remove unnecessary whitespace characters in files except java and m4 …

…files

Makefile.win

@@ -26,7 +26,7 @@ svm.obj: svm.cpp svm.h
 	$(CXX) $(CFLAGS) -c svm.cpp
 
 lib: svm.cpp svm.h svm.def
-	$(CXX) $(CFLAGS) -LD svm.cpp -Fe$(TARGET)\libsvm -link -DEF:svm.def 
+	$(CXX) $(CFLAGS) -LD svm.cpp -Fe$(TARGET)\libsvm -link -DEF:svm.def
 
 clean:
 	-erase /Q *.obj $(TARGET)\*.exe $(TARGET)\*.dll $(TARGET)\*.exp $(TARGET)\*.lib

README

@@ -4,7 +4,7 @@ classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
 regression. It also provides an automatic model selection tool for
 C-SVM classification. This document explains the use of libsvm.
 
-Libsvm is available at 
+Libsvm is available at
 http://www.csie.ntu.edu.tw/~cjlin/libsvm
 Please read the COPYRIGHT file before using libsvm.
 
@@ -18,7 +18,7 @@ Table of Contents
 - `svm-scale' Usage
 - Tips on Practical Use
 - Examples
-- Precomputed Kernels 
+- Precomputed Kernels
 - Library Usage
 - Java Version
 - Building Windows Binaries
@@ -30,8 +30,8 @@ Table of Contents
 Quick Start
 ===========
 
-If you are new to SVM and if the data is not large, please go to 
-`tools' directory and use easy.py after installation. It does 
+If you are new to SVM and if the data is not large, please go to
+`tools' directory and use easy.py after installation. It does
 everything automatic -- from data scaling to parameter selection.
 
 Usage: easy.py training_file [testing_file]
@@ -93,8 +93,8 @@ svm-scale:
 svm-toy:
 
 	This is a simple graphical interface which shows how SVM
-	separate data in a plane. You can click in the window to 
-	draw data points. Use "change" button to choose class 
+	separate data in a plane. You can click in the window to
+	draw data points. Use "change" button to choose class
 	1, 2 or 3 (i.e., up to three classes are supported), "load"
 	button to load data from a file, "save" button to save data to
 	a file, "run" button to obtain an SVM model, and "clear"
@@ -117,7 +117,7 @@ svm-toy:
 
 	You need GTK+ library to build the GTK version.
 	(available from http://www.gtk.org)
-	
+
 	The pre-built Windows binaries are in the `windows'
 	directory. We use Visual C++ on a 64-bit machine.
 
@@ -129,7 +129,7 @@ options:
 -s svm_type : set type of SVM (default 0)
 	0 -- C-SVC		(multi-class classification)
 	1 -- nu-SVC		(multi-class classification)
-	2 -- one-class SVM	
+	2 -- one-class SVM
 	3 -- epsilon-SVR	(regression)
 	4 -- nu-SVR		(regression)
 -t kernel_type : set type of kernel function (default 2)
@@ -206,7 +206,7 @@ Scale each feature of the training data to be in [-1,1]. Scaling
 factors are stored in the file range and then used for scaling the
 test data.
 
-> svm-train -s 0 -c 5 -t 2 -g 0.5 -e 0.1 data_file 
+> svm-train -s 0 -c 5 -t 2 -g 0.5 -e 0.1 data_file
 
 Train a classifier with RBF kernel exp(-0.5|u-v|^2), C=10, and
 stopping tolerance 0.1.
@@ -232,25 +232,25 @@ the parameters C = 100 and gamma = 0.1
 Obtain a model with probability information and predict test data with
 probability estimates
 
-Precomputed Kernels 
+Precomputed Kernels
 ===================
 
 Users may precompute kernel values and input them as training and
 testing files.  Then libsvm does not need the original
 training/testing sets.
 
-Assume there are L training instances x1, ..., xL and. 
+Assume there are L training instances x1, ..., xL and.
 Let K(x, y) be the kernel
 value of two instances x and y. The input formats
 are:
 
 New training instance for xi:
 
-<label> 0:i 1:K(xi,x1) ... L:K(xi,xL) 
+<label> 0:i 1:K(xi,x1) ... L:K(xi,xL)
 
 New testing instance for any x:
 
-<label> 0:? 1:K(x,x1) ... L:K(x,xL) 
+<label> 0:? 1:K(x,x1) ... L:K(x,xL)
 
 That is, in the training file the first column must be the "ID" of
 xi. In testing, ? can be any value.
@@ -277,17 +277,17 @@ Examples:
 	training/testing sets:
 
 	15  0:1 1:4 2:6  3:1
-	45  0:2 1:6 2:18 3:0 
+	45  0:2 1:6 2:18 3:0
 	25  0:3 1:1 2:0  3:1
- 
+
 	15  0:? 1:2 2:0  3:1
 
 	? can be any value.
 
 	Any subset of the above training file is also valid. For example,
 
 	25  0:3 1:1 2:0  3:1
-	45  0:2 1:6 2:18 3:0 
+	45  0:2 1:6 2:18 3:0
 
 	implies that the kernel matrix is
 
@@ -316,18 +316,18 @@ to classify new data.
     the given training data and parameters.
 
     struct svm_problem describes the problem:
-	
+
 	struct svm_problem
 	{
 		int l;
 		double *y;
 		struct svm_node **x;
 	};
- 
+
     where `l' is the number of training data, and `y' is an array containing
     their target values. (integers in classification, real numbers in
     regression) `x' is an array of pointers, each of which points to a sparse
-    representation (array of svm_node) of one training vector. 
+    representation (array of svm_node) of one training vector.
 
     For example, if we have the following training data:
 
@@ -361,7 +361,7 @@ to classify new data.
 
     index = -1 indicates the end of one vector. Note that indices must
     be in ASCENDING order.
- 
+
     struct svm_parameter describes the parameters of an SVM model:
 
 	struct svm_parameter
@@ -402,7 +402,7 @@ to classify new data.
     PRECOMPUTED: kernel values in training_set_file
 
     cache_size is the size of the kernel cache, specified in megabytes.
-    C is the cost of constraints violation. 
+    C is the cost of constraints violation.
     eps is the stopping criterion. (we usually use 0.00001 in nu-SVC,
     0.001 in others). nu is the parameter in nu-SVM, nu-SVR, and
     one-class-SVM. p is the epsilon in epsilon-insensitive loss function
@@ -418,13 +418,13 @@ to classify new data.
     nr_weight is the number of elements in the array weight_label and
     weight. Each weight[i] corresponds to weight_label[i], meaning that
     the penalty of class weight_label[i] is scaled by a factor of weight[i].
-    
+
     If you do not want to change penalty for any of the classes,
     just set nr_weight to 0.
 
     *NOTE* Because svm_model contains pointers to svm_problem, you can
     not free the memory used by svm_problem if you are still using the
-    svm_model produced by svm_train(). 
+    svm_model produced by svm_train().
 
     *NOTE* To avoid wrong parameters, svm_check_parameter() should be
     called before svm_train().
@@ -464,7 +464,7 @@ to classify new data.
     k classes. For data in class j, the corresponding sv_coef includes (k-1) y*alpha vectors,
     where alpha's are solutions of the following two class problems:
     1 vs j, 2 vs j, ..., j-1 vs j, j vs j+1, j vs j+2, ..., j vs k
-    and y=1 for the first j-1 vectors, while y=-1 for the remaining k-j 
+    and y=1 for the first j-1 vectors, while y=-1 for the remaining k-j
     vectors. For example, if there are 4 classes, sv_coef and SV are like:
 
         +-+-+-+--------------------+
@@ -500,8 +500,8 @@ to classify new data.
 
     nSV is the number of support vectors in each class.
 
-    free_sv is a flag used to determine whether the space of SV should 
-    be released in free_model_content(struct svm_model*) and 
+    free_sv is a flag used to determine whether the space of SV should
+    be released in free_model_content(struct svm_model*) and
     free_and_destroy_model(struct svm_model**). If the model is
     generated by svm_train(), then SV points to data in svm_problem
     and should not be removed. For example, free_sv is 0 if svm_model
@@ -527,7 +527,7 @@ to classify new data.
     labels (of all prob's instances) in the validation process are
     stored in the array called target.
 
-    The format of svm_prob is same as that for svm_train(). 
+    The format of svm_prob is same as that for svm_train().
 
 - Function: int svm_get_svm_type(const struct svm_model *model);
 
@@ -540,18 +540,18 @@ to classify new data.
     classes. For a regression or an one-class model, 2 is returned.
 
 - Function: void svm_get_labels(const svm_model *model, int* label)
-    
+
     For a classification model, this function outputs the name of
     labels into an array called label. For regression and one-class
     models, label is unchanged.
 
 - Function: void svm_get_sv_indices(const struct svm_model *model, int *sv_indices)
 
-    This function outputs indices of support vectors into an array called sv_indices. 
-    The size of sv_indices is the number of support vectors and can be obtained by calling svm_get_nr_sv. 
+    This function outputs indices of support vectors into an array called sv_indices.
+    The size of sv_indices is the number of support vectors and can be obtained by calling svm_get_nr_sv.
     Each sv_indices[i] is in the range of [1, ..., num_traning_data].
 
-- Function: int svm_get_nr_sv(const struct svm_model *model) 
+- Function: int svm_get_nr_sv(const struct svm_model *model)
 
     This function gives the number of total support vector.
 
@@ -565,7 +565,7 @@ to classify new data.
     If the model is not for svr or does not contain required
     information, 0 is returned.
 
-- Function: double svm_predict_values(const svm_model *model, 
+- Function: double svm_predict_values(const svm_model *model,
 				    const svm_node *x, double* dec_values)
 
     This function gives decision values on a test vector x given a
@@ -579,17 +579,17 @@ to classify new data.
     label[0] vs. label[nr_class-1], label[1] vs. label[2], ...,
     label[nr_class-2] vs. label[nr_class-1], where label can be
     obtained from the function svm_get_labels. The returned value is
-    the predicted class for x. Note that when nr_class = 1, this 
+    the predicted class for x. Note that when nr_class = 1, this
     function does not give any decision value.
 
     For a regression model, dec_values[0] and the returned value are
     both the function value of x calculated using the model. For a
     one-class model, dec_values[0] is the decision value of x, while
     the returned value is +1/-1.
 
-- Function: double svm_predict_probability(const struct svm_model *model, 
+- Function: double svm_predict_probability(const struct svm_model *model,
 	    const struct svm_node *x, double* prob_estimates);
-    
+
     This function does classification or regression on a test vector x
     given a model with probability information.
 
@@ -645,7 +645,7 @@ to classify new data.
 - Function: void svm_set_print_string_function(void (*print_func)(const char *));
 
     Users can specify their output format by a function. Use
-        svm_set_print_string_function(NULL); 
+        svm_set_print_string_function(NULL);
     for default printing to stdout.
 
 Java Version
@@ -667,7 +667,7 @@ You may need to increase maximum Java heap size.
 Library usages are similar to the C version. These functions are available:
 
 public class svm {
-	public static final int LIBSVM_VERSION=322; 
+	public static final int LIBSVM_VERSION=322;
 	public static svm_model svm_train(svm_problem prob, svm_parameter param);
 	public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target);
 	public static int svm_get_svm_type(svm_model model);
@@ -692,7 +692,7 @@ Note that in Java version, svm_node[] is not ended with a node whose index = -1.
 Users can specify their output format by
 
 	your_print_func = new svm_print_interface()
-	{ 
+	{
 		public void print(String s)
 		{
 			// your own format
@@ -726,7 +726,7 @@ nmake -f Makefile.win lib
 4. (optional) To build 32-bit windows binaries, you must
 	(1) Setup "C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\vcvars32.bat" instead of vcvars64.bat
 	(2) Change CFLAGS in Makefile.win: /D _WIN64 to /D _WIN32
-	
+
 Another way is to build them from Visual C++ environment. See details
 in libsvm FAQ.
 
@@ -761,7 +761,7 @@ http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf
 For any questions and comments, please email [email protected]
 
 Acknowledgments:
-This work was supported in part by the National Science 
+This work was supported in part by the National Science
 Council of Taiwan via the grant NSC 89-2213-E-002-013.
 The authors thank their group members and users
 for many helpful discussions and comments. They are listed in

matlab/README

@@ -24,9 +24,9 @@ the usage and the way of specifying parameters are the same as that of LIBSVM.
 Installation
 ============
 
-On Windows systems, pre-built binary files are already in the 
-directory '..\windows', so no need to conduct installation. Now we 
-provide binary files only for 64bit MATLAB on Windows. If you would 
+On Windows systems, pre-built binary files are already in the
+directory '..\windows', so no need to conduct installation. Now we
+provide binary files only for 64bit MATLAB on Windows. If you would
 like to re-build the package, please rely on the following steps.
 
 We recommend using make.m on both MATLAB and OCTAVE. Just type 'make'
@@ -60,8 +60,8 @@ Example:
 	matlab>> make
 
 On Unix systems, if neither make.m nor 'mex -setup' works, please use
-Makefile and type 'make' in a command window. Note that we assume 
-your MATLAB is installed in '/usr/local/matlab'. If not, please change 
+Makefile and type 'make' in a command window. Note that we assume
+your MATLAB is installed in '/usr/local/matlab'. If not, please change
 MATLABDIR in Makefile.
 
 Example:
@@ -142,7 +142,7 @@ accuracy, is a vector including accuracy (for classification), mean
 squared error, and squared correlation coefficient (for regression).
 The third is a matrix containing decision values or probability
 estimates (if '-b 1' is specified). If k is the number of classes
-in training data, for decision values, each row includes results of 
+in training data, for decision values, each row includes results of
 predicting k(k-1)/2 binary-class SVMs. For classification, k = 1 is a
 special case. Decision value +1 is returned for each testing instance,
 instead of an empty vector. For probabilities, each row contains k values
@@ -153,20 +153,20 @@ in the model structure.
 Other Utilities
 ===============
 
-A matlab function libsvmread reads files in LIBSVM format: 
+A matlab function libsvmread reads files in LIBSVM format:
 
-[label_vector, instance_matrix] = libsvmread('data.txt'); 
+[label_vector, instance_matrix] = libsvmread('data.txt');
 
 Two outputs are labels and instances, which can then be used as inputs
-of svmtrain or svmpredict. 
+of svmtrain or svmpredict.
 
 A matlab function libsvmwrite writes Matlab matrix to a file in LIBSVM format:
 
 libsvmwrite('data.txt', label_vector, instance_matrix)
 
 The instance_matrix must be a sparse matrix. (type must be double)
-For 32bit and 64bit MATLAB on Windows, pre-built binary files are ready 
-in the directory `..\windows', but in future releases, we will only 
+For 32bit and 64bit MATLAB on Windows, pre-built binary files are ready
+in the directory `..\windows', but in future releases, we will only
 include 64bit MATLAB binary files.
 
 These codes are prepared by Rong-En Fan and Kai-Wei Chang from National

matlab/libsvmread.c

@@ -9,8 +9,8 @@
 #ifdef MX_API_VER
 #if MX_API_VER < 0x07030000
 typedef int mwIndex;
-#endif 
-#endif 
+#endif
+#endif
 #ifndef max
 #define max(x,y) (((x)>(y))?(x):(y))
 #endif
@@ -38,7 +38,7 @@ static int max_line_len;
 static char* readline(FILE *input)
 {
 	int len;
-	
+
 	if(fgets(line,max_line_len,input) == NULL)
 		return NULL;