úÎ;Œ:2     portable experimentalamy@nualeargais.ie Safe-InferredEA vector that has been scaled so that all elements in the vector are 8 between zero and one. To scale a set of vectors, use . F Alternatively, if you can identify a maximum and minimum value for D each element in a vector, you can scale individual vectors using  . JA 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. $Compares two patterns and returns a  non-negative number representing / how different the patterns are. A result of 0 indicates that the  patterns are identical.  target amount pattern returns a modified copy of  pattern that is more similar to target than pattern is. The 4 magnitude of the adjustment is controlled by the amount parameter, ? which should be a number between 0 and 1. Larger values for amount " permit greater adjustments. If amount=1, the result should be  identical to the target. If amount=0, the result should be the  unmodified pattern.  pattern c% returns the position of the node in c ( whose pattern best matches the input pattern. pattern `'differences'\` c( returns the positions of all nodes in  c%, paired with the difference between pattern and the node's  pattern. If f dB is a function that returns the learning rate to apply to a node  based on its distance d+from the node that best matches the input  pattern, then  f c pattern returns a modified copy of the  classifier c that has partially learned the target. Same as train$, but applied to multiple patterns. If fA is a function that returns the learning rate to apply to a node = based on its distance from the node that best matches the target, then    f c 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. ECalculates the square of the Euclidean distance between two vectors.   target amount vector adjusts vector to move it closer  to target>. The amount of adjustment is controlled by the learning rate  r6, 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 qs5 of pairs of numbers, where each pair represents the @ maximum and minimum value to be expected at each position in xs,   qs xs scales the vector xs" element by element, mapping the M maximum value expected at that position to one, and the minimum value to  zero. IScales a set of vectors by determining the maximum and minimum values at K each position in the vector, and mapping the maximum value to one, and  the minimum value to zero.       portable experimentalamy@nualeargais.ie Safe-Inferred Calculates ce^(-d^2/2w^2). M This form of the Gaussian function is useful as a learning rate function.  In  c w d, c, specifies the highest learning rate, which H will be applied to the SOM node that best matches the input pattern. K 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 w cause the learning rate * to fall off more slowly with distance.         som-1.0&Data.Datamining.Clustering.SOMInternalData.Datamining.Clustering.SOM ScaledVectorNormalisedVectorPattern difference makeSimilarclassify differencestrain trainBatchclassifyAndTrain adjustNodemagnitudeSquaredeuclideanDistanceSquared adjustVector normalisescalescaleAllgaussiannorm scaleValuequantify quantify'$fPatternScaledVectora$fPatternNormalisedVectora