diff options
Diffstat (limited to 'lib/wrappers/libsvm.nim')
| -rw-r--r-- | lib/wrappers/libsvm.nim | 61 |
1 files changed, 31 insertions, 30 deletions
diff --git a/lib/wrappers/libsvm.nim b/lib/wrappers/libsvm.nim index 00d5ac73c..8cc314412 100644 --- a/lib/wrappers/libsvm.nim +++ b/lib/wrappers/libsvm.nim @@ -21,24 +21,24 @@ else: const svmdll* = "libsvm.so" type - Tnode*{.pure, final.} = object + Node*{.pure, final.} = object index*: cint value*: cdouble - Tproblem*{.pure, final.} = object + Problem*{.pure, final.} = object L*: cint y*: ptr cdouble - x*: ptr ptr Tnode + x*: ptr ptr Node - Ttype*{.size: sizeof(cint).} = enum + Type*{.size: sizeof(cint).} = enum C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR - TKernelType*{.size: sizeof(cint).} = enum + KernelType*{.size: sizeof(cint).} = enum LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED - Tparameter*{.pure, final.} = object - typ*: TType - kernelType*: TKernelType + Parameter*{.pure, final.} = object + typ*: Type + kernelType*: KernelType degree*: cint # for poly gamma*: cdouble # for poly/rbf/sigmoid coef0*: cdouble # for poly/sigmoid @@ -53,18 +53,19 @@ type 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 - TModel*{.pure, final.} = object - param*: Tparameter # parameter + 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 Tnode # SVs (SV[l]) + 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 @@ -74,42 +75,42 @@ type # 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 - + # 0 if svm_model is created by svm_train +{.deprecated: [TModel: Model].} -proc train*(prob: ptr Tproblem, param: ptr Tparameter): ptr Tmodel{.cdecl, +proc train*(prob: ptr Problem, param: ptr Parameter): ptr Model{.cdecl, importc: "svm_train", dynlib: svmdll.} -proc cross_validation*(prob: ptr Tproblem, param: ptr Tparameter, nr_fold: cint, +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 Tmodel): 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 Tmodel{.cdecl, +proc load_model*(model_file_name: cstring): ptr Model{.cdecl, importc: "svm_load_model", dynlib: svmdll.} -proc get_svm_type*(model: ptr Tmodel): 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 Tmodel): 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 Tmodel, 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 Tmodel): cdouble{.cdecl, +proc get_svr_probability*(model: ptr Model): cdouble{.cdecl, importc: "svm_get_svr_probability", dynlib: svmdll.} -proc predict_values*(model: ptr Tmodel, x: ptr Tnode, dec_values: ptr cdouble): cdouble{. +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 Tmodel, x: ptr Tnode): cdouble{.cdecl, +proc predict*(model: ptr Model, x: ptr Node): cdouble{.cdecl, importc: "svm_predict", dynlib: svmdll.} -proc predict_probability*(model: ptr Tmodel, x: ptr Tnode, +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 Tmodel){.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 Tmodel){.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 Tparameter){.cdecl, importc: "svm_destroy_param", +proc destroy_param*(param: ptr Parameter){.cdecl, importc: "svm_destroy_param", dynlib: svmdll.} -proc check_parameter*(prob: ptr Tproblem, param: ptr Tparameter): cstring{. +proc check_parameter*(prob: ptr Problem, param: ptr Parameter): cstring{. cdecl, importc: "svm_check_parameter", dynlib: svmdll.} -proc check_probability_model*(model: ptr Tmodel): 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.}){. |