本文整理汇总了Python中sympy.mpmath.mp.exp方法的典型用法代码示例。如果您正苦于以下问题：Python mp.exp方法的具体用法？Python mp.exp怎么用？Python mp.exp使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sympy.mpmath.mp的用法示例。
在下文中一共展示了mp.exp方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: compute_a
# 需要导入模块: from sympy.mpmath import mp [as 别名]
# 或者: from sympy.mpmath.mp import exp [as 别名]
def compute_a(sigma, q, lmbd, verbose=False):
lmbd_int = int(math.ceil(lmbd))
if lmbd_int == 0:
return 1.0
a_lambda_first_term_exact = 0
a_lambda_second_term_exact = 0
for i in xrange(lmbd_int + 1):
coef_i = scipy.special.binom(lmbd_int, i) * (q ** i)
s1, s2 = 0, 0
for j in xrange(i + 1):
coef_j = scipy.special.binom(i, j) * (-1) ** (i - j)
s1 += coef_j * np.exp((j * j - j) / (2.0 * (sigma ** 2)))
s2 += coef_j * np.exp((j * j + j) / (2.0 * (sigma ** 2)))
a_lambda_first_term_exact += coef_i * s1
a_lambda_second_term_exact += coef_i * s2
a_lambda_exact = ((1.0 - q) * a_lambda_first_term_exact +
q * a_lambda_second_term_exact)
if verbose:
print "A: by binomial expansion {} = {} + {}".format(
a_lambda_exact,
(1.0 - q) * a_lambda_first_term_exact,
q * a_lambda_second_term_exact)
return _to_np_float64(a_lambda_exact) 示例2: _compute_delta
# 需要导入模块: from sympy.mpmath import mp [as 别名]
# 或者: from sympy.mpmath.mp import exp [as 别名]
def _compute_delta(log_moments, eps):
"""Compute delta for given log_moments and eps.
Args:
log_moments: the log moments of privacy loss, in the form of pairs
of (moment_order, log_moment)
eps: the target epsilon.
Returns:
delta
"""
min_delta = 1.0
for moment_order, log_moment in log_moments:
if moment_order == 0:
continue
if math.isinf(log_moment) or math.isnan(log_moment):
sys.stderr.write("The %d-th order is inf or Nan\n" % moment_order)
continue
if log_moment < moment_order * eps:
min_delta = min(min_delta,
math.exp(log_moment - moment_order * eps))
return min_delta 示例3: compute_a
# 需要导入模块: from sympy.mpmath import mp [as 别名]
# 或者: from sympy.mpmath.mp import exp [as 别名]
def compute_a(sigma, q, lmbd, verbose=False):
lmbd_int = int(math.ceil(lmbd))
if lmbd_int == 0:
return 1.0
a_lambda_first_term_exact = 0
a_lambda_second_term_exact = 0
for i in xrange(lmbd_int + 1):
coef_i = scipy.special.binom(lmbd_int, i) * (q ** i)
s1, s2 = 0, 0
for j in xrange(i + 1):
coef_j = scipy.special.binom(i, j) * (-1) ** (i - j)
s1 += coef_j * np.exp((j * j - j) / (2.0 * (sigma ** 2)))
s2 += coef_j * np.exp((j * j + j) / (2.0 * (sigma ** 2)))
a_lambda_first_term_exact += coef_i * s1
a_lambda_second_term_exact += coef_i * s2
a_lambda_exact = ((1.0 - q) * a_lambda_first_term_exact +
q * a_lambda_second_term_exact)
if verbose:
print("A: by binomial expansion {} = {} + {}".format(
a_lambda_exact,
(1.0 - q) * a_lambda_first_term_exact,
q * a_lambda_second_term_exact))
return _to_np_float64(a_lambda_exact) 示例4: pdf_gauss_mp
# 需要导入模块: from sympy.mpmath import mp [as 别名]
# 或者: from sympy.mpmath.mp import exp [as 别名]
def pdf_gauss_mp(x, sigma, mean):
return mp.mpf(1.) / mp.sqrt(mp.mpf("2.") * sigma ** 2 * mp.pi) * mp.exp(
- (x - mean) ** 2 / (mp.mpf("2.") * sigma ** 2))