { BaseLearner::declareArguments(args); args.declareArgument("uoffset", "The offset of u\n", 1, "<offset>"); }
void BaseLearner::declareBaseArguments(nor_utils::Args& args) { args.declareArgument("shypname", "The name of output strong hypothesis (default: " + string(SHYP_NAME) + "." + string(SHYP_EXTENSION) + ").", 1, "<filename>"); args.declareArgument("shypcomp", "The shyp file will be compressed", 1, "<flag 0-1>"); args.setGroup("Basic Algorithm Options"); args.declareArgument("resume", "Resumes a training process using the strong hypothesis file.", 1, "<shypFile>"); args.declareArgument("edgeoffset", "Defines the value of the edge offset (theta) (default: no edge offset).", 1, "<val>"); }
void BanditTreeLearner::declareArguments(nor_utils::Args& args) { BanditLearner::declareArguments(args); args.declareArgument("baselearnertype", "The name of the learner that serves as a basis for the product\n" " and the number of base learners to be multiplied\n" " Don't forget to add its parameters\n", 2, "<baseLearnerType> <numBaseLearners>"); }
void FeaturewiseLearner::declareArguments(nor_utils::Args& args) { AbstainableLearner::declareArguments(args); args.declareArgument("rsample", "Instead of searching for a featurewise in all the possible dimensions (features), select a set of " " size <num> of random dimensions. " "Example: -rsample 50 -> Search over only 50 dimensions" "(Turned off for Haar: use -csample instead)", 1, "<num>"); }
void AbstainableLearner::declareArguments(nor_utils::Args& args) { BaseLearner::declareArguments(args); args.declareArgument("abstention", "Activate the abstention. Available types are:\n" " greedy: sorting and checking in O(k^2)\n" " full: the O(2^k) full search\n" " real: use the AdaBoost.MH with real valued predictions\n" " classwise: abstain if classwise edge <= theta", 1, "<type>"); }