def _run_varLiNGAM(self, xt, verbose=False):
""" Run the VarLiNGAM algorithm on data.
Args:
xt : time series matrix with size n*m (length*num_variables)
Returns:
Tuple: (Bo, Bhat) Instantaneous and lagged causal coefficients
"""
Ident = np.identity(xt.shape[1])
# Step 1: VAR estimation
model = VAR(xt)
results = model.fit(self.lag)
Mt_ = results.params[1:, :]
# Step 2: LiNGAM on Residuals
resid_VAR = results.resid
model = LiNGAM(verbose=verbose)
data = pd.DataFrame(resid_VAR)
Bo_ = model._run_LiNGAM(data)
# Step 3: Get instantaneous matrix Bo from LiNGAM
# Bo_ = pd.read_csv("results.csv").values
# Step 4: Calculation of lagged Bhat
Bhat_ = np.dot((Ident - Bo_), Mt_)
return (Bo_, Bhat_)
def estimateVARLiNGAM(self, xt):
I = np.identity(xt.shape[1])
# -------------------- Step 1: VAR estimation ----------------------------- #
model = VAR(xt)
results = model.fit(self.lag)
Mt_ = results.params[1:, :]
# -------------------- Step 2: LiNGAM on Residuals ------------------------ #
resid_VAR = results.resid
model = LiNGAM()
data = pd.DataFrame(resid_VAR)
model._run_LiNGAM(data)
# ---------------------Step 3: Get instantaneous matrix Bo from LiNGAM----- #
Bo_ = pd.read_csv("results.csv").values
# ---------------------Step 4: Calculation of lagged Bhat ----------------- #
Bhat_ = np.dot((I - Bo_), Mt_)
return (Bo_, Bhat_)
