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diff --git a/b/lib/feast.py b/b/lib/feast.py
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+import numpy as np
+from ctypes import * 
+
+
+'''
+  The FEAST module provides an interface between the C-library
+  for feature selection to Python. 
+
+  References: 
+  1) G. Brown, A. Pocock, M.-J. Zhao, and M. Lujan, "Conditional
+      likelihood maximization: A unifying framework for information
+      theoretic feature selection," Journal of Machine Learning 
+      Research, vol. 13, pp. 27-66, 2012.
+
+
+  __author__ = "Calvin Morrison"
+  __copyright__ = "Copyright 2013, EESI Laboratory"
+  __credits__ = ["Calvin Morrison", "Gregory Ditzler"]
+  __license__ = "GPL"
+  __version__ = "0.1.0"
+  __maintainer__ = "Calvin Morrison"
+  __email__ = "mutantturkey@gmail.com"
+  __status__ = "Release"
+'''
+
+# I listed the function definitions in alphabetical order. Lets
+# keep this up. 
+
+
+try:
+  libFSToolbox = CDLL("libFSToolbox.so"); 
+except:
+  print "Error: could not find libFSToolbox"
+  exit()
+
+
+def BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0):
+  '''
+    BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0)
+
+    This algotihm 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) = 
+        n_observations, and len(data.transpose()) = n_features
+        (REQUIRED)
+      :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
+        redundancy term I(X_k;X_j|Y)
+    Output 
+      :selected_features - returns a list containing the features
+        in the order they were selected. 
+  '''
+  # python values
+  n_observations, n_features = data.shape
+  output = np.zeros(n_select)
+
+  # cast as C types
+  c_n_observations = c_int(n_observations)
+  c_n_select = c_int(n_select)
+  c_n_features = c_int(n_features)
+  c_beta = c_double(beta)
+  c_gamma = c_double(gamma)
+
+  libFSToolbox.BetaGamma.restype = POINTER(c_double * n_select)
+  features = libFSToolbox.BetaGamma(c_n_select,
+                   c_n_observations,
+                   c_n_features, 
+                   data.ctypes.data_as(POINTER(c_double)),
+                   labels.ctypes.data_as(POINTER(c_double)),
+                   output.ctypes.data_as(POINTER(c_double)),
+                   c_beta,
+                   c_gamma
+                   )
+
+  # turn our output into a list
+  selected_features = []
+  for i in features.contents:
+    # recall that feast was implemented with Matlab in mind, so the 
+    # authors assumed the indexing started a one; however, in Python 
+    # the indexing starts at zero. 
+    selected_features.append(i - 1)
+
+  return selected_features
+
+
+
+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) = 
+        n_observations, and len(data.transpose()) = n_features
+        (REQUIRED)
+      :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)
+    Output 
+      :selected_features - returns a list containing the features
+        in the order they were selected. 
+  '''
+
+  # python values
+  n_observations, n_features = data.shape
+  output = np.zeros(n_select)
+
+  # cast as C types
+  c_n_observations = c_int(n_observations)
+  c_n_select = c_int(n_select)
+  c_n_features = c_int(n_features)
+
+  libFSToolbox.CMIM.restype = POINTER(c_double * n_select)
+  features = libFSToolbox.CMIM(c_n_select,
+                   c_n_observations,
+                   c_n_features, 
+                   data.ctypes.data_as(POINTER(c_double)),
+                   labels.ctypes.data_as(POINTER(c_double)),
+                   output.ctypes.data_as(POINTER(c_double))
+                   )
+
+  
+  # turn our output into a list
+  selected_features = []
+  for i in features.contents:
+    # recall that feast was implemented with Matlab in mind, so the 
+    # authors assumed the indexing started a one; however, in Python 
+    # the indexing starts at zero. 
+    selected_features.append(i - 1)
+
+  return selected_features
+
+
+
+def CondMI(data, labels, n_select):
+  '''
+    CondMI(data, labels, n_select)
+
+    This function implements the conditional mutual information
+    maximization feature selection algorithm. 
+
+    Input 
+      :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 
+        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. 
+  '''
+  # python values
+  n_observations, n_features = data.shape
+  output = np.zeros(n_select)
+
+  # cast as C types
+  c_n_observations = c_int(n_observations)
+  c_n_select = c_int(n_select)
+  c_n_features = c_int(n_features)
+
+  libFSToolbox.CondMI.restype = POINTER(c_double * n_select)
+  features = libFSToolbox.CondMI(c_n_select,
+                   c_n_observations,
+                   c_n_features, 
+                   data.ctypes.data_as(POINTER(c_double)),
+                   labels.ctypes.data_as(POINTER(c_double)),
+                   output.ctypes.data_as(POINTER(c_double))
+                   )
+
+  
+  # turn our output into a list
+  selected_features = []
+  for i in features.contents:
+    # recall that feast was implemented with Matlab in mind, so the 
+    # authors assumed the indexing started a one; however, in Python 
+    # the indexing starts at zero. 
+    selected_features.append(i - 1)
+
+  return selected_features
+
+
+
+
+
+
+def DISR(data, labels, n_select):
+  '''
+    DISR(data, labels, n_select)
+
+    This function implements the double input symmetrical relevance
+    feature selection algorithm. 
+
+    Input 
+      :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 
+        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. 
+  '''
+  # python values
+  n_observations, n_features = data.shape
+  output = np.zeros(n_select)
+
+  # cast as C types
+  c_n_observations = c_int(n_observations)
+  c_n_select = c_int(n_select)
+  c_n_features = c_int(n_features)
+
+  libFSToolbox.DISR.restype = POINTER(c_double * n_select)
+  features = libFSToolbox.DISR(c_n_select,
+                   c_n_observations,
+                   c_n_features, 
+                   data.ctypes.data_as(POINTER(c_double)),
+                   labels.ctypes.data_as(POINTER(c_double)),
+                   output.ctypes.data_as(POINTER(c_double))
+                   )
+
+  
+  # turn our output into a list
+  selected_features = []
+  for i in features.contents:
+    # recall that feast was implemented with Matlab in mind, so the 
+    # authors assumed the indexing started a one; however, in Python 
+    # the indexing starts at zero. 
+    selected_features.append(i - 1)
+
+  return selected_features
+
+
+
+
+def ICAP(data, labels, n_select):
+  '''
+    ICAP(data, labels, n_select)
+
+    This function implements the interaction capping feature 
+    selection algorithm. 
+
+    Input 
+      :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 
+        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. 
+  '''
+  # python values
+  n_observations, n_features = data.shape
+  output = np.zeros(n_select)
+
+  # cast as C types
+  c_n_observations = c_int(n_observations)
+  c_n_select = c_int(n_select)
+  c_n_features = c_int(n_features)
+
+  libFSToolbox.ICAP.restype = POINTER(c_double * n_select)
+  features = libFSToolbox.ICAP(c_n_select,
+                   c_n_observations,
+                   c_n_features, 
+                   data.ctypes.data_as(POINTER(c_double)),
+                   labels.ctypes.data_as(POINTER(c_double)),
+                   output.ctypes.data_as(POINTER(c_double))
+                   )
+
+  
+  # turn our output into a list
+  selected_features = []
+  for i in features.contents:
+    # recall that feast was implemented with Matlab in mind, so the 
+    # authors assumed the indexing started a one; however, in Python 
+    # the indexing starts at zero. 
+    selected_features.append(i - 1)
+
+  return selected_features
+
+
+
+
+
+def JMI(data, labels, n_select):
+  '''
+    JMI(data, labels, n_select)
+
+    This function implements the joint mutual information feature
+    selection algorithm. 
+
+    Input 
+      :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 
+        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. 
+  '''
+
+  # python values
+  n_observations, n_features = data.shape
+  output = np.zeros(n_select)
+
+  # cast as C types
+  c_n_observations = c_int(n_observations)
+  c_n_select = c_int(n_select)
+  c_n_features = c_int(n_features)
+
+  libFSToolbox.JMI.restype = POINTER(c_double * n_select)
+  features = libFSToolbox.JMI(c_n_select,
+                   c_n_observations,
+                   c_n_features, 
+                   data.ctypes.data_as(POINTER(c_double)),
+                   labels.ctypes.data_as(POINTER(c_double)),
+                   output.ctypes.data_as(POINTER(c_double))
+                   )
+
+  
+  # turn our output into a list
+  selected_features = []
+  for i in features.contents:
+    # recall that feast was implemented with Matlab in mind, so the 
+    # authors assumed the indexing started a one; however, in Python 
+    # the indexing starts at zero. 
+    selected_features.append(i - 1)
+
+  return selected_features
+
+def mRMR(data, labels, n_select):
+  '''
+    mRMR(data, labels, n_select)
+
+    This funciton implements the max-relevance min-redundancy feature
+    selection algorithm. 
+
+    Input 
+      :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 
+        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. 
+  '''
+
+  # python values
+  n_observations, n_features = data.shape
+  output = np.zeros(n_select)
+
+  # cast as C types
+  c_n_observations = c_int(n_observations)
+  c_n_select = c_int(n_select)
+  c_n_features = c_int(n_features)
+
+  libFSToolbox.mRMR_D.restype = POINTER(c_double * n_select)
+  features = libFSToolbox.mRMR_D(c_n_select,
+                   c_n_observations,
+                   c_n_features, 
+                   data.ctypes.data_as(POINTER(c_double)),
+                   labels.ctypes.data_as(POINTER(c_double)),
+                   output.ctypes.data_as(POINTER(c_double))
+                   )
+
+  
+  # turn our output into a list
+  selected_features = []
+  for i in features.contents:
+    # recall that feast was implemented with Matlab in mind, so the 
+    # authors assumed the indexing started a one; however, in Python 
+    # the indexing starts at zero. 
+    selected_features.append(i - 1)
+
+  return selected_features
+
+
+
+
+