From 8b267fa2c3bb09a782196dabcae75fc05140da6f Mon Sep 17 00:00:00 2001
From: Marcell Vazquez-Chanlatte <mvc@linux.com>
Date: Sat, 10 Dec 2016 13:38:41 -0800
Subject: [PATCH] Implemented smooth_robustness's stl.F encoding

---
 stl/smooth_robustness.py | 35 +++++++++++++++--------------------
 1 file changed, 15 insertions(+), 20 deletions(-)

diff --git a/stl/smooth_robustness.py b/stl/smooth_robustness.py
index 6f97dbd..4ea5b1d 100644
--- a/stl/smooth_robustness.py
+++ b/stl/smooth_robustness.py
@@ -1,15 +1,14 @@
 # TODO: technically incorrect on 0 robustness since conflates < and >
 
 from functools import singledispatch
-from operator import sub, add
 
 import sympy as sym
-from lenses import lens
 from numpy import arange
 from funcy import pairwise, autocurry
 
 import stl.ast
 from stl.ast import t_sym
+from stl.robustness import op_lookup
 
 @singledispatch
 def smooth_robustness(stl, L, h):
@@ -20,47 +19,43 @@ def smooth_robustness(stl, L, h):
 def _(stl, L, H):
     raise NotImplementedError("Call canonicalization function")
 
-def soft_max(rs):
-    return sym.log(sum(sym.exp(r) for r in rs))
+def soft_max(rs, eps=0.1):
+    B = 10
+    return sym.log(sum(B**r for r in rs), B)
 
 
-def LSE(rs):
-    return soft_max(rs) - sym.log(len(rs))
+def LSE(rs, eps=0.1):
+    B = 10
+    return soft_max(rs) - sym.log(len(rs), B)
 
 
 @smooth_robustness.register(stl.Or)
 def _(stl, L, h):
     rl, rh = list(zip(
-        *[smooth_robustness(arg, depth) for arg in stl.args]))
+        *[smooth_robustness(arg, L, h) for arg in stl.args]))
     return soft_max(rl), LSE(rh)
 
 
 @autocurry
-def x_ij(L, h, x_i, x_j):
+def x_ij(L, h, xi_xj):
+    x_i, x_j = xi_xj
     return (L*h + x_i + x_j)/2
 
+
 @smooth_robustness.register(stl.F)
 def _(stl, L, H):
     lo, hi = stl.interval
     times = arange(lo, hi, H)
-    rl, rh = smooth_robustness(stl.arg)
-    los, his = zip(*[rl.subs({t_sym: t}), rh.subs({t_sym: t}) for t in times])
-    return LSE(rl), soft_max(map(x_ij(L, H), his))
+    rl, rh = smooth_robustness(stl.arg, L, H)
+    los, his = zip(*[(rl.subs({t_sym: t_sym + t}), rh.subs({t_sym: t_sym + t})) for t in times]) 
+    return LSE(los), soft_max(map(x_ij(L, H), pairwise(his)))
 
 
 @smooth_robustness.register(stl.Neg)
 def _(stl, L, H):
-    rl, rh = smooth_robustness(arg)
+    rl, rh = smooth_robustness(arg, L, H)
     return -rh, -rl
 
-op_lookup = {
-    ">": sub,
-    ">=": sub,
-    "<": lambda x, y: sub(y, x),
-    "<=": lambda x, y: sub(y, x),
-    "=": lambda a, b: -abs(a - b),
-}
-
 
 @smooth_robustness.register(stl.LinEq)
 def _(stl, L, H):