Giters

blindglobe / clml

Common Lisp Machine Learning Library

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Table of Contents

CL Machine-Learning

CL Machine-Learning is high performance and large scale statistical machine learning package written in Common Lisp developed at MSI.

Author(s):

Original

  • Salvi Péter
  • Naganuma Shigeta
  • Tada Masashi
  • Abe Yusuke
  • Jianshi Huang
  • Fujii Ryo
  • Abe Seika
  • Kuroda Hisao

Current Branch Maintainer(s)/Authors(s):

  • Mike Maul

This repository contains is a modified version of CLML with the following goals in mind:

  • Remove dependent libraries available from the Quicklisp repository
  • Re-factor code to support Quicklisp packaging
  • Organize code into independent systems based on functional category
  • Support for Clozure Common Lisp short term and CLisp and ECL long term
  • Improve documentation

Installation

Requirements

  • Language: SBCL, Clozure Common Lisp, Allegro or Lispworks
  • Platform: Posix compatibile platforms (Windows, Linux, BSD and derivatives)
  • Optionally Intel Math Kernel Library
  • ASDF3 and optionally Quicklisp (This document assumes Quicklisp)

Currently development is taking place mostly on SBCL. For the near future SBCL is most stable platform.

Installation Notes

Obtaining code

Code can be obtained by one of the following methods:

Or download zip archive at

https://github.com/mmaul/clml/archive/master.zip

Installing

  1. For Quicklisp **

    1. Place code in ~/quicklisp/local-projects
    2. Start LISP and enter (ql:quickload :clml)

  2. For ASDF3 only (Non quicklisp users)

    1. Place in a location on your ASDF search path path such as ~/common-lisp
    2. Start LISP and enter (asdf:load-system :clml)

Documentation

User and API Documentation

User and API documentation may be found on line at http://mmaul.github.io/clml/ and also in the project directories docs/clml-manual.html Notes and Algorithmic Details and Background
files in memo, notes and docs

Sample Data

The sample datasets are located outside the CLML repository. Fortunately CLML is able to download sample datasets from remote sites via HTTP and HTTPS via the clml.utility.data:fetch function. Shown below is an example:

(clml.utility.data:fetch "https://mmaul.github.io/clml.data/sample/datafile.csv")

The clml.utility.data:fetch function downloads the file to a cache location and returns the path to the downloaded file. Therefore anywhere a path to a file is required the output from clml.utility.data:fetch can be provided instead.

The contents of the Sample dataset repository can be found at:

Usage

This library is organized as a hierarchical tree of systems.

  • clml
  • clml.association-rule
    • clml.association-rule
  • clml.classifiers
    • clml.classifiers.linear-regression
    • clml.classifiers.logistic-regression
    • clml.classifiers.nbayes
  • clml-clml.statistics
    • clml-clml.statistics
  • clml.clustering
    • clml.clustering.cluster-validation
    • clml.clustering.hc
    • clml.clustering.k-means2
    • clml.clustering.nmf
    • clml.clustering.optics
    • clml.clustering.optics-speed
    • clml.clustering.spectral-clustering
  • clml.decision-tree
    • clml.decision-tree.decision-tree
    • clml-decision-tree.random-forest
  • clml.graph
    • clml.graph.graph-anomaly-detection
    • clml.graph.graph-centrality
    • clml.graph.graph-utils
    • clml.graph.read-graph
    • clml.graph.shortest-path
  • clml.nearest-search
    • clml.nearest-search.k-nn
    • clml.nearest-search.k-nn-new
    • clml.nearest-search.nearest
  • clml.nonparameteric
    • clml.nonparameteric.statistics
    • clml.nonparametric.blocked-hdp-hmm
    • clml.nonparametric.dpm
    • clml.nonparametric.ftm
    • clml.nonparametric.hdp
    • clml.nonparametric.hdp-hmm
    • clml.nonparametric.hdp-hmm
    • clml.nonparametric.hdp-lda
    • clml.nonparametric.ihmm
    • clml.nonparametric.lfm
    • clml.nonparametric.sticky-hdp-hmm
    • clml.numeric.fast-fourier-transform
  • clml.pca
    • clml.pca
  • clml.som
    • clml.som
  • clml.statistics
    • clml.statistics
    • clml.statistics.rand
  • clml.svm
    • clml.svm.mu
    • clml.svm.one-class
    • clml.svm.pwss3
    • clml.svm.smo
    • clml.svm.svr
    • clml.svm.wss3
  • clml.time-series
    • clml.time-series.anomaly-detection
    • clml.time-series.autoregression
    • clml.time-series.burst-detection
    • clml.time-series.changefinder
    • clml.time-series.exponential-smoothing
    • clml.time-series.read-data
    • clml.time-series.state-space
    • clml.time-series.statistics
    • clml.time-series.util
  • clml.utility
    • clml.utility.csv
    • clml.utility.priority-que
  • fork-future
  • future
  • hjs
    • hjs.learn.k-means
    • hjs.learn.read-data
    • hjs.learn.vars
    • hjs.util.eigensystems
    • hjs.util.matrix
    • hjs.util.meta
    • hjs.util.missing-value
    • hjs.util.vector
  • lapack

Each system can be loaded independantly or the the clml system can be loaded which contains dependencies to all child system definitions.

This library requires that default reader float for mat is set to double-float. This should be done before loading the systems.

(setf *read-default-float-format* 'double-float)
  • Example below is using CLML.EXTRAS

Here is a quick demonstration:

CL-USER (ql:quickload :clml)

CL-USER (clml.text.utilities:calculate-levenshtein-similarity "Howdy" "doody")
0.6
CL-USER 
CL-USER (setf *syobu* (hjs.learn.read-data:read-data-from-file 
           (clml.utility.data:fetch "https://mmaul.github.io/clml.data/sample/syobu.csv")
           :type :csv :csv-type-spec '(string integer integer integer integer)))


#<HJS.LEARN.READ-DATA:UNSPECIALIZED-DATASET >
DIMENSIONS: 種類 | がく長 | がく幅 | 花びら長 | 花びら幅
TYPES:      UNKNOWN | UNKNOWN | UNKNOWN | UNKNOWN | UNKNOWN
NUMBER OF DIMENSIONS: 5
DATA POINTS: 150 POINTS

CL-USER (setf *tree* (clml.decision-tree.decision-tree:make-decision-tree *syobu* "種類"))


(((("花びら長" . 30)
   (("花びら幅" . 18) ("花びら幅" . 23) ("花びら幅" . 20) ("花びら幅" . 19) ("花びら幅" . 25)
    ("花びら幅" . 24) ("花びら幅" . 21) ("花びら幅" . 14) ("花びら幅" . 15) ("花びら幅" . 22)
     ("花びら幅" . 16) ("花びら幅" . 17) ("花びら幅" . 13) ("花びら幅" . 11) ("花びら幅" . 12)
  ...
  (("Virginica" . 50) ("Versicolor" . 50) ("Setosa" . 50))
  ((149 148 147 146 145 144 143 142 141 140 139 138 137 136 135 134 133 132 131
  ...
 (((("花びら幅" . 18)
    (("花びら幅" . 23) ("花びら幅" . 20) ("花びら幅" . 19) ("花びら幅" . 25) ("花びら幅" . 24)
     ("花びら幅" . 21) ("花びら幅" . 14) ("花びら幅" . 15) ("花びら幅" . 22) ("花びら幅" . 16)
     ("花びら幅" . 17) ("花びら幅" . 13) ("花びら幅" . 11) ("花びら幅" . 12) ("花びら幅" . 10)
 ...

)))
CL-USER    
CL-USER  (clml.decision-tree.decision-tree:print-decision-tree *tree*)
    [30 <= 花びら長?]((Virginica . 50) (Versicolor . 50) (Setosa . 50))
       Yes->[18 <= 花びら幅?]((Versicolor . 50) (Virginica . 50))
         Yes->[49 <= 花びら長?]((Virginica . 45) (Versicolor . 1))
             Yes->((Virginica . 43))
             No->[60 <= がく長?]((Versicolor . 1) (Virginica . 2))
                Yes->((Virginica . 2))
                No->((Versicolor . 1))
          No->[50 <= 花びら長?]((Virginica . 5) (Versicolor . 49))
             Yes->[16 <= 花びら幅?]((Versicolor . 2) (Virginica . 4))
                Yes->[70 <= がく長?]((Virginica . 1) (Versicolor . 2))
                   Yes->((Virginica . 1))
                   No->((Versicolor . 2))
                No->((Virginica . 3))
             No->[17 <= 花びら幅?]((Versicolor . 47) (Virginica . 1))
                Yes->((Virginica . 1))
                  No->((Versicolor . 47))
       No->((Setosa . 50))

About

Common Lisp Machine Learning Library

License:Other