This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description

CudaTree is an implementation of Leo Breiman’s Random Forests adapted to run on the GPU. A random forest is an ensemble of randomized decision trees which vote together to predict new labels. CudaTree parallelizes the construction of each individual tree in the ensemble and thus is able to train faster than the latest version of scikits-learn.

Usage

import numpy as np
from cudatree import load_data, RandomForestClassifier
x_train, y_train = load_data("digits")
forest = RandomForestClassifier(n_estimators = 50, max_features = 6)
forest.fit(x_train, y_train)
forest.predict(x_train)

Dependencies

CudaTree is writen for Python 2.7 and depends on:

  • scikit-learn
  • NumPy
  • PyCUDA
  • Parakeet
Release History

Release History

0.6

This version

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0.4

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0.3

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0.2.4

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0.2.3

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0.2.2

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0.2.1

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0.2

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0.1.1

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0.1.0

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Download Files

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
cudatree-0.6.tar.gz (22.8 kB) Copy SHA256 Checksum SHA256 Source Dec 8, 2013

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