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Up-> cppad_py library py_lib py_fun py_fun_ctor a_fun_xam.py

library-> py_lib cpp_lib

py_lib-> py_fun py_sparse py_utility more_py

py_fun-> py_independent py_abort_recording py_fun_ctor py_fun_property py_fun_new_dynamic py_fun_jacobian py_fun_hessian py_fun_forward py_fun_reverse py_fun_optimize

py_fun_ctor-> a_fun_xam.py

a_fun_xam.py

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def a_fun_xam() :
     #
     import numpy
     import cppad_py
     #
     # initialize return variable
     ok = True
     # ---------------------------------------------------------------------
     # number of dependent and independent variables
     m = 1
     n = 3
     #
     # Record the function
     #    f(x) = 0.5 * ( x[0]^2 + ... + x[n-1]^2 )
     x = numpy.empty(n, dtype=float)
     for i in range(n) :
          x[i] = i
     ax   = cppad_py.independent(x)
     ay   = numpy.empty(m, dtype=cppad_py.a_double)
     asum = cppad_py.a_double(0.0)
     for i in range(n) :
          asum = asum + ax[i] * ax[i]
     ay[0] = cppad_py.a_double(0.5) * asum
     f     = cppad_py.d_fun(ax, ay)
     af    = cppad_py.a_fun(f)
     #
     # Record the function
     #    g(x) = f'(x) * v
     au   = numpy.empty(m, dtype=cppad_py.a_double)
     av   = numpy.empty(n, dtype=cppad_py.a_double)
     for i in range(n) :
          av[i] = cppad_py.a_double(i)
     ax   = cppad_py.independent(x)
     af.forward(0, ax)
     au = af.forward(1, av)
     g  = cppad_py.d_fun(ax, au)
     #
     # compute g(x)
     u  = g.forward(0, x)
     #
     # compute g'(x)
     uq       = numpy.empty( (m, 1), dtype=float )
     uq[0, 0] = 1.0
     xq       = g.reverse(1, uq)
     #
     # g'(x) = f''(x) * v = v
     for i in range(n) :
          ok = ok and cppad_py.a_double(xq[i, 0]) == av[i]
     return ok