Documentation ¶
Index ¶
- func DTGoLeft(sample *core.MapBasedSample, feature_split core.Feature) bool
- type CART
- func (dt *CART) AppendNodeToTree(samples []*core.MapBasedSample, node *TreeNode, queue *list.List, tree *Tree, ...)
- func (dt *CART) FindBestSplitOfBinaryFeature(samples []*core.MapBasedSample, node *TreeNode, feature_select_prob float64)
- func (dt *CART) FindBestSplitOfContinusousFeature(samples []*core.MapBasedSample, node *TreeNode, feature_select_prob float64)
- func (dt *CART) Init(params map[string]string)
- func (self *CART) LoadModel(path string)
- func (dt *CART) Predict(sample *core.Sample) float64
- func (dt *CART) PredictMultiClass(sample *core.Sample) *core.ArrayVector
- func (dt *CART) RandByFeatureId(fid int64) float64
- func (self *CART) SaveModel(path string)
- func (dt *CART) SingleTreeBuild(samples []*core.MapBasedSample, feature_select_prob float64, bootstrap bool) Tree
- func (dt *CART) Train(dataset *core.DataSet)
- type CARTParams
- type GBDT
- type RDTParams
- type RandomDecisionTree
- func (rdt *RandomDecisionTree) AppendNodeToTree(samples []*core.MapBasedSample, node *TreeNode, queue *list.List, tree *Tree)
- func (rdt *RandomDecisionTree) Init(params map[string]string)
- func (self *RandomDecisionTree) LoadModel(path string)
- func (rdt *RandomDecisionTree) Predict(sample *core.Sample) float64
- func (rdt *RandomDecisionTree) PredictMultiClass(sample *core.Sample) *core.ArrayVector
- func (rdt *RandomDecisionTree) RandomShuffle(features []core.Feature)
- func (self *RandomDecisionTree) SaveModel(path string)
- func (rdt *RandomDecisionTree) SingleTreeBuild(samples []*core.MapBasedSample) Tree
- func (rdt *RandomDecisionTree) Train(dataset *core.DataSet)
- type RandomForest
- func (dt *RandomForest) Init(params map[string]string)
- func (self *RandomForest) LoadModel(path string)
- func (dt *RandomForest) Predict(sample *core.Sample) float64
- func (dt *RandomForest) PredictMultiClass(sample *core.Sample) *core.ArrayVector
- func (self *RandomForest) SaveModel(path string)
- func (dt *RandomForest) Train(dataset *core.DataSet)
- type RandomForestParams
- type RegressionTree
- func (dt *RegressionTree) AppendNodeToTree(samples []*core.MapBasedSample, node *TreeNode, queue *list.List, tree *Tree, ...)
- func (dt *RegressionTree) FindBestSplit(samples []*core.MapBasedSample, node *TreeNode, select_features map[int64]bool)
- func (dt *RegressionTree) GetElementFromQueue(queue *list.List, n int) []*TreeNode
- func (dt *RegressionTree) GoLeft(sample *core.MapBasedSample, feature_split core.Feature) bool
- func (dt *RegressionTree) Init(params map[string]string)
- func (self *RegressionTree) LoadModel(path string)
- func (dt *RegressionTree) Predict(sample *core.Sample) float64
- func (dt *RegressionTree) PredictBySingleTree(tree *Tree, sample *core.MapBasedSample) (*TreeNode, string)
- func (self *RegressionTree) SaveModel(path string)
- func (dt *RegressionTree) SingleTreeBuild(samples []*core.MapBasedSample, select_features map[int64]bool) Tree
- func (dt *RegressionTree) Train(dataset *core.DataSet)
- type Tree
- type TreeNode
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
Types ¶
type CART ¶
type CART struct {
// contains filtered or unexported fields
}
CART is classification and regression tree, this class implement classification tree and use gini to split features
func (*CART) AppendNodeToTree ¶
func (*CART) FindBestSplitOfBinaryFeature ¶
func (dt *CART) FindBestSplitOfBinaryFeature(samples []*core.MapBasedSample, node *TreeNode, feature_select_prob float64)
func (*CART) FindBestSplitOfContinusousFeature ¶
func (dt *CART) FindBestSplitOfContinusousFeature(samples []*core.MapBasedSample, node *TreeNode, feature_select_prob float64)
func (*CART) PredictMultiClass ¶
func (dt *CART) PredictMultiClass(sample *core.Sample) *core.ArrayVector
func (*CART) RandByFeatureId ¶
func (*CART) SingleTreeBuild ¶
type CARTParams ¶
type RandomDecisionTree ¶
type RandomDecisionTree struct {
// contains filtered or unexported fields
}
func (*RandomDecisionTree) AppendNodeToTree ¶
func (rdt *RandomDecisionTree) AppendNodeToTree(samples []*core.MapBasedSample, node *TreeNode, queue *list.List, tree *Tree)
func (*RandomDecisionTree) Init ¶
func (rdt *RandomDecisionTree) Init(params map[string]string)
func (*RandomDecisionTree) LoadModel ¶
func (self *RandomDecisionTree) LoadModel(path string)
func (*RandomDecisionTree) Predict ¶
func (rdt *RandomDecisionTree) Predict(sample *core.Sample) float64
func (*RandomDecisionTree) PredictMultiClass ¶
func (rdt *RandomDecisionTree) PredictMultiClass(sample *core.Sample) *core.ArrayVector
func (*RandomDecisionTree) RandomShuffle ¶
func (rdt *RandomDecisionTree) RandomShuffle(features []core.Feature)
func (*RandomDecisionTree) SaveModel ¶
func (self *RandomDecisionTree) SaveModel(path string)
func (*RandomDecisionTree) SingleTreeBuild ¶
func (rdt *RandomDecisionTree) SingleTreeBuild(samples []*core.MapBasedSample) Tree
func (*RandomDecisionTree) Train ¶
func (rdt *RandomDecisionTree) Train(dataset *core.DataSet)
type RandomForest ¶
type RandomForest struct {
// contains filtered or unexported fields
}
func (*RandomForest) Init ¶
func (dt *RandomForest) Init(params map[string]string)
func (*RandomForest) LoadModel ¶
func (self *RandomForest) LoadModel(path string)
func (*RandomForest) PredictMultiClass ¶
func (dt *RandomForest) PredictMultiClass(sample *core.Sample) *core.ArrayVector
func (*RandomForest) SaveModel ¶
func (self *RandomForest) SaveModel(path string)
func (*RandomForest) Train ¶
func (dt *RandomForest) Train(dataset *core.DataSet)
type RandomForestParams ¶
type RegressionTree ¶
type RegressionTree struct {
// contains filtered or unexported fields
}
func (*RegressionTree) AppendNodeToTree ¶
func (dt *RegressionTree) AppendNodeToTree(samples []*core.MapBasedSample, node *TreeNode, queue *list.List, tree *Tree, select_features map[int64]bool)
func (*RegressionTree) FindBestSplit ¶
func (dt *RegressionTree) FindBestSplit(samples []*core.MapBasedSample, node *TreeNode, select_features map[int64]bool)
func (*RegressionTree) GetElementFromQueue ¶
func (dt *RegressionTree) GetElementFromQueue(queue *list.List, n int) []*TreeNode
func (*RegressionTree) GoLeft ¶
func (dt *RegressionTree) GoLeft(sample *core.MapBasedSample, feature_split core.Feature) bool
func (*RegressionTree) Init ¶
func (dt *RegressionTree) Init(params map[string]string)
func (*RegressionTree) LoadModel ¶
func (self *RegressionTree) LoadModel(path string)
func (*RegressionTree) PredictBySingleTree ¶
func (dt *RegressionTree) PredictBySingleTree(tree *Tree, sample *core.MapBasedSample) (*TreeNode, string)
func (*RegressionTree) SaveModel ¶
func (self *RegressionTree) SaveModel(path string)
func (*RegressionTree) SingleTreeBuild ¶
func (dt *RegressionTree) SingleTreeBuild(samples []*core.MapBasedSample, select_features map[int64]bool) Tree
func (*RegressionTree) Train ¶
func (dt *RegressionTree) Train(dataset *core.DataSet)
type Tree ¶
type Tree struct {
// contains filtered or unexported fields
}
func (*Tree) AddTreeNode ¶
func (*Tree) FromString ¶
type TreeNode ¶
type TreeNode struct {
// contains filtered or unexported fields
}
func PredictBySingleTree ¶
func PredictBySingleTree(tree *Tree, sample *core.MapBasedSample) (*TreeNode, string)
Click to show internal directories.
Click to hide internal directories.