diff options
| author | ziejd2 | 2018-09-13 23:59:20 -0500 |
|---|---|---|
| committer | ziejd2 | 2018-09-13 23:59:20 -0500 |
| commit | e3f7237ffcb19f19db3b68777b5a94b89e07f66a (patch) | |
| tree | 554a8013776ebeae3e2976074020c09c2d1af8b0 /sourcecodes/data/example_chl | |
| parent | a7eb61ff7a09f39bee67014bf24b8919eaccfc19 (diff) | |
| download | BNW-e3f7237ffcb19f19db3b68777b5a94b89e07f66a.tar.gz | |
New parameter learning options
The main change here is in the parameter learning methods. The parameters that are learned at first (i.e., if there is no evidence) are the distributions that are found directly in the data. I had to create or significantly modify several BNT files for this. If there is evidence, the parameters are learned using a Dirichlet prior. This only required a couple of small changes to the BNW parameter learning files.
Diffstat (limited to 'sourcecodes/data/example_chl')
| -rw-r--r-- | sourcecodes/data/example_chl/bWRcontinuous_input_orig.txt | 42 |
1 files changed, 42 insertions, 0 deletions
diff --git a/sourcecodes/data/example_chl/bWRcontinuous_input_orig.txt b/sourcecodes/data/example_chl/bWRcontinuous_input_orig.txt new file mode 100644 index 00000000..62705bb9 --- /dev/null +++ b/sourcecodes/data/example_chl/bWRcontinuous_input_orig.txt @@ -0,0 +1,42 @@ +Ctrq3 MAS Neutrophil Load Weight +2 0.969230769 3 3.252367514 1 +2 0.925170068 1.6 2.46322088 1.033472803 +1 0.427272727 33.8 4.206610024 0.831372549 +2 0.877835951 8.3 3.764250875 0.967153285 +2 0.914862915 4.4 3.691700208 1.046025105 +2 0.560334528 4.9 2.604550033 0.98046875 +1 0.383073497 13.1 4.273556814 0.812316716 +1 0.101010101 18.6 5.089640217 0.771929825 +1 0.106719368 18.9 4.915125346 0.80994152 +2 0.894736842 2.5 2.691700208 0.995515695 +1 0.067226891 19.8 4.878194228 0.846153846 +2 0.921022067 2.9 4.127428778 1.03875969 +2 0.938701923 0.7 4.366310867 0.984732824 +1 0.658008658 15.7 4.531121115 0.884210526 +2 0.9 4.8 2.604550033 0.913194444 +2 0.790923825 10.4 2.390670213 0.892857143 +1 0.295539033 8.6 4.24137213 0.85840708 +2 0.317757009 12.2 4.449648073 0.889830508 +1 0.032418953 20.2 5.053428044 0.811320755 +1 0.603960396 23.4 4.283775979 0.805460751 +2 0.939351199 3.6 2.349277527 0.952380952 +1 0.978448276 0.4 3.495405563 0.8875 +1 0.1 23.2 4.675613388 0.766101695 +1 0.036363636 32.6 4.938109253 0.828125 +1 0.660247593 9.2 3.826398782 0.921052632 +1 0.078651685 35.8 4.513630383 0.852112676 +1 0.186915888 20.7 5.048247532 0.779761905 +1 0.071578947 35.6 4.574586809 0.797356828 +1 0.239520958 15.4 4.331548761 0.941176471 +1 0.205741627 24.5 5.058547488 0.774193548 +1 0.062300319 13.4 4.594790195 0.832236842 +2 0.599675851 5 4.18178644 0.969348659 +1 0.324246772 13.5 4.252367514 0.858585859 +1 0.87628866 14.2 2.929061124 0.869863014 +1 0.146103896 15.6 4.579726449 0.9 +1 0.257383966 22.5 5.210214148 0.75 +1 0.033333333 31.1 4.632578756 0.718644068 +1 0.29739777 36.1 3.880756445 0.798353909 +2 0.872979215 4.2 4.097465554 0.856756757 +2 0.909221902 4.3 3.650307523 0.976 +1 0.10041841 28.3 4.46322088 0.757462687 |