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 ackley(x):
a, b, c = 20.0, -0.2, 2.0 * numpy.pi
len_recip = 1.0 / len(x)
sum_sqrs, sum_cos = 0.0, 0.0
for i in x:
sum_cos += algopy.cos(c * i)
sum_sqrs += i * i
return -a * algopy.exp(b * algopy.sqrt(len_recip * sum_sqrs)) - algopy.exp(len_recip * sum_cos) + a + numpy.e
def ackley(x):
a, b, c = 20.0, -0.2, 2.0 * numpy.pi
len_recip = 1.0 / len(x)
sum_sqrs, sum_cos = 0.0, 0.0
for i in x:
sum_cos += algopy.cos(c * i)
sum_sqrs += i * i
return (-a * algopy.exp(b * algopy.sqrt(len_recip * sum_sqrs)) -
algopy.exp(len_recip * sum_cos) + a + numpy.e)
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
