How to calculate classification accuracy with confusion matrix?
A confusion matrix is a cross tabulation of your predicted values against the true observed values, and (test) accuracy is emperical rate of correct predictions. So in this case you'll need to 


Predict the 'play' attribute for your test set.  (Currently you don't have a method to predict your second test case, so for the sake of argument let's assume your model would predict yes for the sunny example. 
The following method of keeping track of your predictions is reffered to as a confusion matrix.  The top labels are prediced



               Predicted
         +----------------+
         ¦     ¦ yes ¦ no ¦
Oserved  ¦ yes ¦ 1   ¦ 1  ¦
         ¦ no  ¦ 0   ¦ 0  ¦
         +----------------+


Here the first 1 is from your first test case and the second 1 is from the misclassified second test case.


Calculate accuracy,


Accuracy = (# correct predictions)/(# total predictions) = 1 / 2 = .50.