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local dt = require "decisiontree._env"
-- returns a buffer table local to a thread (no serialized)
function dt.getBufferTable(name)
local dt = require 'decisiontree'
assert(torch.type(name) == 'string')
dt.buffer = dt.buffer or {}
dt.buffer[name] = dt.buffer[name] or {}
return dt.buffer[name]
end
function dt.getSparseDummyData(nExample, nCluster, nFeature, overlap, nValid, nActive)
local dt = require 'decisiontree'
if torch.type(nExample) == 'table' then
local opt = nExample
nExample = opt.nExample
nCluster = opt.nCluster
nFeature = opt.nFeature
overlap = opt.overlap
nValid = opt.nValid
nActive = opt.nActive
end
nExample = nExample or 100 -- training set size
nCluster = nCluster or 10
assert(nCluster >= 2)
nFeature = math.max(2, nFeature or 10)
overlap = overlap or 0
nValid = nValid or nExample/10 -- validation set size
nActive = nActive or math.max(2, nFeature / 2)
-- sample nCluster centers
local clusterCenter = torch.rand(nCluster, nFeature)
local clusterLabel = torch.LongTensor(nCluster)
local clusterExamples = {}
for i=1,nCluster do
clusterCenter[i]:add(i)
clusterLabel[i] = i % 2
clusterExamples[i] = {}
end
local sparseCenter = torch.Tensor()
local shuffle = torch.LongTensor()
-- build dataset in pseudo-dense format
local inputs = {}
local targets = torch.Tensor(nExample+nValid)
for i=1,nExample+nValid do
local clusterIdx = torch.random(1,nCluster)
table.insert(clusterExamples[clusterIdx], i)
shuffle:randperm(nFeature)
local keys = torch.LongTensor(nActive):copy(shuffle:narrow(1,1,nActive))
sparseCenter:index(clusterCenter[clusterIdx], 1, keys)
local stdiv = i <= nExample and 100 or 1000
local values = torch.randn(nActive):div(stdiv):add(sparseCenter)
table.insert(inputs, torch.SparseTensor(keys, values))
local label = clusterLabel[clusterIdx]
if math.random() < overlap then
targets[i] = label == 1 and 0 or 1
else
targets[i] = label
end
end
local _ = require 'moses'
local validSet = dt.DataSet(_.slice(inputs, nExample+1, nExample+nValid), targets:narrow(1,nExample+1,nValid))
local trainSet = dt.DataSet(_.slice(inputs, 1, nExample), targets:narrow(1,1,nExample))
return trainSet, validSet, clusterExamples, inputs, targets
end
function dt.getDenseDummyData(nExample, nCluster, nFeature, overlap, nValid)
local dt = require 'decisiontree'
if torch.type(nExample) == 'table' then
local opt = nExample
nExample = opt.nExample
nCluster = opt.nCluster
nFeature = opt.nFeature
overlap = opt.overlap
nValid = opt.nValid
end
nExample = nExample or 100 -- training set size
nCluster = nCluster or 10
assert(nCluster >= 2)
nFeature = math.max(2, nFeature or 10)
overlap = overlap or 0
nValid = nValid or nExample/10 -- validation set size
-- sample nCluster centers
local clusterCenter = torch.rand(nCluster, nFeature)
local clusterLabel = torch.LongTensor(nCluster)
local clusterExamples = {}
for i=1,nCluster do
clusterCenter[i]:add(i)
clusterLabel[i] = i % 2
clusterExamples[i] = {}
end
-- build dataset in pseudo-dense format
local inputs = torch.Tensor(nExample+nValid, nFeature)
local targets = torch.Tensor(nExample+nValid)
for i=1,nExample+nValid do
local clusterIdx = torch.random(1,nCluster)
table.insert(clusterExamples[clusterIdx], i)
local stdiv = i <= nExample and 100 or 1000
inputs[i]:normal():div(stdiv):add(clusterCenter[clusterIdx])
local label = clusterLabel[clusterIdx]
if math.random() < overlap then
targets[i] = label == 1 and 0 or 1
else
targets[i] = label
end
end
local _ = require 'moses'
local validSet = dt.DataSet(inputs:narrow(1,nExample+1,nValid), targets:narrow(1,nExample+1,nValid))
local trainSet = dt.DataSet(inputs:narrow(1,1,nExample), targets:narrow(1,1,nExample))
return trainSet, validSet, clusterExamples, inputs, targets
end
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