skip to navigation
skip to content

cudatree 0.6

Random Forests for the GPU using PyCUDA

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
 
File Type Py Version Uploaded on Size
cudatree-0.6.tar.gz (md5) Source 2013-12-08 22KB