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annotate stat_ops.py @ 431:0f8c81b0776d

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Adding file make_test_datasets to host simple data-generating processes to create artificial datasets meant to test various learning algorithms.
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Tue, 29 Jul 2008 10:19:25 -0400
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1
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2 import theano
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3 from theano import gof
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4 from theano import tensor
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5 import numpy
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6
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7
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8 class ExampleWiseMean(gof.Op):
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9
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10 def __init__(self):
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11 self.destroy_map = {0: [1, 2]}
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12
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13 def make_node(self, x):
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14 return gof.Apply(self,
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15 [x, tensor.value(float('nan')), tensor.value(0)],
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16 [tensor.Tensor(dtype = 'float64',
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17 broadcastable = x.type.broadcastable)()])
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18
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19 def perform(self, node, (x, sum, n), (out,)):
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20 if numpy.isnan(sum).any():
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21 sum.resize(x.shape, refcheck=0)
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22 sum[:] = x
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23 else:
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24 sum += x
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25 n += 1
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26 out[0] = sum / n
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27
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28 def c_code(self, name, node, (x, sum, n), (out, ), sub):
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29 return """
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30 PyObject* multi;
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31 int nelems;
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32 if (isnan(((double*)(%(sum)s->data))[0])) {
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33 PyArray_Dims dims;
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34 dims.len = %(x)s->nd;
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35 dims.ptr = %(x)s->dimensions;
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36 PyArray_Resize(%(sum)s, &dims, 0, PyArray_CORDER);
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37 multi = PyArray_MultiIterNew(2, %(sum)s, %(x)s);
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38 nelems = PyArray_SIZE(%(sum)s);
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39 while (nelems--) {
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40 // Copy %(x)s in %(sum)s
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41 *(double*)PyArray_MultiIter_DATA(multi, 0) = *(double*)PyArray_MultiIter_DATA(multi, 1);
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42 PyArray_MultiIter_NEXT(multi);
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43 }
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44 }
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45 else {
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46 // Add some error checking on the size of x
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47 multi = PyArray_MultiIterNew(2, %(sum)s, %(x)s);
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48 nelems = PyArray_SIZE(%(sum)s);
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49 while (nelems--) {
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50 // Add %(x)s to %(sum)s
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51 *(double*)PyArray_MultiIter_DATA(multi, 0) += *(double*)PyArray_MultiIter_DATA(multi, 1);
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52 PyArray_MultiIter_NEXT(multi);
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53 }
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54 }
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55 ((npy_int64*)(%(n)s->data))[0]++;
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56 int n = ((npy_int64*)(%(n)s->data))[0];
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57 if (%(out)s == NULL) {
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58 %(out)s = (PyArrayObject*)PyArray_EMPTY(%(sum)s->nd, %(sum)s->dimensions, NPY_FLOAT64, 0);
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59 }
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60 multi = PyArray_MultiIterNew(2, %(sum)s, %(out)s);
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61 nelems = PyArray_SIZE(%(sum)s);
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62 while (nelems--) {
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63 // %(out)s <- %(sum)s / %(n)s
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64 *(double*)PyArray_MultiIter_DATA(multi, 1) = *(double*)PyArray_MultiIter_DATA(multi, 0) / n;
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65 PyArray_MultiIter_NEXT(multi);
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66 }
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67 """ % dict(locals(), **sub)
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68
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70
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71 if __name__ == '__main__':
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72
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73 vectors = numpy.random.RandomState(666).rand(10, 2)
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74
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75 x = tensor.dvector()
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76 e = ExampleWiseMean()(x)
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77
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78 # f = theano.function([x], [e], linker = 'py')
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79
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80 # for i, v in enumerate(vectors):
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81 # print v, "->", f(v), numpy.mean(vectors[:i+1], axis=0)
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82
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83 # print
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84
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85 f = theano.function([x], [e], linker = 'c|py')
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86
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87 for i, v in enumerate(vectors):
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88 print v, "->", f(v), numpy.mean(vectors[:i+1], axis=0)
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89
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91
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92