diff options
Diffstat (limited to 'lib/wrappers/libsvm.nim')
| -rw-r--r-- | lib/wrappers/libsvm.nim | 127 |
1 files changed, 64 insertions, 63 deletions
diff --git a/lib/wrappers/libsvm.nim b/lib/wrappers/libsvm.nim index 00d5ac73c..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): @@ -20,96 +20,97 @@ elif defined(macosx): else: const svmdll* = "libsvm.so" -type - Tnode*{.pure, final.} = object +type + 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 - 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 - + + 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 +{.deprecated: [Tnode: Node, Tproblem: Problem, Ttype: Type, + TKernelType: KernelType, Tparameter: Parameter].} # # 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 +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 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, - 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 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, - 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 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.}){. |