The self-organising map (SOM) is a method for unsupervised learning, based on a grid of artificial neurons whose weights are adapted to match input vectors in a training set.
SOMLearner
SOMLearner class constructs an instance of SOMClassifier or if given a classless domain an instance of SOMMap. SOMClassifier is actualy just a SOMMap with a defined call function that returns a majority class at the winning node.
Attributes
- xDim
- X dimension of the map (default 10)
- yDim
- Y dimension of the map (default 10)
- topology
- Topology of the map. Can be a SOMLearner.RetangularTopology for rectangular or SOMLearner.HexagonalTopology (default) for hexagonal topology
- neighborhood
-
- Neighborhood function type. Can be SOMLearner.BubbleNeighborhood (default) or SOMLearner.GaussianNeighborhood
- steps
- Number of steps (default 2)
- alphaType
- A alpha function type. Can be a SOMLearner.LinearFunction (default) or SOMLearner.InverseFunction
- alpha
-
- A list of alpha values (learning rate) to be used at the beginning of each step (default [0.05,0.03]
- iterations
- A list of iterations at each step (default [1000, 10000])
- radius
- A list of radius values for neighborhood function at each step (default [10,5])
- domainContinuizer
- Domain continuizer used to transform the domain
- transformedDomain
- Transformed domain
- randomSeed
- Random seed used to initialize the codebook vectors. Use -1 to use current time as a seed (default 0).
SOMMap
SOMMap holds the resulting 2 dimensional map of SOMNodes
Attributes
- xDim
- X dimension of the map
- yDim
- Y dimension of the map
- topology
- Topology of the map. Can be a SOMLearner.RetangularTopology for rectangular or SOMLearner.HexagonalTopology (default) for hexagonal topology
- neighborhood
-
- Neighborhood function type. Can be SOMLearner.BubbleNeighborhood (default) or SOMLearner.GaussianNeighborhood
- nodes
- A list of SOMNodes
transformedDomain
- Transformed domain
- error
- Quantiztion error of the map
Methods
- getWinner(example) ((example)->SOMNode)
- Returns the node closest to the example
SOMNode
SOMNode holds the codebook vector
Attributes
- vector
- Holds the codebook vector
- examples
- Holds the examples for whitch this is the winning node
Methods
- getDistance(example) ((example)->float)
- Computes the distance to the node
Examples
>>>data=orange.ExampleTable("iris")
>>>map=orange.SOMLearner(data)
>>>print map.nodes[0].examples
...
...
>>>print map.nodes[0].vector
...
>>>map.getDistance(data[0])
1.56