The cl-random-forest Reference Manual

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The cl-random-forest Reference Manual

This is the cl-random-forest Reference Manual, version 0.1, generated automatically by Declt version 2.4 "Will Decker" on Wed Jun 20 11:20:58 2018 GMT+0.


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1 Introduction

* cl-random-forest

[[http://quickdocs.org/cl-random-forest/][http://quickdocs.org/badge/cl-random-forest.svg]]
[[https://travis-ci.org/masatoi/cl-random-forest][https://travis-ci.org/masatoi/cl-random-forest.svg?branch=master]]

Cl-random-forest is a implementation of Random Forest for multiclass classification and univariate regression written in Common Lisp. It also includes a implementation of Global Refinement of Random Forest (Ren, Cao, Wei and Sun. "Global Refinement of Random Forest" CVPR2015). This refinement makes faster and more accurate than standard Random Forest.

** Features and Limitations

- Faster and more accurate than other major implementations such as scikit-learn (Python/Cython) or ranger (R/C++)

|         | scikit-learn     | ranger           | cl-random-forest |
| MNIST   | 96.95%, 41.72sec | 97.17%, 69.34sec | *98.29%*, *12.68sec* |
| letter  | 96.38%, 2.569sec | 96.42%, *1.828sec* | *97.32%*, 3.497sec |
| covtype | 94.89%, 263.7sec | 83.95%, 139.0sec | *96.01%*, *103.9sec* |
| usps    | 93,47%, 3.583sec | 93.57%, 11.70sec | *94.96%*, *0.686sec* |

- Supporting parallelization of training and prediction (tested on SBCL, CCL)

- It also includes Global Pruning algorithm of Random Forest which can make the model extremely compact

- Currently, multivariate regression is not implemented

** Installation

In quicklisp’s local-projects directory,

#+BEGIN_SRC
git clone https://github.com/masatoi/cl-online-learning.git
git clone https://github.com/masatoi/cl-random-forest.git
#+END_SRC

In Lisp,

#+BEGIN_SRC lisp
(ql:quickload :cl-random-forest)
#+END_SRC

When using Roswell,

#+BEGIN_SRC 
ros install masatoi/cl-online-learning masatoi/cl-random-forest
#+END_SRC

** Usage
*** Classification
**** Prepare training dataset
A dataset consists of a target vector and a input data matrix.
For classification, the target vector should be a fixnum simple-vector and the data matrix should be a 2-dimensional double-float array whose row corresponds one datum.
Note that the target is a integer starting from 0.
For example, the following dataset is valid for 4-class classification with 2-dimensional input.

#+BEGIN_SRC lisp
(defparameter *target*
  (make-array 11 :element-type 'fixnum
                 :initial-contents '(0 0 1 1 2 2 2 3 3 3 3)))

(defparameter *datamatrix*
  (make-array '(11 2)
              :element-type 'double-float
              :initial-contents '((-1.0d0 -2.0d0)
                                  (-2.0d0 -1.5d0)
                                  (1.0d0 -2.0d0)
                                  (3.0d0 -1.5d0)
                                  (-2.0d0 2.0d0)
                                  (-3.0d0 1.0d0)
                                  (-2.0d0 1.0d0)
                                  (3.0d0 2.0d0)
                                  (2.0d0 2.0d0)
                                  (1.0d0 2.0d0)
                                  (1.0d0 1.0d0))))
#+END_SRC

[[./docs/img/clrf-example-simple.png]]

**** Make Decision Tree

To construct a decision tree, MAKE-DTREE function is available. This function receives the number of classes, the data matrix and the target vector and then returns a decision tree object. This function also receives optionally the max depth of the tree and the minimum number of samples in the region the tree divides and the number of trials of splits.

#+BEGIN_SRC lisp
(defparameter *n-class* 4)

(defparameter *dtree*
  (make-dtree *n-class* *datamatrix* *target*
              :max-depth 5 :min-region-samples 1 :n-trial 10))
#+END_SRC

Next, make a prediction from the constructed decision tree with PREDICT-DTREE function. For example, to predict the first datum in the data matrix with this decision tree, do as follows.

#+BEGIN_SRC lisp
(predict-dtree *dtree* *datamatrix* 0)
;; => 0 (correct class id)
#+END_SRC

To make predictions for the entire dataset and calculate the accuracy, use TEST-DTREE function.

#+BEGIN_SRC lisp
(test-dtree *dtree* *datamatrix* *target*)
;; Accuracy: 100.0%, Correct: 11, Total: 11
#+END_SRC

**** Make Random Forest

To construct a random forest, MAKE-FOREST function is available. In addition to the MAKE-DTREE function arguments, this function receives optionally the number of decision trees and the bagging ratio that is used for sampling from training data to construct each sub decision trees.

#+BEGIN_SRC lisp
(defparameter *forest*
  (make-forest *n-class* *datamatrix* *target*
               :n-tree 10 :bagging-ratio 1.0
               :max-depth 5 :min-region-samples 1 :n-trial 10))
#+END_SRC

Prediction and test of random forest are done in the almost same way as decision trees. PREDICT-FOREST function and TEST-FOREST function are available for each purpose.

#+BEGIN_SRC lisp
(predict-forest *forest* *datamatrix* 0)
;; => 0 (correct class id)

(test-forest *forest* *datamatrix* *target*)
;; Accuracy: 100.0%, Correct: 11, Total: 11
#+END_SRC

**** Global Refinement of Random Forest

Cl-random-forest has a way to improve pre-trained random forest using global information between each decision trees.
For this purpose, we make an another dataset from original dataset and pre-trained random forest. 
When an original datum input into the random forest, the datum enters into a region which corresponds one leaf node for each decision trees.
The datum of the new dataset represents which position of leaf node the original datum entered for each decision tree.
Then we train a linear classifier (AROW) using this new dataset and the original target.

#+BEGIN_SRC lisp
;; Make refine learner
(defparameter *forest-learner* (make-refine-learner *forest*))

;; Make refine dataset
(defparameter *forest-refine-dataset* (make-refine-dataset *forest* *datamatrix*))

;; Train refine learner
(train-refine-learner *forest-learner* *forest-refine-dataset* *target*)

;; Test refine learner
(test-refine-learner  *forest-learner* *forest-refine-dataset* *target*)
#+END_SRC

This TRAIN-REFINE-LEARNER function can be used to learn the dataset collectively, but it may be necessary to call this function several times until learning converges. TRAIN-REFINE-LEARNER-PROCESS function is used for training until converged.

#+BEGIN_SRC lisp
(train-refine-learner-process *forest-learner* *forest-refine-dataset* *target*
                              *forest-refine-dev-dataset* *dev-target*)
#+END_SRC

**** Global Pruning of Random Forest

***** Pruning
Global pruning is a method for compactization of the model size of the random forest using information of the global-refinement learner. A leaf node in a decision tree is no longer necessary when its corresponding element of the weight vector of the global-refinement learner has a small value norm.

To prune a forest destructively, after training the global-refinement learner, run PRUNING! function.

#+BEGIN_SRC lisp
;; Prune *forest*
(pruning! *forest* *forest-learner* 0.1)
#+END_SRC

The third argument is pruning rate. In this case, 10% leaf nodes are deleted.

***** Re-learning

After pruning, it is required to re-learn the global-refinement learner.

#+BEGIN_SRC lisp
;; Re-learning of refine-learner
(setf *forest-refine-dataset* (make-refine-dataset *forest* *datamatrix*))
(setf *forest-learner* (make-refine-learner *forest*))
(train-refine-learner *forest-learner* *forest-refine-dataset* *target*)
(test-refine-learner  *forest-learner* *forest-refine-dataset* *target*)
#+END_SRC

The following figure shows the accuracy for test dataset and the number of leaf nodes when repeating pruning and re-learning on the MNIST dataset. We can see that the performance hardly changes even if the number of leaf nodes decreases to about 1/10.

[[./docs/img/clrf-mnist-pruning.png]]

**** Parallelization
The following several functions can be parallelized with [[https://lparallel.org/][lparallel]].

- MAKE-FOREST
- MAKE-REGRESSION-FOREST
- MAKE-REFINE-DATASET
- TRAIN-REFINE-LEARNER

To enable/disable parallelization, set lparallel's kernel object. For example, to enable parallelization with 4 threads,

#+BEGIN_SRC lisp
;; Enable parallelization
(setf lparallel:*kernel* (lparallel:make-kernel 4))

;; Disable parallelization
(setf lparallel:*kernel* nil)
#+END_SRC

*** Regression
**** Prepare training dataset
In case of classification, the target is a vector of integer values, whereas in regression is a vector of continuous values.

#+BEGIN_SRC lisp
(defparameter *n* 100)

(defparameter *datamatrix*
  (let ((arr (make-array (list *n* 1) :element-type 'double-float)))
    (loop for i from 0 below *n* do
      (setf (aref arr i 0) (random-uniform (- pi) pi)))
    arr))

(defparameter *target*
  (let ((arr (make-array *n* :element-type 'double-float)))
    (loop for i from 0 below *n* do
      (setf (aref arr i) (+ (sin (aref *datamatrix* i 0))
                            (random-normal :sd 0.1d0))))
    arr))

(defparameter *test*
  (let ((arr (make-array (list *n* 1) :element-type 'double-float)))
    (loop for i from 0 below *n*
          for x from (- pi) to pi by (/ (* 2 pi) *n*)
          do (setf (aref arr i 0) x))
    arr))

(defparameter *test-target*
  (let ((arr (make-array *n* :element-type 'double-float)))
    (loop for i from 0 below *n* do
      (setf (aref arr i) (sin (aref *test* i 0))))
    arr))
#+END_SRC

**** Make Regression Tree

#+BEGIN_SRC lisp
;; Make regression tree
(defparameter *rtree*
  (make-rtree *datamatrix* *target* :max-depth 5 :min-region-samples 5 :n-trial 10))

;; Testing
(test-rtree *rtree* *test* *test-target*)
; RMSE: 0.09220732459820888d0

;; Make a prediction for first data point of test dataset
(predict-rtree *rtree* *test* 0)
; => -0.08374452528780077d0
#+END_SRC

**** Make Random Forest for Regression

#+BEGIN_SRC lisp
;; Make regression tree forest
(defparameter *rforest*
  (make-regression-forest *datamatrix* *target*
                          :n-tree 100 :bagging-ratio 0.6
                          :max-depth 5 :min-region-samples 5 :n-trial 10))

;; Testing
(test-regression-forest *rforest* *test* *test-target*)
; RMSE: 0.05006872795207973d0

;; Make a prediction for first data point of test dataset
(predict-regression-forest *rforest* *test* 0)
; => -0.16540771296145781d0
#+END_SRC

[[./docs/img/clrf-regression.png]]

** Author
Satoshi Imai (satoshi.imai@gmail.com)

** Licence
This software is released under the MIT License, see LICENSE.txt.


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2 Systems

The main system appears first, followed by any subsystem dependency.


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2.1 cl-random-forest

Author

Satoshi Imai

License

MIT Licence

Description

Random Forest and Global Refinement for Common Lisp

Long Description

* cl-random-forest

[[http://quickdocs.org/cl-random-forest/][http://quickdocs.org/badge/cl-random-forest.svg]]
[[https://travis-ci.org/masatoi/cl-random-forest][https://travis-ci.org/masatoi/cl-random-forest.svg?branch=master]]

Cl-random-forest is a implementation of Random Forest for multiclass classification and univariate regression written in Common Lisp. It also includes a implementation of Global Refinement of Random Forest (Ren, Cao, Wei and Sun. "Global Refinement of Random Forest" CVPR2015). This refinement makes faster and more accurate than standard Random Forest.

** Features and Limitations

- Faster and more accurate than other major implementations such as scikit-learn (Python/Cython) or ranger (R/C++)

| | scikit-learn | ranger | cl-random-forest |
| MNIST | 96.95%, 41.72sec | 97.17%, 69.34sec | *98.29%*, *12.68sec* |
| letter | 96.38%, 2.569sec | 96.42%, *1.828sec* | *97.32%*, 3.497sec |
| covtype | 94.89%, 263.7sec | 83.95%, 139.0sec | *96.01%*, *103.9sec* |
| usps | 93,47%, 3.583sec | 93.57%, 11.70sec | *94.96%*, *0.686sec* |

- Supporting parallelization of training and prediction (tested on SBCL, CCL)

- It also includes Global Pruning algorithm of Random Forest which can make the model extremely compact

- Currently, multivariate regression is not implemented

** Installation

In quicklisp’s local-projects directory,

#+BEGIN_SRC
git clone https://github.com/masatoi/cl-online-learning.git
git clone https://github.com/masatoi/cl-random-forest.git
#+END_SRC

In Lisp,

#+BEGIN_SRC lisp
(ql:quickload :cl-random-forest)
#+END_SRC

When using Roswell,

#+BEGIN_SRC
ros install masatoi/cl-online-learning masatoi/cl-random-forest
#+END_SRC

** Usage
*** Classification
**** Prepare training dataset
A dataset consists of a target vector and a input data matrix.
For classification, the target vector should be a fixnum simple-vector and the data matrix should be a 2-dimensional double-float array whose row corresponds one datum.
Note that the target is a integer starting from 0.
For example, the following dataset is valid for 4-class classification with 2-dimensional input.

#+BEGIN_SRC lisp
(defparameter *target*
(make-array 11 :element-type ’fixnum
:initial-contents ’(0 0 1 1 2 2 2 3 3 3 3)))

(defparameter *datamatrix*
(make-array ’(11 2)
:element-type ’double-float
:initial-contents ’((-1.0d0 -2.0d0)
(-2.0d0 -1.5d0)
(1.0d0 -2.0d0)
(3.0d0 -1.5d0)
(-2.0d0 2.0d0)
(-3.0d0 1.0d0)
(-2.0d0 1.0d0)
(3.0d0 2.0d0)
(2.0d0 2.0d0)
(1.0d0 2.0d0)
(1.0d0 1.0d0))))
#+END_SRC

[[./docs/img/clrf-example-simple.png]]

**** Make Decision Tree

To construct a decision tree, MAKE-DTREE function is available. This function receives the number of classes, the data matrix and the target vector and then returns a decision tree object. This function also receives optionally the max depth of the tree and the minimum number of samples in the region the tree divides and the number of trials of splits.

#+BEGIN_SRC lisp
(defparameter *n-class* 4)

(defparameter *dtree*
(make-dtree *n-class* *datamatrix* *target*
:max-depth 5 :min-region-samples 1 :n-trial 10))
#+END_SRC

Next, make a prediction from the constructed decision tree with PREDICT-DTREE function. For example, to predict the first datum in the data matrix with this decision tree, do as follows.

#+BEGIN_SRC lisp
(predict-dtree *dtree* *datamatrix* 0)
;; => 0 (correct class id)
#+END_SRC

To make predictions for the entire dataset and calculate the accuracy, use TEST-DTREE function.

#+BEGIN_SRC lisp
(test-dtree *dtree* *datamatrix* *target*)
;; Accuracy: 100.0%, Correct: 11, Total: 11
#+END_SRC

**** Make Random Forest

To construct a random forest, MAKE-FOREST function is available. In addition to the MAKE-DTREE function arguments, this function receives optionally the number of decision trees and the bagging ratio that is used for sampling from training data to construct each sub decision trees.

#+BEGIN_SRC lisp
(defparameter *forest*
(make-forest *n-class* *datamatrix* *target*
:n-tree 10 :bagging-ratio 1.0
:max-depth 5 :min-region-samples 1 :n-trial 10))
#+END_SRC

Prediction and test of random forest are done in the almost same way as decision trees. PREDICT-FOREST function and TEST-FOREST function are available for each purpose.

#+BEGIN_SRC lisp
(predict-forest *forest* *datamatrix* 0)
;; => 0 (correct class id)

(test-forest *forest* *datamatrix* *target*)
;; Accuracy: 100.0%, Correct: 11, Total: 11
#+END_SRC

**** Global Refinement of Random Forest

Cl-random-forest has a way to improve pre-trained random forest using global information between each decision trees.
For this purpose, we make an another dataset from original dataset and pre-trained random forest.
When an original datum input into the random forest, the datum enters into a region which corresponds one leaf node for each decision trees.
The datum of the new dataset represents which position of leaf node the original datum entered for each decision tree.
Then we train a linear classifier (AROW) using this new dataset and the original target.

#+BEGIN_SRC lisp
;; Make refine learner
(defparameter *forest-learner* (make-refine-learner *forest*))

;; Make refine dataset
(defparameter *forest-refine-dataset* (make-refine-dataset *forest* *datamatrix*))

;; Train refine learner
(train-refine-learner *forest-learner* *forest-refine-dataset* *target*)

;; Test refine learner
(test-refine-learner *forest-learner* *forest-refine-dataset* *target*)
#+END_SRC

This TRAIN-REFINE-LEARNER function can be used to learn the dataset collectively, but it may be necessary to call this function several times until learning converges. TRAIN-REFINE-LEARNER-PROCESS function is used for training until converged.

#+BEGIN_SRC lisp
(train-refine-learner-process *forest-learner* *forest-refine-dataset* *target*
*forest-refine-dev-dataset* *dev-target*)
#+END_SRC

**** Global Pruning of Random Forest

***** Pruning
Global pruning is a method for compactization of the model size of the random forest using information of the global-refinement learner. A leaf node in a decision tree is no longer necessary when its corresponding element of the weight vector of the global-refinement learner has a small value norm.

To prune a forest destructively, after training the global-refinement learner, run PRUNING! function.

#+BEGIN_SRC lisp
;; Prune *forest*
(pruning! *forest* *forest-learner* 0.1)
#+END_SRC

The third argument is pruning rate. In this case, 10% leaf nodes are deleted.

***** Re-learning

After pruning, it is required to re-learn the global-refinement learner.

#+BEGIN_SRC lisp
;; Re-learning of refine-learner
(setf *forest-refine-dataset* (make-refine-dataset *forest* *datamatrix*))
(setf *forest-learner* (make-refine-learner *forest*))
(train-refine-learner *forest-learner* *forest-refine-dataset* *target*)
(test-refine-learner *forest-learner* *forest-refine-dataset* *target*)
#+END_SRC

The following figure shows the accuracy for test dataset and the number of leaf nodes when repeating pruning and re-learning on the MNIST dataset. We can see that the performance hardly changes even if the number of leaf nodes decreases to about 1/10.

[[./docs/img/clrf-mnist-pruning.png]]

**** Parallelization
The following several functions can be parallelized with [[https://lparallel.org/][lparallel]].

- MAKE-FOREST
- MAKE-REGRESSION-FOREST
- MAKE-REFINE-DATASET
- TRAIN-REFINE-LEARNER

To enable/disable parallelization, set lparallel’s kernel object. For example, to enable parallelization with 4 threads,

#+BEGIN_SRC lisp
;; Enable parallelization
(setf lparallel:*kernel* (lparallel:make-kernel 4))

;; Disable parallelization
(setf lparallel:*kernel* nil)
#+END_SRC

*** Regression
**** Prepare training dataset
In case of classification, the target is a vector of integer values, whereas in regression is a vector of continuous values.

#+BEGIN_SRC lisp
(defparameter *n* 100)

(defparameter *datamatrix*
(let ((arr (make-array (list *n* 1) :element-type ’double-float)))
(loop for i from 0 below *n* do
(setf (aref arr i 0) (random-uniform (- pi) pi)))
arr))

(defparameter *target*
(let ((arr (make-array *n* :element-type ’double-float)))
(loop for i from 0 below *n* do
(setf (aref arr i) (+ (sin (aref *datamatrix* i 0))
(random-normal :sd 0.1d0))))
arr))

(defparameter *test*
(let ((arr (make-array (list *n* 1) :element-type ’double-float)))
(loop for i from 0 below *n*
for x from (- pi) to pi by (/ (* 2 pi) *n*)
do (setf (aref arr i 0) x))
arr))

(defparameter *test-target*
(let ((arr (make-array *n* :element-type ’double-float)))
(loop for i from 0 below *n* do
(setf (aref arr i) (sin (aref *test* i 0))))
arr))
#+END_SRC

**** Make Regression Tree

#+BEGIN_SRC lisp
;; Make regression tree
(defparameter *rtree*
(make-rtree *datamatrix* *target* :max-depth 5 :min-region-samples 5 :n-trial 10))

;; Testing
(test-rtree *rtree* *test* *test-target*)
; RMSE: 0.09220732459820888d0

;; Make a prediction for first data point of test dataset
(predict-rtree *rtree* *test* 0)
; => -0.08374452528780077d0
#+END_SRC

**** Make Random Forest for Regression

#+BEGIN_SRC lisp
;; Make regression tree forest
(defparameter *rforest*
(make-regression-forest *datamatrix* *target*
:n-tree 100 :bagging-ratio 0.6
:max-depth 5 :min-region-samples 5 :n-trial 10))

;; Testing
(test-regression-forest *rforest* *test* *test-target*)
; RMSE: 0.05006872795207973d0

;; Make a prediction for first data point of test dataset
(predict-regression-forest *rforest* *test* 0)
; => -0.16540771296145781d0
#+END_SRC

[[./docs/img/clrf-regression.png]]

** Author
Satoshi Imai (satoshi.imai@gmail.com)

** Licence
This software is released under the MIT License, see LICENSE.txt.

Version

0.1

Dependencies
Source

cl-random-forest.asd (file)

Component

src (module)


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3 Modules

Modules are listed depth-first from the system components tree.


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3.1 cl-random-forest/src

Parent

cl-random-forest (system)

Location

src/

Components

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4 Files

Files are sorted by type and then listed depth-first from the systems components trees.


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4.1 Lisp


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4.1.1 cl-random-forest.asd

Location

cl-random-forest.asd

Systems

cl-random-forest (system)

Packages

cl-random-forest-asd


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4.1.2 cl-random-forest/src/utils.lisp

Parent

src (module)

Location

src/utils.lisp

Packages

cl-random-forest.utils

Exported Definitions
Internal Definitions

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4.1.3 cl-random-forest/src/random-forest.lisp

Dependency

utils.lisp (file)

Parent

src (module)

Location

src/random-forest.lisp

Packages

cl-random-forest

Exported Definitions
Internal Definitions

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4.1.4 cl-random-forest/src/reconstruction.lisp

Dependency

random-forest.lisp (file)

Parent

src (module)

Location

src/reconstruction.lisp

Exported Definitions
Internal Definitions

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4.1.5 cl-random-forest/src/feature-importance.lisp

Dependency

random-forest.lisp (file)

Parent

src (module)

Location

src/feature-importance.lisp

Exported Definitions

forest-feature-importance (function)

Internal Definitions

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5 Packages

Packages are listed by definition order.


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5.1 cl-random-forest-asd

Source

cl-random-forest.asd

Use List

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5.2 cl-random-forest.utils

Source

utils.lisp (file)

Nickname

clrf.utils

Use List

common-lisp

Used By List

cl-random-forest

Exported Definitions
Internal Definitions

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5.3 cl-random-forest

Source

random-forest.lisp (file)

Nickname

clrf

Use List
Exported Definitions
Internal Definitions

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6 Definitions

Definitions are sorted by export status, category, package, and then by lexicographic order.


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6.1 Exported definitions


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6.1.1 Macros

Macro: dotimes/pdotimes (VAR N) &body BODY
Package

cl-random-forest.utils

Source

utils.lisp (file)

Macro: mapc/pmapc FN &rest LSTS
Package

cl-random-forest.utils

Source

utils.lisp (file)

Macro: mapcar/pmapcar FN &rest LSTS
Package

cl-random-forest.utils

Source

utils.lisp (file)

Macro: push-ntimes N LST &body BODY
Package

cl-random-forest.utils

Source

utils.lisp (file)

Macro: train-refine-learner-process REFINE-LEARNER TRAIN-DATASET TRAIN-TARGET TEST-DATASET TEST-TARGET &key MAX-EPOCH
Package

cl-random-forest

Source

random-forest.lisp (file)


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6.1.2 Functions

Function: clol-dataset->datamatrix/target DATASET
Package

cl-random-forest.utils

Source

utils.lisp (file)

Function: clol-dataset->datamatrix/target-regression DATASET
Package

cl-random-forest.utils

Source

utils.lisp (file)

Function: cross-validation-forest-with-refine-learner N-FOLD N-CLASS DATAMATRIX TARGET &key N-TREE BAGGING-RATIO MAX-DEPTH MIN-REGION-SAMPLES N-TRIAL GAIN-TEST REMOVE-SAMPLE-INDICES? GAMMA
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: decode-datum FOREST LEAF-INDEX-VECTOR &optional LEAF-NODE-VECTOR
Package

cl-random-forest

Source

reconstruction.lisp (file)

Function: encode-datum FOREST DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

reconstruction.lisp (file)

Function: forest-feature-importance FOREST DATAMATRIX TARGET
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: make-dtree N-CLASS DATAMATRIX TARGET &key MAX-DEPTH MIN-REGION-SAMPLES N-TRIAL GAIN-TEST REMOVE-SAMPLE-INDICES? SAVE-PARENT-NODE? SAMPLE-INDICES
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-forest N-CLASS DATAMATRIX TARGET &key N-TREE BAGGING-RATIO MAX-DEPTH MIN-REGION-SAMPLES N-TRIAL GAIN-TEST REMOVE-SAMPLE-INDICES? SAVE-PARENT-NODE?
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-leaf-node-vector FOREST
Package

cl-random-forest

Source

reconstruction.lisp (file)

Function: make-refine-dataset FOREST DATAMATRIX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-refine-learner FOREST &optional GAMMA
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-refine-vector FOREST DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-regression-forest DATAMATRIX TARGET &key N-TREE BAGGING-RATIO MAX-DEPTH MIN-REGION-SAMPLES N-TRIAL GAIN-TEST REMOVE-SAMPLE-INDICES? SAVE-PARENT-NODE?
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-rtree DATAMATRIX TARGET &key MAX-DEPTH MIN-REGION-SAMPLES N-TRIAL GAIN-TEST REMOVE-SAMPLE-INDICES? SAVE-PARENT-NODE? SAMPLE-INDICES
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: predict-dtree DTREE DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: predict-forest FOREST DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: predict-refine-learner FOREST REFINE-LEARNER DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: predict-regression-forest FOREST DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: predict-rtree RTREE DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: pruning! FOREST LEARNER &optional PRUNING-RATE MIN-DEPTH
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: read-data DATA-PATH DATA-DIMENSION
Package

cl-random-forest.utils

Source

utils.lisp (file)

Function: read-data-regression DATA-PATH DATA-DIMENSION
Package

cl-random-forest.utils

Source

utils.lisp (file)

Function: reconstruction-forest FOREST DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

reconstruction.lisp (file)

Function: test-dtree DTREE DATAMATRIX TARGET &key QUIET-P
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: test-forest FOREST DATAMATRIX TARGET &key QUIET-P
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: test-refine-learner REFINE-LEARNER REFINE-DATASET TARGET &key QUIET-P MINI-BATCH-SIZE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: test-regression-forest FOREST DATAMATRIX TARGET &key QUIET-P
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: test-rtree RTREE DATAMATRIX TARGET &key QUIET-P
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: train-refine-learner REFINE-LEARNER REFINE-DATASET TARGET
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: write-to-r-format-from-clol-dataset DATASET FILE
Package

cl-random-forest.utils

Source

utils.lisp (file)


Previous: , Up: Definitions   [Contents][Index]

6.2 Internal definitions


Next: , Previous: , Up: Internal definitions   [Contents][Index]

6.2.1 Macros

Macro: do-index-value-list (INDEX VALUE LIST) &body BODY
Package

cl-random-forest.utils

Source

utils.lisp (file)

Macro: square X
Package

cl-random-forest

Source

random-forest.lisp (file)


Next: , Previous: , Up: Internal definitions   [Contents][Index]

6.2.2 Functions

Function: %make-dtree &key (N-CLASS N-CLASS) (CLASS-COUNT-ARRAY CLASS-COUNT-ARRAY) (DATUM-DIM DATUM-DIM) (DATAMATRIX DATAMATRIX) (TARGET TARGET) (ROOT ROOT) (MAX-DEPTH MAX-DEPTH) (MIN-REGION-SAMPLES MIN-REGION-SAMPLES) (N-TRIAL N-TRIAL) (GAIN-TEST GAIN-TEST) (REMOVE-SAMPLE-INDICES? REMOVE-SAMPLE-INDICES?) (SAVE-PARENT-NODE? SAVE-PARENT-NODE?) (TMP-ARR1 TMP-ARR1) (TMP-INDEX1 TMP-INDEX1) (TMP-ARR2 TMP-ARR2) (TMP-INDEX2 TMP-INDEX2) (BEST-ARR1 BEST-ARR1) (BEST-INDEX1 BEST-INDEX1) (BEST-ARR2 BEST-ARR2) (BEST-INDEX2 BEST-INDEX2) (MAX-LEAF-INDEX MAX-LEAF-INDEX) (ID ID)
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: %make-forest &key (N-TREE N-TREE) (BAGGING-RATIO BAGGING-RATIO) (DATAMATRIX DATAMATRIX) (TARGET TARGET) (DTREE-LIST DTREE-LIST) (N-CLASS N-CLASS) (CLASS-COUNT-ARRAY CLASS-COUNT-ARRAY) (DATUM-DIM DATUM-DIM) (MAX-DEPTH MAX-DEPTH) (MIN-REGION-SAMPLES MIN-REGION-SAMPLES) (N-TRIAL N-TRIAL) (GAIN-TEST GAIN-TEST) (INDEX-OFFSET INDEX-OFFSET)
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: %make-node &key (SAMPLE-INDICES SAMPLE-INDICES) (N-SAMPLE N-SAMPLE) (DEPTH DEPTH) (TEST-ATTRIBUTE TEST-ATTRIBUTE) (TEST-THRESHOLD TEST-THRESHOLD) (INFORMATION-GAIN INFORMATION-GAIN) (PARENT-NODE PARENT-NODE) (LEFT-NODE LEFT-NODE) (RIGHT-NODE RIGHT-NODE) (DTREE DTREE) (LEAF-INDEX LEAF-INDEX)
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: %print-dtree OBJ STREAM
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: %print-forest OBJ STREAM
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: %print-node OBJ STREAM
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: bootstrap-sample-indices N DATAMATRIX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: calc-accuracy N-CORRECT LEN &key QUIET-P
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: children-l2-norm NODE L2-NORM-ARR FOREST
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: class-distribution SAMPLE-INDICES TERMINATE-INDEX DTREE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: class-distribution-forest FOREST DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: clean-dtree! DTREE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: collect-leaf-parent FOREST
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: collect-leaf-parent-sorted FOREST LEARNER
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: copy-dtree INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: copy-forest INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: copy-node INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: copy-tmp->best! DTREE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: delete-children! NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: do-leaf FN NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-best-arr1 INSTANCE
Function: (setf dtree-best-arr1) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-best-arr2 INSTANCE
Function: (setf dtree-best-arr2) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-best-index1 INSTANCE
Function: (setf dtree-best-index1) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-best-index2 INSTANCE
Function: (setf dtree-best-index2) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-class-count-array INSTANCE
Function: (setf dtree-class-count-array) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-datamatrix INSTANCE
Function: (setf dtree-datamatrix) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-datum-dim INSTANCE
Function: (setf dtree-datum-dim) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-feature-importance DTREE DATAMATRIX TARGET
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: dtree-feature-importance-impurity DTREE
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: dtree-gain-test INSTANCE
Function: (setf dtree-gain-test) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-id INSTANCE
Function: (setf dtree-id) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-max-depth INSTANCE
Function: (setf dtree-max-depth) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-max-leaf-index INSTANCE
Function: (setf dtree-max-leaf-index) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-min-region-samples INSTANCE
Function: (setf dtree-min-region-samples) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-n-class INSTANCE
Function: (setf dtree-n-class) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-n-trial INSTANCE
Function: (setf dtree-n-trial) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-oob-sample-indices DTREE
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: dtree-p OBJECT
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-remove-sample-indices? INSTANCE
Function: (setf dtree-remove-sample-indices?) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-root INSTANCE
Function: (setf dtree-root) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-save-parent-node? INSTANCE
Function: (setf dtree-save-parent-node?) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-target INSTANCE
Function: (setf dtree-target) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-tmp-arr1 INSTANCE
Function: (setf dtree-tmp-arr1) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-tmp-arr2 INSTANCE
Function: (setf dtree-tmp-arr2) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-tmp-index1 INSTANCE
Function: (setf dtree-tmp-index1) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: dtree-tmp-index2 INSTANCE
Function: (setf dtree-tmp-index2) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: entropy SAMPLE-INDICES TERMINATE-INDEX DTREE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: find-leaf NODE DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: find-leaf-randomized NODE DATAMATRIX DATUM-INDEX RANDOMIZED-ATTRIBUTE OOB-SAMPLE-INDICES
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: forest-bagging-ratio INSTANCE
Function: (setf forest-bagging-ratio) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-class-count-array INSTANCE
Function: (setf forest-class-count-array) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-datamatrix INSTANCE
Function: (setf forest-datamatrix) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-datum-dim INSTANCE
Function: (setf forest-datum-dim) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-dtree-list INSTANCE
Function: (setf forest-dtree-list) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-feature-importance-impurity FOREST
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: forest-gain-test INSTANCE
Function: (setf forest-gain-test) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-index-offset INSTANCE
Function: (setf forest-index-offset) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-max-depth INSTANCE
Function: (setf forest-max-depth) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-min-region-samples INSTANCE
Function: (setf forest-min-region-samples) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-n-class INSTANCE
Function: (setf forest-n-class) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-n-tree INSTANCE
Function: (setf forest-n-tree) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-n-trial INSTANCE
Function: (setf forest-n-trial) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-p OBJECT
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: forest-target INSTANCE
Function: (setf forest-target) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: format-datum DATUM STREAM
Package

cl-random-forest.utils

Source

utils.lisp (file)

Function: gini SAMPLE-INDICES TERMINATE-INDEX DTREE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: leaf? NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-l2-norm LEARNER
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-l2-norm-binary LEARNER
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-l2-norm-multiclass LEARNER
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-node SAMPLE-INDICES PARENT-NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-oob-sample-indices TOTAL-SIZE SAMPLE-INDICES
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: make-partial-arr ARR LEN
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-random-test NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-regression-refine-dataset FOREST DATAMATRIX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-regression-refine-learner FOREST &optional GAMMA
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-regression-refine-vector FOREST DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: make-root-node DTREE &key SAMPLE-INDICES
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: maximize-activation/count ACTIVATION-MATRIX END-OF-MINI-BATCH TARGET CYCLE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-class-distribution NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-depth INSTANCE
Function: (setf node-depth) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-dtree INSTANCE
Function: (setf node-dtree) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-information-gain INSTANCE
Function: (setf node-information-gain) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-leaf-index INSTANCE
Function: (setf node-leaf-index) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-left-node INSTANCE
Function: (setf node-left-node) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-n-sample INSTANCE
Function: (setf node-n-sample) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-p OBJECT
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-parent-node INSTANCE
Function: (setf node-parent-node) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-regression-mean NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-right-node INSTANCE
Function: (setf node-right-node) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-sample-indices INSTANCE
Function: (setf node-sample-indices) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-test-attribute INSTANCE
Function: (setf node-test-attribute) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: node-test-threshold INSTANCE
Function: (setf node-test-threshold) VALUE INSTANCE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: normalize-arr! ARR
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: predict-dtree-randomized DTREE DATAMATRIX DATUM-INDEX RANDOMIZED-ATTRIBUTE OOB-SAMPLE-INDICES
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: predict-regression-refine-learner FOREST REFINE-LEARNER DATAMATRIX DATUM-INDEX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: predict-rtree-randomized RTREE DATAMATRIX DATUM-INDEX RANDOMIZED-ATTRIBUTE OOB-SAMPLE-INDICES
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: random-uniform START END
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: reconstruction-backward NODE INPUT-RANGE-ARRAY
Package

cl-random-forest

Source

reconstruction.lisp (file)

Function: reconstruction-dtree LEAF-NODE INPUT-RANGE-ARRAY
Package

cl-random-forest

Source

reconstruction.lisp (file)

Function: region-min/max SAMPLE-INDICES DATAMATRIX ATTRIBUTE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: rtree-feature-importance RTREE DATAMATRIX TARGET
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: rtree? DTREE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: set-activation-matrix! ACTIVATION-MATRIX REFINE-LEARNER N-CLASS SV-VEC REFINE-DATASET CYCLE END-OF-MINI-BATCH
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: set-best-children! N-TRIAL NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: set-leaf-index! DTREE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: set-leaf-index-forest! FOREST
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: split-node! NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: split-sample-indices SAMPLE-INDICES TRUE-ARRAY FALSE-ARRAY ATTRIBUTE THRESHOLD DATAMATRIX
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: stop-split? NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: test-dtree-oob DTREE DATAMATRIX TARGET &key QUIET-P OOB-SAMPLE-INDICES
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: test-dtree-oob-randomized DTREE DATAMATRIX TARGET RANDOMIZED-ATTRIBUTE &key QUIET-P OOB-SAMPLE-INDICES
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: test-refine-learner-binary REFINE-LEARNER REFINE-DATASET TARGET &key QUIET-P
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: test-refine-learner-multiclass REFINE-LEARNER REFINE-DATASET TARGET &key QUIET-P MINI-BATCH-SIZE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: test-regression-refine-learner REFINE-LEARNER REFINE-DATASET TARGET &key QUIET-P
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: test-rtree-oob RTREE DATAMATRIX TARGET &key QUIET-P OOB-SAMPLE-INDICES
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: test-rtree-oob-randomized RTREE DATAMATRIX TARGET RANDOMIZED-ATTRIBUTE &key QUIET-P OOB-SAMPLE-INDICES
Package

cl-random-forest

Source

feature-importance.lisp (file)

Function: train-refine-learner-binary REFINE-LEARNER REFINE-DATASET TARGET
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: train-refine-learner-multiclass REFINE-LEARNER REFINE-DATASET TARGET
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: train-refine-learner-process-inner REFINE-LEARNER TRAIN-DATASET TRAIN-TARGET TEST-DATASET TEST-TARGET &key MAX-EPOCH
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: train-regression-refine-learner REFINE-LEARNER REFINE-DATASET TARGET
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: traverse FN NODE
Package

cl-random-forest

Source

random-forest.lisp (file)

Function: variance SAMPLE-INDICES TERMINATE-INDEX RTREE
Package

cl-random-forest

Source

random-forest.lisp (file)


Previous: , Up: Internal definitions   [Contents][Index]

6.2.3 Structures

Structure: dtree ()
Package

cl-random-forest

Source

random-forest.lisp (file)

Direct superclasses

structure-object (structure)

Direct methods

print-object (method)

Direct slots
Slot: n-class
Readers

dtree-n-class (function)

Writers

(setf dtree-n-class) (function)

Slot: class-count-array
Readers

dtree-class-count-array (function)

Writers

(setf dtree-class-count-array) (function)

Slot: datum-dim
Readers

dtree-datum-dim (function)

Writers

(setf dtree-datum-dim) (function)

Slot: datamatrix
Readers

dtree-datamatrix (function)

Writers

(setf dtree-datamatrix) (function)

Slot: target
Readers

dtree-target (function)

Writers

(setf dtree-target) (function)

Slot: root
Readers

dtree-root (function)

Writers

(setf dtree-root) (function)

Slot: max-depth
Readers

dtree-max-depth (function)

Writers

(setf dtree-max-depth) (function)

Slot: min-region-samples
Readers

dtree-min-region-samples (function)

Writers

(setf dtree-min-region-samples) (function)

Slot: n-trial
Readers

dtree-n-trial (function)

Writers

(setf dtree-n-trial) (function)

Slot: gain-test
Readers

dtree-gain-test (function)

Writers

(setf dtree-gain-test) (function)

Slot: remove-sample-indices?
Readers

dtree-remove-sample-indices? (function)

Writers

(setf dtree-remove-sample-indices?) (function)

Slot: save-parent-node?
Readers

dtree-save-parent-node? (function)

Writers

(setf dtree-save-parent-node?) (function)

Slot: tmp-arr1
Readers

dtree-tmp-arr1 (function)

Writers

(setf dtree-tmp-arr1) (function)

Slot: tmp-index1
Readers

dtree-tmp-index1 (function)

Writers

(setf dtree-tmp-index1) (function)

Slot: tmp-arr2
Readers

dtree-tmp-arr2 (function)

Writers

(setf dtree-tmp-arr2) (function)

Slot: tmp-index2
Readers

dtree-tmp-index2 (function)

Writers

(setf dtree-tmp-index2) (function)

Slot: best-arr1
Readers

dtree-best-arr1 (function)

Writers

(setf dtree-best-arr1) (function)

Slot: best-index1
Readers

dtree-best-index1 (function)

Writers

(setf dtree-best-index1) (function)

Slot: best-arr2
Readers

dtree-best-arr2 (function)

Writers

(setf dtree-best-arr2) (function)

Slot: best-index2
Readers

dtree-best-index2 (function)

Writers

(setf dtree-best-index2) (function)

Slot: max-leaf-index
Readers

dtree-max-leaf-index (function)

Writers

(setf dtree-max-leaf-index) (function)

Slot: id
Readers

dtree-id (function)

Writers

(setf dtree-id) (function)

Structure: forest ()
Package

cl-random-forest

Source

random-forest.lisp (file)

Direct superclasses

structure-object (structure)

Direct methods

print-object (method)

Direct slots
Slot: n-tree
Readers

forest-n-tree (function)

Writers

(setf forest-n-tree) (function)

Slot: bagging-ratio
Readers

forest-bagging-ratio (function)

Writers

(setf forest-bagging-ratio) (function)

Slot: datamatrix
Readers

forest-datamatrix (function)

Writers

(setf forest-datamatrix) (function)

Slot: target
Readers

forest-target (function)

Writers

(setf forest-target) (function)

Slot: dtree-list
Readers

forest-dtree-list (function)

Writers

(setf forest-dtree-list) (function)

Slot: n-class
Readers

forest-n-class (function)

Writers

(setf forest-n-class) (function)

Slot: class-count-array
Readers

forest-class-count-array (function)

Writers

(setf forest-class-count-array) (function)

Slot: datum-dim
Readers

forest-datum-dim (function)

Writers

(setf forest-datum-dim) (function)

Slot: max-depth
Readers

forest-max-depth (function)

Writers

(setf forest-max-depth) (function)

Slot: min-region-samples
Readers

forest-min-region-samples (function)

Writers

(setf forest-min-region-samples) (function)

Slot: n-trial
Readers

forest-n-trial (function)

Writers

(setf forest-n-trial) (function)

Slot: gain-test
Readers

forest-gain-test (function)

Writers

(setf forest-gain-test) (function)

Slot: index-offset
Readers

forest-index-offset (function)

Writers

(setf forest-index-offset) (function)

Structure: node ()
Package

cl-random-forest

Source

random-forest.lisp (file)

Direct superclasses

structure-object (structure)

Direct methods

print-object (method)

Direct slots
Slot: sample-indices
Readers

node-sample-indices (function)

Writers

(setf node-sample-indices) (function)

Slot: n-sample
Readers

node-n-sample (function)

Writers

(setf node-n-sample) (function)

Slot: depth
Readers

node-depth (function)

Writers

(setf node-depth) (function)

Slot: test-attribute
Readers

node-test-attribute (function)

Writers

(setf node-test-attribute) (function)

Slot: test-threshold
Readers

node-test-threshold (function)

Writers

(setf node-test-threshold) (function)

Slot: information-gain
Readers

node-information-gain (function)

Writers

(setf node-information-gain) (function)

Slot: parent-node
Readers

node-parent-node (function)

Writers

(setf node-parent-node) (function)

Slot: left-node
Readers

node-left-node (function)

Writers

(setf node-left-node) (function)

Slot: right-node
Readers

node-right-node (function)

Writers

(setf node-right-node) (function)

Slot: dtree
Readers

node-dtree (function)

Writers

(setf node-dtree) (function)

Slot: leaf-index
Readers

node-leaf-index (function)

Writers

(setf node-leaf-index) (function)


Previous: , Up: Top   [Contents][Index]

Appendix A Indexes


Next: , Previous: , Up: Indexes   [Contents][Index]

A.1 Concepts

Jump to:   C   F   L   M  
Index Entry  Section

C
cl-random-forest.asd: The cl-random-forest<dot>asd file
cl-random-forest/src: The cl-random-forest/src module
cl-random-forest/src/feature-importance.lisp: The cl-random-forest/src/feature-importance<dot>lisp file
cl-random-forest/src/random-forest.lisp: The cl-random-forest/src/random-forest<dot>lisp file
cl-random-forest/src/reconstruction.lisp: The cl-random-forest/src/reconstruction<dot>lisp file
cl-random-forest/src/utils.lisp: The cl-random-forest/src/utils<dot>lisp file

F
File, Lisp, cl-random-forest.asd: The cl-random-forest<dot>asd file
File, Lisp, cl-random-forest/src/feature-importance.lisp: The cl-random-forest/src/feature-importance<dot>lisp file
File, Lisp, cl-random-forest/src/random-forest.lisp: The cl-random-forest/src/random-forest<dot>lisp file
File, Lisp, cl-random-forest/src/reconstruction.lisp: The cl-random-forest/src/reconstruction<dot>lisp file
File, Lisp, cl-random-forest/src/utils.lisp: The cl-random-forest/src/utils<dot>lisp file

L
Lisp File, cl-random-forest.asd: The cl-random-forest<dot>asd file
Lisp File, cl-random-forest/src/feature-importance.lisp: The cl-random-forest/src/feature-importance<dot>lisp file
Lisp File, cl-random-forest/src/random-forest.lisp: The cl-random-forest/src/random-forest<dot>lisp file
Lisp File, cl-random-forest/src/reconstruction.lisp: The cl-random-forest/src/reconstruction<dot>lisp file
Lisp File, cl-random-forest/src/utils.lisp: The cl-random-forest/src/utils<dot>lisp file

M
Module, cl-random-forest/src: The cl-random-forest/src module

Jump to:   C   F   L   M  

Next: , Previous: , Up: Indexes   [Contents][Index]

A.2 Functions

Jump to:   %   (  
B   C   D   E   F   G   L   M   N   P   R   S   T   V   W  
Index Entry  Section

%
%make-dtree: Internal functions
%make-forest: Internal functions
%make-node: Internal functions
%print-dtree: Internal functions
%print-forest: Internal functions
%print-node: Internal functions

(
(setf dtree-best-arr1): Internal functions
(setf dtree-best-arr2): Internal functions
(setf dtree-best-index1): Internal functions
(setf dtree-best-index2): Internal functions
(setf dtree-class-count-array): Internal functions
(setf dtree-datamatrix): Internal functions
(setf dtree-datum-dim): Internal functions
(setf dtree-gain-test): Internal functions
(setf dtree-id): Internal functions
(setf dtree-max-depth): Internal functions
(setf dtree-max-leaf-index): Internal functions
(setf dtree-min-region-samples): Internal functions
(setf dtree-n-class): Internal functions
(setf dtree-n-trial): Internal functions
(setf dtree-remove-sample-indices?): Internal functions
(setf dtree-root): Internal functions
(setf dtree-save-parent-node?): Internal functions
(setf dtree-target): Internal functions
(setf dtree-tmp-arr1): Internal functions
(setf dtree-tmp-arr2): Internal functions
(setf dtree-tmp-index1): Internal functions
(setf dtree-tmp-index2): Internal functions
(setf forest-bagging-ratio): Internal functions
(setf forest-class-count-array): Internal functions
(setf forest-datamatrix): Internal functions
(setf forest-datum-dim): Internal functions
(setf forest-dtree-list): Internal functions
(setf forest-gain-test): Internal functions
(setf forest-index-offset): Internal functions
(setf forest-max-depth): Internal functions
(setf forest-min-region-samples): Internal functions
(setf forest-n-class): Internal functions
(setf forest-n-tree): Internal functions
(setf forest-n-trial): Internal functions
(setf forest-target): Internal functions
(setf node-depth): Internal functions
(setf node-dtree): Internal functions
(setf node-information-gain): Internal functions
(setf node-leaf-index): Internal functions
(setf node-left-node): Internal functions
(setf node-n-sample): Internal functions
(setf node-parent-node): Internal functions
(setf node-right-node): Internal functions
(setf node-sample-indices): Internal functions
(setf node-test-attribute): Internal functions
(setf node-test-threshold): Internal functions

B
bootstrap-sample-indices: Internal functions

C
calc-accuracy: Internal functions
children-l2-norm: Internal functions
class-distribution: Internal functions
class-distribution-forest: Internal functions
clean-dtree!: Internal functions
clol-dataset->datamatrix/target: Exported functions
clol-dataset->datamatrix/target-regression: Exported functions
collect-leaf-parent: Internal functions
collect-leaf-parent-sorted: Internal functions
copy-dtree: Internal functions
copy-forest: Internal functions
copy-node: Internal functions
copy-tmp->best!: Internal functions
cross-validation-forest-with-refine-learner: Exported functions

D
decode-datum: Exported functions
delete-children!: Internal functions
do-index-value-list: Internal macros
do-leaf: Internal functions
dotimes/pdotimes: Exported macros
dtree-best-arr1: Internal functions
dtree-best-arr2: Internal functions
dtree-best-index1: Internal functions
dtree-best-index2: Internal functions
dtree-class-count-array: Internal functions
dtree-datamatrix: Internal functions
dtree-datum-dim: Internal functions
dtree-feature-importance: Internal functions
dtree-feature-importance-impurity: Internal functions
dtree-gain-test: Internal functions
dtree-id: Internal functions
dtree-max-depth: Internal functions
dtree-max-leaf-index: Internal functions
dtree-min-region-samples: Internal functions
dtree-n-class: Internal functions
dtree-n-trial: Internal functions
dtree-oob-sample-indices: Internal functions
dtree-p: Internal functions
dtree-remove-sample-indices?: Internal functions
dtree-root: Internal functions
dtree-save-parent-node?: Internal functions
dtree-target: Internal functions
dtree-tmp-arr1: Internal functions
dtree-tmp-arr2: Internal functions
dtree-tmp-index1: Internal functions
dtree-tmp-index2: Internal functions

E
encode-datum: Exported functions
entropy: Internal functions

F
find-leaf: Internal functions
find-leaf-randomized: Internal functions
forest-bagging-ratio: Internal functions
forest-class-count-array: Internal functions
forest-datamatrix: Internal functions
forest-datum-dim: Internal functions
forest-dtree-list: Internal functions
forest-feature-importance: Exported functions
forest-feature-importance-impurity: Internal functions
forest-gain-test: Internal functions
forest-index-offset: Internal functions
forest-max-depth: Internal functions
forest-min-region-samples: Internal functions
forest-n-class: Internal functions
forest-n-tree: Internal functions
forest-n-trial: Internal functions
forest-p: Internal functions
forest-target: Internal functions
format-datum: Internal functions
Function, %make-dtree: Internal functions
Function, %make-forest: Internal functions
Function, %make-node: Internal functions
Function, %print-dtree: Internal functions
Function, %print-forest: Internal functions
Function, %print-node: Internal functions
Function, (setf dtree-best-arr1): Internal functions
Function, (setf dtree-best-arr2): Internal functions
Function, (setf dtree-best-index1): Internal functions
Function, (setf dtree-best-index2): Internal functions
Function, (setf dtree-class-count-array): Internal functions
Function, (setf dtree-datamatrix): Internal functions
Function, (setf dtree-datum-dim): Internal functions
Function, (setf dtree-gain-test): Internal functions
Function, (setf dtree-id): Internal functions
Function, (setf dtree-max-depth): Internal functions
Function, (setf dtree-max-leaf-index): Internal functions
Function, (setf dtree-min-region-samples): Internal functions
Function, (setf dtree-n-class): Internal functions
Function, (setf dtree-n-trial): Internal functions
Function, (setf dtree-remove-sample-indices?): Internal functions
Function, (setf dtree-root): Internal functions
Function, (setf dtree-save-parent-node?): Internal functions
Function, (setf dtree-target): Internal functions
Function, (setf dtree-tmp-arr1): Internal functions
Function, (setf dtree-tmp-arr2): Internal functions
Function, (setf dtree-tmp-index1): Internal functions
Function, (setf dtree-tmp-index2): Internal functions
Function, (setf forest-bagging-ratio): Internal functions
Function, (setf forest-class-count-array): Internal functions
Function, (setf forest-datamatrix): Internal functions
Function, (setf forest-datum-dim): Internal functions
Function, (setf forest-dtree-list): Internal functions
Function, (setf forest-gain-test): Internal functions
Function, (setf forest-index-offset): Internal functions
Function, (setf forest-max-depth): Internal functions
Function, (setf forest-min-region-samples): Internal functions
Function, (setf forest-n-class): Internal functions
Function, (setf forest-n-tree): Internal functions
Function, (setf forest-n-trial): Internal functions
Function, (setf forest-target): Internal functions
Function, (setf node-depth): Internal functions
Function, (setf node-dtree): Internal functions
Function, (setf node-information-gain): Internal functions
Function, (setf node-leaf-index): Internal functions
Function, (setf node-left-node): Internal functions
Function, (setf node-n-sample): Internal functions
Function, (setf node-parent-node): Internal functions
Function, (setf node-right-node): Internal functions
Function, (setf node-sample-indices): Internal functions
Function, (setf node-test-attribute): Internal functions
Function, (setf node-test-threshold): Internal functions
Function, bootstrap-sample-indices: Internal functions
Function, calc-accuracy: Internal functions
Function, children-l2-norm: Internal functions
Function, class-distribution: Internal functions
Function, class-distribution-forest: Internal functions
Function, clean-dtree!: Internal functions
Function, clol-dataset->datamatrix/target: Exported functions
Function, clol-dataset->datamatrix/target-regression: Exported functions
Function, collect-leaf-parent: Internal functions
Function, collect-leaf-parent-sorted: Internal functions
Function, copy-dtree: Internal functions
Function, copy-forest: Internal functions
Function, copy-node: Internal functions
Function, copy-tmp->best!: Internal functions
Function, cross-validation-forest-with-refine-learner: Exported functions
Function, decode-datum: Exported functions
Function, delete-children!: Internal functions
Function, do-leaf: Internal functions
Function, dtree-best-arr1: Internal functions
Function, dtree-best-arr2: Internal functions
Function, dtree-best-index1: Internal functions
Function, dtree-best-index2: Internal functions
Function, dtree-class-count-array: Internal functions
Function, dtree-datamatrix: Internal functions
Function, dtree-datum-dim: Internal functions
Function, dtree-feature-importance: Internal functions
Function, dtree-feature-importance-impurity: Internal functions
Function, dtree-gain-test: Internal functions
Function, dtree-id: Internal functions
Function, dtree-max-depth: Internal functions
Function, dtree-max-leaf-index: Internal functions
Function, dtree-min-region-samples: Internal functions
Function, dtree-n-class: Internal functions
Function, dtree-n-trial: Internal functions
Function, dtree-oob-sample-indices: Internal functions
Function, dtree-p: Internal functions
Function, dtree-remove-sample-indices?: Internal functions
Function, dtree-root: Internal functions
Function, dtree-save-parent-node?: Internal functions
Function, dtree-target: Internal functions
Function, dtree-tmp-arr1: Internal functions
Function, dtree-tmp-arr2: Internal functions
Function, dtree-tmp-index1: Internal functions
Function, dtree-tmp-index2: Internal functions
Function, encode-datum: Exported functions
Function, entropy: Internal functions
Function, find-leaf: Internal functions
Function, find-leaf-randomized: Internal functions
Function, forest-bagging-ratio: Internal functions
Function, forest-class-count-array: Internal functions
Function, forest-datamatrix: Internal functions
Function, forest-datum-dim: Internal functions
Function, forest-dtree-list: Internal functions
Function, forest-feature-importance: Exported functions
Function, forest-feature-importance-impurity: Internal functions
Function, forest-gain-test: Internal functions
Function, forest-index-offset: Internal functions
Function, forest-max-depth: Internal functions
Function, forest-min-region-samples: Internal functions
Function, forest-n-class: Internal functions
Function, forest-n-tree: Internal functions
Function, forest-n-trial: Internal functions
Function, forest-p: Internal functions
Function, forest-target: Internal functions
Function, format-datum: Internal functions
Function, gini: Internal functions
Function, leaf?: Internal functions
Function, make-dtree: Exported functions
Function, make-forest: Exported functions
Function, make-l2-norm: Internal functions
Function, make-l2-norm-binary: Internal functions
Function, make-l2-norm-multiclass: Internal functions
Function, make-leaf-node-vector: Exported functions
Function, make-node: Internal functions
Function, make-oob-sample-indices: Internal functions
Function, make-partial-arr: Internal functions
Function, make-random-test: Internal functions
Function, make-refine-dataset: Exported functions
Function, make-refine-learner: Exported functions
Function, make-refine-vector: Exported functions
Function, make-regression-forest: Exported functions
Function, make-regression-refine-dataset: Internal functions
Function, make-regression-refine-learner: Internal functions
Function, make-regression-refine-vector: Internal functions
Function, make-root-node: Internal functions
Function, make-rtree: Exported functions
Function, maximize-activation/count: Internal functions
Function, node-class-distribution: Internal functions
Function, node-depth: Internal functions
Function, node-dtree: Internal functions
Function, node-information-gain: Internal functions
Function, node-leaf-index: Internal functions
Function, node-left-node: Internal functions
Function, node-n-sample: Internal functions
Function, node-p: Internal functions
Function, node-parent-node: Internal functions
Function, node-regression-mean: Internal functions
Function, node-right-node: Internal functions
Function, node-sample-indices: Internal functions
Function, node-test-attribute: Internal functions
Function, node-test-threshold: Internal functions
Function, normalize-arr!: Internal functions
Function, predict-dtree: Exported functions
Function, predict-dtree-randomized: Internal functions
Function, predict-forest: Exported functions
Function, predict-refine-learner: Exported functions
Function, predict-regression-forest: Exported functions
Function, predict-regression-refine-learner: Internal functions
Function, predict-rtree: Exported functions
Function, predict-rtree-randomized: Internal functions
Function, pruning!: Exported functions
Function, random-uniform: Internal functions
Function, read-data: Exported functions
Function, read-data-regression: Exported functions
Function, reconstruction-backward: Internal functions
Function, reconstruction-dtree: Internal functions
Function, reconstruction-forest: Exported functions
Function, region-min/max: Internal functions
Function, rtree-feature-importance: Internal functions
Function, rtree?: Internal functions
Function, set-activation-matrix!: Internal functions
Function, set-best-children!: Internal functions
Function, set-leaf-index!: Internal functions
Function, set-leaf-index-forest!: Internal functions
Function, split-node!: Internal functions
Function, split-sample-indices: Internal functions
Function, stop-split?: Internal functions
Function, test-dtree: Exported functions
Function, test-dtree-oob: Internal functions
Function, test-dtree-oob-randomized: Internal functions
Function, test-forest: Exported functions
Function, test-refine-learner: Exported functions
Function, test-refine-learner-binary: Internal functions
Function, test-refine-learner-multiclass: Internal functions
Function, test-regression-forest: Exported functions
Function, test-regression-refine-learner: Internal functions
Function, test-rtree: Exported functions
Function, test-rtree-oob: Internal functions
Function, test-rtree-oob-randomized: Internal functions
Function, train-refine-learner: Exported functions
Function, train-refine-learner-binary: Internal functions
Function, train-refine-learner-multiclass: Internal functions
Function, train-refine-learner-process-inner: Internal functions
Function, train-regression-refine-learner: Internal functions
Function, traverse: Internal functions
Function, variance: Internal functions
Function, write-to-r-format-from-clol-dataset: Exported functions

G
gini: Internal functions

L
leaf?: Internal functions

M
Macro, do-index-value-list: Internal macros
Macro, dotimes/pdotimes: Exported macros
Macro, mapc/pmapc: Exported macros
Macro, mapcar/pmapcar: Exported macros
Macro, push-ntimes: Exported macros
Macro, square: Internal macros
Macro, train-refine-learner-process: Exported macros
make-dtree: Exported functions
make-forest: Exported functions
make-l2-norm: Internal functions
make-l2-norm-binary: Internal functions
make-l2-norm-multiclass: Internal functions
make-leaf-node-vector: Exported functions
make-node: Internal functions
make-oob-sample-indices: Internal functions
make-partial-arr: Internal functions
make-random-test: Internal functions
make-refine-dataset: Exported functions
make-refine-learner: Exported functions
make-refine-vector: Exported functions
make-regression-forest: Exported functions
make-regression-refine-dataset: Internal functions
make-regression-refine-learner: Internal functions
make-regression-refine-vector: Internal functions
make-root-node: Internal functions
make-rtree: Exported functions
mapc/pmapc: Exported macros
mapcar/pmapcar: Exported macros
maximize-activation/count: Internal functions

N
node-class-distribution: Internal functions
node-depth: Internal functions
node-dtree: Internal functions
node-information-gain: Internal functions
node-leaf-index: Internal functions
node-left-node: Internal functions
node-n-sample: Internal functions
node-p: Internal functions
node-parent-node: Internal functions
node-regression-mean: Internal functions
node-right-node: Internal functions
node-sample-indices: Internal functions
node-test-attribute: Internal functions
node-test-threshold: Internal functions
normalize-arr!: Internal functions

P
predict-dtree: Exported functions
predict-dtree-randomized: Internal functions
predict-forest: Exported functions
predict-refine-learner: Exported functions
predict-regression-forest: Exported functions
predict-regression-refine-learner: Internal functions
predict-rtree: Exported functions
predict-rtree-randomized: Internal functions
pruning!: Exported functions
push-ntimes: Exported macros

R
random-uniform: Internal functions
read-data: Exported functions
read-data-regression: Exported functions
reconstruction-backward: Internal functions
reconstruction-dtree: Internal functions
reconstruction-forest: Exported functions
region-min/max: Internal functions
rtree-feature-importance: Internal functions
rtree?: Internal functions

S
set-activation-matrix!: Internal functions
set-best-children!: Internal functions
set-leaf-index!: Internal functions
set-leaf-index-forest!: Internal functions
split-node!: Internal functions
split-sample-indices: Internal functions
square: Internal macros
stop-split?: Internal functions

T
test-dtree: Exported functions
test-dtree-oob: Internal functions
test-dtree-oob-randomized: Internal functions
test-forest: Exported functions
test-refine-learner: Exported functions
test-refine-learner-binary: Internal functions
test-refine-learner-multiclass: Internal functions
test-regression-forest: Exported functions
test-regression-refine-learner: Internal functions
test-rtree: Exported functions
test-rtree-oob: Internal functions
test-rtree-oob-randomized: Internal functions
train-refine-learner: Exported functions
train-refine-learner-binary: Internal functions
train-refine-learner-multiclass: Internal functions
train-refine-learner-process: Exported macros
train-refine-learner-process-inner: Internal functions
train-regression-refine-learner: Internal functions
traverse: Internal functions

V
variance: Internal functions

W
write-to-r-format-from-clol-dataset: Exported functions

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A.3 Variables

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Index Entry  Section

B
bagging-ratio: Internal structures
best-arr1: Internal structures
best-arr2: Internal structures
best-index1: Internal structures
best-index2: Internal structures

C
class-count-array: Internal structures
class-count-array: Internal structures

D
datamatrix: Internal structures
datamatrix: Internal structures
datum-dim: Internal structures
datum-dim: Internal structures
depth: Internal structures
dtree: Internal structures
dtree-list: Internal structures

G
gain-test: Internal structures
gain-test: Internal structures

I
id: Internal structures
index-offset: Internal structures
information-gain: Internal structures

L
leaf-index: Internal structures
left-node: Internal structures

M
max-depth: Internal structures
max-depth: Internal structures
max-leaf-index: Internal structures
min-region-samples: Internal structures
min-region-samples: Internal structures

N
n-class: Internal structures
n-class: Internal structures
n-sample: Internal structures
n-tree: Internal structures
n-trial: Internal structures
n-trial: Internal structures

P
parent-node: Internal structures

R
remove-sample-indices?: Internal structures
right-node: Internal structures
root: Internal structures

S
sample-indices: Internal structures
save-parent-node?: Internal structures
Slot, bagging-ratio: Internal structures
Slot, best-arr1: Internal structures
Slot, best-arr2: Internal structures
Slot, best-index1: Internal structures
Slot, best-index2: Internal structures
Slot, class-count-array: Internal structures
Slot, class-count-array: Internal structures
Slot, datamatrix: Internal structures
Slot, datamatrix: Internal structures
Slot, datum-dim: Internal structures
Slot, datum-dim: Internal structures
Slot, depth: Internal structures
Slot, dtree: Internal structures
Slot, dtree-list: Internal structures
Slot, gain-test: Internal structures
Slot, gain-test: Internal structures
Slot, id: Internal structures
Slot, index-offset: Internal structures
Slot, information-gain: Internal structures
Slot, leaf-index: Internal structures
Slot, left-node: Internal structures
Slot, max-depth: Internal structures
Slot, max-depth: Internal structures
Slot, max-leaf-index: Internal structures
Slot, min-region-samples: Internal structures
Slot, min-region-samples: Internal structures
Slot, n-class: Internal structures
Slot, n-class: Internal structures
Slot, n-sample: Internal structures
Slot, n-tree: Internal structures
Slot, n-trial: Internal structures
Slot, n-trial: Internal structures
Slot, parent-node: Internal structures
Slot, remove-sample-indices?: Internal structures
Slot, right-node: Internal structures
Slot, root: Internal structures
Slot, sample-indices: Internal structures
Slot, save-parent-node?: Internal structures
Slot, target: Internal structures
Slot, target: Internal structures
Slot, test-attribute: Internal structures
Slot, test-threshold: Internal structures
Slot, tmp-arr1: Internal structures
Slot, tmp-arr2: Internal structures
Slot, tmp-index1: Internal structures
Slot, tmp-index2: Internal structures

T
target: Internal structures
target: Internal structures
test-attribute: Internal structures
test-threshold: Internal structures
tmp-arr1: Internal structures
tmp-arr2: Internal structures
tmp-index1: Internal structures
tmp-index2: Internal structures

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A.4 Data types

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Index Entry  Section

C
cl-random-forest: The cl-random-forest system
cl-random-forest: The cl-random-forest package
cl-random-forest-asd: The cl-random-forest-asd package
cl-random-forest.utils: The cl-random-forest<dot>utils package

D
dtree: Internal structures

F
forest: Internal structures

N
node: Internal structures

P
Package, cl-random-forest: The cl-random-forest package
Package, cl-random-forest-asd: The cl-random-forest-asd package
Package, cl-random-forest.utils: The cl-random-forest<dot>utils package

S
Structure, dtree: Internal structures
Structure, forest: Internal structures
Structure, node: Internal structures
System, cl-random-forest: The cl-random-forest system

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