Mercurial > pylearn

annotate _test_dataset.py @ 75:90e4c0784d6e

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Added draft of LinearRegression learner
author bengioy@bengiomac.local
date Sat, 03 May 2008 21:59:26 -0400
parents 46c5c90019c2
children 5b3afda2f1ad
rev   line source
4
f7dcfb5f9d5b Added test for dataset.
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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
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5 def _sum_all(a):
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6 s=a
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7 while isinstance(s,numpy.ndarray):
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8 s=sum(s)
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9 return s
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10
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11 class T_arraydataset(unittest.TestCase):
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12 def setUp(self):
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13 numpy.random.seed(123456)
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14
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15
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16 def test_ctor_len(self):
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17 n = numpy.random.rand(8,3)
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18 a=ArrayDataSet(n)
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19 self.failUnless(a.data is n)
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20 self.failUnless(a.fields is None)
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21
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22 self.failUnless(len(a) == n.shape[0])
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23 self.failUnless(a[0].shape == (n.shape[1],))
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24
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25 def test_iter(self):
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26 arr = numpy.random.rand(8,3)
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27 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,3)})
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28 for i, example in enumerate(a):
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29 self.failUnless(numpy.all( example['x'] == arr[i,:2]))
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30 self.failUnless(numpy.all( example['y'] == arr[i,1:3]))
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31
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32 def test_zip(self):
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33 arr = numpy.random.rand(8,3)
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34 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,3)})
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35 for i, x in enumerate(a.zip("x")):
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36 self.failUnless(numpy.all( x == arr[i,:2]))
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37
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38 def test_minibatch_basic(self):
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39 arr = numpy.random.rand(10,4)
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40 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)})
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41 for i, mb in enumerate(a.minibatches(minibatch_size=2)): #all fields
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42 self.failUnless(numpy.all( mb['x'] == arr[i*2:i*2+2,0:2]))
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43 self.failUnless(numpy.all( mb['y'] == arr[i*2:i*2+2,1:4]))
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44
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45 def test_getattr(self):
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46 arr = numpy.random.rand(10,4)
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47 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)})
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48 a_y = a.y
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49 self.failUnless(numpy.all( a_y == arr[:,1:4]))
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50
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51 def test_minibatch_wraparound_even(self):
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52 arr = numpy.random.rand(10,4)
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53 arr2 = ArrayDataSet.Iterator.matcat(arr,arr)
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54
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55 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)})
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56
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57 #print arr
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58 for i, x in enumerate(a.minibatches(["x"], minibatch_size=2, n_batches=8)):
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59 #print 'x' , x
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60 self.failUnless(numpy.all( x == arr2[i*2:i*2+2,0:2]))
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61
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62 def test_minibatch_wraparound_odd(self):
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63 arr = numpy.random.rand(10,4)
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64 arr2 = ArrayDataSet.Iterator.matcat(arr,arr)
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65
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66 a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)})
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67
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68 for i, x in enumerate(a.minibatches(["x"], minibatch_size=3, n_batches=6)):
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69 self.failUnless(numpy.all( x == arr2[i*3:i*3+3,0:2]))
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71
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72 class T_renamingdataset(unittest.TestCase):
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73 def setUp(self):
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74 numpy.random.seed(123456)
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75
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76
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77 def test_hasfield(self):
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78 n = numpy.random.rand(3,8)
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79 a=ArrayDataSet(data=n,fields={"x":slice(2),"y":slice(1,4),"z":slice(4,6)})
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80 b=a.rename({'xx':'x','zz':'z'})
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81 self.failUnless(b.hasFields('xx','zz') and not b.hasFields('x') and not b.hasFields('y'))
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82
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83 class T_applyfunctiondataset(unittest.TestCase):
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84 def setUp(self):
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85 numpy.random.seed(123456)
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86
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87 def test_function(self):
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88 n = numpy.random.rand(3,8)
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89 a=ArrayDataSet(data=n,fields={"x":slice(2),"y":slice(1,4),"z":slice(4,6)})
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90 b=a.apply_function(lambda x,y: x+y,x+1, ['x','y'], ['x+y','x+1'], False,False,False)
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91 print b.fieldNames()
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92 print b('x+y')
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4
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95 if __name__ == '__main__':
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96 unittest.main()
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97