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local dt = require "decisiontree._env"
-- Interface for all decisionForestTrainers
local DFT = torch.class("dt.DecisionForestTrainer", dt)
-- Train a DecisionForest with examples, a table of valid featureIds and a dataset (i.e. sortedExamplesByFeatureId)
function DFT:train(examples, validFeatureIds, dataset)
assert(torch.type(examples) == "table")
assert(torch.isTypeOf(examples[1], "dt.LabeledExample"))
assert(torch.type(validFeatureIds) == 'table')
assert(torch.type(dataset) == 'table')
for k,v in pairs(dataset) do
assert(torch.type(v) == 'table')
assert(torch.isTypeOf(v[1], 'dt.LabeledExample'))
break
end
-- dataset is a table mapping featureIds to sorted lists of LabeledExamples
-- e.g. {featureId={example1,example2,example3}}
error"Not Implemented"
end
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