ea_conditioning - Compute the entropy of a conditioning function

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ea_conditioning 1.1.5                              April 2023                                 EA_CONDITIONING(1)

       Compute the entropy of a conditioning function.

       <n_in>: input number of bits to conditioning function.

       <n_out>: output number of bits from conditioning function.

       <nw>: narrowest internal width of conditioning function.

       <h_in>: input entropy to conditioning function.

       <-v|-n>: '-v' for vetted  conditioning  function,  '-n'  for  non-vetted  conditioning  function.  Vetted
              conditioning  is  the  default.   <h'>: entropy estimate per bit of conditioned sequential dataset
              (only for '-n' option).

       -q: Quiet mode, less output to screen.

       -i: Input file name, to run an entropy assessment on a non-vetted conditioned  data  file  and  use  that
              value as h'.

              This  program  computes  the  entropy  of  the  output of a conditioning function 'h_out' (Section
              3.1.5).  If the conditioning function is vetted, then

              h_out = Output_Entropy(n_in, n_out, nw, h_in)

              where 'Output_Entropy' is  specified  in  Section  3.1.5.1.2.  If  the  conditioning  function  is
              non-vetted then

              h_out = min(Output_Entropy(n_in, n_out, nw, h_in), 0.999*n_out, h'*n_out)

              as stated in Section 3.1.5.2.

       -o: Set Output Type to JSON

              Changes the output format to JSON and sets the file location for the output file.

       --version: Prints tool version information

       ea_conditioning - Compute the entropy of a conditioning function

ea_conditioning-v  [-q] <n_in> <n_out> <nw> <h_in> [-o filename.json] ea_conditioning-n <n_in> <n_out>
       <nw> <h_in> [h' | -i filename] [-o filename.json]