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Package rdkit :: Package ML :: Package DecTree :: Module BuildQuantTree
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Module BuildQuantTree

source code
Functions [hide private]
 
FindBest(resCodes, examples, nBoundsPerVar, nPossibleRes, nPossibleVals, attrs, exIndices=None, **kwargs) source code
 
BuildQuantTree(examples, target, attrs, nPossibleVals, nBoundsPerVar, depth=0, maxDepth=-1, exIndices=None, **kwargs)
**Arguments**		 source code
 
QuantTreeBoot(examples, attrs, nPossibleVals, nBoundsPerVar, initialVar=None, maxDepth=-1, **kwargs)
Bootstrapping code for the QuantTree		 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

Imports: numpy, random, QuantTree, ID3, entropy, Quantize, range


Function Details [hide private]

BuildQuantTree(examples, target, attrs, nPossibleVals, nBoundsPerVar, depth=0, maxDepth=-1, exIndices=None, **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.

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