def residuals_totalcases(params):
"""
Wanted to start with solve_ivp because figures will be faster to debug
params: (alpha, days, r_s, r_dth, p_dth, k1, k2), fitted parameters of the model
"""
# Variables Initialization for the ODE system
alpha, days, r_s, r_dth, p_dth, k1, k2 = params
params = max(alpha, 0), days, max(r_s, 0), max(r_dth, 0), max(min(p_dth, 1), 0), max(k1, 0), max(k2, 0)
x_0_cases = get_initial_conditions(
params_fitted=params,
global_params_fixed=GLOBAL_PARAMS_FIXED
)
x_sol = solve_ivp(
fun=model_covid,
y0=x_0_cases,
t_span=[t_cases[0], t_cases[-1]],
t_eval=t_cases,
args=tuple(params),
).y
weights = list(range(1, len(fitcasesnd) + 1))
# focus on last 5 days
weights[-5:] =[x + 50 for x in weights[-5:]]
residuals_value = sum(
np.multiply((x_sol[15, :] - fitcasesnd) ** 2, weights)
+ balance * balance * np.multiply((x_sol[14, :] - fitcasesd) ** 2, weights)
)
return residuals_value
def solve_best_params_and_predict(optimal_params):
# Variables Initialization for the ODE system
x_0_cases = get_initial_conditions(
params_fitted=optimal_params,
global_params_fixed=GLOBAL_PARAMS_FIXED)
x_sol_best = solve_ivp(
fun=model_covid,
y0=x_0_cases,
t_span=[t_cases_all[0], t_cases_all[-1]],
t_eval=t_cases_all,
args=tuple(optimal_params),
).y
return x_sol_best
