class_docstr: XLearn is the core interface used by python API.
class_docstr_tok: ["XLearn","is","the","core","interface","used","by","python","API","."]
docstr: Set hyper-parameter for xlearn handle
  
  Parameters
  ----------
  param : dict
      xlearn hyper-parameters
docstr_tok: ["Set","hyper-parameter","for","xlearn","handle","Parameters","--","--","--","--","--","param",":","dict","xlearn","hyper-parameters"]
code: def _set_Param(self, param):
      """Set hyper-parameter for xlearn handle
  
      Parameters
      ----------
      param : dict
          xlearn hyper-parameters
      """
      for key, value in param.items():
          if key == "task":
              _check_call(
                  _LIB.XLearnSetStr(
                      ctypes.byref(self.handle),
                      c_str(key),
                      c_str(value),
                  )
              )
          elif key == "metric":
              _check_call(
                  _LIB.XLearnSetStr(
                      ctypes.byref(self.handle),
                      c_str(key),
                      c_str(value),
                  )
              )
          elif key == "opt":
              _check_call(
                  _LIB.XLearnSetStr(
                      ctypes.byref(self.handle),
                      c_str(key),
                      c_str(value),
                  )
              )
          elif key == "log":
              _check_call(
                  _LIB.XLearnSetStr(
                      ctypes.byref(self.handle),
                      c_str(key),
                      c_str(value),
                  )
              )
          elif key == "lr":
              _check_call(
                  _LIB.XLearnSetFloat(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_float(value),
                  )
              )
          elif key == "k":
              _check_call(
                  _LIB.XLearnSetInt(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_uint(value),
                  )
              )
          elif key == "lambda":
              _check_call(
                  _LIB.XLearnSetFloat(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_float(value),
                  )
              )
          elif key == "init":
              _check_call(
                  _LIB.XLearnSetFloat(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_float(value),
                  )
              )
          elif key == "epoch":
              _check_call(
                  _LIB.XLearnSetInt(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_uint(value),
                  )
              )
          elif key == "fold":
              _check_call(
                  _LIB.XLearnSetInt(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_uint(value),
                  )
              )
          elif key == "alpha":
              _check_call(
                  _LIB.XLearnSetFloat(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_float(value),
                  )
              )
          elif key == "beta":
              _check_call(
                  _LIB.XLearnSetFloat(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_float(value),
                  )
              )
          elif key == "lambda_1":
              _check_call(
                  _LIB.XLearnSetFloat(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_float(value),
                  )
              )
          elif key == "lambda_2":
              _check_call(
                  _LIB.XLearnSetFloat(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_float(value),
                  )
              )
          elif key == "nthread":
              _check_call(
                  _LIB.XLearnSetInt(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_uint(value),
                  )
              )
          elif key == "block_size":
              _check_call(
                  _LIB.XLearnSetInt(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_uint(value),
                  )
              )
          elif key == "stop_window":
              _check_call(
                  _LIB.XLearnSetInt(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_uint(value),
                  )
              )
          elif key == "seed":
              _check_call(
                  _LIB.XLearnSetInt(
                      ctypes.byref(self.handle),
                      c_str(key),
                      ctypes.c_uint(value),
                  )
              )
          else:
              raise Exception("Invalid key!", key)
  
imports: import sys
  import os
  import ctypes
  from numpy import ndarray
  from .base import Series, DataFrame
  import numpy
  from .base import _LIB, XLearnHandle
  from .base import _check_call, c_str
  from .data import DMatrix
label_desc: Use image node_id 3 for calling the XLearn obj's underlying member method code with example usage: obj._set_Param(param) without return types