Weka ADTree doesn't match if-then rules output

I am using weka 3.7.13 ADTree classifier. I wrote a parser to convert the output tree to if-then rules. Even though the if-then rules match the visualization tree the confusion matrix differs. Here is the ADTree generated:

: -0.248
|  (1)M_L < 6.5: 0.284
|  |  (2)Fav = 0: -0.114
|  |  |  (4)M_L < 4.25: 0.117
|  |  |  |  (7)M_L < 0.15: -0.695
|  |  |  |  (7)M_L >= 0.15: 0.013
|  |  |  (4)M_L >= 4.25: -0.125
|  |  (2)Fav = 1: 0.359
|  |  (8)M_L < 2.2: 0.181
|  |  (8)M_L >= 2.2: -0.036
|  (1)M_L >= 6.5: -0.38
|  |  (3)M_L < 15.5: 0.102
|  |  |  (6)M_L < 8.5: 0.153
|  |  |  (6)M_L >= 8.5: -0.059
|  |  (3)M_L >= 15.5: -0.381
|  |  |  (9)M_L < 20.5: 0.111
|  |  |  (9)M_L >= 20.5: -0.267
|  (5)AvgSpd < 46.5: -0.129
|  (5)AvgSpd >= 46.5: 0.036
|  (10)Horse = 1A: -0.409
|  (10)Horse != 1A: 0.004

And this is the if-then routine

Function test(ByRef p) ' E:\Horses\ADTree10 03-20-17.txt
Dim t

t = -0.248
    If p("M_L") < 6.5 Then
       t = t + 0.284
       If p("Fav") = 0 Then
          t = t - 0.114
          If p("M_L") < 4.25 Then
             t = t + 0.117
             If p("M_L") < 0.15 Then
                t = t - 0.695
             End If ' p("M_L") < 0.15
             If p("M_L") >= 0.15 Then
                t = t + 0.013
             End If ' p("M_L") >= 0.15
          End If ' p("M_L") < 4.25
          If p("M_L") >= 4.25 Then
             t = t - 0.125
          End If ' p("M_L") >= 4.25
       End If ' p("Fav") = 0
       If p("Fav") = 1 Then
          t = t + 0.359
       End If ' p("Fav") = 1
       If p("M_L") < 2.2 Then
          t = t + 0.181
       End If ' p("M_L") < 2.2
       If p("M_L") >= 2.2 Then
          t = t - 0.036
       End If ' p("M_L") >= 2.2
    End If ' p("M_L") < 6.5
    If p("M_L") >= 6.5 Then
       t = t - 0.38
       If p("M_L") < 15.5 Then
          t = t + 0.102
          If p("M_L") < 8.5 Then
             t = t + 0.153
          End If ' p("M_L") < 8.5
          If p("M_L") >= 8.5 Then
             t = t - 0.059
          End If ' p("M_L") >= 8.5
       End If ' p("M_L") < 15.5
       If p("M_L") >= 15.5 Then
          t = t - 0.381
          If p("M_L") < 20.5 Then
             t = t + 0.111
          End If ' p("M_L") < 20.5
          If p("M_L") >= 20.5 Then
             t = t - 0.267
          End If ' p("M_L") >= 20.5
       End If ' p("M_L") >= 15.5
    End If ' p("M_L") >= 6.5
    If p("AvgSpd") < 46.5 Then
       t = t - 0.129
    End If ' p("AvgSpd") < 46.5
    If p("AvgSpd") >= 46.5 Then
       t = t + 0.036
    End If ' p("AvgSpd") >= 46.5
    If p("Horse") = "1A" Then
       t = t - 0.409
    End If ' p("Horse") = "1A"
    If p("Horse") <> "1A" Then
       t = t + 0.004
    End If ' p("Horse") <> "1A"
 if t < 0 Then
  t = 0
 Else
 t = 1
 End If ' t < 0
 test = t
End Function

Now the weka confusion matrix is

=== Weka Confusion Matrix ===

    a    b   <-- classified as
 5971 1415 |    a = 0
 2353 2145 |    b = 1

And the routine output is as follow

=== Confusion Matrix ===

    a    b   <-- classified as
 5995 1391 |    a = 0
 2342 2156 |    b = 1

And I cannot find out what is wrong with the routine. The routines generated by the parser work great with other classification but not with ADTree. Thanks in advance