som/basic_som.py
def _gaussian(self, node1, node2, t):
        """Calculates a neighbourhood value following a Gaussian distribution.
        This assumes the nodes are GridNodes.
        """
        dist_sq = self.distance_squared(node1, node2)
        variance = self._gaussian_variance(t)
        return exp(-dist_sq / (2 * variance * variance))
Calculates a neighbourhood value following a Gaussian distribution.
        This assumes the nodes are GridNodes.
        
Calculates a neighbourhood value following a Gaussian distribution.
This assumes the nodes are GridNodes.
Calculates a neighbourhood value following a Gaussian distribution . This assumes the nodes are GridNodes .
def _gaussian(self, node1, node2, t):
        dist_sq = self.distance_squared(node1, node2)
        variance = self._gaussian_variance(t)
        return exp(-dist_sq / (2 * variance * variance))
def _gaussian ( self , node1 , node2 , t ) : dist_sq = self . distance_squared ( node1 , node2 ) variance = self . _gaussian_variance ( t ) return exp ( - dist_sq / ( 2 * variance * variance ) )
Calculates a neighbourhood value following a Gaussian distribution.
Calculates a neighbourhood value following a Gaussian distribution .
"""Calculates a neighbourhood value following a Gaussian distribution.
        This assumes the nodes are GridNodes.
        """
self
node1
node2
t