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117 lines
4.7 KiB
Nim
117 lines
4.7 KiB
Nim
#
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#
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# Nimrod's Runtime Library
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# (c) Copyright 2012 Andreas Rumpf
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#
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# See the file "copying.txt", included in this
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# distribution, for details about the copyright.
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#
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## This module is a low level wrapper for `libsvm`:idx:.
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{.deadCodeElim: on.}
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const
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LIBSVM_VERSION* = 312
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when defined(windows):
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const svmdll* = "libsvm.dll"
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elif defined(macosx):
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const svmdll* = "libsvm.dylib"
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else:
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const svmdll* = "libsvm.so"
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type
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Tnode*{.pure, final.} = object
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index*: cint
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value*: cdouble
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Tproblem*{.pure, final.} = object
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L*: cint
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y*: ptr cdouble
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x*: ptr ptr Tnode
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Ttype*{.size: sizeof(cint).} = enum
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C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR
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TKernelType*{.size: sizeof(cint).} = enum
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LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED
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Tparameter*{.pure, final.} = object
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typ*: TType
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kernelType*: TKernelType
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degree*: cint # for poly
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gamma*: cdouble # for poly/rbf/sigmoid
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coef0*: cdouble # for poly/sigmoid
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# these are for training only
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cache_size*: cdouble # in MB
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eps*: cdouble # stopping criteria
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C*: cdouble # for C_SVC, EPSILON_SVR and NU_SVR
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nr_weight*: cint # for C_SVC
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weight_label*: ptr cint # for C_SVC
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weight*: ptr cdouble # for C_SVC
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nu*: cdouble # for NU_SVC, ONE_CLASS, and NU_SVR
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p*: cdouble # for EPSILON_SVR
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shrinking*: cint # use the shrinking heuristics
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probability*: cint # do probability estimates
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#
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# svm_model
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#
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type
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TModel*{.pure, final.} = object
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param*: Tparameter # parameter
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nr_class*: cint # number of classes, = 2 in regression/one class svm
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L*: cint # total #SV
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SV*: ptr ptr Tnode # SVs (SV[l])
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sv_coef*: ptr ptr cdouble # coefficients for SVs in decision functions (sv_coef[k-1][l])
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rho*: ptr cdouble # constants in decision functions (rho[k*(k-1)/2])
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probA*: ptr cdouble # pariwise probability information
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probB*: ptr cdouble # for classification only
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label*: ptr cint # label of each class (label[k])
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nSV*: ptr cint # number of SVs for each class (nSV[k])
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# nSV[0] + nSV[1] + ... + nSV[k-1] = l
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# XXX
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free_sv*: cint # 1 if svm_model is created by svm_load_model
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# 0 if svm_model is created by svm_train
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proc train*(prob: ptr Tproblem, param: ptr Tparameter): ptr Tmodel{.cdecl,
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importc: "svm_train", dynlib: svmdll.}
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proc cross_validation*(prob: ptr Tproblem, param: ptr Tparameter, nr_fold: cint,
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target: ptr cdouble){.cdecl,
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importc: "svm_cross_validation", dynlib: svmdll.}
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proc save_model*(model_file_name: cstring, model: ptr Tmodel): cint{.cdecl,
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importc: "svm_save_model", dynlib: svmdll.}
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proc load_model*(model_file_name: cstring): ptr Tmodel{.cdecl,
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importc: "svm_load_model", dynlib: svmdll.}
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proc get_svm_type*(model: ptr Tmodel): cint{.cdecl, importc: "svm_get_svm_type",
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dynlib: svmdll.}
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proc get_nr_class*(model: ptr Tmodel): cint{.cdecl, importc: "svm_get_nr_class",
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dynlib: svmdll.}
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proc get_labels*(model: ptr Tmodel, label: ptr cint){.cdecl,
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importc: "svm_get_labels", dynlib: svmdll.}
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proc get_svr_probability*(model: ptr Tmodel): cdouble{.cdecl,
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importc: "svm_get_svr_probability", dynlib: svmdll.}
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proc predict_values*(model: ptr Tmodel, x: ptr Tnode, dec_values: ptr cdouble): cdouble{.
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cdecl, importc: "svm_predict_values", dynlib: svmdll.}
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proc predict*(model: ptr Tmodel, x: ptr Tnode): cdouble{.cdecl,
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importc: "svm_predict", dynlib: svmdll.}
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proc predict_probability*(model: ptr Tmodel, x: ptr Tnode,
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prob_estimates: ptr cdouble): cdouble{.cdecl,
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importc: "svm_predict_probability", dynlib: svmdll.}
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proc free_model_content*(model_ptr: ptr Tmodel){.cdecl,
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importc: "svm_free_model_content", dynlib: svmdll.}
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proc free_and_destroy_model*(model_ptr_ptr: ptr ptr Tmodel){.cdecl,
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importc: "svm_free_and_destroy_model", dynlib: svmdll.}
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proc destroy_param*(param: ptr Tparameter){.cdecl, importc: "svm_destroy_param",
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dynlib: svmdll.}
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proc check_parameter*(prob: ptr Tproblem, param: ptr Tparameter): cstring{.
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cdecl, importc: "svm_check_parameter", dynlib: svmdll.}
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proc check_probability_model*(model: ptr Tmodel): cint{.cdecl,
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importc: "svm_check_probability_model", dynlib: svmdll.}
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proc set_print_string_function*(print_func: proc (arg: cstring) {.cdecl.}){.
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cdecl, importc: "svm_set_print_string_function", dynlib: svmdll.}
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