update

buffaloiron · Mar 15, 2016 · f90b080 · f90b080
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Showing 10 changed files with 11 additions and 11 deletions.
diff --git a/skfeature/function/information_theoretical_based/CIFE.py b/skfeature/function/information_theoretical_based/CIFE.py
@@ -18,13 +18,13 @@ def cife(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape (n_features,)
-        index of selected features, F[1] is the most important feature
+        index of selected features, F[0] is the most important feature
 
     Reference
     ---------
     Brown, Gavin et al. "Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection." JMLR 2012.
     """
-
+    
     if 'n_selected_features' in kwargs.keys():
         n_selected_features = kwargs['n_selected_features']
         F = LCSI.lcsi(X, y, beta=1, gamma=1, n_selected_features=n_selected_features)

diff --git a/skfeature/function/information_theoretical_based/CMIM.py b/skfeature/function/information_theoretical_based/CMIM.py
@@ -19,7 +19,7 @@ def cmim(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape (n_features,)
-        index of selected features, F(1) is the most important feature
+        index of selected features, F[0] is the most important feature
 
     Reference
     ---------

diff --git a/skfeature/function/information_theoretical_based/DISR.py b/skfeature/function/information_theoretical_based/DISR.py
@@ -21,7 +21,7 @@ def disr(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape (n_features, )
-        index of selected features, F[1] is the most important feature
+        index of selected features, F[0] is the most important feature
 
     Reference
     ---------

diff --git a/skfeature/function/information_theoretical_based/FCBF.py b/skfeature/function/information_theoretical_based/FCBF.py
@@ -19,7 +19,7 @@ def fcbf(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape (n_features,)
-        index of selected features, F[1] is the most important feature
+        index of selected features, F[0] is the most important feature
 
     Reference
     ---------

diff --git a/skfeature/function/information_theoretical_based/ICAP.py b/skfeature/function/information_theoretical_based/ICAP.py
@@ -19,7 +19,7 @@ def icap(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape (n_features,)
-        index of selected features, F(1) is the most important feature
+        index of selected features, F[0] is the most important feature
     """
     n_samples, n_features = X.shape
     # index of selected features, initialized to be empty

diff --git a/skfeature/function/information_theoretical_based/JMI.py b/skfeature/function/information_theoretical_based/JMI.py
@@ -18,7 +18,7 @@ def jmi(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape (n_features,)
-        index of selected features, F[1] is the most important feature
+        index of selected features, F[0] is the most important feature
 
     Reference
     ---------

diff --git a/skfeature/function/information_theoretical_based/LCSI.py b/skfeature/function/information_theoretical_based/LCSI.py
@@ -26,7 +26,7 @@ def lcsi(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape: (n_features,)
-        index of selected features, F[1] is the most important feature
+        index of selected features, F[0] is the most important feature
 
     Reference
     ---------

diff --git a/skfeature/function/information_theoretical_based/MIFS.py b/skfeature/function/information_theoretical_based/MIFS.py
@@ -18,7 +18,7 @@ def mifs(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape (n_features,)
-        index of selected features, F[1] is the most important feature
+        index of selected features, F[0] is the most important feature
 
     Reference
     ---------

diff --git a/skfeature/function/information_theoretical_based/MIM.py b/skfeature/function/information_theoretical_based/MIM.py
@@ -18,7 +18,7 @@ def mim(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape (n_features, )
-        index of selected features, F[1] is the most important feature
+        index of selected features, F[0] is the most important feature
 
     Reference
     ---------

diff --git a/skfeature/function/information_theoretical_based/MRMR.py b/skfeature/function/information_theoretical_based/MRMR.py
@@ -18,7 +18,7 @@ def mrmr(X, y, **kwargs):
     Output
     ------
     F: {numpy array}, shape (n_features,)
-        index of selected features, F[1] is the most important feature
+        index of selected features, F[0] is the most important feature
 
     Reference
     ---------