úÎGEu     portable experimentalamy@nualeargais.ie Safe-InferredBA vector that has been scaled so that all elements in the vector = are between zero and one. To scale a set of vectors, use  4. Alternatively, if you can identify a maximum and > minimum value for each element in a vector, you can scale  individual vectors using . ?A vector that has been normalised, i.e., the magnitude of the  vector = 1. @A pattern to be learned or classified by a self-organising map. $Compares two patterns and returns a  non-negative number < representing how different the patterns are. A result of 0 . indicates that the patterns are identical.  target amount pattern returns a modified copy of  pattern that is more similar to target than pattern is. The 4 magnitude of the adjustment is controlled by the amount @ parameter, which should be a number between 0 and 1. Larger  values for amount permit greater adjustments. If amount=1, ) the result should be identical to the target. If amount=0, ' the result should be the unmodified pattern.  c pattern( returns the positions of all nodes in  c%, paired with the difference between pattern and the node's  pattern. classify c pattern% returns the position of the node in c ( whose pattern best matches the input pattern. If f d< is a function that returns the learning rate to apply to a  node based on its distance d$from the node that best matches the  input pattern, then  c f pattern returns a modified copy  of the classifier c that has partially learned the target. Same as train$, but applied to multiple patterns. If f< is a function that returns the learning rate to apply to a C node based on its distance from the node that best matches the  target, then   c f target returns a tuple * containing the position of the node in c whose pattern best  matches the input target), and a modified copy of the classifier  c that has partially learned the target.  Invoking classifyAndTrain c f p may be faster than invoking  (p  c, train c f p)!, but they should give identical  results. If f< is a function that returns the learning rate to apply to a C node based on its distance from the node that best matches the  target, then   c f target returns a tuple  containing: $ 1. The positions of all nodes in c, paired with the difference  between pattern and the node' s pattern ( 2. A modified copy of the classifier c that has partially  learned the target.  Invoking diffAndTrain c f p may be faster than invoking  (p  c, train c f p)!, but they should give identical  results. =Calculates the square of the Euclidean distance between two  vectors.  target amount vector adjusts vector to move it  closer to target0. The amount of adjustment is controlled by the  learning rate r3, which is a number between 0 and 1. Larger values  of r permit more adjustment. If r=1, the result will be  identical to the target. If amount=0, the result will be the  unmodified pattern. Normalises a vector Given a vector qs1 of pairs of numbers, where each pair represents E the maximum and minimum value to be expected at each position in  xs,  qs xs scales the vector xs element by element, G mapping the maximum value expected at that position to one, and the  minimum value to zero. ?Scales a set of vectors by determining the maximum and minimum C values at each position in the vector, and mapping the maximum 0 value to one, and the minimum value to zero.           portable experimentalamy@nualeargais.ie Safe-Inferred Calculates ce^(-d^2/2w^2). C This form of the Gaussian function is useful as a learning rate  function. In  c w d, c specifies the highest learning E rate, which will be applied to the SOM node that best matches the D input pattern. The learning rate applied to other nodes will be # applied based on their distance d from the best matching node.  The value w controls the 'width' of the Gaussian. Higher values  of w7 cause the learning rate to fall off more slowly with  distance.          som-3.1&Data.Datamining.Clustering.SOMInternalData.Datamining.Clustering.SOM ScaledVectorNormalisedVectorPatternMetric difference makeSimilardiffclassifytrain trainBatchclassifyAndTrain diffAndTrain adjustNodemagnitudeSquaredeuclideanDistanceSquared adjustVector normalisescalescaleAllgaussianreportAndTrain trainWithBMUnorm scaleValuequantify quantify'$fPatternScaledVector$fPatternNormalisedVector