def lognormal_adsolver(pts): ec = util.ecdf(np.array(pts), issorted=False) x = ec[:,0] xrev = util.reverse(x) n = float((len(x))) i = np.array(range(len(x)), dtype=float) l1 = logn.Lognormal.fromFit(pts) imu = l1.mu() isig = l1.sigma() ivs = [imu, isig] ovs = (i,x,xrev,n) print ovs (fvals, infodict, ier, mesg) = opt.fsolve(solve_admin, ivs, ovs, None, 1, 0) f_mu = fvals[0] f_sigma = fvals[1] if ier != 1: raise logn.LognormalConvergenceError(mesg, (f_mu, f_sigma)) return logn.Lognormal(f_mu, f_sigma)
def lognormal_adsolver(pts): ec = util.ecdf(np.array(pts), issorted=False) x = ec[:, 0] xrev = util.reverse(x) n = float((len(x))) i = np.array(range(len(x)), dtype=float) l1 = logn.Lognormal.fromFit(pts) imu = l1.mu() isig = l1.sigma() ivs = [imu, isig] ovs = (i, x, xrev, n) print ovs (fvals, infodict, ier, mesg) = opt.fsolve(solve_admin, ivs, ovs, None, 1, 0) f_mu = fvals[0] f_sigma = fvals[1] if ier != 1: raise logn.LognormalConvergenceError(mesg, (f_mu, f_sigma)) return logn.Lognormal(f_mu, f_sigma)
def lognormal_nrsolver(pts): ec = util.ecdf(np.array(pts), issorted=False) x = ec[:,0] xrev = util.reverse(x) n = float(len(x)) i = np.array(range(len(x)), dtype=float) l1 = logn.Lognormal.fromFit(pts) imu = l1.mu() isig = l1.sigma() ivs = [imu, isig] ovs = (i, x, xrev, n) [mu, sigma] = opt.root(solve_admin, ivs, ovs) return [mu, sigma]
def lognormal_nrsolver(pts): ec = util.ecdf(np.array(pts), issorted=False) x = ec[:, 0] xrev = util.reverse(x) n = float(len(x)) i = np.array(range(len(x)), dtype=float) l1 = logn.Lognormal.fromFit(pts) imu = l1.mu() isig = l1.sigma() ivs = [imu, isig] ovs = (i, x, xrev, n) [mu, sigma] = opt.root(solve_admin, ivs, ovs) return [mu, sigma]
def tlladmin_solver(pts): ec = util.ecdf(pts) x = ec[:, 1] xrev = util.reverse(x) i = np.array(range(len(x)), dtype=float) n = float(len(x)) ib = 1.0 ic = float(np.median(x)) id = ic / float(x.max()) ivs = [ib, ic, id] ovs = (i, x, xrev, n) (fvals, infodict, ier, mesg) = opt.fsolve(tll_admin, ivs, ovs, None, 1, 0) f_b = fvals[0] f_c = fvals[1] f_d = fvals[2] if ier != 1: raise ml.ModLavConvergenceError(mesg, (f_b, f_c, f_d)) return ml.ModLav(f_b, f_c, f_d)
def tlladmin_solver(pts): ec = util.ecdf(pts) x = ec[:,1] xrev = util.reverse(x) i = np.array(range(len(x)), dtype=float) n = float(len(x)) ib = 1.0 ic = float(np.median(x)) id = ic/float(x.max()) ivs = [ib,ic,id] ovs = (i,x,xrev,n) (fvals, infodict, ier, mesg) = opt.fsolve(tll_admin, ivs, ovs, None, 1, 0) f_b = fvals[0] f_c = fvals[1] f_d = fvals[2] if ier != 1: raise ml.ModLavConvergenceError(mesg, (f_b,f_c,f_d)) return ml.ModLav(f_b, f_c, f_d)