Skip to main content

A prioritized sampling tool.

Project description

# Priority Memory

A prioritized sampling tool for priority memory replay.

The implementation is based on sum tree, or segmentation tree.

- Set the priority of each sample at anytime.
- When you do not know the priority of the sample, you can append
them to the buffer, and they will show up in the next sampling batch.
- When the buffer is full, drop the samples with lowest priority.

The time complexity for sampling a batch with batch size m
from a dataset with n samples is O(mlogn), for setting priority
for the batch is O(mlogn).

# Usage

> pip install priority_memory


from priority_memory import FastPriorReplayBuffer

buffer = FastPriorReplayBuffer(8000)
buffer.append(features=[0.1, 0.1, 0.1], prior=1)
buffer.append(features=[0.2, 0.2, 0.2], prior=2)
buffer.append(features=[0.3, 0.3, 0.3], prior=3)
buffer.append(features=[0.4, 0.4, 0.4], prior=4)
indexes, data, weights = buffer.sample_with_weights(batch_size=2)

mae = [10, 20]
buffer.set_weights(indexes, mae)


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for priority-memory, version 0.0.2
Filename, size File type Python version Upload date Hashes
Filename, size priority_memory-0.0.2.tar.gz (7.4 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page