pysummarization.iteratabledata package¶

Subpackages¶

pysummarization.iteratabledata._mxnet package

Submodules¶

pysummarization.iteratabledata.token_iterator module¶

class pysummarization.iteratabledata.token_iterator.TokenIterator(vectorizable_token, token_arr, epochs=1000, batch_size=25, seq_len=5, test_size=0.3, norm_mode=None, noiseable_data=None)[source]¶

Bases: accelbrainbase.iteratable_data.IteratableData

batch_size¶: getter

epochs¶: getter

generate_inferenced_samples()[source]¶

Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.

Returns:	Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.

generate_learned_samples()[source]¶

Draw and generate data.

Returns:	Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.

get_batch_size()[source]¶: getter

get_epochs()[source]¶: getter

get_norm_mode()[source]¶: getter

get_scale()[source]¶: getter

get_seq_len()[source]¶: getter

get_vectorizable_token()[source]¶: getter

norm_mode¶: getter

pre_normalize(arr)[source]¶

Normalize before observation.

Parameters:	arr – Tensor.
Returns:	Tensor.

scale¶: getter

seq_len¶: getter

set_batch_size(value)[source]¶: setter

set_epochs(value)[source]¶: setter

set_norm_mode(value)[source]¶: setter

set_readonly(value)[source]¶: setter

set_scale(value)[source]¶: setter

set_seq_len(value)[source]¶: setter

set_vectorizable_token(value)[source]¶: setter

vectorizable_token¶: getter

pysummarization.iteratabledata package¶

Subpackages¶

Submodules¶

pysummarization.iteratabledata.token_iterator module¶

Module contents¶