Remove trailing white-space

diff --git a/README b/README
index 74def8ad8c106b7e55db4c9fda34e689effca11a..4dbca97c752c9158b24568a2d5a9a342da1ce9e8 100644 (file)
--- a/README
+++ b/README
@@ -141,8 +141,8 @@ Option -v randomly splits the data into n parts and calculates cross
 validation accuracy on them.
 
 Option -C conducts cross validation under different parameters and finds
-the best one. This option is supported only by -s 0, -s 2 (for finding 
-C) and -s 11 (for finding C, p). If the solver is not specified, -s 2 
+the best one. This option is supported only by -s 0, -s 2 (for finding
+C) and -s 11 (for finding C, p). If the solver is not specified, -s 2
 is used.
 
 Formulations:
@@ -274,12 +274,12 @@ For parameter selection by -C, users can specify other
 solvers (currently -s 0, -s 2 and -s 11 are supported) and
 different number of CV folds. Further, users can use
 the -c option to specify the smallest C value of the
-search range. This option is useful when users want to 
+search range. This option is useful when users want to
 rerun the parameter selection procedure from a specified
 C under a different setting, such as a stricter stopping
 tolerance -e 0.0001 in the above example. Similarly, for
--s 11, users can use the -p option to specify the 
-maximal p value of the search range. 
+-s 11, users can use the -p option to specify the
+maximal p value of the search range.
 
 > train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file
 
@@ -458,9 +458,9 @@ in linear.h, so you can check the version number.
     conducts cross validation many times under parameters C = start_C,
     2*start_C, 4*start_C, 8*start_C, ..., and finds the best one with
     the highest cross validation accuracy. For -s 11, it conducts cross
-    validation many times with a two-fold loop. The outer loop considers a 
+    validation many times with a two-fold loop. The outer loop considers a
     default sequence of p = 19/20*max_p, ..., 1/20*max_p, 0 and
-    under each p value the inner loop considers a sequence of parameters 
+    under each p value the inner loop considers a sequence of parameters
     C = start_C, 2*start_C, 4*start_C, ..., and finds the best one with the
     lowest mean squared error.
 
@@ -469,12 +469,12 @@ in linear.h, so you can check the version number.
     all folds become stable or C reaches max_C.
 
     If start_p <= 0, then this procedure calculates a maximal p for prob as
-    the start_p. Otherwise, the procedure starts with the first 
-    i/20*max_p <= start_p so the outer sequence is i/20*max_p, 
+    the start_p. Otherwise, the procedure starts with the first
+    i/20*max_p <= start_p so the outer sequence is i/20*max_p,
     (i-1)/20*max_p, ..., 0.
-    
-    The best C, the best p, and the corresponding accuracy (or MSE) are 
-    assigned to *best_C, *best_p and *best_score, respectively. For 
+
+    The best C, the best p, and the corresponding accuracy (or MSE) are
+    assigned to *best_C, *best_p and *best_score, respectively. For
     classification, *best_p is not used, and the returned value is -1.
 
 - Function: double predict(const model *model_, const feature_node *x);

diff --git a/matlab/README b/matlab/README
index 58651f6514f1783cdbdfd0dcdc516ae345597eca..a084a6e960ece64609dec2c32cfa55849f346d05 100644 (file)
--- a/matlab/README
+++ b/matlab/README
@@ -133,11 +133,11 @@ If the '-v' option is specified, cross validation is conducted and the
 returned model is just a scalar: cross-validation accuracy for
 classification and mean-squared error for regression.
 
-If the '-C' option is specified, best parameters are found by cross 
+If the '-C' option is specified, best parameters are found by cross
 validation. The parameter selection utility is supported only by -s 0,
--s 2 (for finding C) and -s 11 (for finding C, p). The returned 
-model is a three dimensional vector with the best C, the best p, and 
-the corresponding cross-validation accuracy or mean squared error. The 
+-s 2 (for finding C) and -s 11 (for finding C, p). The returned
+model is a three dimensional vector with the best C, the best p, and
+the corresponding cross-validation accuracy or mean squared error. The
 returned best p for -s 0 and -s 2 is set to -1 because the p parameter
 is not used by classification models.
 

diff --git a/python/README b/python/README
index 4644e4eee15801f2f8b135fa43abd542a4aec1e7..f4e52f6365bdf2831e60956fc38d5ef15882b297 100644 (file)
--- a/python/README
+++ b/python/README
@@ -335,13 +335,13 @@ The above command loads
            structure. If '-v' is specified, cross validation is
            conducted and the returned model is just a scalar: cross-validation
            accuracy for classification and mean-squared error for regression.
-           
+
            If the '-C' option is specified, best parameters are found
-           by cross validation. The parameter selection utility is supported 
+           by cross validation. The parameter selection utility is supported
            only by -s 0, -s 2 (for finding C) and -s 11 (for finding C, p).
-           The returned structure is a triple with the best C, the best p, 
-           and the corresponding cross-validation accuracy or mean squared 
-           error. The returned best p for -s 0 and -s 2 is set to -1 because 
+           The returned structure is a triple with the best C, the best p,
+           and the corresponding cross-validation accuracy or mean squared
+           error. The returned best p for -s 0 and -s 2 is set to -1 because
            the p parameter is not used by classification models.