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