Source Code for Module parsimony.datasets.Russett

  1  # -*- coding: utf-8 -*- 
  2  """ 
  3  Created on Mon Apr 22 11:13:02 2013 
  4   
  5  Copyright (c) 2013-2014, CEA/DSV/I2BM/Neurospin. All rights reserved. 
  6   
  7  @author:  Tommy Löfstedt 
  8  @email:   tommy.loefstedt@cea.fr 
  9  @license: BSD 3-clause. 
 10  """ 
 11  import numpy as np 
 12   
 13  __all__ = ['load', 'labels'] 
 14   
 15   
 16 -def load(): 
 17      X_agric = np.asarray([[86.3, 98.2, 3.52], 
 18                            [92.9, 99.6, 3.27], 
 19                            [74.0, 97.4, 2.46], 
 20                            [58.7, 85.8, 4.15], 
 21                            [93.8, 97.7, 3.04], 
 22                            [83.7, 98.5, 2.31], 
 23                            [49.7, 82.9, 2.10], 
 24                            [93.8, 99.7, 2.67], 
 25                            [84.9, 98.1, 2.57], 
 26                            [88.1, 99.1, 1.86], 
 27                            [79.2, 97.8, 4.00], 
 28                            [45.8, 79.3, 1.50], 
 29                            [79.5, 98.5, 3.08], 
 30                            [86.4, 99.3, 2.75], 
 31                            [74.0, 98.1, 2.53], 
 32                            [82.8, 98.8, 2.78], 
 33                            [59.9, 86.3, 1.22], 
 34                            [58.3, 86.1, 3.30], 
 35                            [86.0, 99.7, 2.89], 
 36                            [74.7, 99.4, 2.93], 
 37                            [75.7, 97.4, 2.87], 
 38                            [52.2, 86.9, 3.99], 
 39                            [88.1, 99.3, 4.33], 
 40                            [59.8, 85.9, 1.25], 
 41                            [80.3, 98.0, 3.21], 
 42                            [47.0, 81.5, 1.36], 
 43                            [70.0, 93.0, 2.25], 
 44                            [63.8, 87.7, 2.99], 
 45                            [60.5, 86.2, 3.99], 
 46                            [77.3, 95.5, 3.15], 
 47                            [75.7, 96.4, 2.39], 
 48                            [66.9, 87.5, 2.14], 
 49                            [73.7, 95.0, 2.59], 
 50                            [87.5, 96.9, 2.61], 
 51                            [56.4, 88.2, 3.65], 
 52                            [45.0, 77.7, 0.00], 
 53                            [67.1, 94.6, 3.04], 
 54                            [78.0, 99.5, 3.80], 
 55                            [57.7, 87.2, 2.99], 
 56                            [49.8, 81.5, 2.99], 
 57                            [65.2, 94.1, 3.71], 
 58                            [71.0, 93.4, 3.82], 
 59                            [70.5, 95.4, 3.06], 
 60                            [81.7, 96.6, 3.58], 
 61                            [90.9, 99.3, 3.07], 
 62                            [67.4, 93.0, 1.90], 
 63                            [43.7, 79.8, 0.00]]) 
 64   
 65      X_ind = np.asarray([[5.92, 3.22], 
 66                          [7.10, 2.64], 
 67                          [6.28, 3.47], 
 68                          [6.92, 2.30], 
 69                          [4.19, 4.28], 
 70                          [5.57, 4.11], 
 71                          [7.42, 2.48], 
 72                          [5.19, 3.40], 
 73                          [5.80, 4.01], 
 74                          [5.73, 4.01], 
 75                          [5.89, 3.74], 
 76                          [6.82, 3.14], 
 77                          [5.32, 4.03], 
 78                          [5.32, 3.97], 
 79                          [4.89, 4.16], 
 80                          [5.50, 4.14], 
 81                          [6.85, 3.83], 
 82                          [6.95, 3.26], 
 83                          [5.19, 4.22], 
 84                          [5.48, 3.87], 
 85                          [4.92, 4.19], 
 86                          [4.28, 4.26], 
 87                          [5.27, 4.39], 
 88                          [6.23, 3.69], 
 89                          [6.09, 3.37], 
 90                          [5.48, 3.69], 
 91                          [4.50, 4.32], 
 92                          [7.09, 3.14], 
 93                          [6.56, 2.40], 
 94                          [7.14, 2.77], 
 95                          [5.54, 4.22], 
 96                          [6.88, 3.26], 
 97                          [5.86, 3.99], 
 98                          [4.94, 4.09], 
 99                          [5.30, 4.08], 
100                          [6.15, 4.04], 
101                          [4.89, 4.17], 
102                          [5.54, 3.91], 
103                          [7.06, 2.56], 
104                          [7.11, 2.30], 
105                          [4.88, 3.91], 
106                          [6.91, 1.61], 
107                          [7.76, 2.30], 
108                          [6.34, 3.61], 
109                          [6.64, 3.74], 
110                          [6.64, 2.64], 
111                          [5.69, 4.20]]) 
112   
113      X_polit = np.asarray([[0, 0], 
114                            [1, 0], 
115                            [0, 0], 
116                            [1, 0], 
117                            [0, 1], 
118                            [0, 0], 
119                            [1, 0], 
120                            [0, 0], 
121                            [0, 0], 
122                            [0, 0], 
123                            [0, 1], 
124                            [1, 0], 
125                            [0, 1], 
126                            [0, 1], 
127                            [0, 1], 
128                            [0, 1], 
129                            [0, 0], 
130                            [0, 0], 
131                            [0, 1], 
132                            [0, 0], 
133                            [0, 1], 
134                            [1, 0], 
135                            [0, 1], 
136                            [1, 0], 
137                            [0, 0], 
138                            [0, 0], 
139                            [0, 1], 
140                            [1, 0], 
141                            [1, 0], 
142                            [1, 0], 
143                            [0, 1], 
144                            [1, 0], 
145                            [0, 1], 
146                            [0, 1], 
147                            [0, 1], 
148                            [0, 1], 
149                            [0, 1], 
150                            [0, 1], 
151                            [1, 0], 
152                            [1, 0], 
153                            [0, 1], 
154                            [1, 0], 
155                            [1, 0], 
156                            [1, 0], 
157                            [0, 1], 
158                            [0, 0], 
159                            [0, 1]]) 
160   
161      return X_agric, X_ind, X_polit 
162   
163   
164 -def labels(): 
165      return [("gini", "farm", "rent"), 
166              ("gnpr", "labo"), 
167              ("demostab", "dictatur")] 
168