Spam filter - step 3

Create additional functions needed to evaluate the filter quality.

Function ''quality_score()''

Task:

`quality_score(tp, tn, fp, fn)` Compute the quality score based on the confusion matrix.
Inputs	4 nonnegative integers for TP, TN, FP, FN.
Outputs	A number between 0 and 1 showing the prediction quality measure.

Task:

In module quality.py, create function compute_quality_for_corpus(corpus_dir) which evaluates the filter quality based on the information contained in files !truth.txt and !prediction.txt in the given corpus.
The true and predicted classification can be read in the form of dictionaries using function read_classification_from_file().
The confusion matrix for the given corpus can be computed from the dictionaries using method compute_confusion_matrix() function from step 2.
The quality score can be computed from the confusion matrix using function quality_score().

Why do we need it?

`compute_quality_for_corpus(corpus_dir)`	Compute the quality of predictions for given corpus.
Inputs	A corpus directory evaluated by a filter (i.e. a directory containing `!truth.txt` and `!prediction.txt` files).
Outputs	Quality of the filter as a number between 0 and 1.