def test_most_drivers(self):
def f(x):
return x[0]*x[1]*x[2] + 7*x[1]
def g(x):
out = algopy.zeros(3, dtype=x)
out[0] = 2*x[0]**2
out[1] = 7*x[0]*x[1]
out[2] = 23*x[0] + x[2]
return out
x = numpy.array([1,2,3],dtype=float)
v = numpy.array([1,1,1],dtype=float)
w = numpy.array([4,5,6],dtype=float)
# forward mode gradient
res1 = UTPM.extract_jacobian(f(UTPM.init_jacobian(x)))
# forward mode Jacobian
res2 = UTPM.extract_jacobian(g(UTPM.init_jacobian(x)))
# forward mode Jacobian-vector
res3 = UTPM.extract_jac_vec(g(UTPM.init_jac_vec(x, v)))
# trace f
cg = algopy.CGraph()
fx = algopy.Function(x)
fy = f(fx)
cg.trace_off()
cg.independentFunctionList = [fx]
cg.dependentFunctionList = [fy]
# trace g
cg2 = algopy.CGraph()
fx = algopy.Function(x)
fy = g(fx)
cg2.trace_off()
cg2.independentFunctionList = [fx]
cg2.dependentFunctionList = [fy]
# reverse mode gradient
res4 = cg.gradient(x)
assert_array_almost_equal(numpy.array( [x[1]*x[2],
x[0]*x[2]+7,
x[0]*x[1]]), res4)
# forward/reverse mode Hessian
res5 = cg.hessian(x)
assert_array_almost_equal(numpy.array( [[0, x[2], x[1]],
[x[2], 0., x[0]],
[x[1], x[0], 0]]), res5)
# forward/reverse mode Hessian-vector
res6 = cg.hess_vec(x,v)
assert_array_almost_equal(numpy.dot(res5, v), res6)
# reverese mode Jacobian
res7 = cg2.jacobian(x)
assert_array_almost_equal(numpy.array( [[4*x[0], 0, 0],
[7*x[1], 7*x[0], 0],
[23., 0, 1]]), res7)
# reverse mode vector-Jacobian
res8 = cg2.vec_jac(w,x)
assert_array_almost_equal(numpy.dot(w,res7), res8)
# forward mode Jacobian-vector
res9 = cg2.jac_vec(x,v)
assert_array_almost_equal(numpy.dot(res7,v), res9)
# forward/reverse mode vector-Hessian-vector
res10 = cg2.vec_hess_vec(w,x,v)
assert_array_almost_equal(numpy.array([4*v[0]*w[0]+ 7*v[1]*w[1],
7*w[1],
0]), res10)
