def eval_g(x, y):
""" some vector-valued function """
retval = algopy.zeros(3, dtype=x)
retval[0] = algopy.sin(x**2 + y)
retval[1] = algopy.cos(x+y) - x
retval[2] = algopy.sin(x)**2 + algopy.cos(x)**2
return retval
def eval_g(x, y):
""" some vector-valued function """
retval = algopy.zeros(3, dtype=x)
retval[0] = algopy.sin(x**2 + y)
retval[1] = algopy.cos(x + y) - x
retval[2] = algopy.sin(x)**2 + algopy.cos(x)**2
return retval
def f(x):
nobs = x.shape[1:]
f0 = x[0]**2 * sin(x[1])**2
f1 = x[0]**2 * cos(x[1])**2
out = zeros((2,) + nobs, dtype=x)
out[0,:] = f0
out[1,:] = f1
return out
def test_tangent_gradient(self):
cg = CGraph()
x = Function(1.)
y1 = algopy.tan(x)
cg.trace_off()
cg.independentFunctionList = [x]
cg.dependentFunctionList = [y1]
g1 = cg.gradient([1.])[0]
x = UTPM.init_jacobian(1.)
assert_array_almost_equal(g1,UTPM.extract_jacobian(algopy.sin(x)/algopy.cos(x)))
assert_array_almost_equal(g1,UTPM.extract_jacobian(algopy.tan(x)))
def rotate(x, y, U_direction_radians):
x_r = x*cos(U_direction_radians)-y*sin(U_direction_radians)
y_r = x*sin(U_direction_radians)+y*cos(U_direction_radians)
return x_r, y_r
def rotate(x, y, U_direction_radians):
x_r = x * cos(U_direction_radians) - y * sin(U_direction_radians)
y_r = x * sin(U_direction_radians) + y * cos(U_direction_radians)
return x_r, y_r
cstart = clock()
r8 = test_numdifftools_std()
cstop = clock()
LOGGER.debug('test numdifftools w/std-dev only:\n\telapsed time> %g [s]\n',
cstop - cstart)
cstart = clock()
r9 = test_numdifftools_cov()
cstop = clock()
LOGGER.debug('test numdifftools w/covariance:\n\telapsed time> %g [s]\n',
cstop - cstart)
# test numdifftools with algopy
G = lambda x: sin(x)
def test_algopy_std(avg=AVG, std=STD, f=G):
c = clock()
jac = nda.Derivative(f)
LOGGER.debug('\t1> %g [s]', clock() - c)
j = jac(avg)
LOGGER.debug('\t2> %g [s]', clock() - c)
std = np.abs(j*std) # np.sqrt(j*std*std*j)
LOGGER.debug('\t3> %g [s]', clock() - c)
avg = f(avg)
LOGGER.debug('\t4> %g [s]', clock() - c)
dt = np.dtype([('avg', float), ('std', float)])
LOGGER.debug('\t5> %g [s]', clock() - c)
return np.array(zip(avg, std), dtype=dt)
