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Up-> cppad_py library py_lib py_fun py_fun_jacobian fun_jacobian_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_jacobian-> fun_jacobian_xam.py

fun_jacobian_xam.py

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def fun_jacobian_xam() :
     #
     import numpy
     import cppad_py
     #
     # initialize return variable
     ok = True
     # ---------------------------------------------------------------------
     # number of dependent and independent variables
     n_dep = 1
     n_ind = 3
     #
     # create the independent variables ax
     x = numpy.empty(n_ind, dtype=float)
     for i in range( n_ind  ) :
          x[i] = i + 2.0
     #
     ax = cppad_py.independent(x)
     #
     # create dependent variables ay with ay0 = ax_0 * ax_1 * ax_2
     ax_0  = ax[0]
     ax_1  = ax[1]
     ax_2  = ax[2]
     ay    = numpy.empty(n_dep, dtype=cppad_py.a_double)
     ay[0] = ax_0 * ax_1 * ax_2
     #
     # define af corresponding to f(x) = x_0 * x_1 * x_2
     f  = cppad_py.d_fun(ax, ay)
     #
     # compute the Jacobian f'(x) = ( x_1*x_2, x_0*x_2, x_0*x_1 )
     fp = f.jacobian(x)
     #
     # check Jacobian
     x_0 = x[0]
     x_1 = x[1]
     x_2 = x[2]
     ok = ok and fp[0, 0] == x_1 * x_2
     ok = ok and fp[0, 1] == x_0 * x_2
     ok = ok and fp[0, 2] == x_0 * x_1
     # ---------------------------------------------------------------------
     af = cppad_py.a_fun(f)
     #
     ax   = numpy.empty(n_ind, dtype=cppad_py.a_double)
     for i in range( n_ind ) :
          ax[i] = x[i]
     #
     # compute the Jacobian f'(x) = ( x_1*x_2, x_0*x_2, x_0*x_1 )
     afp = af.jacobian(ax)
     #
     # check Jacobian
     ok = ok and afp[0, 0] == cppad_py.a_double(x_1 * x_2)
     ok = ok and afp[0, 1] == cppad_py.a_double(x_0 * x_2)
     ok = ok and afp[0, 2] == cppad_py.a_double(x_0 * x_1)
     #
     return( ok )
#