Mercurial > pylearn

comparison _test_dataset.py @ 220:1f527fe65e22

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
test on simple slicing works
author Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
date Fri, 23 May 2008 13:44:25 -0400
parents 5b3afda2f1ad
children 58e17421c69c
comparison
equal deleted inserted replaced
219:5b3afda2f1ad 220:1f527fe65e22
1 from dataset import * 1 from dataset import *
2 from math import * 2 from math import *
3 import unittest 3 import unittest
4 import sys
5 import numpy as N
4 6
5 def _sum_all(a): 7 def _sum_all(a):
6 s=a 8 s=a
7 while isinstance(s,numpy.ndarray): 9 while isinstance(s,numpy.ndarray):
8 s=sum(s) 10 s=sum(s)
90 b=a.apply_function(lambda x,y: x+y,x+1, ['x','y'], ['x+y','x+1'], False,False,False) 92 b=a.apply_function(lambda x,y: x+y,x+1, ['x','y'], ['x+y','x+1'], False,False,False)
91 print b.fieldNames() 93 print b.fieldNames()
92 print b('x+y') 94 print b('x+y')
93 95
94 96
97
98
95 # to be used with a any new dataset 99 # to be used with a any new dataset
96 class T_dataset_tester(object): 100 class T_dataset_tester(object):
97 """ 101 """
98 This class' goal is to test any new dataset that is created 102 This class' goal is to test any new dataset that is created
103 Tests are (will be!) designed to check the normal behaviours
104 of a dataset, as defined in dataset.py
99 """ 105 """
100 106
101 107
102 def __init__(self,ds,runall=True) : 108 def __init__(self,ds,runall=True) :
103 """if interested in only a subset of test, init with runall=False""" 109 """if interested in only a subset of test, init with runall=False"""
119 125
120 def test2_slicing(self,ds) : 126 def test2_slicing(self,ds) :
121 """test if slicing works properly""" 127 """test if slicing works properly"""
122 print 'testing slicing...', 128 print 'testing slicing...',
123 sys.stdout.flush() 129 sys.stdout.flush()
130
124 middle = len(ds) / 2 131 middle = len(ds) / 2
125 tenpercent = int(len(ds) * .1) 132 tenpercent = int(len(ds) * .1)
126 set1 = ds[:middle+tenpercent] 133 set1 = ds[:middle+tenpercent]
127 set2 = ds[middle-tenpercent:] 134 set2 = ds[middle-tenpercent:]
135 for k in range(tenpercent + tenpercent -1):
136 for k2 in ds.fieldNames() :
137 if type(set1[middle-tenpercent+k](k2)[0]) == N.ndarray :
138 for k3 in range(len(set1[middle-tenpercent+k](k2)[0])) :
139 assert set1[middle-tenpercent+k](k2)[0][k3] == set2[k](k2)[0][k3]
140 else :
141 assert set1[middle-tenpercent+k](k2)[0] == set2[k](k2)[0]
142
143 print 'done'
128 144
129 145
130 ################################################################### 146 ###################################################################
131 # main 147 # main
132 if __name__ == '__main__': 148 if __name__ == '__main__':