Fix infinite recursion.

This commit is contained in:
Rusty Klophaus
2013-01-15 14:45:18 -05:00
parent a714dcecf7
commit dafd3ea7f8
2 changed files with 21 additions and 1 deletions

View File

@@ -69,9 +69,14 @@ module DecisionTree
# return classification if all examples have the same classification # return classification if all examples have the same classification
return data.first.last if data.classification.uniq.size == 1 return data.first.last if data.classification.uniq.size == 1
# Choose best attribute (1. enumerate all attributes / 2. Pick best attribute) # Choose best attribute:
# 1. enumerate all attributes
# 2. Pick best attribute
# 3. If attributes all score the same, then pick a random one to avoid infinite recursion.
performance = attributes.collect { |attribute| fitness_for(attribute).call(data, attributes, attribute) } performance = attributes.collect { |attribute| fitness_for(attribute).call(data, attributes, attribute) }
max = performance.max { |a,b| a[0] <=> b[0] } max = performance.max { |a,b| a[0] <=> b[0] }
min = performance.min { |a,b| a[0] <=> b[0] }
max = performance.shuffle.first if max == min
best = Node.new(attributes[performance.index(max)], max[1], max[0]) best = Node.new(attributes[performance.index(max)], max[1], max[0])
best.threshold = nil if @type == :discrete best.threshold = nil if @type == :discrete
@used.has_key?(best.attribute) ? @used[best.attribute] += [best.threshold] : @used[best.attribute] = [best.threshold] @used.has_key?(best.attribute) ? @used[best.attribute] += [best.threshold] : @used[best.attribute] = [best.threshold]

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@@ -74,4 +74,19 @@ describe describe DecisionTree::ID3Tree do
Then { tree.predict([2, "blue"]).should == "not angry" } Then { tree.predict([2, "blue"]).should == "not angry" }
end end
describe "infinite recursion case" do
Given(:labels) { [:a, :b, :c] }
Given(:data) do
[
["a1", "b0", "c0", "RED"],
["a1", "b1", "c1", "RED"],
["a1", "b1", "c0", "BLUE"],
["a1", "b0", "c1", "BLUE"]
]
end
Given(:tree) { DecisionTree::ID3Tree.new(labels, data, "RED", :discrete) }
When { tree.train }
Then { tree.predict(["a1","b0","c0"]).should == "RED" }
end
end end