mirror of
https://github.com/vale981/ray
synced 2025-03-10 13:26:39 -04:00
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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def flatten(weights, start=0, stop=2):
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"""This methods reshapes all values in a dictionary.
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The indices from start to stop will be flattened into a single index.
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Args:
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weights: A dictionary mapping keys to numpy arrays.
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start: The starting index.
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stop: The ending index.
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"""
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for key, val in weights.items():
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new_shape = val.shape[0:start] + (-1,) + val.shape[stop:]
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weights[key] = val.reshape(new_shape)
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return weights
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def concatenate(weights_list):
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keys = weights_list[0].keys()
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result = {}
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for key in keys:
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result[key] = np.concatenate([l[key] for l in weights_list])
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return result
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def shuffle(trajectory):
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permutation = np.random.permutation(trajectory["dones"].shape[0])
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for key, val in trajectory.items():
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trajectory[key] = val[permutation][permutation]
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return trajectory
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def iterate(trajectory, batchsize):
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trajectory = shuffle(trajectory)
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curr_index = 0
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# TODO(pcm): This drops some data at the end of the batch.
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while curr_index + batchsize < trajectory["dones"].shape[0]:
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batch = dict()
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for key in trajectory:
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batch[key] = trajectory[key][curr_index:curr_index+batchsize]
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curr_index += batchsize
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yield batch
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