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"""Utilities for preprocessing sequence data.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from keras_preprocessing import sequence
from .. import utils
pad_sequences = sequence.pad_sequences
make_sampling_table = sequence.make_sampling_table
skipgrams = sequence.skipgrams
_remove_long_seq = sequence._remove_long_seq # TODO: make it public?
class TimeseriesGenerator(sequence.TimeseriesGenerator, utils.Sequence):
"""Utility class for generating batches of temporal data.
This class takes in a sequence of data-points gathered at
equal intervals, along with time series parameters such as
stride, length of history, etc., to produce batches for
# Arguments
data: Indexable generator (such as list or Numpy array)
containing consecutive data points (timesteps).
The data should be at 2D, and axis 0 is expected
to be the time dimension.
targets: Targets corresponding to timesteps in `data`.
It should have same length as `data`.
length: Length of the output sequences (in number of timesteps).
sampling_rate: Period between successive individual timesteps
within sequences. For rate `r`, timesteps
`data[i]`, `data[i-r]`, ... `data[i - length]`
are used for create a sample sequence.
stride: Period between successive output sequences.
For stride `s`, consecutive output samples would
be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.
start_index: Data points earlier than `start_index` will not be used
in the output sequences. This is useful to reserve part of the
data for test or validation.
end_index: Data points later than `end_index` will not be used
in the output sequences. This is useful to reserve part of the
data for test or validation.
shuffle: Whether to shuffle output samples,
or instead draw them in chronological order.
reverse: Boolean: if `true`, timesteps in each output sample will be
in reverse chronological order.
batch_size: Number of timeseries samples in each batch
(except maybe the last one).
# Returns
A [Sequence](/utils/#sequence) instance.
# Examples
from keras.preprocessing.sequence import TimeseriesGenerator
import numpy as np
data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])
data_gen = TimeseriesGenerator(data, targets,
length=10, sampling_rate=2,
assert len(data_gen) == 20
batch_0 = data_gen[0]
x, y = batch_0
assert np.array_equal(x,
np.array([[[0], [2], [4], [6], [8]],
[[1], [3], [5], [7], [9]]]))
assert np.array_equal(y,
np.array([[10], [11]]))