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LTU(3) User Contributed Perl Documentation LTU(3)NAMEStatistics::LTU - An implementation of Linear Threshold UnitsSYNOPSISuse Statistics::LTU; my $acr_ltu = new Statistics::LTU::ACR(3, 1); # 3 attributes, scaled $ltu->train([1,3,2], $LTU_PLUS); $ltu->train([-1,3,0], $LTU_MINUS); ... print "LTU looks like this:\n"; $ltu->print; print "[1,5,2] is in class "; if ($ltu->test([1,5,2]) > $LTU_THRESHOLD) { print "PLUS" } else { print "MINUS" }; $ltu->save("ACR.saved") or die "Save failed!"; $ltu2 = restore Statistics::LTU("ACR.saved");EXPORTSFor readability, LTU.pm exports three scalar constants: $LTU_PLUS (+1), $LTU_MINUS (-1) and $LTU_THRESHOLD (0).DESCRIPTIONStatistics::LTU defines methods for creating, destroying, training and testing Linear Threshold Units. A linear threshold unit is a 1-layer neural network, also called a perceptron. LTU's are used to learn classifications from examples. An LTU learns to distinguish between two classes based on the data given to it. After training on a number of examples, the LTU can then be used to classify new (unseen) examples. Technically, LTU's learn to distinguish two classes by fitting a hyperplane between examples; if the examples have n features, the hyperplane will have n dimensions. In general, the LTU's weights will converge to a define the separating hyperplane. The LTU.pm file defines an uninstantiable base class, LTU, and four other instantiable classes built on top of LTU. The four individual classes differs in the training rules used: ACR - Absolute Correction Rule TACR - Thermal Absolute Correction Rule (thermal annealing) LMS - Least Mean Squares rule RLS - Recursive Least Squares rule Each of these training rules behaves somewhat differently. Exact details of how these work are beyond the scope of this document; see the additional documentation file (ltu.doc) for discussion.SCALARS$LTU_PLUS and $LTU_MINUS (+1 and -1, respectively) may be passed to thetrainmethod. $LTU_THRESHOLD (set to zero) may be used to compare values returned from thetestmethod.METHODSEach LTU has the following methods:newTYPE(n_features,scaling)Creates an LTU of the given "TYPE". "TYPE" must be one of: Statistics::LTU::ACR, Statistics::LTU::TACR, Statistics::LTU::LMS, Statistics::LTU::RLS. "n_features" sets the number of attributes in the examples. If "scaling" is 1, the LTU will automatically scale the input features to the range (-1, +1). For example: $ACR_ltu = new Statistics::LTU::ACR(5, 1); creates an LTU that will train using the absolute correction rule. It will have 5 variables and scale features automatically.copyCopies the LTU and returns the copy.destroyDestroys the LTU (undefines its substructures). This method is kept for compatibility; it's probably sufficient simply to callundef($ltu).save(filename)Saves the LTU to the filefilename. All the weights and necessary permanent data are saved. Returns 1 if the LTU was saved successfully, else 0.restoreLTU(filename)Static method. Creates and returns a new LTU fromfilename. The new LTU will be of the same type.test(instance)Tests the LTU oninstance, the instance vector, which must be a reference to an array. Returns the raw (non-thresholded) result. A typical use of this is: if ($ltu->test($instance) >= $LTU_PLUS) { # instance is in class 1 } else { # instance is in class 2 }correctly_classifies(instance,realclass)Tests the LTU against an instance vectorinstance, which must be a reference to an array.realclassmust be a number. Returns 1 if the LTU classifiesinstancein the same class asrealclass. Technically: Returns 1 iff instance is on therealclassside of the LTU's hyperplane.weightsReturns a reference to a copy of the LTU's weights.set_origin_restriction(orig)Sets LTU's origin restriction toorig, which should be 1 or 0. If an LTU is origin-restricted, its hyperplane must pass through the origin (ie, so its intercept is zero). This is usually used for preference predicates, whose classifications must be symmetrical.is_cycling(n)Returns 1 if the LTU's weights seem to be cycling. This is a heuristic test, based on whether the LTU's weights have been pushed out in the pastntraining instances. See comments with the code.versionReturns the version of the LTU implementation. In addition to the methods above, each of the four classes of LTU defines atrainmethod. Thetrainmethod "trains" the LTU that an instance belongs in a particular class. For eachtrainmethod,instancemust be a reference to an array of numbers, andvaluemust be a number. For convenience, two constants are defined: $LTU_PLUS and $LTU_MINUS, set to +1 and -1 respectively. These can be given as arguments to thetrainmethod. A typicaltraincall looks like: $ltu->train([1,3,-5], $Statistics_LTU_PLUS); which trains the LTU that the instance vector (1,3,-5) should be in the PLUS class.oFor ACR:train(instance,value)Returns 1 iff the LTU already classified the instance correctly, else 0.oFor RLS:train(instance,value)Returns undef.oFor LMS:train(instance,value,rho)Returns 1 if the LTU already classified theinstancecorrectly, else 0.Rhodetermines how much the weights are adjusted on each training instance. It must be a positive number.oFor TACR:train(instance,value,temperature,rate)Uses the thermal perceptron (absolute correction) rule to train the specified linear threshold unit on a particular instance_vector. The instance_vector is a vector of numbers; each number is one attribute. The desired_value should be either $LTU_PLUS (for positive instances) or $LTU_MINUS (for negative instances). Thetemperatureandratemust be floating point numbers. This method returns 1 if the linear threshold unit already classified the instance correctly, otherwise it returns 0. The TACR rule only trains on instances that it does not already classify correctly.AUTHORfawcett@nynexst.com (Tom Fawcett) LTU.pm is based on a C implementation by James Callan at the University of Massachusetts. His version has been in use for a long time, is stable, and seems to be bug-free. This Perl module was created by Tom Fawcett, and any bugs you find were probably introduced in translation. Send bugs, comments and suggestions tofawcett@nynexst.com.BUGSNone known. This Perl module has been moderately exercised but I don't guarantee anything. perl v5.24.1 1997-02-27 LTU(3)

NAME | SYNOPSIS | EXPORTS | DESCRIPTION | SCALARS | METHODS | AUTHOR | BUGS

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