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fixed pyipopt test in tests/experimental_tests.py

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1 parent 2279b3d commit 5faea4a5121211c88528e3fcf4f8cca14b44c594 @b45ch1 committed Apr 14, 2011
Showing with 68 additions and 29 deletions.
  1. +68 −29 tests/experimental_tests.py
@@ -6,7 +6,7 @@
or complicated_tests.py
"""
import numpy
-import scipy
+import scipy;
from numpy.testing import *
from adolc import *
@@ -35,17 +35,17 @@ def test_big_tape():
#tape_to_latex(0,numpy.array([1.]),numpy.array([0.]))
-def test_arc_hyperbolic_functions():
- x = 3.
- ax = adouble(x)
+# def test_arc_hyperbolic_functions():
+# x = 3.
+# ax = adouble(x)
- aarcsh = numpy.arcsinh(ax)
- aarcch = numpy.arccosh(ax)
- aarcth = numpy.arctanh(ax)
+# aarcsh = numpy.arcsinh(ax)
+# aarcch = numpy.arccosh(ax)
+# aarcth = numpy.arctanh(ax)
- assert_almost_equal(aarcsh.val, numpy.arcsinh(x))
- assert_almost_equal(aarcch.val, numpy.arccosh(x))
- assert_almost_equal(aarcth.val, numpy.arctanh(x))
+# assert_almost_equal(aarcsh.val, numpy.arcsinh(x))
+# assert_almost_equal(aarcch.val, numpy.arccosh(x))
+# assert_almost_equal(aarcth.val, numpy.arctanh(x))
@@ -74,6 +74,14 @@ def test_ipopt_optimization():
#print '"pyipopt is not installed, skipping test'
#return
raise NotImplementedError("pyipopt is not installed, skipping test")
+
+ try:
+ import scipy.sparse as sparse
+ except:
+ #print '"pyipopt is not installed, skipping test'
+ #return
+ raise NotImplementedError("scipy is not installed, skipping test")
+
import time
nvar = 4
@@ -120,7 +128,9 @@ def eval_jac_g(x, flag, user_data = None):
2.0*x[1],
2.0*x[2],
2.0*x[3] ])
-
+
+
+
nnzh = 10
def eval_h(x, lagrange, obj_factor, flag, user_data = None):
if flag:
@@ -207,30 +217,59 @@ def eval_jac_g_adolc(x, flag, user_data = None):
return (numpy.asarray(result[1],dtype=int), numpy.asarray(result[2],dtype=int))
else:
return result[3]
-
+
def eval_h_adolc(x, lagrange, obj_factor, flag, user_data = None):
- options = numpy.array([0,0],dtype=int)
- assert numpy.ndim(x) == 1
- assert numpy.size(x) == 4
- result_f = colpack.sparse_hess_no_repeat(1, x, options)
- result_g0 = colpack.sparse_hess_no_repeat(3, x,options)
- result_g1 = colpack.sparse_hess_no_repeat(4, x,options)
- Hf = scipy.linalg.sparse.coo_matrix( (result_f[3], (result_f[1], result_f[2])), shape=(4, 4))
- Hg0 = scipy.linalg.sparse.coo_matrix( (result_g0[3], (result_g0[1], result_g0[2])), shape=(4, 4))
- Hg1 = scipy.linalg.sparse.coo_matrix( (result_g1[3], (result_g1[1], result_g1[2])), shape=(4, 4))
-
- H = Hf + Hg0 + Hg1
- H = H.tocoo()
+
if flag:
- hrow = H.row
- hcol = H.col
- return (numpy.array(hcol,dtype=int), numpy.array(hrow,dtype=int))
+ # return sparsity pattern of the hessian
+
+ if eval_h_adolc.firstrun == True:
+
+ # this can be done more elegantly based directly on
+ # the sparsity pattern returned by adolc.sparse.hess_pat
+ options = numpy.array([0,0],dtype=int)
+ result_f = colpack.sparse_hess_no_repeat(1, x, options)
+ result_g0 = colpack.sparse_hess_no_repeat(3, x,options)
+ result_g1 = colpack.sparse_hess_no_repeat(4, x,options)
+ Hf = sparse.coo_matrix( (result_f[3], (result_f[1], result_f[2])), shape=(4, 4))
+ Hg0 = sparse.coo_matrix( (result_g0[3], (result_g0[1], result_g0[2])), shape=(4, 4))
+ Hg1 = sparse.coo_matrix( (result_g1[3], (result_g1[1], result_g1[2])), shape=(4, 4))
+
+ H = Hf + Hg0 + Hg1
+ H = H.tocoo()
+ hrow = H.row
+ hcol = H.col
+ hpat = (numpy.array(hcol,dtype=int), numpy.array(hrow,dtype=int))
+
+ eval_h_adolc.hpat = hpat
+ eval_h_adolc.firstrun = False
+
+ return eval_h_adolc.hpat
else:
+ # compute sparse hessian
+ assert numpy.ndim(x) == 1
+ assert numpy.size(x) == 4
+
+ options = numpy.array([0,0],dtype=int)
+ result_f = colpack.sparse_hess_no_repeat(1, x, options)
+ result_g0 = colpack.sparse_hess_no_repeat(3, x,options)
+ result_g1 = colpack.sparse_hess_no_repeat(4, x,options)
+ Hf = sparse.coo_matrix( (result_f[3], (result_f[1], result_f[2])), shape=(4, 4))
+ Hg0 = sparse.coo_matrix( (result_g0[3], (result_g0[1], result_g0[2])), shape=(4, 4))
+ Hg1 = sparse.coo_matrix( (result_g1[3], (result_g1[1], result_g1[2])), shape=(4, 4))
+
+ H = Hf + Hg0 + Hg1
+ H = H.tocoo()
+
values = numpy.zeros((10), float)
values[:] = H.data
return values
+ eval_h_adolc.firstrun = True
+
+
+
# function of f
assert_almost_equal(eval_f(x0), eval_f_adolc(x0))
@@ -252,8 +291,8 @@ def eval_h_adolc(x, lagrange, obj_factor, flag, user_data = None):
x0 = numpy.random.rand(4)
result = (eval_h(x0, lagrange, obj_factor, False), eval_h(x0, lagrange, obj_factor, True))
result_adolc = (eval_h_adolc(x0, lagrange, obj_factor, False), eval_h_adolc(x0, lagrange, obj_factor, True))
- H = scipy.linalg.sparse.coo_matrix( result, shape=(4, 4))
- H_adolc = scipy.linalg.sparse.coo_matrix( result_adolc, shape=(4, 4))
+ H = sparse.coo_matrix( result, shape=(4, 4))
+ H_adolc = sparse.coo_matrix( result_adolc, shape=(4, 4))
H = H.todense()
H_adolc = H_adolc.todense()
assert_array_almost_equal( H, H_adolc.T)

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