The only virtue of using the natural base is that you get a nice asymptotic
distribution for random data.
On Fri, Apr 12, 2013 at 1:10 AM, Sean Owen wrote:
> Yes that's true, it is more usually bits. Here it's natural log / nats.
> Since it's unnormalized anyway another constant factor doesn'
Yes that's true, it is more usually bits. Here it's natural log / nats.
Since it's unnormalized anyway another constant factor doesn't hurt and it
means not having to change the base.
On Fri, Apr 12, 2013 at 8:01 AM, Phoenix Bai wrote:
> I got 168, because I use log base 2 instead of e.
> ([?])
I got 168, because I use log base 2 instead of e.
([?]) if memory serves right, I read it in entropy definition that people
normally use base 2, so I just assumed it was 2 in code. (my bad).
And now I have a better understanding, so thank you both for the
explanation.
On Fri, Apr 12, 2013 at 6:0
Yes I also get (er, Mahout gets) 117 (116.69), FWIW.
I think the second question concerned counts vs relative frequencies
-- normalized, or not. Like whether you divide all the counts by their
sum or not. For a fixed set of observations that does change the LLR
because it is unnormalized, not beca
Counts are critical here.
Suppose that two rare events occur together the first time you ever see
them. How exciting is this? Not very in my mind, but not necessarily
trivial.
Now suppose that they occur together 20 times and never occur alone after
you have collected 20 times more data. This i
These numbers don't match what I get.
I get LLR = 117.
This is wildly anomalous so this pair should definitely be connected. Both
items are quite rare (15/300,000 or 20/300,000 rates) but they occur
together most of the time that they appear.
On Wed, Apr 10, 2013 at 2:15 AM, Phoenix Bai wrot
Yes using counts is more efficient. Certainly it makes the LLR value
different since the results are not normalized; all the input values are N
times larger (N = sum k), and so the LLR is N times larger. 2x more events
in the same ratio will make the LLR 2x larger too.
That's just fine if you're c
Good point.
btw, why use counts instead of probabilities? for easy and efficient
implementation?
also, do you think the similarity score using counts might quite differ
from using probabilities?
thank you very much for your prompt reply. [?]
On Wed, Apr 10, 2013 at 5:50 PM, Sean Owen wrote:
>
These events do sound 'similar'. They occur together about half the
time either one of them occurs. You might have many pairs that end up
being similar for the same reason, and this is not surprising. They're
all "really" similar.
The mapping here from LLR's range of [0,inf) to [0,1] is pretty
arb
Hi,
the counts for two events are:
* **Event A**Everything but A**Event B**k11=7**k12=8**Everything but B**
k21=13**k22=300,000*
according to the code, I will get:
rowEntropy = entropy(7,8) + entropy(13, 300,000) = 222
colEntropy = entropy(7,13) + entropy(8, 300,000) = 152
matrixEntropy(entropy(7
10 matches
Mail list logo
