From e26434979adc33bd949566ba7bf02dff64e80a3e Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cab.ark.cs.cmu.edu>
Date: Tue, 2 Oct 2012 00:19:43 -0400
Subject: cdec cleanup, remove bayesian stuff, parsing stuff

---
 gi/posterior-regularisation/linesearch.py | 58 -------------------------------
 1 file changed, 58 deletions(-)
 delete mode 100644 gi/posterior-regularisation/linesearch.py

(limited to 'gi/posterior-regularisation/linesearch.py')

diff --git a/gi/posterior-regularisation/linesearch.py b/gi/posterior-regularisation/linesearch.py
deleted file mode 100644
index 5a3f2e9c..00000000
--- a/gi/posterior-regularisation/linesearch.py
+++ /dev/null
@@ -1,58 +0,0 @@
-## Automatically adapted for scipy Oct 07, 2005 by convertcode.py
-
-from scipy.optimize import minpack2
-import numpy
-
-import __builtin__
-pymin = __builtin__.min
-
-def line_search(f, myfprime, xk, pk, gfk, old_fval, old_old_fval,
-                args=(), c1=1e-4, c2=0.9, amax=50):
-
-    fc = 0
-    gc = 0
-    phi0 = old_fval
-    derphi0 = numpy.dot(gfk,pk)
-    alpha1 = pymin(1.0,1.01*2*(phi0-old_old_fval)/derphi0)
-    # trevor: added this test
-    alpha1 = pymin(alpha1,amax)
-
-    if isinstance(myfprime,type(())):
-        eps = myfprime[1]
-        fprime = myfprime[0]
-        newargs = (f,eps) + args
-        gradient = False
-    else:
-        fprime = myfprime
-        newargs = args
-        gradient = True
-
-    xtol = 1e-14
-    amin = 1e-8
-    isave = numpy.zeros((2,), numpy.intc)
-    dsave = numpy.zeros((13,), float)
-    task = 'START'
-    fval = old_fval
-    gval = gfk
-
-    while 1:
-        stp,fval,derphi,task = minpack2.dcsrch(alpha1, phi0, derphi0, c1, c2,
-                                               xtol, task, amin, amax,isave,dsave)
-        #print 'minpack2.dcsrch', alpha1, phi0, derphi0, c1, c2, xtol, task, amin, amax,isave,dsave
-        #print 'returns', stp,fval,derphi,task
-
-        if task[:2] == 'FG':
-            alpha1 = stp
-            fval = f(xk+stp*pk,*args)
-            fc += 1
-            gval = fprime(xk+stp*pk,*newargs)
-            if gradient: gc += 1
-            else: fc += len(xk) + 1
-            phi0 = fval
-            derphi0 = numpy.dot(gval,pk)
-        else:
-            break
-
-    if task[:5] == 'ERROR' or task[1:4] == 'WARN':
-        stp = None  # failed
-    return stp, fc, gc, fval, old_fval, gval
-- 
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