From f1762904c648b2089031ba6ce46ccaaac4f3514c Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Mon, 30 Nov 2015 19:57:58 -0500
Subject: Big code cleanup

---
 simulation/mleNode.py | 72 ---------------------------------------------------
 1 file changed, 72 deletions(-)
 delete mode 100644 simulation/mleNode.py

(limited to 'simulation/mleNode.py')

diff --git a/simulation/mleNode.py b/simulation/mleNode.py
deleted file mode 100644
index c6b2e85..0000000
--- a/simulation/mleNode.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import numpy as np
-from scipy.optimize import minimize
-
-
-def likelihood(p, x, y):
-    a = np.dot(x, p)
-    return np.log(1. - np.exp(-a[y])).sum() - a[~y].sum()
-
-
-def likelihood_gradient(p, x, y):
-    a = np.dot(x, p)
-    l = np.log(1. - np.exp(-a[y])).sum() - a[~y].sum()
-    g1 = 1. / (np.exp(a[y]) - 1.)
-    g = (x[y] * g1[:, np.newaxis]).sum(0) - x[~y].sum(0)
-    return l, g
-
-
-def test_gradient(x, y):
-    eps = 1e-10
-    for i in xrange(x.shape[1]):
-        p = 0.5 * np.ones(x.shape[1])
-        a = np.dot(x, p)
-        g1 = 1. / (np.exp(a[y]) - 1.)
-        g = (x[y] * g1[:, np.newaxis]).sum(0) - x[~y].sum(0)
-        p[i] += eps
-        f1 = likelihood(p, x, y)
-        p[i] -= 2 * eps
-        f2 = likelihood(p, x, y)
-        print(g[i], (f1 - f2) / (2 * eps))
-
-
-def infer(x, y):
-    def f(p):
-        l, g = likelihood_gradient(p, x, y)
-        return -l, -g
-    x0 = np.ones(x.shape[1])
-    bounds = [(1e-10, None)] * x.shape[1]
-    return minimize(f, x0, jac=True, bounds=bounds, method="L-BFGS-B").x
-
-
-def bootstrap(x, y, n_iter=100):
-    rval = np.zeros((n_iter, x.shape[1]))
-    for i in xrange(n_iter):
-        indices = np.random.choice(len(y), replace=False, size=int(len(y)*.9))
-        rval[i] = infer(x[indices], y[indices])
-    return rval
-
-
-def confidence_interval(counts, bins):
-    k = 0
-    while np.sum(counts[len(counts)/2-k:len(counts)/2+k]) <= .95*np.sum(counts):
-        k += 1
-    return bins[len(bins)/2-k], bins[len(bins)/2+k]
-
-
-def build_matrix(cascades, node):
-
-    def aux(cascade, node):
-        xlist, slist = zip(*cascade)
-        indices = [i for i, s in enumerate(slist) if s[node] and i >= 1]
-        if indices:
-            x = np.vstack(xlist[i-1] for i in indices)
-            y = np.array([xlist[i][node] for i in indices])
-            return x, y
-        else:
-            return None
-
-    pairs = (aux(cascade, node) for cascade in cascades)
-    xs, ys = zip(*(pair for pair in pairs if pair))
-    x = np.vstack(xs)
-    y = np.concatenate(ys)
-    return x, y
-- 
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