def nllf(p):
beta0C, beta, phi, q, x[x_index] = p[0], p[1], p[2:6].reshape(2, 2), p[6], p[7:]
p = ilogit(beta0C + beta*np.floor(x))
x_int, d_int = [np.asarray(np.floor(v), 'i') for v in (x, d)]
cost = 0
cost += np.sum(dbern_llf(d, q))
cost += np.sum(dbern_llf(d, p))
cost += np.sum(dbern_llf(w, phi[x_int, d_int]))
cost += dnorm_llf(beta0C, 0, 0.00001)
cost += dnorm_llf(beta, 0, 0.00001)
return -cost
def nllf(p):
alpha, beta = p
p = exp(alpha * xa + beta * xs)
cost = 0
cost += np.sum(dbern_llf(y, p))
return -cost
def nllf(p):
alpha, beta, theta = p[:T], p[T], p[T + 1:]
p = ilogit(beta * theta[:, None] - alpha[None, :])
cost = 0
cost += np.sum(dbern_llf(r, p))
cost += np.sum(dnorm_llf(theta, 0.0, 1.0))
cost += np.sum(dnorm_llf(alpha, 0, 0.0001))
cost += dflat_llf(beta)
return -cost
def nllf(p):
delta, alpha, state = p[0], p[1], p[2:]
beta = exp(alpha)
theta = ilogit(delta)
P = np.array([ilogit(alpha), 0])
state = np.asarray(np.floor(state), 'i')
#state1 = state + 1 # zero-indexing in numpy
#prop = P[state] # unused
cost = 0
cost += np.sum(dbin_llf(y, P[state], t))
cost += np.sum(dbern_llf(state, theta))
cost += dnorm_llf(alpha, 0, 1e-4)
cost += dnorm_llf(delta, 0, 1e-4)
return -cost