1 files changed, 86 insertions, 0 deletions

diff --git a/gen/rv.py b/gen/rv.py
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+++ b/gen/rv.py
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+from __future__ import division
+import random
+
+def uniform_int(minval, maxval):
+    "Create a function that draws ints uniformly from {minval, ..., maxval}"
+    def _draw():
+        return random.randint(minval, maxval)
+    return _draw
+
+def uniform(minval, maxval):
+    "Create a function that draws floats uniformly from [minval, maxval]"
+    def _draw():
+        return random.uniform(minval, maxval)
+    return _draw
+
+def bernoulli(p):
+    "Create a function that flips a weight coin with probability p"
+    def _draw():
+        return random.random() < p
+    return _draw
+
+def uniform_choice(choices):
+    "Create a function that draws uniformly elements from choices"
+    selector = uniform_int(0, len(choices) - 1)
+    def _draw():
+        return choices[selector()]
+    return _draw
+
+def truncate(minval, maxval):
+    def _limit(fun):
+        def _f(*args, **kargs):
+            val = fun(*args, **kargs)
+            return min(maxval, max(minval, val))
+        return _f
+    return _limit
+
+def redraw(minval, maxval):
+    def _redraw(dist):
+        def _f(*args, **kargs):
+            in_range = False
+            while not in_range:
+                val = dist(*args, **kargs)
+                in_range = minval <= val <= maxval
+            return val
+        return _f
+    return _redraw
+
+def exponential(minval, maxval, mean, limiter=redraw):
+    """Create a function that draws floats from an exponential
+    distribution with expected value 'mean'. If a drawn value is less
+    than minval or greater than maxval, then either another value is
+    drawn (if limiter=redraw) or the drawn value is set to minval or
+    maxval (if limiter=truncate)."""
+    def _draw():
+        return random.expovariate(1.0 / mean)
+    return limiter(minval, maxval)(_draw)
+
+def multimodal(weighted_distributions):
+    """Create a function that draws values from several distributions
+    with probability according to the given weights in a list of
+    (distribution, weight) pairs."""
+    total_weight = sum([w for (d, w) in weighted_distributions])
+    selector = uniform(0, total_weight)
+    def _draw():
+        x = selector()
+        wsum = 0
+        for (d, w) in weighted_distributions:
+            wsum += w
+            if wsum >= x:
+                return d()
+        assert False # should never drop off
+    return _draw
+
+def uniform_slack(min_slack_ratio, max_slack_ratio):
+    """Choose deadlines uniformly such that the slack
+       is within [cost + min_slack_ratio * (period - cost),
+                  cost + max_slack_ratio * (period - cost)].
+                  
+        Setting max_slack_ratio = 1 implies constrained deadlines.
+    """
+    def choose_deadline(cost, period):
+        slack = period - cost
+        earliest = slack * min_slack_ratio
+        latest   = slack * max_slack_ratio
+        return cost + random.uniform(earliest, latest)
+    return choose_deadline