lib: Trim .nim files trailing whitespace

via OSX: find . -name '*.nim' -exec sed -i '' -E 's/[[:space:]]+$//' {} +

1 files changed, 54 insertions, 54 deletions

diff --git a/lib/wrappers/libsvm.nim b/lib/wrappers/libsvm.nim
index 8cc314412..ac5889410 100644
--- a/lib/wrappers/libsvm.nim
+++ b/lib/wrappers/libsvm.nim
@@ -10,9 +10,9 @@
 ## This module is a low level wrapper for `libsvm`:idx:.
 
 {.deadCodeElim: on.}
-const 
+const
   LIBSVM_VERSION* = 312
-  
+
 when defined(windows):
   const svmdll* = "libsvm.dll"
 elif defined(macosx):
@@ -20,97 +20,97 @@ elif defined(macosx):
 else:
   const svmdll* = "libsvm.so"
 
-type 
-  Node*{.pure, final.} = object 
+type
+  Node*{.pure, final.} = object
     index*: cint
     value*: cdouble
 
-  Problem*{.pure, final.} = object 
+  Problem*{.pure, final.} = object
     L*: cint
     y*: ptr cdouble
     x*: ptr ptr Node
 
-  Type*{.size: sizeof(cint).} = enum 
+  Type*{.size: sizeof(cint).} = enum
     C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR
-  
-  KernelType*{.size: sizeof(cint).} = enum 
+
+  KernelType*{.size: sizeof(cint).} = enum
     LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED
-  
-  Parameter*{.pure, final.} = object 
+
+  Parameter*{.pure, final.} = object
     typ*: Type
     kernelType*: KernelType
-    degree*: cint             # for poly 
-    gamma*: cdouble           # for poly/rbf/sigmoid 
-    coef0*: cdouble           # for poly/sigmoid 
-                              # these are for training only 
-    cache_size*: cdouble      # in MB 
-    eps*: cdouble             # stopping criteria 
-    C*: cdouble               # for C_SVC, EPSILON_SVR and NU_SVR 
-    nr_weight*: cint          # for C_SVC 
-    weight_label*: ptr cint   # for C_SVC 
-    weight*: ptr cdouble      # for C_SVC 
-    nu*: cdouble              # for NU_SVC, ONE_CLASS, and NU_SVR 
-    p*: cdouble               # for EPSILON_SVR 
-    shrinking*: cint          # use the shrinking heuristics 
-    probability*: cint        # do probability estimates 
+    degree*: cint             # for poly
+    gamma*: cdouble           # for poly/rbf/sigmoid
+    coef0*: cdouble           # for poly/sigmoid
+                              # these are for training only
+    cache_size*: cdouble      # in MB
+    eps*: cdouble             # stopping criteria
+    C*: cdouble               # for C_SVC, EPSILON_SVR and NU_SVR
+    nr_weight*: cint          # for C_SVC
+    weight_label*: ptr cint   # for C_SVC
+    weight*: ptr cdouble      # for C_SVC
+    nu*: cdouble              # for NU_SVC, ONE_CLASS, and NU_SVR
+    p*: cdouble               # for EPSILON_SVR
+    shrinking*: cint          # use the shrinking heuristics
+    probability*: cint        # do probability estimates
 {.deprecated: [Tnode: Node, Tproblem: Problem, Ttype: Type,
               TKernelType: KernelType, Tparameter: Parameter].}
 
 #
 # svm_model
-# 
+#
 
-type 
-  Model*{.pure, final.} = object 
-    param*: Parameter         # parameter 
-    nr_class*: cint           # number of classes, = 2 in regression/one class svm 
-    L*: cint                  # total #SV 
+type
+  Model*{.pure, final.} = object
+    param*: Parameter         # parameter
+    nr_class*: cint           # number of classes, = 2 in regression/one class svm
+    L*: cint                  # total #SV
     SV*: ptr ptr Node         # SVs (SV[l])
-    sv_coef*: ptr ptr cdouble # coefficients for SVs in decision functions (sv_coef[k-1][l]) 
-    rho*: ptr cdouble         # constants in decision functions (rho[k*(k-1)/2]) 
-    probA*: ptr cdouble       # pariwise probability information 
-    probB*: ptr cdouble       # for classification only 
-    label*: ptr cint          # label of each class (label[k]) 
-    nSV*: ptr cint            # number of SVs for each class (nSV[k]) 
-                              # nSV[0] + nSV[1] + ... + nSV[k-1] = l 
-                              # XXX 
+    sv_coef*: ptr ptr cdouble # coefficients for SVs in decision functions (sv_coef[k-1][l])
+    rho*: ptr cdouble         # constants in decision functions (rho[k*(k-1)/2])
+    probA*: ptr cdouble       # pariwise probability information
+    probB*: ptr cdouble       # for classification only
+    label*: ptr cint          # label of each class (label[k])
+    nSV*: ptr cint            # number of SVs for each class (nSV[k])
+                              # nSV[0] + nSV[1] + ... + nSV[k-1] = l
+                              # XXX
     free_sv*: cint            # 1 if svm_model is created by svm_load_model
                               # 0 if svm_model is created by svm_train
 {.deprecated: [TModel: Model].}
 
-proc train*(prob: ptr Problem, param: ptr Parameter): ptr Model{.cdecl, 
+proc train*(prob: ptr Problem, param: ptr Parameter): ptr Model{.cdecl,
     importc: "svm_train", dynlib: svmdll.}
-proc cross_validation*(prob: ptr Problem, param: ptr Parameter, nr_fold: cint, 
-                       target: ptr cdouble){.cdecl, 
+proc cross_validation*(prob: ptr Problem, param: ptr Parameter, nr_fold: cint,
+                       target: ptr cdouble){.cdecl,
     importc: "svm_cross_validation", dynlib: svmdll.}
-proc save_model*(model_file_name: cstring, model: ptr Model): cint{.cdecl, 
+proc save_model*(model_file_name: cstring, model: ptr Model): cint{.cdecl,
     importc: "svm_save_model", dynlib: svmdll.}
-proc load_model*(model_file_name: cstring): ptr Model{.cdecl, 
+proc load_model*(model_file_name: cstring): ptr Model{.cdecl,
     importc: "svm_load_model", dynlib: svmdll.}
-proc get_svm_type*(model: ptr Model): cint{.cdecl, importc: "svm_get_svm_type", 
+proc get_svm_type*(model: ptr Model): cint{.cdecl, importc: "svm_get_svm_type",
     dynlib: svmdll.}
-proc get_nr_class*(model: ptr Model): cint{.cdecl, importc: "svm_get_nr_class", 
+proc get_nr_class*(model: ptr Model): cint{.cdecl, importc: "svm_get_nr_class",
     dynlib: svmdll.}
-proc get_labels*(model: ptr Model, label: ptr cint){.cdecl, 
+proc get_labels*(model: ptr Model, label: ptr cint){.cdecl,
     importc: "svm_get_labels", dynlib: svmdll.}
-proc get_svr_probability*(model: ptr Model): cdouble{.cdecl, 
+proc get_svr_probability*(model: ptr Model): cdouble{.cdecl,
     importc: "svm_get_svr_probability", dynlib: svmdll.}
 proc predict_values*(model: ptr Model, x: ptr Node, dec_values: ptr cdouble): cdouble{.
     cdecl, importc: "svm_predict_values", dynlib: svmdll.}
-proc predict*(model: ptr Model, x: ptr Node): cdouble{.cdecl, 
+proc predict*(model: ptr Model, x: ptr Node): cdouble{.cdecl,
     importc: "svm_predict", dynlib: svmdll.}
-proc predict_probability*(model: ptr Model, x: ptr Node, 
-                          prob_estimates: ptr cdouble): cdouble{.cdecl, 
+proc predict_probability*(model: ptr Model, x: ptr Node,
+                          prob_estimates: ptr cdouble): cdouble{.cdecl,
     importc: "svm_predict_probability", dynlib: svmdll.}
-proc free_model_content*(model_ptr: ptr Model){.cdecl, 
+proc free_model_content*(model_ptr: ptr Model){.cdecl,
     importc: "svm_free_model_content", dynlib: svmdll.}
-proc free_and_destroy_model*(model_ptr_ptr: ptr ptr Model){.cdecl, 
+proc free_and_destroy_model*(model_ptr_ptr: ptr ptr Model){.cdecl,
     importc: "svm_free_and_destroy_model", dynlib: svmdll.}
-proc destroy_param*(param: ptr Parameter){.cdecl, importc: "svm_destroy_param", 
+proc destroy_param*(param: ptr Parameter){.cdecl, importc: "svm_destroy_param",
     dynlib: svmdll.}
 proc check_parameter*(prob: ptr Problem, param: ptr Parameter): cstring{.
     cdecl, importc: "svm_check_parameter", dynlib: svmdll.}
-proc check_probability_model*(model: ptr Model): cint{.cdecl, 
+proc check_probability_model*(model: ptr Model): cint{.cdecl,
     importc: "svm_check_probability_model", dynlib: svmdll.}
 
 proc set_print_string_function*(print_func: proc (arg: cstring) {.cdecl.}){.