Magic Learner-JPL and others

FOR JPL

Study 1:

1. Predict LogicalDelivered, excluding EKLOC, inheritance.
2. Predict LogicalEKLOC, excluding delivered, and including inheritance.

Study 2:

1. Boring COCOMO using 1.1
2. Boring COCOMO using 1.2

Boring COCOMO ready and set to go.

Effort Predictions(val2):

so, we need a LOO experiment where we:

    0) cluster the dense columns 

    1)  take the "left out" example,  described in terms of:

                a) the new columns ("new" means "not traditional cocomo")

                b) its "class" e.g. "flight systems"
                c) act effort 

    2)  find the "left out" example's cluster and extract the LOC in that cluster
               - note: until step 5 we will ignore the actual effort

    3)  go to darren's rig and generate effort predictions (constrained by (2) and (1b))

    4) show the 50 to 70th percentile range of (3)

    5) mark on that  range the actual effort

    6) dance and sing if the the actual effort (from 1c) is in the range of (5

http://unbox.org/things/var/nave/lpj/out/master/effort_check_with_cocomo_50_70.xlsx

Graphs learnt on whole *old+new* with specific conditions(val0):

OTHER THAN JPL

Results for tables showing different techniques:

http://unbox.org/things/var/nave/lpj/out/master/*preptab*.dat

Contrast sets sorted:

http://unbox.org/things/var/nave/lpj/out/master/*contrast_sets_sorted.dat

Trees for different models:

http://unbox.org/things/var/nave/lpj/out/master/*dtree*.dat

Other graphs:

MRE with and without learning 20 records around cluster centroid: