parameters: (self, pts, **kwargs)
return_statement: return params
docstring: This builds and internally stores an index to be used for
          future nearest neighbor matchings.  It erases any previously
          stored indexes, so use multiple instances of this class to
          work with multiple stored indices.  Use nn_index(...) to find
          the nearest neighbors in this index.
  
          pts is a 2d numpy array or matrix. All the computation is done
          in float32 type, but pts may be any type that is convertable
          to float32.
docstring_summary: This builds and internally stores an index to be used for
          future nearest neighbor matchings.  It erases any previously
          stored indexes, so use multiple instances of this class to
          work with multiple stored indices.  Use nn_index(...) to find
          the nearest neighbors in this index.
docstring_tokens: This builds and internally stores an index to be used for future nearest neighbor matchings . It erases any previously stored indexes so use multiple instances of this class to work with multiple stored indices . Use nn_index ( ... ) to find the nearest neighbors in this index .
function: def build_index(self, pts, **kwargs):
          """
          This builds and internally stores an index to be used for
          future nearest neighbor matchings.  It erases any previously
          stored indexes, so use multiple instances of this class to
          work with multiple stored indices.  Use nn_index(...) to find
          the nearest neighbors in this index.
  
          pts is a 2d numpy array or matrix. All the computation is done
          in float32 type, but pts may be any type that is convertable
          to float32. 
          """
          
          if not pts.dtype.type in allowed_types:
              raise FLANNException("Cannot handle type: %s"%pts.dtype)
  
          pts = ensure_2d_array(pts,default_flags) 
          npts, dim = pts.shape
          
          self.__ensureRandomSeed(kwargs)
          
          self.__flann_parameters.update(kwargs)
  
          if self.__curindex != None:
              flann.free_index[self.__curindex_type](self.__curindex, pointer(self.__flann_parameters))
              self.__curindex = None
                  
          speedup = c_float(0)
          self.__curindex = flann.build_index[pts.dtype.type](pts, npts, dim, byref(speedup), pointer(self.__flann_parameters))
          self.__curindex_data = pts
          self.__curindex_type = pts.dtype.type
          
          params = dict(self.__flann_parameters)
          params["speedup"] = speedup.value
          
          return params
function_tokens: def build_index ( self , pts , * * kwargs ) : if not pts . dtype . type in allowed_types : raise FLANNException ( "Cannot handle type: %s" % pts . dtype ) pts = ensure_2d_array ( pts , default_flags ) npts , dim = pts . shape self . __ensureRandomSeed ( kwargs ) self . __flann_parameters . update ( kwargs ) if self . __curindex != None : flann . free_index [ self . __curindex_type ] ( self . __curindex , pointer ( self . __flann_parameters ) ) self . __curindex = None speedup = c_float ( 0 ) self . __curindex = flann . build_index [ pts . dtype . type ] ( pts , npts , dim , byref ( speedup ) , pointer ( self . __flann_parameters ) ) self . __curindex_data = pts self . __curindex_type = pts . dtype . type params = dict ( self . __flann_parameters ) params [ "speedup" ] = speedup . value return params