from mpmath import mp mp.dps = 110 def compute(): n = 7 K = 40 # series terms for small-t evaluation of L(t) with mp.extradps(50): # Precompute moments E[D^(2k)] for D = X-Y with X,Y ~ U(-1,1) # E[D^(2k)] = 2^(2k+1) / ((2k+1)(2k+2)), k>=1; and moment_0 = 1 moments = [mp.mpf(0)] * (K + 1) facts = [mp.mpf(0)] * (K + 1) moments[0] = mp.mpf(1) facts[0] = mp.mpf(1) for k in range(1, K + 1): moments[k] = mp.power(2, 2*k + 1) / ((2*k + 1) * (2*k + 2)) facts[k] = facts[k - 1] * k def L_series(t): s = mp.mpf(1) p = -t for k in range(1, K + 1): s += p * moments[k] / facts[k] p *= -t return s def L(t): if t == 0: return mp.mpf(1) # Use series where the closed form has cancellation (t -> 0) if t < mp.mpf("0.02"): return L_series(t) rt = mp.sqrt(t) term1 = mp.sqrt(mp.pi) * mp.erf(2 * rt) / (2 * rt) term2 = -mp.expm1(-4 * t) / (4 * t) # (1 - exp(-4t)) / (4t) return term1 - term2 def integrand(u): if u == 0: # limit u->0 of (1 - L(t)^n)/u^2 with t=(u/(1-u))^2: # 1 - L(t)^n ~ n*E[D^2]*t, E[D^2]=2/3, and t~u^2 return mp.mpf(14) / 3 if u == 1: return mp.mpf(1) a = u / (1 - u) t = a * a Lt = L(t) if abs(Lt - 1) < mp.mpf("0.1"): logLt = mp.log1p(Lt - 1) else: logLt = mp.log(Lt) one_minus_phi = -mp.expm1(n * logLt) # 1 - Lt^n, stable for Lt~1 return one_minus_phi / (u * u) # E[||D||] = 1/sqrt(pi) * ∫_0^1 (1 - E[e^{-t||D||^2}]) / u^2 du val = mp.quad(integrand, [0, mp.mpf("0.5"), mp.mpf("0.9"), mp.mpf("0.99"), mp.mpf("0.999"), 1]) return +(val / mp.sqrt(mp.pi)) if __name__ == "__main__": print(str(compute()))