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Up-> cppad_py library py_lib py_fun py_fun_ctor

cppad_py-> setup.py library whats_new_2018

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

Headings-> Syntax ---..d_fun ---..a_fun ax ay f af Example

@(@\newcommand{\B}[1]{ {\bf #1} } \newcommand{\R}[1]{ {\rm #1} }@)@Stop Current Recording and Store Function Object
Syntax

d_fun
f = cppad_py.d_fun(ax, ay)

a_fun
af = cppad_py.a_fun(f)

ax
This argument must be the same as ax returned by the previous call to independent; i.e., it must be the independent variable vector. We use n to denote the number of independent variables (the size of ax ).

ay
This argument is a numpy array with a_double elements. It specifies the dependent variables. We use m to denote the number of dependent variables (the size of ay ).

f
This result is a function object that has a representation for the floating point operations that mapped the independent variables to the dependent variables. This object computes function and derivative values using double

af
This result is a function object that has a representation for the same function as f . This object computes function and derivative values using a_double Initially, there are not Taylor coefficient stored in af ; i.e., af.size_order() is zero.

Example
All of the examples use the d_fun constructor. The example a_fun_xam.py demonstrates the purpose of a_fun objects.

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Input File: lib/python/fun_ctor.py