ak9im/p3
2023-03-12 16:18:22 +01:00
..
data p3: add theta{,T{,_p{0,1,2,3}}}, estim. err files 2023-03-12 16:18:22 +01:00
lrls p3: implement Recursive Least Squares estimation 2023-03-12 16:13:45 +01:00
data.go p3: implement Recursive Least Squares estimation 2023-03-12 16:13:45 +01:00
go.mod p3: add lrls/helper.go 2023-03-05 17:48:31 +01:00
go.sum flake,go,pre-commit: add p3 2023-03-04 14:51:37 +01:00
main.go flake,go,pre-commit: add p3 2023-03-04 14:51:37 +01:00
README.md flake,go,pre-commit: add p3 2023-03-04 14:51:37 +01:00
run.go p3: implement Recursive Least Squares estimation 2023-03-12 16:13:45 +01:00
visualise.py flake,go,pre-commit: add p3 2023-03-04 14:51:37 +01:00

p3

this is a Go subproject containing code for task no. 3. Python is used for visualisation.

compile

go build -v .

run

to compute correlations, impulse function:

# pass a csv data file.
./p2 -datafile=./data/m.csv

to visualise the computed data:

python visualise.py

alternatively, from current folder you could do both of the above in a single step:

# compute stuff and visualise.
-./p2 -datafile=./data/m.csv
+./p2 -datafile=./data/m.csv -vis