def learn_bocpd(X, model_f, hazard_f, conversion): max_minimize_iter = 30 theta = hazard_f.hazard_params theta.extend(model_f.post_params) theta, nlml, _ = rt_minimize(theta, bocpd_dwrap1D, -max_minimize_iter, X, model_f, hazard_f, conversion, hazard_f.num_hazard_params) print "we make it to here!" hazard_f.hazard_params = theta[0:num_hazard_params] model_f.model_params = theta[num_hazard_params:] hazard_f.hazard_params[0] = logistic(hazard_f.hazard_params[0]) model_f.model_params[1:] = np.exp(model_f.model_params[1:]) return hazard_params, model_params, nlml
def learn_bocpd(X, model_f, hazard_f, conversion):
max_minimize_iter = 30
theta = hazard_f.hazard_params
theta.extend(model_f.post_params)
theta, nlml, _ = rt_minimize(theta, bocpd_dwrap1D, -max_minimize_iter, X,
model_f, hazard_f, conversion,
hazard_f.num_hazard_params)
print "we make it to here!"
hazard_f.hazard_params = theta[0:num_hazard_params]
model_f.model_params = theta[num_hazard_params:]
hazard_f.hazard_params[0] = logistic(hazard_f.hazard_params[0])
model_f.model_params[1:] = np.exp(model_f.model_params[1:])
return hazard_params, model_params, nlml
def bocpd_dwrap1D(theta, X, model_f, hazard_f, conversion): from bocpd_deriv import bocpd_deriv # Warning: this code assumes: theta_h are in logit scale, theta_m(1) is in # linear, and theta_m(2:end) are in log scale! theta[conversion == 2] = logistic(theta[conversion == 2]) theta[conversion == 1] = np.exp(theta[conversion == 1]) # Seperate theta into hazard and model hypers theta_h = theta[:hazard_f.num_hazard_params] theta_m = theta[hazard_f.num_hazard_params:] nlml, dnlml_h, dnlml_m = bocpd_deriv(theta_h, theta_m, X, hazard_f, model_f) # Put back into one vector for minimize dnlml = dnlml_h.extend(dnlml_m) # Correct for the distortion dnlml[conversion == 2] = dnlml[conversion == 2] * theta[conversion == 2] * (1 - theta[conversion == 2]) dnlml[conversion == 1] = dnlml[conversion == 1] * theta[conversion == 1] return nlml, dnlml
def bocpd_dwrap1D(theta, X, model_f, hazard_f, conversion):
from bocpd_deriv import bocpd_deriv
# Warning: this code assumes: theta_h are in logit scale, theta_m(1) is in
# linear, and theta_m(2:end) are in log scale!
theta[conversion == 2] = logistic(theta[conversion == 2])
theta[conversion == 1] = np.exp(theta[conversion == 1])
# Seperate theta into hazard and model hypers
theta_h = theta[:hazard_f.num_hazard_params]
theta_m = theta[hazard_f.num_hazard_params:]
nlml, dnlml_h, dnlml_m = bocpd_deriv(theta_h, theta_m, X, hazard_f,
model_f)
# Put back into one vector for minimize
dnlml = dnlml_h.extend(dnlml_m)
# Correct for the distortion
dnlml[conversion == 2] = dnlml[conversion == 2] * theta[
conversion == 2] * (1 - theta[conversion == 2])
dnlml[conversion == 1] = dnlml[conversion == 1] * theta[conversion == 1]
return nlml, dnlml