Working with an actual matrix

The core capability of transitionMatrix is to produce estimated matrices but getting a realistic example requires quite some work. In this section we assume we have estimated one.

Lets look at a realistic example from the JLT paper

# Reproduce JLT Generator
# We load it using different sources
E = tm.TransitionMatrix(values=JLT)
E_2 = tm.TransitionMatrix(json_file=dataset_path + "JLT.json")
E_3 = tm.TransitionMatrix(csv_file=dataset_path + "JLT.csv")
# Lets check there are no errors
Error = E - E_3
print(np.linalg.norm(Error))
# Lets look at validation and generators"
# Empirical matrices will not satisfy constraints exactly
print(E.validate(accuracy=1e-3))
print(E.characterize())
print(E.generator())
Error = E - expm(E.generator())
# Frobenious norm
print(np.linalg.norm(Error))
# L1 norm
print(np.linalg.norm(Error, 1))
# Use pandas style API for saving to files
E.to_csv("JLT.csv")
E.to_json("JLT.json")