diff options
| -rw-r--r-- | feast.py | 22 |
1 files changed, 10 insertions, 12 deletions
@@ -106,7 +106,7 @@ def CIFE(data, labels, n_select): @type n_select: integer @return selected_features: returns a list containing the features in the order they were selected. - @return type: ndarray + @rtype: ndarray ''' return BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0) @@ -116,25 +116,23 @@ def CIFE(data, labels, n_select): def CMIM(data, labels, n_select): ''' - CMIM(data, labels, n_select) - This function implements the conditional mutual information maximization feature selection algorithm. Note that this implementation does not allow for the weighting of the redundancy terms that BetaGamma will allow you to do. - Input - :data - data in a Numpy array such that len(data) = + @param data: 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 array 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) - Output - :selected_features - returns a list containing the features - in the order they were selected. + @type labels: ndarray + @param n_select: number of features to select. + @type n_select: integer + @return selected_features: A list containing the features + in the order that they were selected. + @rtype: ndarray ''' data, labels = check_data(data, labels) |