本文整理汇总了Python中pyds.MassFunction.from_bel方法的典型用法代码示例。如果您正苦于以下问题：Python MassFunction.from_bel方法的具体用法？Python MassFunction.from_bel怎么用？Python MassFunction.from_bel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pyds.MassFunction的用法示例。
在下文中一共展示了MassFunction.from_bel方法的2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: test_from_bel
# 需要导入模块: from pyds import MassFunction [as 别名]
# 或者: from pyds.MassFunction import from_bel [as 别名]
def test_from_bel(self):
self._assert_equal_belief(self.m1, MassFunction.from_bel(self.m1.bel()), 8)
self._assert_equal_belief(self.m2, MassFunction.from_bel(self.m2.bel()), 8)
self._assert_equal_belief(self.m3, MassFunction.from_bel(self.m3.bel()), 8)示例2: MassFunction
# 需要导入模块: from pyds import MassFunction [as 别名]
# 或者: from pyds.MassFunction import from_bel [as 别名]
m1 = MassFunction({'ab':0.6, 'bc':0.3, 'a':0.1, 'ad':0.0}) # using a dictionary
print('m_1 =', m1)
m2 = MassFunction([({'a', 'b', 'c'}, 0.2), ({'a', 'c'}, 0.5), ({'c'}, 0.3)]) # using a list of tuples
print('m_2 =', m2)
m3 = MassFunction()
m3['bc'] = 0.8
m3[{}] = 0.2
print('m_3 =', m3, ('(unnormalized mass function)'))
print('\n=== belief, plausibility, and commonality ===')
print('bel_1({a, b}) =', m1.bel({'a', 'b'}))
print('pl_1({a, b}) =', m1.pl({'a', 'b'}))
print('q_1({a, b}) =', m1.q({'a', 'b'}))
print('bel_1 =', m1.bel()) # entire belief function
print('bel_3 =', m3.bel())
print('m_3 from bel_3 =', MassFunction.from_bel(m3.bel())) # construct a mass function from a belief function
print('\n=== frame of discernment, focal sets, and core ===')
print('frame of discernment of m_1 =', m1.frame())
print('focal sets of m_1 =', m1.focal())
print('core of m_1 =', m1.core())
print('combined core of m_1 and m_3 =', m1.core(m3))
print('\n=== Dempster\'s combination rule, unnormalized conjunctive combination (exact and approximate) ===')
print('Dempster\'s combination rule for m_1 and m_2 =', m1 & m2)
print('Dempster\'s combination rule for m_1 and m_2 (Monte-Carlo, importance sampling) =', m1.combine_conjunctive(m2, sample_count=1000, importance_sampling=True))
print('Dempster\'s combination rule for m_1, m_2, and m_3 =', m1.combine_conjunctive(m2, m3))
print('unnormalized conjunctive combination of m_1 and m_2 =', m1.combine_conjunctive(m2, normalization=False))
print('unnormalized conjunctive combination of m_1 and m_2 (Monte-Carlo) =', m1.combine_conjunctive(m2, normalization=False, sample_count=1000))
print('unnormalized conjunctive combination of m_1, m_2, and m_3 =', m1.combine_conjunctive(m2, m3, normalization=False))