added libsvm wrapper

lib/wrappers/libsvm.nim

@@ -0,0 +1,116 @@
+#
+#
+#            Nimrod's Runtime Library
+#        (c) Copyright 2012 Andreas Rumpf
+#
+#    See the file "copying.txt", included in this
+#    distribution, for details about the copyright.
+#
+
+## This module is a low level wrapper for `libsvm`:idx:.
+
+{.deadCodeElim: on.}
+const 
+  LIBSVM_VERSION* = 312
+  
+when defined(windows):
+  const svmdll* = "libsvm.dll"
+elif defined(macosx):
+  const svmdll* = "libsvm.dylib"
+else:
+  const svmdll* = "libsvm.so"
+
+type 
+  Tnode*{.pure, final.} = object 
+    index*: cint
+    value*: cdouble
+
+  Tproblem*{.pure, final.} = object 
+    L*: cint
+    y*: ptr cdouble
+    x*: ptr ptr Tnode
+
+  Ttype*{.size: sizeof(cint).} = enum 
+    C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR
+  
+  TKernelType*{.size: sizeof(cint).} = enum 
+    LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED
+  
+  Tparameter*{.pure, final.} = object 
+    typ*: TType
+    kernelType*: TKernelType
+    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 
+  
+
+#
+# svm_model
+# 
+
+type 
+  TModel*{.pure, final.} = object 
+    param*: Tparameter        # parameter 
+    nr_class*: cint           # number of classes, = 2 in regression/one class svm 
+    L*: cint                  # total #SV 
+    SV*: ptr ptr Tnode        # 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 
+    free_sv*: cint            # 1 if svm_model is created by svm_load_model
+                              # 0 if svm_model is created by svm_train 
+  
+
+proc train*(prob: ptr Tproblem, param: ptr Tparameter): ptr Tmodel{.cdecl, 
+    importc: "svm_train", dynlib: svmdll.}
+proc cross_validation*(prob: ptr Tproblem, param: ptr Tparameter, nr_fold: cint, 
+                       target: ptr cdouble){.cdecl, 
+    importc: "svm_cross_validation", dynlib: svmdll.}
+proc save_model*(model_file_name: cstring, model: ptr Tmodel): cint{.cdecl, 
+    importc: "svm_save_model", dynlib: svmdll.}
+proc load_model*(model_file_name: cstring): ptr Tmodel{.cdecl, 
+    importc: "svm_load_model", dynlib: svmdll.}
+proc get_svm_type*(model: ptr Tmodel): cint{.cdecl, importc: "svm_get_svm_type", 
+    dynlib: svmdll.}
+proc get_nr_class*(model: ptr Tmodel): cint{.cdecl, importc: "svm_get_nr_class", 
+    dynlib: svmdll.}
+proc get_labels*(model: ptr Tmodel, label: ptr cint){.cdecl, 
+    importc: "svm_get_labels", dynlib: svmdll.}
+proc get_svr_probability*(model: ptr Tmodel): cdouble{.cdecl, 
+    importc: "svm_get_svr_probability", dynlib: svmdll.}
+proc predict_values*(model: ptr Tmodel, x: ptr Tnode, dec_values: ptr cdouble): cdouble{.
+    cdecl, importc: "svm_predict_values", dynlib: svmdll.}
+proc predict*(model: ptr Tmodel, x: ptr Tnode): cdouble{.cdecl, 
+    importc: "svm_predict", dynlib: svmdll.}
+proc predict_probability*(model: ptr Tmodel, x: ptr Tnode, 
+                          prob_estimates: ptr cdouble): cdouble{.cdecl, 
+    importc: "svm_predict_probability", dynlib: svmdll.}
+proc free_model_content*(model_ptr: ptr Tmodel){.cdecl, 
+    importc: "svm_free_model_content", dynlib: svmdll.}
+proc free_and_destroy_model*(model_ptr_ptr: ptr ptr Tmodel){.cdecl, 
+    importc: "svm_free_and_destroy_model", dynlib: svmdll.}
+proc destroy_param*(param: ptr Tparameter){.cdecl, importc: "svm_destroy_param", 
+    dynlib: svmdll.}
+proc check_parameter*(prob: ptr Tproblem, param: ptr Tparameter): cstring{.
+    cdecl, importc: "svm_check_parameter", dynlib: svmdll.}
+proc check_probability_model*(model: ptr Tmodel): cint{.cdecl, 
+    importc: "svm_check_probability_model", dynlib: svmdll.}
+
+proc set_print_string_function*(print_func: proc (arg: cstring) {.cdecl.}){.
+    cdecl, importc: "svm_set_print_string_function", dynlib: svmdll.}