cl-random-forest

API Reference

cl-random-forest

Random Forest and Global Refinement for Common Lisp

CL-RANDOM-FOREST.UTILS

  • Macro DOTIMES/PDOTIMES ((var n) &body body)
  • Macro MAPCAR/PMAPCAR (fn &rest lsts)
  • Macro MAPC/PMAPC (fn &rest lsts)
  • Macro PUSH-NTIMES (n lst &body body)
  • Function CLOL-DATASET->DATAMATRIX/TARGET (dataset)
  • Function CLOL-DATASET->DATAMATRIX/TARGET-REGRESSION (dataset)
  • Function READ-DATA (data-path data-dimension)
  • Function READ-DATA-REGRESSION (data-path data-dimension)
  • Function WRITE-TO-R-FORMAT-FROM-CLOL-DATASET (dataset file)

CL-RANDOM-FOREST

  • Function MAKE-DTREE (n-class datamatrix target &key (max-depth 5) (min-region-samples 1) (n-trial 10) (gain-test #'entropy) (remove-sample-indices? t) (save-parent-node? nil) sample-indices)
  • Function MAKE-RTREE (datamatrix target &key (max-depth 5) (min-region-samples 1) (n-trial 10) (gain-test #'variance) (remove-sample-indices? t) (save-parent-node? nil) sample-indices)
  • Function PREDICT-DTREE (dtree datamatrix datum-index)
  • Function TEST-DTREE (dtree datamatrix target &key quiet-p)
  • Function PREDICT-RTREE (rtree datamatrix datum-index)
  • Function TEST-RTREE (rtree datamatrix target &key quiet-p)
  • Function MAKE-FOREST (n-class datamatrix target &key (n-tree 100) (bagging-ratio 0.1) (max-depth 5) (min-region-samples 1) (n-trial 10) (gain-test #'entropy) (remove-sample-indices? t) (save-parent-node? nil))
  • Function MAKE-REGRESSION-FOREST (datamatrix target &key (n-tree 100) (bagging-ratio 0.1) (max-depth 5) (min-region-samples 1) (n-trial 10) (gain-test #'variance) (remove-sample-indices? t) (save-parent-node? nil))
  • Function PREDICT-FOREST (forest datamatrix datum-index)
  • Function TEST-FOREST (forest datamatrix target &key quiet-p)
  • Function PREDICT-REGRESSION-FOREST (forest datamatrix datum-index)
  • Function TEST-REGRESSION-FOREST (forest datamatrix target &key quiet-p)
  • Function MAKE-REFINE-VECTOR (forest datamatrix datum-index)
  • Function MAKE-REFINE-LEARNER (forest &optional (gamma 10.0d0))
  • Function PREDICT-REFINE-LEARNER (forest refine-learner datamatrix datum-index)
  • Function MAKE-REFINE-DATASET (forest datamatrix)
  • Function TRAIN-REFINE-LEARNER (refine-learner refine-dataset target)
  • Function TEST-REFINE-LEARNER (refine-learner refine-dataset target &key quiet-p (mini-batch-size 1000))
  • Macro TRAIN-REFINE-LEARNER-PROCESS (refine-learner train-dataset train-target test-dataset test-target &key (max-epoch 100))
  • Function CROSS-VALIDATION-FOREST-WITH-REFINE-LEARNER (n-fold n-class datamatrix target &key (n-tree 100) (bagging-ratio 0.1) (max-depth 5) (min-region-samples 1) (n-trial 10) (gain-test #'entropy) (remove-sample-indices? t) (gamma 10.0d0))
  • Function PRUNING! (forest learner &optional (pruning-rate 0.1) (min-depth 1))
  • Function RECONSTRUCTION-FOREST (forest datamatrix datum-index)
  • Function ENCODE-DATUM (forest datamatrix datum-index)
  • Function MAKE-LEAF-NODE-VECTOR (forest)
  • Function DECODE-DATUM (forest leaf-index-vector &optional leaf-node-vector)
  • Function FOREST-FEATURE-IMPORTANCE (forest datamatrix target)

cl-random-forest-test

Test system for cl-random-forest

No packages.