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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from . import MiniBatches
from padding import pad_dataset
import numpy as np
PADDING_SIDE = "right"
class SequentialMiniBatches(MiniBatches):
"""
Mini batch class for sequential data.
def __init__(self, dataset, batch_size=20, padding_length=-1, fix_batch_size=False):
super(SequentialMiniBatches, self).__init__(dataset, batch_size=batch_size)
self.padding_length = padding_length
self._fix_batch_size = fix_batch_size
def _yield_data(self, subset):
for i in xrange(0, len(subset), self.size):
x_set, y_set = [], []
batch = pad_dataset(subset[i:i + self.size], PADDING_SIDE, self.padding_length)
for x, y in batch:
x_set.append(x)
y_set.append(y)
x_set = np.array(x_set)
y_set = np.array(y_set)
if self._fix_batch_size and x_set.shape[0] != self.size:
continue
yield x_set, y_set
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