first commit

.gitignore

@@ -87,3 +87,9 @@ ENV/
 
 # Rope project settings
 .ropeproject
+
+# Mac folder meta
+.DS_Store
+
+# results folder
+results/

cisc.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""Causal inference on discrete data using stochastic complexity of multinomial.
+
+"""
+from collections import defaultdict
+from sc import stochastic_complexity
+
+
+__author__ = "Kailash Budhathoki"
+__email__ = "kbudhath@mpi-inf.mpg.de"
+__copyright__ = "Copyright (c) 2017"
+__license__ = "MIT"
+
+
+def marginals(X, Y):
+    Ys = defaultdict(list)
+    for i, x in enumerate(X):
+        Ys[x].append(Y[i])
+    return Ys
+
+
+def cisc(X, Y):
+    scX = stochastic_complexity(X)
+    scY = stochastic_complexity(Y)
+
+    mYgX = marginals(X, Y)
+    mXgY = marginals(Y, X)
+
+    scYgX = sum(stochastic_complexity(Z) for Z in mYgX.itervalues())
+    scXgY = sum(stochastic_complexity(Z) for Z in mXgY.itervalues())
+
+    ciscXtoY = scX + scYgX
+    ciscYtoX = scY + scXgY
+
+    return (ciscXtoY, ciscYtoX)
+
+
+if __name__ == "__main__":
+    import random
+    from test_anm import map_randomly
+
+    n = 1000
+    Xd = range(1, 4)
+    fXd = range(1, 4)
+    f = map_randomly(Xd, fXd)
+    N = range(-2, 3)
+
+    X = [random.choice(Xd) for i in xrange(n)]
+    Y = [f[X[i]] + random.choice(N) for i in xrange(n)]
+
+    print cisc(X, Y)