Understanding the confusion matrix

Hi

In an academic paper that I am trying to follow (with difficulty :)) 
they talk about a measure of accuracy

being (TP + TN) / N

TP = no. of correctly forecasted TRUE instances of the variable

TN = no. of correctly forecasted FALSE instances of the variable

N = total no. of instances

But the confusion matrix I get using Auto-weka looks like:-

     a   b   <-- classified as
   376   0 |   a = TRUE
   225   0 |   b = FALSE

i.e. the predictions are 'TRUE' for every instance

This I don't understand ?

The paper gets accuracy above 80% but the above yields 62.5%

and if the predictions are always 'TRUE' who needs a model ?

What am I missing here ?

Bob M

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