def fun_property_xam():
#
import numpy
import cppad_py
#
# initialize return variable
ok = True
# ---------------------------------------------------------------------
n_ind = 1 # number of independent variables
n_dep = 2 # number of dependent variables
n_var = 1 # phantom variable at address 0
n_op = 1 # special operator at beginning
#
# dimension some vectors
# x = numpy.empty(n_ind, dtype=float)
x = numpy.empty(n_ind, dtype=float)
ay = numpy.empty(n_dep, dtype=cppad_py.a_double)
#
# independent variables
x[0] = 1.0
ax = cppad_py.independent(x)
n_var = n_var + n_ind # one for each indpendent
n_op = n_op + n_ind
#
# first dependent variable
ay[0] = ax[0] + ax[0]
n_var = n_var + 1 # one variable and operator
n_op = n_op + 1
#
# second dependent variable
ax0 = ax[0]
ay[1] = ax0.sin()
n_var = n_var + 2 # two varialbes, one operator
n_op = n_op + 1
#
# define f(x) = y
f = cppad_py.d_fun(ax, ay)
n_op = n_op + 1 # speical operator at end
#
# check af properties
ok = ok and f.size_domain() == n_ind
ok = ok and f.size_range() == n_dep
ok = ok and f.size_var() == n_var
ok = ok and f.size_op() == n_op
ok = ok and f.size_order() == 0
#
# compute zero order Taylor coefficients
y = f.forward(0, x)
ok = ok and f.size_order() == 1
# ---------------------------------------------------------------------
af = cppad_py.a_fun(f)
#
# check af properties
ok = ok and af.size_domain() == n_ind
ok = ok and af.size_range() == n_dep
ok = ok and af.size_var() == n_var
ok = ok and af.size_op() == n_op
ok = ok and af.size_order() == 0
#
return (ok)
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 )
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
def fun_reverse_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 xp = numpy.empty(n_ind, dtype=float) for i in range( n_ind ) : xp[i] = i # ax = cppad_py.independent(xp) # # 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) # ----------------------------------------------------------------------- # define X(t) = (x_0 + t, x_1 + t, x_2 + t) # it follows that Y(t) = f(X(t)) = (x_0 + t) * (x_1 + t) * (x_2 + t) # and that Y'(0) = x_1 * x_2 + x_0 * x_2 + x_0 * x_1 # ----------------------------------------------------------------------- # zero order forward mode p = 0 xp[0] = 2.0 xp[1] = 3.0 xp[2] = 4.0 yp = f.forward(p, xp) ok = ok and yp[0] == 24.0 # ----------------------------------------------------------------------- # first order reverse (derivative of zero order forward) # define G( Y ) = y_0 = x_0 * x_1 * x_2 m = f.size_range() q = 1 yq1 = numpy.empty( (m, q), dtype=float) yq1[0, 0] = 1.0 xq1 = f.reverse(q, yq1) # partial G w.r.t x_0 ok = ok and xq1[0,0] == 3.0 * 4.0 # partial G w.r.t x_1 ok = ok and xq1[1,0] == 2.0 * 4.0 # partial G w.r.t x_2 ok = ok and xq1[2,0] == 2.0 * 3.0 # ----------------------------------------------------------------------- # first order forward mode p = 1 xp[0] = 1.0 xp[1] = 1.0 xp[2] = 1.0 yp = f.forward(p, xp) ok = ok and yp[0] == 3.0*4.0 + 2.0*4.0 + 2.0*3.0 # ----------------------------------------------------------------------- # second order reverse (derivative of first order forward) # define G( y_0^0 , y_0^1 ) = y_0^1 # = x_1^0 * x_2^0 + x_0^0 * x_2^0 + x_0^0 * x_1^0 q = 2 yq2 = numpy.empty( (m, q), dtype=float) yq2[0, 0] = 0.0 # partial of G w.r.t y_0^0 yq2[0, 1] = 1.0 # partial of G w.r.t y_0^1 xq2 = f.reverse(q, yq2) # partial G w.r.t x_0^0 ok = ok and xq2[0, 0] == 3.0 + 4.0 # partial G w.r.t x_1^0 ok = ok and xq2[1, 0] == 2.0 + 4.0 # partial G w.r.t x_2^0 ok = ok and xq2[2, 0] == 2.0 + 3.0 # ----------------------------------------------------------------------- af = cppad_py.a_fun(f) # # zero order forward axp = numpy.empty(n_ind, dtype=cppad_py.a_double) p = 0 axp[0] = 2.0 axp[1] = 3.0 axp[2] = 4.0 ayp = af.forward(p, axp) ok = ok and ayp[0] == cppad_py.a_double(24.0) # # first order reverse q = 1 ayq1 = numpy.empty( (m, q), dtype=cppad_py.a_double) ayq1[0, 0] = 1.0 axq1 = af.reverse(q, ayq1) # partial G w.r.t x_0 ok = ok and axq1[0,0] == cppad_py.a_double(3.0 * 4.0) # partial G w.r.t x_1 ok = ok and axq1[1,0] == cppad_py.a_double(2.0 * 4.0) # partial G w.r.t x_2 ok = ok and axq1[2,0] == cppad_py.a_double(2.0 * 3.0) # return( ok )
def fun_forward_xam() : # import numpy import cppad_py # # initialize return variable ok = True # --------------------------------------------------------------------- # number of dependent and independent variables n_dep = 1 n_ind = 2 # # create the independent variables ax xp = numpy.empty(n_ind, dtype=float) for i in range( n_ind ) : xp[i] = i + 1.0 # ax = cppad_py.independent(xp) # # create dependent varialbes ay with ay0 = ax0 * ax1 ax0 = ax[0] ax1 = ax[1] ay = numpy.empty(n_dep, dtype=cppad_py.a_double) ay[0] = ax0 * ax1 # # define af corresponding to f(x) = x0 * x1 f = cppad_py.d_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 = f.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 = f.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 = f.forward(p, xp) ok = ok and yp[0] == 1.0 # --------------------------------------------------------------------- af = cppad_py.a_fun(f) ok = ok and af.size_order() == 0 # # zero order forward p = 0 axp = numpy.empty(n_ind, dtype=cppad_py.a_double) axp[0] = 3.0 axp[1] = 2.0 ayp = af.forward(p, axp) ok = ok and ayp[0] == cppad_py.a_double(6.0) ok = ok and af.size_order() == 1 # return( ok )
def fun_hessian_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)
#
# g(x) = w_0 * f_0 (x) = f(x)
w = numpy.empty(n_dep, dtype=float)
w[0] = 1.0
#
# compute Hessian
fpp = f.hessian(x, w)
#
# [ 0.0 , x_2 , x_1 ]
# f''(x) = [ x_2 , 0.0 , x_0 ]
# [ x_1 , x_0 , 0.0 ]
ok = ok and fpp[0, 0] == 0.0
ok = ok and fpp[0, 1] == x[2]
ok = ok and fpp[0, 2] == x[1]
#
ok = ok and fpp[1, 0] == x[2]
ok = ok and fpp[1, 1] == 0.0
ok = ok and fpp[1, 2] == x[0]
#
ok = ok and fpp[2, 0] == x[1]
ok = ok and fpp[2, 1] == x[0]
ok = ok and fpp[2, 2] == 0.0
# ---------------------------------------------------------------------
af = cppad_py.a_fun(f)
#
# compute and check Hessian
aw = numpy.empty(n_dep, dtype=cppad_py.a_double)
aw[0] = w[0]
afpp = af.hessian(ax, aw)
ok = ok and afpp.shape == fpp.shape
(nr, nc) = fpp.shape
for i in range(nr):
for j in range(nc):
ok = ok and afpp[i, j] == cppad_py.a_double(fpp[i, j])
#
return (ok)
