From 2aa8b73636ad82e1ede0f91da03793c4f61f9f59 Mon Sep 17 00:00:00 2001 From: Calvin Date: Thu, 4 Apr 2013 17:10:14 -0400 Subject: convert beta-gamma to epydoc style docs --- feast.py | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) (limited to 'feast.py') diff --git a/feast.py b/feast.py index 9bb2b9f..96cf59d 100644 --- a/feast.py +++ b/feast.py @@ -29,28 +29,30 @@ except: def BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0): ''' - BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0) - This algorithm implements conditional mutual information feature select, such that beta and gamma control the weight attached to the redundant mutual and conditional mutual information, respectively. - Input - :data - data in a Numpy array such that len(data) = + @param data: data in a Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy list with n_observations as the number of elements. That is len(labels) = len(data) = n_observations. (REQUIRED) - :n_select - number of features to select. (REQUIRED) - :beta - penalty attacted to I(X_j;X_k) - :gamma - positive weight attached to the conditional + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @param beta: penalty attacted to I(X_j;X_k) + @type beta: float between 0 and 1.0 + @param gamma: positive weight attached to the conditional redundancy term I(X_k;X_j|Y) - Output - :selected_features - returns a list containing the features + @type gamma: float between 0 and 1.0 + @return:selected_features - returns a list containing the features in the order they were selected. + @rtype: ndarray ''' data, labels = check_data(data, labels) -- cgit v1.2.3