Mercurial > pylearn

annotate _test_dataset.py @ 231:38beb81f4e8b

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Automated merge with ssh://projects@lgcm.iro.umontreal.ca/hg/pylearn
author Frederic Bastien <bastienf@iro.umontreal.ca>
date Tue, 27 May 2008 13:46:03 -0400
parents 58e17421c69c
children 174374d59405
rev   line source
4
f7dcfb5f9d5b Added test for dataset.
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diff changeset
1 from dataset import *
f7dcfb5f9d5b Added test for dataset.
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2 from math import *
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3 import unittest
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4 import sys
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5 import numpy as N
4
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6
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7 def _sum_all(a):
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8 s=a
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9 while isinstance(s,numpy.ndarray):
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10 s=sum(s)
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11 return s
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12
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13 class T_arraydataset(unittest.TestCase):
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14 def setUp(self):
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15 numpy.random.seed(123456)
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16
17
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17
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18 def test_ctor_len(self):
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19 n = numpy.random.rand(8,3)
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20 a=ArrayDataSet(n)
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21 self.failUnless(a.data is n)
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22 self.failUnless(a.fields is None)
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23
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24 self.failUnless(len(a) == n.shape[0])
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25 self.failUnless(a[0].shape == (n.shape[1],))
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26
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27 def test_iter(self):
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28 arr = numpy.random.rand(8,3)
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29 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,3)})
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30 for i, example in enumerate(a):
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31 self.failUnless(numpy.all( example['x'] == arr[i,:2]))
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32 self.failUnless(numpy.all( example['y'] == arr[i,1:3]))
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33
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34 def test_zip(self):
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35 arr = numpy.random.rand(8,3)
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36 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,3)})
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37 for i, x in enumerate(a.zip("x")):
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38 self.failUnless(numpy.all( x == arr[i,:2]))
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39
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40 def test_minibatch_basic(self):
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41 arr = numpy.random.rand(10,4)
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42 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)})
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43 for i, mb in enumerate(a.minibatches(minibatch_size=2)): #all fields
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44 self.failUnless(numpy.all( mb['x'] == arr[i*2:i*2+2,0:2]))
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45 self.failUnless(numpy.all( mb['y'] == arr[i*2:i*2+2,1:4]))
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46
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47 def test_getattr(self):
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48 arr = numpy.random.rand(10,4)
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49 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)})
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50 a_y = a.y
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51 self.failUnless(numpy.all( a_y == arr[:,1:4]))
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52
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53 def test_minibatch_wraparound_even(self):
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54 arr = numpy.random.rand(10,4)
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55 arr2 = ArrayDataSet.Iterator.matcat(arr,arr)
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56
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57 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)})
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58
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59 #print arr
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60 for i, x in enumerate(a.minibatches(["x"], minibatch_size=2, n_batches=8)):
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61 #print 'x' , x
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62 self.failUnless(numpy.all( x == arr2[i*2:i*2+2,0:2]))
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63
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64 def test_minibatch_wraparound_odd(self):
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65 arr = numpy.random.rand(10,4)
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66 arr2 = ArrayDataSet.Iterator.matcat(arr,arr)
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67
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68 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)})
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69
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70 for i, x in enumerate(a.minibatches(["x"], minibatch_size=3, n_batches=6)):
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71 self.failUnless(numpy.all( x == arr2[i*3:i*3+3,0:2]))
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72
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73
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74 class T_renamingdataset(unittest.TestCase):
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75 def setUp(self):
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76 numpy.random.seed(123456)
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77
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78
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79 def test_hasfield(self):
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80 n = numpy.random.rand(3,8)
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81 a=ArrayDataSet(data=n,fields={"x":slice(2),"y":slice(1,4),"z":slice(4,6)})
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82 b=a.rename({'xx':'x','zz':'z'})
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83 self.failUnless(b.hasFields('xx','zz') and not b.hasFields('x') and not b.hasFields('y'))
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84
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85 class T_applyfunctiondataset(unittest.TestCase):
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86 def setUp(self):
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87 numpy.random.seed(123456)
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88
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89 def test_function(self):
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90 n = numpy.random.rand(3,8)
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91 a=ArrayDataSet(data=n,fields={"x":slice(2),"y":slice(1,4),"z":slice(4,6)})
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92 b=a.apply_function(lambda x,y: x+y,x+1, ['x','y'], ['x+y','x+1'], False,False,False)
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93 print b.fieldNames()
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94 print b('x+y')
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95
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98
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99 # to be used with a any new dataset
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100 class T_dataset_tester(object):
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101 """
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102 This class' goal is to test any new dataset that is created
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103 Tests are (will be!) designed to check the normal behaviours
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104 of a dataset, as defined in dataset.py
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105 """
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106
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107
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108 def __init__(self,ds,runall=True) :
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109 """if interested in only a subset of test, init with runall=False"""
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110 self.ds = ds
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111
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112 if runall :
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113 self.test1_basicstats(ds)
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114 self.test2_slicing(ds)
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115 self.test3_fields_iterator_consistency(ds)
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116
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117 def test1_basicstats(self,ds) :
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118 """print basics stats on a dataset, like length"""
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119
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120 print 'len(ds) = ',len(ds)
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121 print 'num fields = ', len(ds.fieldNames())
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122 print 'types of field: ',
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123 for k in ds.fieldNames() :
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124 print type(ds[0](k)[0]),
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125 print ''
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126
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127 def test2_slicing(self,ds) :
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128 """test if slicing works properly"""
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129 print 'testing slicing...',
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130 sys.stdout.flush()
220
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131
219
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132 middle = len(ds) / 2
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133 tenpercent = int(len(ds) * .1)
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134 set1 = ds[:middle+tenpercent]
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135 set2 = ds[middle-tenpercent:]
220
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136 for k in range(tenpercent + tenpercent -1):
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137 for k2 in ds.fieldNames() :
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138 if type(set1[middle-tenpercent+k](k2)[0]) == N.ndarray :
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139 for k3 in range(len(set1[middle-tenpercent+k](k2)[0])) :
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140 assert set1[middle-tenpercent+k](k2)[0][k3] == set2[k](k2)[0][k3]
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141 else :
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142 assert set1[middle-tenpercent+k](k2)[0] == set2[k](k2)[0]
221
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143 assert tenpercent > 1
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144 set3 = ds[middle-tenpercent:middle+tenpercent:2]
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145 for k2 in ds.fieldNames() :
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146 if type(set2[2](k2)[0]) == N.ndarray :
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147 for k3 in range(len(set2[2](k2)[0])) :
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148 assert set2[2](k2)[0][k3] == set3[1](k2)[0][k3]
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149 else :
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150 assert set2[2](k2)[0] == set3[1](k2)[0]
220
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151
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152 print 'done'
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153
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154
221
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155 def test3_fields_iterator_consistency(self,ds) :
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156 """ check if the number of iterator corresponds to the number of fields"""
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157 print 'testing fields/iterator consistency...',
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158 sys.stdout.flush()
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159
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160 # basic test
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161 maxsize = min(len(ds)-1,100)
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162 for iter in ds[:maxsize] :
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163 assert len(iter) == len(ds.fieldNames())
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164 if len(ds.fieldNames()) == 1 :
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165 print 'done'
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166 return
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167
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168 # with minibatches iterator
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169 ds2 = ds.minibatches[:maxsize]([ds.fieldNames()[0],ds.fieldNames()[1]],minibatch_size=2)
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170 for iter in ds2 :
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171 assert len(iter) == 2
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172
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173 print 'done'
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174
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175
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176
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177
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178
219
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179 ###################################################################
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180 # main
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181 if __name__ == '__main__':
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182 unittest.main()
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183