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<?xml version="1.0" encoding="utf-8"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en">
<head>
<title>CL Machine-Learning</title>
<meta http-equiv="Content-Type" content="text/html;charset=utf-8"/>
<meta name="title" content="CL Machine-Learning"/>
<meta name="generator" content="Org-mode"/>
<meta name="generated" content="2013-07-19T18:29+0900"/>
<meta name="author" content="Salvi Péter, Naganuma Shigeta, Tada Masashi, Abe Yusuke, Jianshi Huang, Fujii Ryo, Abe Seika, Kuroda Hisao"/>
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<script type="text/javascript">
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<body>
<div id="preamble">
</div>
<div id="content">
<h1 class="title">CL Machine-Learning</h1>
<p>
CL Machine-Learning is high performance and large scale statistical
machine learning package written in Common Lisp. It runs almost all
major platforms and supports several Common Lisp implementations --
Allegro, Lispworks and Sbcl.
</p>
<p>
<a href="http://cl-www.msi.co.jp/solutions/knowledge/lisp-world/products/CLML.html">日本語版はここ</a>
</p>
<div id="table-of-contents">
<h2>Table of Contents</h2>
<div id="text-table-of-contents">
<ul>
<li><a href="#sec-1">1 Installation Notes</a>
<ul>
<li><a href="#sec-1-1">1.1 For Windows Users</a></li>
<li><a href="#sec-1-2">1.2 For Linux Users</a></li>
</ul>
</li>
<li><a href="#sec-2">2 Loading/Using the library</a></li>
<li><a href="#sec-3">3 Machine-Learning Packages</a>
<ul>
<li><a href="#sec-3-1">3.1 Read-Data</a></li>
<li><a href="#sec-3-2">3.2 Principal-Component-Analysis</a></li>
<li><a href="#sec-3-3">3.3 K-means</a></li>
<li><a href="#sec-3-4">3.4 Cluster-Validation</a></li>
<li><a href="#sec-3-5">3.5 Linear-Regression</a></li>
<li><a href="#sec-3-6">3.6 Hierarchical-Clustering</a></li>
<li><a href="#sec-3-7">3.7 Non-negative-Matrix-Factorization</a></li>
<li><a href="#sec-3-8">3.8 Spectral-Clustering</a></li>
<li><a href="#sec-3-9">3.9 Optics</a></li>
<li><a href="#sec-3-10">3.10 Association-Rule</a></li>
<li><a href="#sec-3-11">3.11 Decision-Tree</a></li>
<li><a href="#sec-3-12">3.12 Random-Forest</a></li>
<li><a href="#sec-3-13">3.13 K-Nearest-Neighbor</a></li>
<li><a href="#sec-3-14">3.14 Support-Vector-Machine</a></li>
<li><a href="#sec-3-15">3.15 Support-Vector-Machine (Soft Margin)</a></li>
<li><a href="#sec-3-16">3.16 Support-Vector-Regression</a></li>
<li><a href="#sec-3-17">3.17 One-Class-SVM</a></li>
<li><a href="#sec-3-18">3.18 Naive-Bayes</a></li>
<li><a href="#sec-3-19">3.19 Self-Organizing-Map</a></li>
<li><a href="#sec-3-20">3.20 Time-Series-Read-Data</a></li>
<li><a href="#sec-3-21">3.21 Time-Series-Statistics</a></li>
<li><a href="#sec-3-22">3.22 Time-Series-State-Space-Model</a></li>
<li><a href="#sec-3-23">3.23 Time-Series-Auto-Regression</a></li>
<li><a href="#sec-3-24">3.24 Exponential-Smoothing (HoltWinters)</a></li>
<li><a href="#sec-3-25">3.25 ChangeFinder</a></li>
<li><a href="#sec-3-26">3.26 Time-Series-Anomaly-detection</a></li>
<li><a href="#sec-3-27">3.27 Read-Graph</a></li>
<li><a href="#sec-3-28">3.28 Graph-Centrality</a></li>
<li><a href="#sec-3-29">3.29 Text-Utilities</a></li>
<li><a href="#sec-3-30">3.30 Hierarchical-Dirichlet-Process-Latent-Dirichlet-Allocation</a></li>
<li><a href="#sec-3-31">3.31 Dirichlet-Process-Mixture</a></li>
</ul>
</li>
<li><a href="#sec-4">4 Statistics</a>
<ul>
<li><a href="#sec-4-1">4.1 Requirements</a></li>
<li><a href="#sec-4-2">4.2 Usage</a></li>
<li><a href="#sec-4-3">4.3 Notes</a></li>
</ul>
</li>
<li><a href="#sec-5">5 Test package</a>
<ul>
<li><a href="#sec-5-1">5.1 lisp-unit</a></li>
<li><a href="#sec-5-2">5.2 STEFIL</a></li>
</ul>
</li>
<li><a href="#sec-6">6 Licensing</a></li>
<li><a href="#sec-7">7 Supported CL implementations</a></li>
<li><a href="#sec-8">8 Download</a>
<ul>
<li><a href="#sec-8-1">8.1 fasl packages</a></li>
<li><a href="#sec-8-2">8.2 sample data</a></li>
<li><a href="#sec-8-3">8.3 test scripts</a></li>
</ul>
</li>
</ul>
</div>
</div>
<div id="outline-container-1" class="outline-2">
<h2 id="sec-1"><span class="section-number-2">1</span> Installation Notes</h2>
<div class="outline-text-2" id="text-1">
</div>
<div id="outline-container-1-1" class="outline-3">
<h3 id="sec-1-1"><span class="section-number-3">1.1</span> For Windows Users</h3>
<div class="outline-text-3" id="text-1-1">
<ul>
<li>OpenMP library
<p>
Just copy
</p>
<p>
lib/mkl<sub>win</sub>(32/64)/libiomp5md.dll
</p>
<p>
to \WINDOWS\system32 directory, MKL customized library depends on
this file that must be loaded from search-able PATHs on Windows.
</p></li>
</ul>
</div>
</div>
<div id="outline-container-1-2" class="outline-3">
<h3 id="sec-1-2"><span class="section-number-3">1.2</span> For Linux Users</h3>
<div class="outline-text-3" id="text-1-2">
<p>
Latest Linux distributions, it should be OK just loading the fasl
files.
</p>
<p>
If the libraries cannot be loaded, please check the version of GLIBC
on your Linux system is 2.X. Or check that libc6 package has been
installed.
</p>
</div>
</div>
</div>
<div id="outline-container-2" class="outline-2">
<h2 id="sec-2"><span class="section-number-2">2</span> Loading/Using the library</h2>
<div class="outline-text-2" id="text-2">
<pre>(let ((*read-default-float-format* 'double-float))
(load "defsystem.cl")
(load-system :machine-learning :compile t))
or (in AllegroCL)
CL-USER(2): (setf *read-default-float-format* 'double-float)
CL-USER(3): :ld defsystem
CL-USER(4): (excl:load-system :machine-learning :compile t)
</pre>
</div>
</div>
<div id="outline-container-3" class="outline-2">
<h2 id="sec-3"><span class="section-number-2">3</span> Machine-Learning Packages</h2>
<div class="outline-text-2" id="text-3">
</div>
<div id="outline-container-3-1" class="outline-3">
<h3 id="sec-3-1"><span class="section-number-3">3.1</span> Read-Data</h3>
<div class="outline-text-3" id="text-3-1">
<p> package of reading data for machine learning
</p><ul>
<li id="sec-3-1-1">Class<br/>
<ul>
<li id="sec-3-1-1-1">dataset (base class)<br/>
<ul>
<li>accessor:
<ul>
<li>dataset-dimensions : info vector describes each column
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-1-2">unspecialized-dataset (dataset without column type)<br/>
<ul>
<li>accessor:
<ul>
<li>dataset-points : data except column names
</li>
</ul>
</li>
<li>parent: dataset
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-1-3">specialized-dataset (dataset with column type)<br/>
<ul>
<li>parent: dataset
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-1-4">numeric-dataset (dataset which column type is numeric)<br/>
<ul>
<li>accessor:
<ul>
<li>dataset-numeric-points : vector of numeric data
</li>
</ul>
</li>
<li>parent: specialized-dataset
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-1-5">category-dataset (dataset which column type is category)<br/>
<ul>
<li>accessor:
<ul>
<li>dataset-category-points : vector of numeric data
</li>
</ul>
</li>
<li>parent: specialized-dataset
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-1-6">numeric-and-category-dataset (dataset which column type is category or numeric)<br/>
<ul>
<li>accessor:
<ul>
<li>dataset-numeric-points : vector of numeric data
</li>
<li>dataset-category-points : vector of category data
</li>
</ul>
</li>
<li>parent: (numeric-dataset category-dataset)
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-2">read-data-from-file (filename &key (type :sexp) external-format csv-type-spec (csv-header-p t) (missing-value-check t) missing-values-list)<br/>
<ul>
<li>return: <unspecialized-dataset>
</li>
<li>arguments:
<ul>
<li>filename : <string>
</li>
<li>type : :sexp | :csv
</li>
<li>external-format : <acl-external-format>
</li>
<li>csv-header-p : <boolean>, the first line is column names or not. default is t.
</li>
<li>csv-type-spec : <list symbol>, specifies type of each column, e.g. '(string integer double-float double-float)
</li>
<li>missing-value-check : <boolean>, whether or not missing value check. default is t.
</li>
<li>missing-value-list : <list>, represents missing value, default is '(nil "" "NA")
</li>
</ul>
</li>
<li>comment:
<ul>
<li>default external-format is :default when <i>type</i> is :sexp, :932 when <i>type</i> is :csv
</li>
<li>CSV conforms <a href="http://www.ietf.org/rfc/rfc4180.txt?number=4180">RFC4180</a>.
<ul>
<li>a column value cannot include <i>return</i> or <i>newline</i>.
</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-3">pick-and-specialize-data ((d unspecialized-dataset) &key (range :all) except data-types)<br/>
<ul>
<li>return: <numeric-dataset>, <category-dataset> or <numeric-and-category-dataset> (determined by <i>data-type</i>)
</li>
<li>arguments:
<ul>
<li>d : <unspecialized-dataset>
</li>
<li>range : :all | <list integer>, specifies which column are picked up. e.g. '(0 1 3 4)
</li>
<li>except : <list integer>, specifies which column are <b>not</b> picked up. e.g. '(2)
</li>
<li>data-types : list of data type. e.g. '(:category :numeric :numeric)
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-4">divide-dataset ((specialized-d specialized-dataset) &key divide-ratio random (range :all) except)<br/>
<ul>
<li>return: (values of <unspecialized-dataset>, <numeric-dataset>, <category-dataset> or <numeric-and-category-dataset>)
</li>
<li>arguments:
<ul>
<li>d : <unspecialized-dataset> | <specialized-dataset>
</li>
<li>divide-ratio : <list non-negative-integer>, specifies how to divide the data. e.g. '(1 2 3) means data will be divided into 1:2:3.
</li>
<li>random : <boolean>, if t, order of line is random.
</li>
<li>range : :all | <list integer>, specifies which column are picked up. e.g. '(0 1 3 4)
</li>
<li>except : <list integer>, specifies which column are <b>not</b> picked up. e.g. '(2)
</li>
</ul>
</li>
<li>comments:
<ul>
<li>devide-dataset divide data into ration specified by <i>divide-ratio</i>. <br/>
you can choose or remove specified columns using <i>range</i> or <i>except</i>.
</li>
<li>order of line stay in the original order.
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-5">choice-dimensions (names data)<br/>
<ul>
<li>return: <vector vector>
</li>
<li>arguments:
<ul>
<li>names : <list string>, the list of column names
</li>
<li>data : <unspecialized-dataset> | <specialized-dataset>
</li>
</ul>
</li>
<li>comments:
<ul>
<li>This function takes out the data of the columns which have names specified with <i>names</i>.
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-6">choice-a-dimension (name data)<br/>
<ul>
<li>return: <vector>
</li>
<li>arguments:
<ul>
<li>name : <string>, a name of column
</li>
<li>data : <unspecialized-dataset> | <specialized-dataset>
</li>
</ul>
</li>
<li>comments:
<ul>
<li>This function takes out the data of the column which has name specified with <i>name</i>.
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-7">make-unspecialized-dataset (all-column-names data)<br/>
<ul>
<li>return: <unspecialized-dataset>
</li>
<li>arguments:
<ul>
<li>all-column-names : <list string>
</li>
<li>data : <vector vector>
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-8">dataset-cleaning (d &key interp-types outlier-types outlier-values)<br/>
<ul>
<li>return: <numeric-dataset> | <category-dataset> | <numeric-and-category-dataset>
</li>
<li>arguments:
<ul>
<li>d : <numeric-dataset> | <category-dataset> | <numeric-and-category-dataset>
</li>
<li>interp-types-alist : a-list (key: column name, datum: interpolation(:zero :min :max :mean :median :mode :spline)) | nil<br/>
</li>
<li>outlier-types-alist : a-list (key: column name, datum: outlier-verification(:std-dev :mean-dev :user :smirnov-grubbs :freq)) | nil<br/>
</li>
<li>outlier-values-alist : a-list (key: outlier-verification datum: the value according to outlier-verification) | nil
</li>
</ul>
</li>
<li>descriptions:
<ul>
<li>This function performs Outlier-verification and Interpolation.
At first, outlier is detected then interpolate missing values and those outlier values.
</li>
<li>Outlier verification<br/>
Outlier verification execute on values of columns which specified by <i>key</i> of <i>outlier-types-alist</i>, by the method specified by <i>datum</i>.<br/>
When judged the outlier, it is substituted for the missing value. when <i>outlier-types-alist</i> is nil, verification will not execute.<br/>
Parameter for each verification method is specified by <i>outlier-values-alist</i>. <br/>
If it is not specified, default value is taken.
<ul>
<li>Methods of Outlier verification
<ul>
<li>Methods for numerical column( :numeric )
<ul>
<li>Standard deviation (:std-dev)<br/>
When the difference with the mean value is greater than n time standard deviation, it is assumed the outlier.
Default value for n is 3.
</li>
<li>Mean deviation (:mean-dev)<br/>
When the difference with the mean value is greater than n time mean deviation, it is assumed the outlier.
Default value for n is 3.
</li>
<li>Smirnov-grubbs verification(:smirnov-grubbs)
<ul>
<li>reference: <a href="http://aoki2.si.gunma-u.ac.jp/lecture/Grubbs/Grubbs.html">http://aoki2.si.gunma-u.ac.jp/lecture/Grubbs/Grubbs.html</a>
</li>
<li>Significance level is specified by parameter. Default value is 0.05.
</li>
</ul>
</li>
<li>specified by user(:user)<br/>
The value specified for a parameter is assumed to be an outlier. It is necessary to specify the parameter.
</li>
</ul>
</li>
<li>Methods for categorical column( :category )
<ul>
<li>frequency(:freq)<br/>
Assume the threshold be total number of data times the parameter.
And the value of frequency that is less than the threshold is assumed to be an outlier.<br/>
Default value is 0.01.
</li>
<li>specified by user (:user)<br/>
The value specified for a parameter is assumed to be an outlier. It is necessary to specify the parameter.
</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
<li>Interpolation
Interpolate values of columns which specified by <i>key</i> of <i>interp-types-alist</i>, by the method specified by <i>datum</i>.<br/>
when <i>interp-types-alist</i> is nil, interpolation will not execute.
<ul>
<li>Methods of Interpolation
<ul>
<li>Methods for numerical column( :numeric )
<ul>
<li>:zero, interpolated by 0
</li>
<li>:min, interpolated by minimum
</li>
<li>:max, interpolated by maximum
</li>
<li>:mean, interpolated by mean
</li>
<li>:median, interpolated by median
</li>
<li>:spline, 3-dimensional Spline interpolation
<ul>
<li>reference: William H. Press "NUMERICAL RECIPES in C", Chapter3
</li>
</ul>
</li>
</ul>
</li>
<li>Methods for categorical column( :category )
<ul>
<li>:mode, interpolated by most frequent value
</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-9">make-bootstrap-sample-datasets (dataset &key number-of-datasets)<br/>
<ul>
<li>return: <list <unspecialized-dataset> | <numeric-dataset> | <category-dataset> | <numeric-and-category-dataset>>
</li>
<li>arguments:
<ul>
<li>dataset : <unspecialized-dataset> | <numeric-dataset> | <category-dataset> | <numeric-and-category-dataset>
</li>
<li>number-of-datasets : <positive-integer>, default is 10
</li>
</ul>
</li>
<li>comments:
<ul>
<li>Make bootstrap sample data of the number specified with number-of-datasets.
</li>
<li>reference: C.M.Bishop "Pattern Recognition and Machine Learning"
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-1-10">sample usage<br/>
<pre> READ-DATA(18): (setf dataset (read-data-from-file "sample/original-airquality.sexp"))
#<UNSPECIALIZED-DATASET>
DIMENSIONS: id | Ozone | Solar.R | Wind | Temp | Month | Day
TYPES: UNKNOWN | UNKNOWN | UNKNOWN | UNKNOWN | UNKNOWN | UNKNOWN | UNKNOWN
DATA POINTS: 153 POINTS
READ-DATA(19): (setf dataset (pick-and-specialize-data
dataset :range :all
:data-types '(:category :numeric :numeric :numeric :numeric
:category :category)))
#<NUMERIC-AND-CATEGORY-DATASET>
DIMENSIONS: id | Ozone | Solar.R | Wind | Temp | Month | Day
TYPES: CATEGORY | NUMERIC | NUMERIC | NUMERIC | NUMERIC | CATEGORY | CATEGORY
CATEGORY DATA POINTS: 153 POINTS
NUMERIC DATA POINTS: 153 POINTS
READ-DATA(20): (dataset-numeric-points dataset)
#(#(41.0 190.0 7.4 67.0) #(36.0 118.0 8.0 72.0) #(12.0 149.0 #.EXCL:*NAN-DOUBLE* 74.0) #(18.0 313.0 11.5 62.0)
#(#.EXCL:*NAN-DOUBLE* #.EXCL:*NAN-DOUBLE* 14.3 56.0) #(28.0 #.EXCL:*NAN-DOUBLE* 14.9 66.0) #(23.0 299.0 8.6 65.0)
#(19.0 99.0 13.8 #.EXCL:*NAN-DOUBLE*) #(8.0 19.0 #.EXCL:*NAN-DOUBLE* #.EXCL:*NAN-DOUBLE*) #(#.EXCL:*NAN-DOUBLE* 194.0 8.6 69.0) ...)
READ-DATA(21): (dataset-category-points dataset)
#(#(1 5 1) #(2 5 2) #(3 5 3) #(4 0 0) #(5 5 5) #(6 5 6) #(7 5 7) #(8 5 8) #(9 5 9) #(10 5 10) ...)
READ-DATA(22): (setf dataset
(dataset-cleaning dataset
:interp-types '(nil :spline :min :max :median :mode :mode)
:outlier-types '(nil :std-dev :mean-dev :smirnov-grubbs nil
:user :freq)
:outlier-values '(nil 2d0 2d0 0.05d0 nil 5 nil)))
#<NUMERIC-AND-CATEGORY-DATASET>
DIMENSIONS: id | Ozone | Solar.R | Wind | Temp | Month | Day
TYPES: CATEGORY | NUMERIC | NUMERIC | NUMERIC | NUMERIC | CATEGORY | CATEGORY
CATEGORY DATA POINTS: 153 POINTS
NUMERIC DATA POINTS: 153 POINTS
READ-DATA(23): (dataset-numeric-points dataset)
#(#(41.0 190.0 7.4 67.0) #(36.0 118.0 8.0 72.0) #(12.0 149.0 20.7 74.0) #(18.0 313.0 11.5 62.0) #(27.093168555852095 36.0 14.3 56.0)
#(28.0 36.0 14.9 66.0) #(23.0 299.0 8.6 65.0) #(19.0 99.0 13.8 79.0) #(8.0 36.0 20.7 79.0)
#(2.4104000463381468 194.0 8.6 69.0) ...)
READ-DATA(24): (dataset-category-points dataset)
#(#(1 8 1) #(2 8 2) #(3 8 3) #(4 8 30) #(5 8 5) #(6 8 6) #(7 8 7) #(8 8 8) #(9 8 9) #(10 8 10) ...)
READ-DATA(25): (divide-dataset dataset :divide-ratio '(3 2) :except '(2 3 4))
#<NUMERIC-AND-CATEGORY-DATASET>
DIMENSIONS: id | Ozone | Month | Day
TYPES: CATEGORY | NUMERIC | CATEGORY | CATEGORY
CATEGORY DATA POINTS: 91 POINTS
NUMERIC DATA POINTS: 91 POINTS
#<NUMERIC-AND-CATEGORY-DATASET>
DIMENSIONS: id | Ozone | Month | Day
TYPES: CATEGORY | NUMERIC | CATEGORY | CATEGORY
CATEGORY DATA POINTS: 62 POINTS
NUMERIC DATA POINTS: 62 POINTS
READ-DATA(26): (choice-dimensions '("Day" "Month" "Temp" "Wind") dataset)
#(#(1 8 67.0 7.4) #(2 8 72.0 8.0) #(3 8 74.0 20.7) #(30 8 62.0 11.5) #(5 8 56.0 14.3) #(6 8 66.0 14.9)
#(7 8 65.0 8.6) #(8 8 79.0 13.8) #(9 8 79.0 20.7) #(10 8 69.0 8.6) ...)
READ-DATA(27): (choice-a-dimension "Ozone" dataset)
#(41.0 36.0 12.0 18.0 27.093168555852095 28.0 23.0 19.0 8.0 2.4104000463381468 ...)
READ-DATA(26): (make-bootstrap-sample-datasets dataset :number-of-datasets 3)
(#<NUMERIC-AND-CATEGORY-DATASET>
DIMENSIONS: id | Ozone | Solar.R | Wind | Temp | Month | Day
TYPES: CATEGORY | NUMERIC | NUMERIC | NUMERIC | NUMERIC | CATEGORY | CATEGORY
CATEGORY DATA POINTS: 153 POINTS
NUMERIC DATA POINTS: 153 POINTS
#<NUMERIC-AND-CATEGORY-DATASET>
DIMENSIONS: id | Ozone | Solar.R | Wind | Temp | Month | Day
TYPES: CATEGORY | NUMERIC | NUMERIC | NUMERIC | NUMERIC | CATEGORY | CATEGORY
CATEGORY DATA POINTS: 153 POINTS
NUMERIC DATA POINTS: 153 POINTS
#<NUMERIC-AND-CATEGORY-DATASET>
DIMENSIONS: id | Ozone | Solar.R | Wind | Temp | Month | Day
TYPES: CATEGORY | NUMERIC | NUMERIC | NUMERIC | NUMERIC | CATEGORY | CATEGORY
CATEGORY DATA POINTS: 153 POINTS
NUMERIC DATA POINTS: 153 POINTS
)
;; make unspecialized-dataset from vector
READ-DATA(11): sample-vec
#(#("1967 DEC" 1720) #("1968 JAN" 1702) #("1968 FEB" 1707) #("1968 MAR" 1708) #("1968 APR" 1727)
#("1968 MAY" 1789) #("1968 JUN" 1829) #("1968 JUL" 1880) #("1968 AUG" 1920) #("1968 SEP" 1872) ...)
READ-DATA(12): (make-unspecialized-dataset '("Year Month" "Amount of food") ;; column names
sample-vec)
#<UNSPECIALIZED-DATASET >
DIMENSIONS: Year Month | Amount of food
TYPES: UNKNOWN | UNKNOWN
NUMBER OF DIMENSIONS: 2
DATA POINTS: 156 POINTS
</pre>
</li>
</ul>
</div>
</div>
<div id="outline-container-3-2" class="outline-3">
<h3 id="sec-3-2"><span class="section-number-3">3.2</span> Principal-Component-Analysis</h3>
<div class="outline-text-3" id="text-3-2">
<ul>
<li id="sec-3-2-1">Class<br/>
<ul>
<li id="sec-3-2-1-1">pca-result (the result of principle component analysis)<br/>
<ul>
<li>accessor:
<ul>
<li>components : <vector of datapoints>, principle components、score
</li>
<li>contributions : <vector of double-float>
</li>
<li>loading-factors : <matrix> (pay attention the representation of the matrix is row major)
</li>
<li>pca-method : :covariance | :correlation
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-2-1-2">pca-model (for calculating score)<br/>
<ul>
<li>accessor:
<ul>
<li>loading-factors : <matrix>, (pay attention the representation of the matrix is row major)
</li>
<li>pca-method : :covariance | :correlation
</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-2-2">princomp (dataset &key (method :correlation))<br/>
<ul>
<li>return: (values pca-result pca-model)
</li>
<li>arugments:
<ul>
<li>dataset : <numeric-dataset>
</li>
<li>method : :covariance | :correlation
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-2-3">princomp-projection (dataset pca-model)<br/>
<ul>
<li>return: score (vector of datapoints)
</li>
<li>arguments:
<ul>
<li>dataset : <numeric-dataset>
</li>
<li>pca-model : <pca-model>, model by P.C.A.
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-2-4">sub-princomp (dataset &key (method :correlation) (dimension-thld 0.8d0))<br/>
<ul>
<li>return: (values pca-result pca-model)
</li>
<li>arugments:
<ul>
<li>dataset : <numeric-dataset>
</li>
<li>method : :covariance | :correlation
</li>
<li>dimension-thld : 0 < <number> < 1 | 1 <= <integer>, threshold for deciding principal components
</li>
</ul>
</li>
<li>note:
<ul>
<li>When 0 < <i>dimension-thld</i> < 1, it means the threshold for accumulated
contribution ratio. A principle component's contribution ratio means
its proportion in all principle components' contributions.
</li>
<li>When 1 <= <i>dimension-thld</i> ( integer ), it means the number of principle components.
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-2-5">make-face-estimator ((face-dataset numeric-and-category-dataset)<br/>
&key id-column dimension-thld method
pca-method d-fcn pca-result pca-model)
<ul>
<li>return: (values estimator hash)
</li>
<li>arguments:
<ul>
<li>face-dataset : <numeric-and-category-dataset>
</li>
<li>id-column : <string>, the name for the face ID column, default value is "personID"
</li>
<li>dimension-thld : 0 < <number> < 1 | 1 <= <integer>, the threshold for determining the number of dimensions to use.
</li>
<li>method : :eigenface | :subspace, method for face recognition, eigenface or subspace method.
</li>
<li>pca-method : :covariance | :correlation, only valid when method is :subspace
</li>
<li>d-fcn : distance function for eigenface, default value is euclid-distance
</li>
<li>pca-result : <pca-result>, necessary for :eigenface
</li>
<li>pca-model : <pca-model>, necessary for :eigenface
</li>
</ul>
</li>
<li>note:
<ul>
<li>When 0 < <i>dimension-thld</i> < 1, it means the threshold for accumulated
contribution ratio. A principle component's contribution ratio means
its proportion in all principle components' contributions.
</li>
<li>When 1 <= <i>dimension-thld</i> ( integer ), it means the number of principle components.
</li>
</ul>
</li>
<li>reference:
<ul>
<li><a href="http://www.ism.ac.jp/editsec/toukei/pdf/49-1-023.pdf">坂野 鋭 "パターン認識における主成分分析 顔画像認識を例として" </a>
</li>
<li><a href="http://www.face-rec.org/">Face Recognition Homepage</a>
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-2-6">face-estimate ((d numeric-dataset) estimator)<br/>
<ul>
<li>return: <numeric-and-category-dataset>
</li>
<li>arguments:
<ul>
<li>d : <numeric-dataset>
</li>
<li>estimator : <closure>, the first return value for make-face-estimator
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-2-7">Note<br/>
<ul>
<li>when using princomp and sub-princomp, if there exists two columns
that are of same value, the result for :correlation
method will not be converged. Therefore pick-and-specialize-data or
divide-dataset must be used to remove one column.
</li>
</ul>
</li>
</ul>
<ul>
<li id="sec-3-2-8">sample usage<br/>
<pre>PCA(10): (setf dataset (read-data-from-file "sample/pos.sexp" :external-format #+allegro :932 #-allegro :sjis))
PCA(11): (setf dataset (pick-and-specialize-data dataset :range '(2 3) :data-types '(:numeric :numeric)))
PCA(12): (princomp dataset :method :correlation)
#<PCA-RESULT @ #x20fcd88a>
#<PCA-MODEL @ #x20fcd8c2>
PCA(13): (princomp-projection dataset (cadr /))
#(#(-0.18646787691278618 -0.5587877417431286)
#(-0.2586922124306382 -0.6310120772609806)
#(0.08929776779173992 -0.2830220970386028)
#(-0.311219001898167 -0.6835388667285094)
#(-0.19303372559622725 -0.5653535904265697)