lua/src/regress_linear.h

   1 /***
   2  linear regression
   3  @classmod a.regress_linear
   4 */
   5
   6 #ifndef LUA_LIBA_REGRESS_LINEAR_H
   7 #define LUA_LIBA_REGRESS_LINEAR_H
   8
   9 #include "a.h"
  10
  11 /***
  12  linear regression
  13  @field coef regression coefficient
  14  @field bias intercept
  15  @table a.regress_linear
  16 */
  17 #if defined(__cplusplus)
  18 extern "C" {
  19 #endif /* __cplusplus */
  20
  21 /***
  22  destructor for linear regression
  23  @function die
  24 */
  25 int liba_regress_linear_die(lua_State *L);
  26
  27 /***
  28  constructor for linear regression
  29  @tparam number coef regression coefficient
  30  @tparam number bias intercept
  31  @treturn a.regress_linear linear regression userdata
  32  @function new
  33 */
  34 int liba_regress_linear_new(lua_State *L);
  35
  36 /***
  37  initialize for linear regression
  38  @tparam number coef regression coefficient
  39  @tparam number bias intercept
  40  @treturn a.regress_linear linear regression userdata
  41  @function init
  42 */
  43 int liba_regress_linear_init(lua_State *L);
  44
  45 /***
  46  calculate predicted value for linear regression
  47  @tparam number val independent variable
  48  @treturn number predicted value
  49  @function eval
  50 */
  51 int liba_regress_linear_eval(lua_State *L);
  52
  53 /***
  54  calculate residuals for linear regression
  55  @tparam table x predictor data, specified as a numeric matrix
  56  @tparam table y response data, specified as a numeric vector
  57  @treturn table residuals, specified as a numeric vector
  58  @function err
  59 */
  60 int liba_regress_linear_err(lua_State *L);
  61
  62 /***
  63  gradient descent for linear regression
  64  @tparam table input predictor data, specified as a numeric vector
  65  @tparam number error residual, specified as a numeric scalar
  66  @tparam number alpha learning rate for gradient descent
  67  @treturn a.regress_linear linear regression userdata
  68  @function gd
  69 */
  70 int liba_regress_linear_gd(lua_State *L);
  71
  72 /***
  73  stochastic gradient descent for linear regression
  74  @tparam table x predictor data, specified as a numeric matrix
  75  @tparam table y response data, specified as a numeric vector
  76  @tparam number alpha learning rate for gradient descent
  77  @treturn a.regress_linear linear regression userdata
  78  @function sgd
  79 */
  80 int liba_regress_linear_sgd(lua_State *L);
  81
  82 /***
  83  batch gradient descent for linear regression
  84  @tparam table x predictor data, specified as a numeric matrix
  85  @tparam table y response data, specified as a numeric vector
  86  @tparam number alpha learning rate for gradient descent
  87  @treturn a.regress_linear linear regression userdata
  88  @function bgd
  89 */
  90 int liba_regress_linear_bgd(lua_State *L);
  91
  92 /***
  93  mini-batch gradient descent for linear regression
  94  @tparam table x predictor data, specified as a numeric matrix
  95  @tparam table y response data, specified as a numeric vector
  96  @tparam number delta threshold for gradient descent value
  97  @tparam number lrmax maximum learning rate of iterations
  98  @tparam number lrmin minimum learning rate of iterations
  99  @tparam[opt] integer lrtim total number of learning rate steps
 100  @tparam[opt] integer epoch maximum number of epochs
 101  @tparam[opt] integer batch batch size of data
 102  @treturn number change in loss function
 103  @function mgd
 104 */
 105 int liba_regress_linear_mgd(lua_State *L);
 106
 107 /***
 108  zeroing for linear regression
 109  @treturn a.regress_linear linear regression userdata
 110  @function zero
 111 */
 112 int liba_regress_linear_zero(lua_State *L);
 113
 114 #if defined(__cplusplus)
 115 } /* extern "C" */
 116 #endif /* __cplusplus */
 117
 118 #endif /* regress_linear.h */