ssh & model-storyboard: changed ssh API, added node.execute* methods

1 files changed, 65 insertions, 0 deletions

diff --git a/rumba/recpoisson.py b/rumba/recpoisson.py
new file mode 100644
index 0000000..3c1e6fe
--- /dev/null
+++ b/rumba/recpoisson.py
@@ -0,0 +1,65 @@
+import math
+import random
+
+import sys
+
+if sys.version_info < (3, 2):
+    from repoze.lru import lru_cache
+    # from functools32 import lru_cache
+else:
+    from functools import lru_cache
+
+
+@lru_cache(1000)
+def _get_poisson_var(parameter):
+    return Poisson(parameter)
+
+
+class Poisson(object):
+
+    def __init__(self, parameter):
+        self.parameter = parameter
+
+        def c_p(k):
+            """Compute the Poisson CDF for k iteratively."""
+            if k == 0:
+                return self._p(0)
+            else:
+                return self._compute_poisson_cdf(k - 1) + self._p(k)
+        self._compute_poisson_cdf = lru_cache(int(2.5*self.parameter) + 1)(c_p)
+
+    @staticmethod
+    def _get_random():
+        return random.random()
+
+    def _p(self, k):
+        # l^k * e^-l / k!
+        # Computed as exp(klog(l) - l - log(k!))
+        l = self.parameter
+        l_to_the_k = k * math.log(l)
+        k_fact = sum([math.log(i + 1) for i in range(k)])
+        return math.exp(l_to_the_k - l - k_fact)
+
+    def sample(self):
+        # The idea is:
+        # take a sample from U(0,1) and call it f.
+        # Let x be s.t. x = min_N F(x) > f
+        # where F is the cumulative distribution function of Poisson(parameter)
+        # return x
+        f = self._get_random()
+
+        # We compute x iteratively by computing
+        # \sum_k(P=k)
+        # where P ~ Poisson(parameter) and stopping as soon as
+        # it is greater than f.
+        # We use the cache to store results.
+        current_cdf = -1
+        current_x = -1
+        while current_cdf < f:
+            current_x += 1
+            current_cdf = self._compute_poisson_cdf(current_x)
+        return current_x
+
+
+def poisson(parameter):
+    return _get_poisson_var(parameter).sample()