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71 lines
2.0 KiB
Python
Executable File
71 lines
2.0 KiB
Python
Executable File
# $Id: QuantTree.py 3596 2004-07-23 01:45:57Z glandrum $
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#
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# Copyright (C) 2001, 2003 greg Landrum and Rational Discovery LLC
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# All Rights Reserved
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#
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""" Defines the class _QuantTreeNode_, used to represent decision trees with automatic
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quantization bounds
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_QuantTreeNode_ is derived from _DecTree.DecTreeNode_
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"""
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from ML.DecTree import DecTree,Tree
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class QuantTreeNode(DecTree.DecTreeNode):
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"""
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"""
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def __init__(self,*args,**kwargs):
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apply(DecTree.DecTreeNode.__init__,(self,)+args,kwargs)
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self.qBounds = []
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self.nBounds = 0
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def ClassifyExample(self,example,appendExamples=0):
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""" Recursively classify an example by running it through the tree
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**Arguments**
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- example: the example to be classified
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- appendExamples: if this is nonzero then this node (and all children)
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will store the example
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**Returns**
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the classification of _example_
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**NOTE:**
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In the interest of speed, I don't use accessor functions
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here. So if you subclass DecTreeNode for your own trees, you'll
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have to either include ClassifyExample or avoid changing the names
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of the instance variables this needs.
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"""
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if appendExamples:
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self.examples.append(example)
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if self.terminalNode:
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return self.label
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else:
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val = example[self.label]
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if not hasattr(self,'nBounds'): self.nBounds = len(self.qBounds)
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if self.nBounds:
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for i,bound in enumerate(self.qBounds):
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if val < bound:
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val = i
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break
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else:
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val = i+1
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else:
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val = int(val)
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return self.children[val].ClassifyExample(example,appendExamples=appendExamples)
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def SetQuantBounds(self,qBounds):
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self.qBounds = qBounds[:]
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self.nBounds = len(self.qBounds)
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def GetQuantBounds(self):
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return self.qBounds
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def __cmp__(self,other):
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return cmp(type(self),type(other)) or \
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cmp(self.qBounds,other.qBounds) or \
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Tree.TreeNode.__cmp__(self,other)
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