Package ML :: Package DecTree :: Module BuildQuantTree

Module BuildQuantTree

Functions

FindBest(resCodes, examples, nBoundsPerVar, nPossibleRes, nPossibleVals, attrs, **kwargs)

source code

BuildQuantTree(examples, target, attrs, nPossibleVals, nBoundsPerVar, depth=0, maxDepth=-1, **kwargs)
**Arguments** - examples: a list of lists (nInstances x nVariables+1) of variable values + instance values - target: an int - attrs: a list of ints indicating which variables can be used in the tree - nPossibleVals: a list containing the number of possible values of every variable.

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QuantTreeBoot(examples, attrs, nPossibleVals, nBoundsPerVar, initialVar=None, maxDepth=-1, **kwargs)
Bootstrapping code for the QuantTree If _initialVar_ is not set, the algorithm will automatically choose the first variable in the tree (the standard greedy approach).

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TestTree()
testing code for named trees...

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TestQuantTree()
testing code for named trees...

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TestQuantTree2()
testing code for named trees...

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Function Details

[hide private]

BuildQuantTree(examples, target, attrs, nPossibleVals, nBoundsPerVar, depth=0, maxDepth=-1, **kwargs)

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**Arguments**

  - examples: a list of lists (nInstances x nVariables+1) of variable
    values + instance values

  - target: an int

  - attrs: a list of ints indicating which variables can be used in the tree

  - nPossibleVals: a list containing the number of possible values of
               every variable.

  - nBoundsPerVar: the number of bounds to include for each variable

  - depth: (optional) the current depth in the tree

  - maxDepth: (optional) the maximum depth to which the tree
               will be grown
**Returns**

 a QuantTree.QuantTreeNode with the decision tree

**NOTE:** This code cannot bootstrap (start from nothing...)
      use _QuantTreeBoot_ (below) for that.

QuantTreeBoot(examples, attrs, nPossibleVals, nBoundsPerVar, initialVar=None, maxDepth=-1, **kwargs)

source code

Bootstrapping code for the QuantTree

If _initialVar_ is not set, the algorithm will automatically
 choose the first variable in the tree (the standard greedy
 approach).  Otherwise, _initialVar_ will be used as the first
 split.

TestTree()

source code

testing code for named trees

TestQuantTree()

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testing code for named trees

TestQuantTree2()

source code

testing code for named trees