Mercurial > pylearn
comparison dataset.py @ 188:f01ac276c6fb
added __contains__ to Dataset, added parent constructor call to ArrayDataSet
| author | James Bergstra <bergstrj@iro.umontreal.ca> |
|---|---|
| date | Wed, 14 May 2008 14:49:08 -0400 |
| parents | 895b4b60f5e8 |
| children | cb6b945acf5a |
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| 187:ebbb0e749565 | 188:f01ac276c6fb |
|---|---|
| 205 | 205 |
| 206 The default implementation calls the minibatches iterator and extracts the first example of each field. | 206 The default implementation calls the minibatches iterator and extracts the first example of each field. |
| 207 """ | 207 """ |
| 208 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) | 208 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) |
| 209 | 209 |
| 210 def __contains__(self, fieldname): | |
| 211 return (fieldname in self.fieldNames()) \ | |
| 212 or (fieldname in self.attributeNames()) | |
| 210 | 213 |
| 211 class MinibatchWrapAroundIterator(object): | 214 class MinibatchWrapAroundIterator(object): |
| 212 """ | 215 """ |
| 213 An iterator for minibatches that handles the case where we need to wrap around the | 216 An iterator for minibatches that handles the case where we need to wrap around the |
| 214 dataset because n_batches*minibatch_size > len(dataset). It is constructed from | 217 dataset because n_batches*minibatch_size > len(dataset). It is constructed from |
| 935 whose first axis iterates over examples, second axis determines fields. | 938 whose first axis iterates over examples, second axis determines fields. |
| 936 If the underlying array is N-dimensional (has N axes), then the field | 939 If the underlying array is N-dimensional (has N axes), then the field |
| 937 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). | 940 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). |
| 938 """ | 941 """ |
| 939 | 942 |
| 940 def __init__(self, data_array, fields_columns): | 943 def __init__(self, data_array, fields_columns, **kwargs): |
| 941 """ | 944 """ |
| 942 Construct an ArrayDataSet from the underlying numpy array (data) and | 945 Construct an ArrayDataSet from the underlying numpy array (data) and |
| 943 a map (fields_columns) from fieldnames to field columns. The columns of a field are specified | 946 a map (fields_columns) from fieldnames to field columns. The columns of a field are specified |
| 944 using the standard arguments for indexing/slicing: integer for a column index, | 947 using the standard arguments for indexing/slicing: integer for a column index, |
| 945 slice for an interval of columns (with possible stride), or iterable of column indices. | 948 slice for an interval of columns (with possible stride), or iterable of column indices. |
| 946 """ | 949 """ |
| 950 ArrayFieldsDataSet.__init__(self, **kwargs) | |
| 947 self.data=data_array | 951 self.data=data_array |
| 948 self.fields_columns=fields_columns | 952 self.fields_columns=fields_columns |
| 949 | 953 |
| 950 # check consistency and complete slices definitions | 954 # check consistency and complete slices definitions |
| 951 for fieldname, fieldcolumns in self.fields_columns.items(): | 955 for fieldname, fieldcolumns in self.fields_columns.items(): |