Multivariate Regression in Pykalman with Fixed Intercept and Compute R2 of Pykalman Regression

Question

I am using the pykalman module to do the following regression: Y = b1 * x1 + b2 * x2 + intercept. I would like to keep the intercept constant for the entire regression.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

trans_cov = delta / (1 - delta) * np.eye(3) \
obs_mat = np.vstack( [df['X1'], df['X2'],np.ones(df['X1'].shape)]).T[:, np.newaxis]

from pykalman import KalmanFilter
    kf = KalmanFilter(
    n_dim_obs=1,\
    n_dim_state=3,\
    initial_state_mean=np.zeros(3),\
    initial_state_covariance=np.ones((3, 3)),\
    transition_matrices=np.eye(3),\
    observation_matrices=obs_mat,\
    observation_covariance=1.0,\
    transition_covariance=trans_cov )

    **state_means**, state_covs = kf.filter(df['Y'].values) 

The output of state_means has time-varying intercept values.
- Is there a way in pykalman to get fixed intercept
- How can I compute the R**2 of the pykalman regression?

DATA: enter image description here

RESULTS:

enter image description here

I am looking for a result with constant intercept.