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Up-> cppad_swig library a_fun a_fun_forward a_fun_forward_xam.py

cppad_swig-> testing swig_xam.i library whats_new_2018

library-> module a_double vector a_fun sparse error xam.m4

a_fun-> independent abort_recording a_fun_ctor a_fun_jacobian a_fun_hessian a_fun_forward a_fun_reverse a_fun_optimize a_fun_property

a_fun_forward-> a_fun_forward_xam.cpp a_fun_forward_xam.m a_fun_forward_xam.pm a_fun_forward_xam.py

a_fun_forward_xam.py

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def a_fun_forward_xam() :
     #
     # load the Cppad Swig library
     import py_cppad
     #
     # initialize return variable
     ok = True
     # ---------------------------------------------------------------------
     # number of dependent and independent variables
     n_dep = 1
     n_ind = 2
     #
     # create the independent variables ax
     xp = py_cppad.vec_double(n_ind)
     for i in range( n_ind  ) :
          xp[i] = i + 1.0
     #
     ax = py_cppad.independent(xp)
     #
     # create dependent varialbes ay with ay0 = ax0 * ax1
     ax0 = ax[0]
     ax1 = ax[1]
     ay = py_cppad.vec_a_double(n_dep)
     ay[0] = ax0 * ax1
     #
     # define af corresponding to f(x) = x0 * x1
     af = py_cppad.a_fun(ax, ay)
     #
     # define X(t) = (3 + t, 2 + t)
     # it follows that Y(t) = f(X(t)) = (3 + t) * (2 + t)
     #
     # Y(0) = 6 and p ! = 1
     p = 0
     xp[0] = 3.0
     xp[1] = 2.0
     yp = af.forward(p, xp)
     ok = ok and yp[0] == 6.0
     #
     # first order Taylor coefficients for X(t)
     p = 1
     xp[0] = 1.0
     xp[1] = 1.0
     #
     # first order Taylor coefficient for Y(t)
     # Y'(0) = 3 + 2 = 5 and p ! = 1
     yp = af.forward(p, xp)
     ok = ok and yp[0] == 5.0
     #
     # second order Taylor coefficients for X(t)
     p = 2
     xp[0] = 0.0
     xp[1] = 0.0
     #
     # second order Taylor coefficient for Y(t)
     # Y''(0) = 2.0 and p ! = 2
     yp = af.forward(p, xp)
     ok = ok and yp[0] == 1.0
     #
     return( ok )
#