ML.DecTree.BuildQuantTree

Package ML :: Package DecTree :: Module BuildQuantTree |
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# Module BuildQuantTree

source code

Functions | [hide private]
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. | source code
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). | source code
TestTree() testing code for named trees... | source code
TestQuantTree() testing code for named trees... | source code
TestQuantTree2() testing code for named trees... | source code

Function Details | [hide private]

### BuildQuantTree(examples, target, attrs, nPossibleVals, nBoundsPerVar, depth=0, maxDepth=-1, **kwargs) | source code
    **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() | source code
    testing code for named trees

### TestQuantTree2() | source code
    testing code for named trees