Hello,
I am quite new to maximum entropy and unfortunately, I'm fighting with input
to TADM (tadm.sf.net) and to OpenNLP. Maybe my question is off-topic, however
I would like to know the answer. However, I would like to have an equivalent
inputs for OpenNLP GIS and TADM.
For example to open nlp the input are is the set of observations with the
format:
(outcome_i, events_i) where events_i is a set of events.
For example, let have a database of binary vectors:
1 0 0 1 1 1 1 1 1 1
0 1 0 0 1 1 1 0 0 0
0 0 0 1 1 1 1 1 1 1
1 0 1 1 1 0 1 1 1 1
0 0 0 0 0 0 0 1 1 1
1 0 0 0 1 0 1 1 1 1
0 0 0 1 0 1 0 1 1 0
1 0 1 1 1 1 1 1 0 0
0 1 0 1 1 0 1 1 1 0
let model the column zero using all other columns. The input can be:
outcome | strings representing the observations/events
class=1 | 1=0 2=0 3=1 4=1 5=1 6=1 7=1 8=1 9=1
class=0 | 1=1 2=0 3=0 4=1 5=1 6=1 7=0 8=0 9=0
class=0 | 1=0 2=0 3=1 4=1 5=1 6=1 7=1 8=1 9=1
class=1 | 1=0 2=1 3=1 4=1 5=0 6=1 7=1 8=1 9=1
class=0 | 1=0 2=0 3=0 4=0 5=0 6=0 7=1 8=1 9=1
class=1 | 1=0 2=0 3=0 4=1 5=0 6=1 7=1 8=1 9=1
class=0 | 1=0 2=0 3=1 4=0 5=1 6=0 7=1 8=1 9=0
class=1 | 1=0 2=1 3=1 4=1 5=1 6=1 7=1 8=0 9=0
class=0 | 1=1 2=0 3=1 4=1 5=0 6=1 7=1 8=1 9=0
class=0 | 1=0 2=0 3=1 4=1 5=1 6=1 7=1 8=0 9=0
class=1 | 1=0 2=0 3=1 4=0 5=1 6=1 7=0 8=0 9=1
class=0 | 1=1 2=0 3=1 4=1 5=1 6=1 7=1 8=0 9=0
class=1 | 1=1 2=0 3=1 4=1 5=0 6=1 7=0 8=0 9=0
class=0 | 1=1 2=0 3=1 4=0 5=1 6=1 7=1 8=0 9=0
The string n=m means the bit n is set to the value m, counting n from 0.
Now, what is the equivalent input for TADM ? I suspect that the input for TADM
consists of all observations. Is this correct ? However as I have only a very
limited
number of observations I get a probability distribution that gives probability
of
1/n for n bits.
I understand that this question is maybe a bit out of topic, but maybe there is
someone who knows
answer to my question.
Thanks,
Robert
