72 lines
1.6 KiB
Python
72 lines
1.6 KiB
Python
# TODO: technically incorrect on 0 robustness since conflates < and >
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from functools import singledispatch
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import sympy as sym
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from numpy import arange
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from funcy import pairwise, autocurry
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import stl.ast
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from stl.ast import t_sym
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from stl.robustness import op_lookup
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@singledispatch
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def smooth_robustness(stl, L, h):
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raise NotImplementedError
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@smooth_robustness.register(stl.And)
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@smooth_robustness.register(stl.G)
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def _(stl, L, H):
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raise NotImplementedError("Call canonicalization function")
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def soft_max(rs, eps=0.1):
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B = 10
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return sym.log(sum(B**r for r in rs), B)
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def LSE(rs, eps=0.1):
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B = 10
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return soft_max(rs) - sym.log(len(rs), B)
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@smooth_robustness.register(stl.Or)
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def _(stl, L, h):
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rl, rh = list(zip(
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*[smooth_robustness(arg, L, h) for arg in stl.args]))
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return soft_max(rl), LSE(rh)
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@autocurry
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def x_ij(L, h, xi_xj):
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x_i, x_j = xi_xj
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return (L*h + x_i + x_j)/2
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@smooth_robustness.register(stl.F)
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def _(stl, L, H):
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lo, hi = stl.interval
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times = arange(lo, hi, H)
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rl, rh = smooth_robustness(stl.arg, L, H)
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los, his = zip(*[(rl.subs({t_sym: t_sym + t}), rh.subs({t_sym: t_sym + t})) for t in times])
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return LSE(los), soft_max(map(x_ij(L, H), pairwise(his)))
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@smooth_robustness.register(stl.Neg)
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def _(stl, L, H):
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rl, rh = smooth_robustness(arg, L, H)
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return -rh, -rl
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@smooth_robustness.register(stl.LinEq)
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def _(stl, L, H):
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op = op_lookup[stl.op]
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retval = op(eval_terms(stl), stl.const)
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return retval, retval
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def eval_terms(lineq):
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return sum(map(eval_term, lineq.terms))
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def eval_term(term):
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return term.coeff*sym.Function(term.id.name)(t_sym)
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