def solve_best_params_and_predict(optimal_params):
# Variables Initialization for the ODE system
alpha, days, r_s, r_dth, p_dth, r_dthdecay, k1, k2, jump, t_jump, std_normal = optimal_params
optimal_params = [
max(alpha, 0), days,
max(r_s, 0),
max(min(r_dth, 1), 0.02),
max(min(p_dth, 1), 0),
max(r_dthdecay, 0),
max(k1, 0),
max(k2, 0),
max(jump, 0),
max(t_jump, 0),
max(std_normal, 1)
]
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_predictions[0], t_predictions[-1]],
t_eval=t_predictions,
args=tuple(optimal_params),
).y
return x_sol_best
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, r_dthdecay, k1, k2, jump, t_jump, std_normal = params
params = (
max(alpha, 0), days, max(r_s, 0), max(min(r_dth, 1), 0.02), max(min(p_dth, 1), 0), max(r_dthdecay, 0),
max(k1, 0), max(k2, 0), max(jump, 0), max(t_jump, 0),max(std_normal, 1)
)
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))
weights = [(x/len(fitcasesnd))**2 for x in weights]
# weights[-15:] =[x + 50 for x in weights[-15:]]
residuals_value = sum(
np.multiply((x_sol[15, :] - fitcasesnd) ** 2, weights)
+ balance * balance * np.multiply((x_sol[14, :] - fitcasesd) ** 2, weights)) + sum(
np.multiply((x_sol[15, 7:] - x_sol[15, :-7] - fitcasesnd[7:] + fitcasesnd[:-7]) ** 2, weights[7:])
+ balance * balance * np.multiply((x_sol[14, 7:] - x_sol[14, :-7] - fitcasesd[7:] + fitcasesd[:-7]) ** 2, weights[7:])
)
# 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_predictions[0], t_predictions[-1]],
t_eval=t_predictions,
args=tuple(optimal_params),
).y
return x_sol_best