Mercurial > pylearn
annotate stat_ops.py @ 338:7d4792fc28ae
Automated merge with ssh://projects@lgcm.iro.umontreal.ca/hg/pylearn
author | Frederic Bastien <bastienf@iro.umontreal.ca> |
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date | Mon, 16 Jun 2008 17:17:50 -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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69 |
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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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90 |
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91 |
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92 |