added all FS functions

1 files changed, 189 insertions, 9 deletions

diff --git a/python/feast.py b/python/feast.py
index 9955650..c30c405 100644
--- a/python/feast.py
+++ b/python/feast.py
@@ -1,24 +1,170 @@
 import numpy as np
 from ctypes import * 
 
-def select(data, labels, n_observations, n_features, n_select, method):
+try:
+  libFSToolbox = CDLL("libFSToolbox.so"); 
+except:
+  print "Error: could not find libFSToolbox"
+  exit()
+
+
+
+def BetaGamma(data, labels, n_select, beta=2.0, gamma=2.0):
+
+  # 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:
+    selected_features.append(i - 1)
+
+  return selected_features
+
+def JMI(data, labels, n_select):
+
+  # 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:
+    selected_features.append(i - 1)
+
+  return selected_features
+
+def mRMR_D(data, labels, n_select):
+
+  # 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:
+    selected_features.append(i - 1)
+
+  return selected_features
+
+def CMIM(data, labels, n_select):
+
+  # 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:
+    selected_features.append(i - 1)
+
+  return selected_features
+
+def DISR(data, labels, n_select):
+
+  # 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:
+    selected_features.append(i - 1)
 
-  try:
-    libFSToolbox = CDLL("libFSToolbox.so"); 
-  except:
-    print "Error: could not find libFSToolbox"
-    exit()
+  return selected_features
+
+def ICAP(data, labels, n_select):
 
-# JMI(n_features_to_ret, int n_samples, int n_feats, double *featureMatrix, double *classcol, outputFeatures);
+  # 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)
 
-  # right now just call only JMI, work out the rest later
-  libFSToolbox.JMI(c_n_select,
+  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)),
@@ -26,4 +172,38 @@ def select(data, labels, n_observations, n_features, n_select, method):
                    output.ctypes.data_as(POINTER(c_double))
                    )
 
+  
+  # turn our output into a list
+  selected_features = []
+  for i in features.contents:
+    selected_features.append(i - 1)
+
+  return selected_features
+
+def CondMI(data, labels, n_select):
+
+  # 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:
+    selected_features.append(i - 1)
+
   return selected_features