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How to implement this Sample Entropy Algorithm?

Mariusz Iwaniuk

Mariusz Iwaniuk, engineer, math hobbyist.

Posted 9 years ago

Hello, I am very new to mathematica and also new to this community. I have a problem given below.I want to implement a Sample Entropy Algorithm click here ,and here ,and here in Mathematica.I wrote the code , but do not know is there correct. data = Table[Sin[i], {i, 1, 100, .2}];f[n_] := data[[n ;; n]]; d[i_, j_, m_, n_] := Max[Table[Abs[f[i + k] - f[j + k]], {k, 0, m - 1}]]; nm[i_, r_, m_, n_] := (1/(n - m - 1))* Sum[If[d[i, j, m, n] < r, 1, 0], {j, 1, n - m}]; nm1[i_, r_, m_, n_] := (1/(n - m - 1))Sum[If[d[i, j, m + 1, n] < r, 1, 0], {j, 1, n - m}]; B[r_, m_, n_] := (1/(n - m))Sum[nm[i, r, m, n], {i, 1, n - m}]; A[r_, m_, n_] := (1/(n - m))*Sum[nm1[i, r, m, n], {i, 1, n - m}]; SampEn[n_, m_, r_] := -Log[A[r, m, n]/B[r, m, n]]; SampEn[Length[data], 2, 0.2 StandardDeviation[data]] // N with this document click here SampEn= 0.2359326 ,but with my code is SampEn=0.230084. I do not know at which point I made a mistake.Any help will be appreciated!

Hello, I am very new to mathematica and also new to this community. I have a problem given below.I want to implement a Sample Entropy Algorithm click here ,and here ,and here in Mathematica.I wrote the code , but do not know is there correct.

 data = Table[Sin[i], {i, 1, 100, .2}];f[n_] := data[[n ;; n]]; 

 d[i_, j_, m_, n_] := Max[Table[Abs[f[i + k] - f[j + k]], {k, 0, m - 1}]];
 nm[i_, r_, m_, n_] := (1/(n - m - 1))* Sum[If[d[i, j, m, n] < r, 1, 0], {j, 1, n - m}];
 nm1[i_, r_, m_, n_] := (1/(n - m - 1))*Sum[If[d[i, j, m + 1, n] < r, 1, 0], {j, 1, n - m}];
 B[r_, m_, n_] := (1/(n - m))*Sum[nm[i, r, m, n], {i, 1, n - m}];
 A[r_, m_, n_] := (1/(n - m))*Sum[nm1[i, r, m, n], {i, 1, n - m}];
 SampEn[n_, m_, r_] := -Log[A[r, m, n]/B[r, m, n]]; 
 SampEn[Length[data], 2, 0.2 StandardDeviation[data]] // N

with this document click here SampEn= 0.2359326 ,but with my code is SampEn=0.230084.

I do not know at which point I made a mistake.Any help will be appreciated!

POSTED BY: Mariusz Iwaniuk

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Mariusz Iwaniuk

Mariusz Iwaniuk, engineer, math hobbyist.

Posted 9 years ago

I found code on this site https://mathematica.stackexchange.com.Works for MMA 11.2. data = Table[Sin[i], {i, 1, 100, .2}]; ClearAll[sampleEnt2] sampleEnt2[data_, ws_, t_] := Module[{nF1, nF2, diff1, diff2, va1, va2}, va1 = Partition[data, ws, 1]; va2 = Partition[data, 1 + ws, 1]; nF1 = Nearest[va1 -> Automatic, DistanceFunction -> ChessboardDistance]; nF2 = Nearest[va2 -> Automatic, DistanceFunction -> ChessboardDistance]; diff1 = Total[(Length /@ nF1[va1, {All, t}])] - Length[va1]; diff2 = Total[(Length /@ nF2[va2, {All, t}])] - Length[va2]; -Log[N@(diff2/diff1)] ] m = 2; r = 0.2StandardDeviation[data]; sampleEnt2[data, m, r] // AbsoluteTiming ( {0.00581366, 0.230651} *)

I found code on this site https://mathematica.stackexchange.com.Works for MMA 11.2.

 data = Table[Sin[i], {i, 1, 100, .2}];

 ClearAll[sampleEnt2]
 sampleEnt2[data_, ws_, t_] :=  Module[{nF1, nF2, diff1, diff2, va1, va2},
  va1 = Partition[data, ws, 1];
  va2 = Partition[data, 1 + ws, 1];
  nF1 = Nearest[va1 -> Automatic, DistanceFunction -> ChessboardDistance];
  nF2 = Nearest[va2 -> Automatic, DistanceFunction -> ChessboardDistance];
  diff1 = Total[(Length /@ nF1[va1, {All, t}])] - Length[va1];
  diff2 = Total[(Length /@ nF2[va2, {All, t}])] - Length[va2];
  -Log[N@(diff2/diff1)]
  ]


   m = 2;
   r = 0.2*StandardDeviation[data];
   sampleEnt2[data, m, r] // AbsoluteTiming
   (* {0.00581366, 0.230651} *)

POSTED BY: Mariusz Iwaniuk