machine-learning.data-sets: load commonly used test data sets.

db4
John Benediktsson 2012-12-04 10:00:02 -08:00
parent 53382f4472
commit bb3d028d30
3 changed files with 244 additions and 0 deletions

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! Copyright (C) 2012 John Benediktsson
! See http://factorcode.org/license.txt for BSD license
USING: assocs csv io.encodings.utf8 io.files kernel math.parser
sequences ;
IN: machine-learning.data-sets
TUPLE: data-set data target target-names description
feature-names ;
C: <data-set> data-set
<PRIVATE
: load-file ( name -- contents )
"resource:extra/machine-learning/data-sets/" prepend
utf8 file-contents ;
PRIVATE>
: load-iris ( -- data-set )
"iris.csv" load-file string>csv unclip [
[
unclip-last
[ [ string>number ] map ]
[ string>number ] bi*
] { } map>assoc unzip
] [ 2 tail ] bi*
"iris.rst" load-file
{
"sepal length (cm)" "sepal width (cm)"
"petal length (cm)" "petal width (cm)"
} <data-set> ;

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150,4,setosa,versicolor,virginica
5.1,3.5,1.4,0.2,0
4.9,3.0,1.4,0.2,0
4.7,3.2,1.3,0.2,0
4.6,3.1,1.5,0.2,0
5.0,3.6,1.4,0.2,0
5.4,3.9,1.7,0.4,0
4.6,3.4,1.4,0.3,0
5.0,3.4,1.5,0.2,0
4.4,2.9,1.4,0.2,0
4.9,3.1,1.5,0.1,0
5.4,3.7,1.5,0.2,0
4.8,3.4,1.6,0.2,0
4.8,3.0,1.4,0.1,0
4.3,3.0,1.1,0.1,0
5.8,4.0,1.2,0.2,0
5.7,4.4,1.5,0.4,0
5.4,3.9,1.3,0.4,0
5.1,3.5,1.4,0.3,0
5.7,3.8,1.7,0.3,0
5.1,3.8,1.5,0.3,0
5.4,3.4,1.7,0.2,0
5.1,3.7,1.5,0.4,0
4.6,3.6,1.0,0.2,0
5.1,3.3,1.7,0.5,0
4.8,3.4,1.9,0.2,0
5.0,3.0,1.6,0.2,0
5.0,3.4,1.6,0.4,0
5.2,3.5,1.5,0.2,0
5.2,3.4,1.4,0.2,0
4.7,3.2,1.6,0.2,0
4.8,3.1,1.6,0.2,0
5.4,3.4,1.5,0.4,0
5.2,4.1,1.5,0.1,0
5.5,4.2,1.4,0.2,0
4.9,3.1,1.5,0.1,0
5.0,3.2,1.2,0.2,0
5.5,3.5,1.3,0.2,0
4.9,3.1,1.5,0.1,0
4.4,3.0,1.3,0.2,0
5.1,3.4,1.5,0.2,0
5.0,3.5,1.3,0.3,0
4.5,2.3,1.3,0.3,0
4.4,3.2,1.3,0.2,0
5.0,3.5,1.6,0.6,0
5.1,3.8,1.9,0.4,0
4.8,3.0,1.4,0.3,0
5.1,3.8,1.6,0.2,0
4.6,3.2,1.4,0.2,0
5.3,3.7,1.5,0.2,0
5.0,3.3,1.4,0.2,0
7.0,3.2,4.7,1.4,1
6.4,3.2,4.5,1.5,1
6.9,3.1,4.9,1.5,1
5.5,2.3,4.0,1.3,1
6.5,2.8,4.6,1.5,1
5.7,2.8,4.5,1.3,1
6.3,3.3,4.7,1.6,1
4.9,2.4,3.3,1.0,1
6.6,2.9,4.6,1.3,1
5.2,2.7,3.9,1.4,1
5.0,2.0,3.5,1.0,1
5.9,3.0,4.2,1.5,1
6.0,2.2,4.0,1.0,1
6.1,2.9,4.7,1.4,1
5.6,2.9,3.6,1.3,1
6.7,3.1,4.4,1.4,1
5.6,3.0,4.5,1.5,1
5.8,2.7,4.1,1.0,1
6.2,2.2,4.5,1.5,1
5.6,2.5,3.9,1.1,1
5.9,3.2,4.8,1.8,1
6.1,2.8,4.0,1.3,1
6.3,2.5,4.9,1.5,1
6.1,2.8,4.7,1.2,1
6.4,2.9,4.3,1.3,1
6.6,3.0,4.4,1.4,1
6.8,2.8,4.8,1.4,1
6.7,3.0,5.0,1.7,1
6.0,2.9,4.5,1.5,1
5.7,2.6,3.5,1.0,1
5.5,2.4,3.8,1.1,1
5.5,2.4,3.7,1.0,1
5.8,2.7,3.9,1.2,1
6.0,2.7,5.1,1.6,1
5.4,3.0,4.5,1.5,1
6.0,3.4,4.5,1.6,1
6.7,3.1,4.7,1.5,1
6.3,2.3,4.4,1.3,1
5.6,3.0,4.1,1.3,1
5.5,2.5,4.0,1.3,1
5.5,2.6,4.4,1.2,1
6.1,3.0,4.6,1.4,1
5.8,2.6,4.0,1.2,1
5.0,2.3,3.3,1.0,1
5.6,2.7,4.2,1.3,1
5.7,3.0,4.2,1.2,1
5.7,2.9,4.2,1.3,1
6.2,2.9,4.3,1.3,1
5.1,2.5,3.0,1.1,1
5.7,2.8,4.1,1.3,1
6.3,3.3,6.0,2.5,2
5.8,2.7,5.1,1.9,2
7.1,3.0,5.9,2.1,2
6.3,2.9,5.6,1.8,2
6.5,3.0,5.8,2.2,2
7.6,3.0,6.6,2.1,2
4.9,2.5,4.5,1.7,2
7.3,2.9,6.3,1.8,2
6.7,2.5,5.8,1.8,2
7.2,3.6,6.1,2.5,2
6.5,3.2,5.1,2.0,2
6.4,2.7,5.3,1.9,2
6.8,3.0,5.5,2.1,2
5.7,2.5,5.0,2.0,2
5.8,2.8,5.1,2.4,2
6.4,3.2,5.3,2.3,2
6.5,3.0,5.5,1.8,2
7.7,3.8,6.7,2.2,2
7.7,2.6,6.9,2.3,2
6.0,2.2,5.0,1.5,2
6.9,3.2,5.7,2.3,2
5.6,2.8,4.9,2.0,2
7.7,2.8,6.7,2.0,2
6.3,2.7,4.9,1.8,2
6.7,3.3,5.7,2.1,2
7.2,3.2,6.0,1.8,2
6.2,2.8,4.8,1.8,2
6.1,3.0,4.9,1.8,2
6.4,2.8,5.6,2.1,2
7.2,3.0,5.8,1.6,2
7.4,2.8,6.1,1.9,2
7.9,3.8,6.4,2.0,2
6.4,2.8,5.6,2.2,2
6.3,2.8,5.1,1.5,2
6.1,2.6,5.6,1.4,2
7.7,3.0,6.1,2.3,2
6.3,3.4,5.6,2.4,2
6.4,3.1,5.5,1.8,2
6.0,3.0,4.8,1.8,2
6.9,3.1,5.4,2.1,2
6.7,3.1,5.6,2.4,2
6.9,3.1,5.1,2.3,2
5.8,2.7,5.1,1.9,2
6.8,3.2,5.9,2.3,2
6.7,3.3,5.7,2.5,2
6.7,3.0,5.2,2.3,2
6.3,2.5,5.0,1.9,2
6.5,3.0,5.2,2.0,2
6.2,3.4,5.4,2.3,2
5.9,3.0,5.1,1.8,2
1 150 4 setosa versicolor virginica
2 5.1 3.5 1.4 0.2 0
3 4.9 3.0 1.4 0.2 0
4 4.7 3.2 1.3 0.2 0
5 4.6 3.1 1.5 0.2 0
6 5.0 3.6 1.4 0.2 0
7 5.4 3.9 1.7 0.4 0
8 4.6 3.4 1.4 0.3 0
9 5.0 3.4 1.5 0.2 0
10 4.4 2.9 1.4 0.2 0
11 4.9 3.1 1.5 0.1 0
12 5.4 3.7 1.5 0.2 0
13 4.8 3.4 1.6 0.2 0
14 4.8 3.0 1.4 0.1 0
15 4.3 3.0 1.1 0.1 0
16 5.8 4.0 1.2 0.2 0
17 5.7 4.4 1.5 0.4 0
18 5.4 3.9 1.3 0.4 0
19 5.1 3.5 1.4 0.3 0
20 5.7 3.8 1.7 0.3 0
21 5.1 3.8 1.5 0.3 0
22 5.4 3.4 1.7 0.2 0
23 5.1 3.7 1.5 0.4 0
24 4.6 3.6 1.0 0.2 0
25 5.1 3.3 1.7 0.5 0
26 4.8 3.4 1.9 0.2 0
27 5.0 3.0 1.6 0.2 0
28 5.0 3.4 1.6 0.4 0
29 5.2 3.5 1.5 0.2 0
30 5.2 3.4 1.4 0.2 0
31 4.7 3.2 1.6 0.2 0
32 4.8 3.1 1.6 0.2 0
33 5.4 3.4 1.5 0.4 0
34 5.2 4.1 1.5 0.1 0
35 5.5 4.2 1.4 0.2 0
36 4.9 3.1 1.5 0.1 0
37 5.0 3.2 1.2 0.2 0
38 5.5 3.5 1.3 0.2 0
39 4.9 3.1 1.5 0.1 0
40 4.4 3.0 1.3 0.2 0
41 5.1 3.4 1.5 0.2 0
42 5.0 3.5 1.3 0.3 0
43 4.5 2.3 1.3 0.3 0
44 4.4 3.2 1.3 0.2 0
45 5.0 3.5 1.6 0.6 0
46 5.1 3.8 1.9 0.4 0
47 4.8 3.0 1.4 0.3 0
48 5.1 3.8 1.6 0.2 0
49 4.6 3.2 1.4 0.2 0
50 5.3 3.7 1.5 0.2 0
51 5.0 3.3 1.4 0.2 0
52 7.0 3.2 4.7 1.4 1
53 6.4 3.2 4.5 1.5 1
54 6.9 3.1 4.9 1.5 1
55 5.5 2.3 4.0 1.3 1
56 6.5 2.8 4.6 1.5 1
57 5.7 2.8 4.5 1.3 1
58 6.3 3.3 4.7 1.6 1
59 4.9 2.4 3.3 1.0 1
60 6.6 2.9 4.6 1.3 1
61 5.2 2.7 3.9 1.4 1
62 5.0 2.0 3.5 1.0 1
63 5.9 3.0 4.2 1.5 1
64 6.0 2.2 4.0 1.0 1
65 6.1 2.9 4.7 1.4 1
66 5.6 2.9 3.6 1.3 1
67 6.7 3.1 4.4 1.4 1
68 5.6 3.0 4.5 1.5 1
69 5.8 2.7 4.1 1.0 1
70 6.2 2.2 4.5 1.5 1
71 5.6 2.5 3.9 1.1 1
72 5.9 3.2 4.8 1.8 1
73 6.1 2.8 4.0 1.3 1
74 6.3 2.5 4.9 1.5 1
75 6.1 2.8 4.7 1.2 1
76 6.4 2.9 4.3 1.3 1
77 6.6 3.0 4.4 1.4 1
78 6.8 2.8 4.8 1.4 1
79 6.7 3.0 5.0 1.7 1
80 6.0 2.9 4.5 1.5 1
81 5.7 2.6 3.5 1.0 1
82 5.5 2.4 3.8 1.1 1
83 5.5 2.4 3.7 1.0 1
84 5.8 2.7 3.9 1.2 1
85 6.0 2.7 5.1 1.6 1
86 5.4 3.0 4.5 1.5 1
87 6.0 3.4 4.5 1.6 1
88 6.7 3.1 4.7 1.5 1
89 6.3 2.3 4.4 1.3 1
90 5.6 3.0 4.1 1.3 1
91 5.5 2.5 4.0 1.3 1
92 5.5 2.6 4.4 1.2 1
93 6.1 3.0 4.6 1.4 1
94 5.8 2.6 4.0 1.2 1
95 5.0 2.3 3.3 1.0 1
96 5.6 2.7 4.2 1.3 1
97 5.7 3.0 4.2 1.2 1
98 5.7 2.9 4.2 1.3 1
99 6.2 2.9 4.3 1.3 1
100 5.1 2.5 3.0 1.1 1
101 5.7 2.8 4.1 1.3 1
102 6.3 3.3 6.0 2.5 2
103 5.8 2.7 5.1 1.9 2
104 7.1 3.0 5.9 2.1 2
105 6.3 2.9 5.6 1.8 2
106 6.5 3.0 5.8 2.2 2
107 7.6 3.0 6.6 2.1 2
108 4.9 2.5 4.5 1.7 2
109 7.3 2.9 6.3 1.8 2
110 6.7 2.5 5.8 1.8 2
111 7.2 3.6 6.1 2.5 2
112 6.5 3.2 5.1 2.0 2
113 6.4 2.7 5.3 1.9 2
114 6.8 3.0 5.5 2.1 2
115 5.7 2.5 5.0 2.0 2
116 5.8 2.8 5.1 2.4 2
117 6.4 3.2 5.3 2.3 2
118 6.5 3.0 5.5 1.8 2
119 7.7 3.8 6.7 2.2 2
120 7.7 2.6 6.9 2.3 2
121 6.0 2.2 5.0 1.5 2
122 6.9 3.2 5.7 2.3 2
123 5.6 2.8 4.9 2.0 2
124 7.7 2.8 6.7 2.0 2
125 6.3 2.7 4.9 1.8 2
126 6.7 3.3 5.7 2.1 2
127 7.2 3.2 6.0 1.8 2
128 6.2 2.8 4.8 1.8 2
129 6.1 3.0 4.9 1.8 2
130 6.4 2.8 5.6 2.1 2
131 7.2 3.0 5.8 1.6 2
132 7.4 2.8 6.1 1.9 2
133 7.9 3.8 6.4 2.0 2
134 6.4 2.8 5.6 2.2 2
135 6.3 2.8 5.1 1.5 2
136 6.1 2.6 5.6 1.4 2
137 7.7 3.0 6.1 2.3 2
138 6.3 3.4 5.6 2.4 2
139 6.4 3.1 5.5 1.8 2
140 6.0 3.0 4.8 1.8 2
141 6.9 3.1 5.4 2.1 2
142 6.7 3.1 5.6 2.4 2
143 6.9 3.1 5.1 2.3 2
144 5.8 2.7 5.1 1.9 2
145 6.8 3.2 5.9 2.3 2
146 6.7 3.3 5.7 2.5 2
147 6.7 3.0 5.2 2.3 2
148 6.3 2.5 5.0 1.9 2
149 6.5 3.0 5.2 2.0 2
150 6.2 3.4 5.4 2.3 2
151 5.9 3.0 5.1 1.8 2

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Iris Plants Database
Notes
-----
Data Set Characteristics:
:Number of Instances: 150 (50 in each of three classes)
:Number of Attributes: 4 numeric, predictive attributes and the class
:Attribute Information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris-Setosa
- Iris-Versicolour
- Iris-Virginica
:Summary Statistics:
============== ==== ==== ======= ===== ====================
Min Max Mean SD Class Correlation
============== ==== ==== ======= ===== ====================
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
============== ==== ==== ======= ===== ====================
:Missing Attribute Values: None
:Class Distribution: 33.3% for each of 3 classes.
:Creator: R.A. Fisher
:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
:Date: July, 1988
This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris
The famous Iris database, first used by Sir R.A Fisher
This is perhaps the best known database to be found in the
pattern recognition literature. Fisher's paper is a classic in the field and
is referenced frequently to this day. (See Duda & Hart, for example.) The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant. One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.
References
----------
- Fisher,R.A. "The use of multiple measurements in taxonomic problems"
Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
Mathematical Statistics" (John Wiley, NY, 1950).
- Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments". IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.
- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
on Information Theory, May 1972, 431-433.
- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
conceptual clustering system finds 3 classes in the data.
- Many, many more ...