diff --git a/scikits/learn/datasets/iris/data.py b/scikits/learn/datasets/iris/data.py
index 4e0b0fbbd9ff11d22b25ebde6b5cd77686c0d004..ce470280edd25192eaa771ee912af7ba2434895c 100644
--- a/scikits/learn/datasets/iris/data.py
+++ b/scikits/learn/datasets/iris/data.py
@@ -1,6 +1,6 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
-# Last Change: Sun Jul 01 08:00 PM 2007 J
+# Last Change: Tue Jul 17 04:00 PM 2007 J
# The code and descriptive text is copyrighted and offered under the terms of
# the BSD License from the authors; see below. However, the actual dataset may
@@ -98,25 +98,45 @@ def load():
"""load the iris data and returns them.
:returns:
- data: recordarray
- a record array of the data.
+ d : dict
+ contains the following values:
+ - 'data' : a record array with the actual data
+ - 'label' : label[i] = label index of data[i]
+ - 'class' : class[i] is the string corresponding to label index i.
+
+ Example
+ -------
+
+ Let's say you are interested in the samples 10, 25, and 50, and want to
+ know their class name.
+
+ >>>> d = load()
+ >>>> ind = [10, 25, 50]
+ >>>> lind = d['label'][ind] # returns the label index of each sample
+ >>>> d['class'][lind] # returns the class name of each sample
+
"""
import numpy
- from iris import SL, SW, PL, PW, CLI
+ from iris import SL, SW, PL, PW, LABELS, LI2LN
PW = numpy.array(PW).astype(numpy.float)
PL = numpy.array(PL).astype(numpy.float)
SW = numpy.array(SW).astype(numpy.float)
SL = numpy.array(SL).astype(numpy.float)
data = {}
- for i in CLI.items():
- name = i[0][5:]
- data[name] = numpy.empty(len(i[1]), [('petal width', numpy.int),\
- ('petal length', numpy.int),
- ('sepal width', numpy.int),
- ('sepal length', numpy.int)])
- data[name]['petal width'] = numpy.round(PW[i[1]] * 10)
- data[name]['petal length'] = numpy.round(PL[i[1]] * 10)
- data[name]['sepal width'] = numpy.round(SW[i[1]] * 10)
- data[name]['sepal length'] = numpy.round(SL[i[1]] * 10)
+ data['data'] = numpy.empty(len(PW),
+ [('petal width', numpy.int),
+ ('petal length', numpy.int),
+ ('sepal width', numpy.int),
+ ('sepal length', numpy.int)])
+
+ data['data']['petal width'] = numpy.round(PW * 10)
+ data['data']['petal length'] = numpy.round(PL * 10)
+ data['data']['sepal width'] = numpy.round(SW * 10)
+ data['data']['sepal length'] = numpy.round(SL * 10)
+ data['label'] = numpy.array(LABELS).astype(numpy.int)
+ data['class'] = numpy.empty(len(LI2LN),
+ 'S%d' % numpy.max([len(i) for i in LI2LN.values()]))
+ for i,c in LI2LN.items():
+ data['class'][i] = c
return data
diff --git a/scikits/learn/datasets/iris/iris.py b/scikits/learn/datasets/iris/iris.py
index 7348f910bd2422042aa9b1b349d79f37f1fb4ab1..94da7409f794167b518d88c8269b1feb4f764689 100644
--- a/scikits/learn/datasets/iris/iris.py
+++ b/scikits/learn/datasets/iris/iris.py
@@ -1,12 +1,71 @@
-# Autogenerated by convert.py at Sun, 01 Jul 2007 10:36:56 +0000
+# Autogenerated by convert.py at Tue, 17 Jul 2007 06:47:17 +0000
-SL = ['5.1', '4.9', '4.7', '4.6', '5.0', '5.4', '4.6', '5.0', '4.4', '4.9', '5.4', '4.8', '4.8', '4.3', '5.8', '5.7', '5.4', '5.1', '5.7', '5.1', '5.4', '5.1', '4.6', '5.1', '4.8', '5.0', '5.0', '5.2', '5.2', '4.7', '4.8', '5.4', '5.2', '5.5', '4.9', '5.0', '5.5', '4.9', '4.4', '5.1', '5.0', '4.5', '4.4', '5.0', '5.1', '4.8', '5.1', '4.6', '5.3', '5.0', '7.0', '6.4', '6.9', '5.5', '6.5', '5.7', '6.3', '4.9', '6.6', '5.2', '5.0', '5.9', '6.0', '6.1', '5.6', '6.7', '5.6', '5.8', '6.2', '5.6', '5.9', '6.1', '6.3', '6.1', '6.4', '6.6', '6.8', '6.7', '6.0', '5.7', '5.5', '5.5', '5.8', '6.0', '5.4', '6.0', '6.7', '6.3', '5.6', '5.5', '5.5', '6.1', '5.8', '5.0', '5.6', '5.7', '5.7', '6.2', '5.1', '5.7', '6.3', '5.8', '7.1', '6.3', '6.5', '7.6', '4.9', '7.3', '6.7', '7.2', '6.5', '6.4', '6.8', '5.7', '5.8', '6.4', '6.5', '7.7', '7.7', '6.0', '6.9', '5.6', '7.7', '6.3', '6.7', '7.2', '6.2', '6.1', '6.4', '7.2', '7.4', '7.9', '6.4', '6.3', '6.1', '7.7', '6.3', '6.4', '6.0', '6.9', '6.7', '6.9', '5.8', '6.8', '6.7', '6.7', '6.3', '6.5', '6.2', '5.9']
+SL = ['5.1', '4.9', '4.7', '4.6', '5.0', '5.4', '4.6', '5.0', '4.4', '4.9',
+'5.4', '4.8', '4.8', '4.3', '5.8', '5.7', '5.4', '5.1', '5.7', '5.1', '5.4',
+'5.1', '4.6', '5.1', '4.8', '5.0', '5.0', '5.2', '5.2', '4.7', '4.8', '5.4',
+'5.2', '5.5', '4.9', '5.0', '5.5', '4.9', '4.4', '5.1', '5.0', '4.5', '4.4',
+'5.0', '5.1', '4.8', '5.1', '4.6', '5.3', '5.0', '7.0', '6.4', '6.9', '5.5',
+'6.5', '5.7', '6.3', '4.9', '6.6', '5.2', '5.0', '5.9', '6.0', '6.1', '5.6',
+'6.7', '5.6', '5.8', '6.2', '5.6', '5.9', '6.1', '6.3', '6.1', '6.4', '6.6',
+'6.8', '6.7', '6.0', '5.7', '5.5', '5.5', '5.8', '6.0', '5.4', '6.0', '6.7',
+'6.3', '5.6', '5.5', '5.5', '6.1', '5.8', '5.0', '5.6', '5.7', '5.7', '6.2',
+'5.1', '5.7', '6.3', '5.8', '7.1', '6.3', '6.5', '7.6', '4.9', '7.3', '6.7',
+'7.2', '6.5', '6.4', '6.8', '5.7', '5.8', '6.4', '6.5', '7.7', '7.7', '6.0',
+'6.9', '5.6', '7.7', '6.3', '6.7', '7.2', '6.2', '6.1', '6.4', '7.2', '7.4',
+'7.9', '6.4', '6.3', '6.1', '7.7', '6.3', '6.4', '6.0', '6.9', '6.7', '6.9',
+'5.8', '6.8', '6.7', '6.7', '6.3', '6.5', '6.2', '5.9']
-SW = ['3.5', '3.0', '3.2', '3.1', '3.6', '3.9', '3.4', '3.4', '2.9', '3.1', '3.7', '3.4', '3.0', '3.0', '4.0', '4.4', '3.9', '3.5', '3.8', '3.8', '3.4', '3.7', '3.6', '3.3', '3.4', '3.0', '3.4', '3.5', '3.4', '3.2', '3.1', '3.4', '4.1', '4.2', '3.1', '3.2', '3.5', '3.1', '3.0', '3.4', '3.5', '2.3', '3.2', '3.5', '3.8', '3.0', '3.8', '3.2', '3.7', '3.3', '3.2', '3.2', '3.1', '2.3', '2.8', '2.8', '3.3', '2.4', '2.9', '2.7', '2.0', '3.0', '2.2', '2.9', '2.9', '3.1', '3.0', '2.7', '2.2', '2.5', '3.2', '2.8', '2.5', '2.8', '2.9', '3.0', '2.8', '3.0', '2.9', '2.6', '2.4', '2.4', '2.7', '2.7', '3.0', '3.4', '3.1', '2.3', '3.0', '2.5', '2.6', '3.0', '2.6', '2.3', '2.7', '3.0', '2.9', '2.9', '2.5', '2.8', '3.3', '2.7', '3.0', '2.9', '3.0', '3.0', '2.5', '2.9', '2.5', '3.6', '3.2', '2.7', '3.0', '2.5', '2.8', '3.2', '3.0', '3.8', '2.6', '2.2', '3.2', '2.8', '2.8', '2.7', '3.3', '3.2', '2.8', '3.0', '2.8', '3.0', '2.8', '3.8', '2.8', '2.8', '2.6', '3.0', '3.4', '3.1', '3.0', '3.1', '3.1', '3.1', '2.7', '3.2', '3.3', '3.0', '2.5', '3.0', '3.4', '3.0']
+SW = ['3.5', '3.0', '3.2', '3.1', '3.6', '3.9', '3.4', '3.4', '2.9', '3.1',
+'3.7', '3.4', '3.0', '3.0', '4.0', '4.4', '3.9', '3.5', '3.8', '3.8', '3.4',
+'3.7', '3.6', '3.3', '3.4', '3.0', '3.4', '3.5', '3.4', '3.2', '3.1', '3.4',
+'4.1', '4.2', '3.1', '3.2', '3.5', '3.1', '3.0', '3.4', '3.5', '2.3', '3.2',
+'3.5', '3.8', '3.0', '3.8', '3.2', '3.7', '3.3', '3.2', '3.2', '3.1', '2.3',
+'2.8', '2.8', '3.3', '2.4', '2.9', '2.7', '2.0', '3.0', '2.2', '2.9', '2.9',
+'3.1', '3.0', '2.7', '2.2', '2.5', '3.2', '2.8', '2.5', '2.8', '2.9', '3.0',
+'2.8', '3.0', '2.9', '2.6', '2.4', '2.4', '2.7', '2.7', '3.0', '3.4', '3.1',
+'2.3', '3.0', '2.5', '2.6', '3.0', '2.6', '2.3', '2.7', '3.0', '2.9', '2.9',
+'2.5', '2.8', '3.3', '2.7', '3.0', '2.9', '3.0', '3.0', '2.5', '2.9', '2.5',
+'3.6', '3.2', '2.7', '3.0', '2.5', '2.8', '3.2', '3.0', '3.8', '2.6', '2.2',
+'3.2', '2.8', '2.8', '2.7', '3.3', '3.2', '2.8', '3.0', '2.8', '3.0', '2.8',
+'3.8', '2.8', '2.8', '2.6', '3.0', '3.4', '3.1', '3.0', '3.1', '3.1', '3.1',
+'2.7', '3.2', '3.3', '3.0', '2.5', '3.0', '3.4', '3.0']
-PL = ['1.4', '1.4', '1.3', '1.5', '1.4', '1.7', '1.4', '1.5', '1.4', '1.5', '1.5', '1.6', '1.4', '1.1', '1.2', '1.5', '1.3', '1.4', '1.7', '1.5', '1.7', '1.5', '1.0', '1.7', '1.9', '1.6', '1.6', '1.5', '1.4', '1.6', '1.6', '1.5', '1.5', '1.4', '1.5', '1.2', '1.3', '1.5', '1.3', '1.5', '1.3', '1.3', '1.3', '1.6', '1.9', '1.4', '1.6', '1.4', '1.5', '1.4', '4.7', '4.5', '4.9', '4.0', '4.6', '4.5', '4.7', '3.3', '4.6', '3.9', '3.5', '4.2', '4.0', '4.7', '3.6', '4.4', '4.5', '4.1', '4.5', '3.9', '4.8', '4.0', '4.9', '4.7', '4.3', '4.4', '4.8', '5.0', '4.5', '3.5', '3.8', '3.7', '3.9', '5.1', '4.5', '4.5', '4.7', '4.4', '4.1', '4.0', '4.4', '4.6', '4.0', '3.3', '4.2', '4.2', '4.2', '4.3', '3.0', '4.1', '6.0', '5.1', '5.9', '5.6', '5.8', '6.6', '4.5', '6.3', '5.8', '6.1', '5.1', '5.3', '5.5', '5.0', '5.1', '5.3', '5.5', '6.7', '6.9', '5.0', '5.7', '4.9', '6.7', '4.9', '5.7', '6.0', '4.8', '4.9', '5.6', '5.8', '6.1', '6.4', '5.6', '5.1', '5.6', '6.1', '5.6', '5.5', '4.8', '5.4', '5.6', '5.1', '5.1', '5.9', '5.7', '5.2', '5.0', '5.2', '5.4', '5.1']
+PL = ['1.4', '1.4', '1.3', '1.5', '1.4', '1.7', '1.4', '1.5', '1.4', '1.5',
+'1.5', '1.6', '1.4', '1.1', '1.2', '1.5', '1.3', '1.4', '1.7', '1.5', '1.7',
+'1.5', '1.0', '1.7', '1.9', '1.6', '1.6', '1.5', '1.4', '1.6', '1.6', '1.5',
+'1.5', '1.4', '1.5', '1.2', '1.3', '1.5', '1.3', '1.5', '1.3', '1.3', '1.3',
+'1.6', '1.9', '1.4', '1.6', '1.4', '1.5', '1.4', '4.7', '4.5', '4.9', '4.0',
+'4.6', '4.5', '4.7', '3.3', '4.6', '3.9', '3.5', '4.2', '4.0', '4.7', '3.6',
+'4.4', '4.5', '4.1', '4.5', '3.9', '4.8', '4.0', '4.9', '4.7', '4.3', '4.4',
+'4.8', '5.0', '4.5', '3.5', '3.8', '3.7', '3.9', '5.1', '4.5', '4.5', '4.7',
+'4.4', '4.1', '4.0', '4.4', '4.6', '4.0', '3.3', '4.2', '4.2', '4.2', '4.3',
+'3.0', '4.1', '6.0', '5.1', '5.9', '5.6', '5.8', '6.6', '4.5', '6.3', '5.8',
+'6.1', '5.1', '5.3', '5.5', '5.0', '5.1', '5.3', '5.5', '6.7', '6.9', '5.0',
+'5.7', '4.9', '6.7', '4.9', '5.7', '6.0', '4.8', '4.9', '5.6', '5.8', '6.1',
+'6.4', '5.6', '5.1', '5.6', '6.1', '5.6', '5.5', '4.8', '5.4', '5.6', '5.1',
+'5.1', '5.9', '5.7', '5.2', '5.0', '5.2', '5.4', '5.1']
-PW = ['0.2', '0.2', '0.2', '0.2', '0.2', '0.4', '0.3', '0.2', '0.2', '0.1', '0.2', '0.2', '0.1', '0.1', '0.2', '0.4', '0.4', '0.3', '0.3', '0.3', '0.2', '0.4', '0.2', '0.5', '0.2', '0.2', '0.4', '0.2', '0.2', '0.2', '0.2', '0.4', '0.1', '0.2', '0.1', '0.2', '0.2', '0.1', '0.2', '0.2', '0.3', '0.3', '0.2', '0.6', '0.4', '0.3', '0.2', '0.2', '0.2', '0.2', '1.4', '1.5', '1.5', '1.3', '1.5', '1.3', '1.6', '1.0', '1.3', '1.4', '1.0', '1.5', '1.0', '1.4', '1.3', '1.4', '1.5', '1.0', '1.5', '1.1', '1.8', '1.3', '1.5', '1.2', '1.3', '1.4', '1.4', '1.7', '1.5', '1.0', '1.1', '1.0', '1.2', '1.6', '1.5', '1.6', '1.5', '1.3', '1.3', '1.3', '1.2', '1.4', '1.2', '1.0', '1.3', '1.2', '1.3', '1.3', '1.1', '1.3', '2.5', '1.9', '2.1', '1.8', '2.2', '2.1', '1.7', '1.8', '1.8', '2.5', '2.0', '1.9', '2.1', '2.0', '2.4', '2.3', '1.8', '2.2', '2.3', '1.5', '2.3', '2.0', '2.0', '1.8', '2.1', '1.8', '1.8', '1.8', '2.1', '1.6', '1.9', '2.0', '2.2', '1.5', '1.4', '2.3', '2.4', '1.8', '1.8', '2.1', '2.4', '2.3', '1.9', '2.3', '2.5', '2.3', '1.9', '2.0', '2.3', '1.8']
+PW = ['0.2', '0.2', '0.2', '0.2', '0.2', '0.4', '0.3', '0.2', '0.2', '0.1',
+'0.2', '0.2', '0.1', '0.1', '0.2', '0.4', '0.4', '0.3', '0.3', '0.3', '0.2',
+'0.4', '0.2', '0.5', '0.2', '0.2', '0.4', '0.2', '0.2', '0.2', '0.2', '0.4',
+'0.1', '0.2', '0.1', '0.2', '0.2', '0.1', '0.2', '0.2', '0.3', '0.3', '0.2',
+'0.6', '0.4', '0.3', '0.2', '0.2', '0.2', '0.2', '1.4', '1.5', '1.5', '1.3',
+'1.5', '1.3', '1.6', '1.0', '1.3', '1.4', '1.0', '1.5', '1.0', '1.4', '1.3',
+'1.4', '1.5', '1.0', '1.5', '1.1', '1.8', '1.3', '1.5', '1.2', '1.3', '1.4',
+'1.4', '1.7', '1.5', '1.0', '1.1', '1.0', '1.2', '1.6', '1.5', '1.6', '1.5',
+'1.3', '1.3', '1.3', '1.2', '1.4', '1.2', '1.0', '1.3', '1.2', '1.3', '1.3',
+'1.1', '1.3', '2.5', '1.9', '2.1', '1.8', '2.2', '2.1', '1.7', '1.8', '1.8',
+'2.5', '2.0', '1.9', '2.1', '2.0', '2.4', '2.3', '1.8', '2.2', '2.3', '1.5',
+'2.3', '2.0', '2.0', '1.8', '2.1', '1.8', '1.8', '1.8', '2.1', '1.6', '1.9',
+'2.0', '2.2', '1.5', '1.4', '2.3', '2.4', '1.8', '1.8', '2.1', '2.4', '2.3',
+'1.9', '2.3', '2.5', '2.3', '1.9', '2.0', '2.3', '1.8']
-CLI = {'Iris-virginica': [100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149], 'Iris-setosa': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], 'Iris-versicolor': [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]}
+LABELS = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,
+2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
+
+LI2LN = {0: 'Iris-setosa', 1: 'Iris-versicolor', 2: 'Iris-virginica'}
diff --git a/scikits/learn/datasets/iris/src/convert.py b/scikits/learn/datasets/iris/src/convert.py
index 0f1fb7b7b473c5b619972a8880a03090066ff50a..86111fd77ce3b4cd9779ddf666045dc2fc287ebd 100755
--- a/scikits/learn/datasets/iris/src/convert.py
+++ b/scikits/learn/datasets/iris/src/convert.py
@@ -1,10 +1,19 @@
#! /usr/bin/env python
-# Last Change: Sun Jul 01 07:00 PM 2007 J
+# Last Change: Tue Jul 17 03:00 PM 2007 J
# This script generates a python file from the txt data
import time
import csv
+from scikits.learn.datasets.misc import dumpvar
+
+# array for equivalence label index <> label name
+ln2li = {'Iris-setosa' : 0, 'Iris-versicolor': 1, 'Iris-virginica' :2}
+li2ln = {}
+for c,i in ln2li.items():
+ li2ln[i] = c
+
+# Load the data
dataname = 'iris.data'
f = open(dataname, 'r')
a = csv.reader(f)
@@ -19,22 +28,17 @@ pl = [i[2] for i in el]
pw = [i[3] for i in el]
cl = [i[4] for i in el]
-dcl = dict([(i, []) for i in cl])
-for i in range(len(cl)):
- dcl[cl[i]].append(i)
+# dcl[i] = label index of data[i]
+dcl = [ln2li[i] for i in cl]
# Write the data in oldfaitful.py
-a = open("iris.py", "w")
+a = open("../iris.py", "w")
a.write('# Autogenerated by convert.py at %s\n\n' %
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime()))
-def dump_var(var, varname):
- a.write(varname + " = ")
- a.write(str(var))
- a.write("\n\n")
-
-dump_var(sl, 'SL')
-dump_var(sw, 'SW')
-dump_var(pl, 'PL')
-dump_var(pw, 'PW')
-dump_var(dcl, 'CLI')
+a.writelines(dumpvar(sl, 'SL'))
+a.writelines(dumpvar(sw, 'SW'))
+a.writelines(dumpvar(pl, 'PL'))
+a.writelines(dumpvar(pw, 'PW'))
+a.writelines(dumpvar(dcl, 'LABELS'))
+a.writelines(dumpvar(li2ln, 'LI2LN'))