def fmin_cg(fn, init_args=None, init_kwargs=None, **scipy_kwargs):
"""
Minimize a scalar valued function using SciPy's nonlinear conjugate
gradient algorithm. The initial parameter guess is 'init_args'.
"""
init_args = utils.as_seq(init_args, tuple)
init_kwargs = utils.as_seq(init_kwargs, dict)
f = VectorArg(fn,
init_args=init_args,
init_kwargs=init_kwargs,
compile_fn=True)
fprime = VectorArg(fn,
init_args=init_args,
init_kwargs=init_kwargs,
compile_grad=True)
x0 = f.vector_from_args(init_args, init_kwargs)
x_opt = scipy.optimize.fmin_cg(
f=f,
x0=x0,
fprime=fprime,
full_output=False,
**scipy_kwargs)
x_reshaped = f.args_from_vector(x_opt)
if len(x_reshaped) == 1:
x_reshaped = x_reshaped[0]
return x_reshaped
def fmin_cg(fn, init_args=None, init_kwargs=None, **scipy_kwargs):
"""
Minimize a scalar valued function using SciPy's nonlinear conjugate
gradient algorithm. The initial parameter guess is 'init_args'.
"""
init_args = utils.as_seq(init_args, tuple)
init_kwargs = utils.as_seq(init_kwargs, dict)
f = VectorArg(fn,
init_args=init_args,
init_kwargs=init_kwargs,
compile_fn=True)
fprime = VectorArg(fn,
init_args=init_args,
init_kwargs=init_kwargs,
compile_grad=True)
x0 = f.vector_from_args(init_args, init_kwargs)
x_opt = scipy.optimize.fmin_cg(f=f,
x0=x0,
fprime=fprime,
full_output=False,
**scipy_kwargs)
x_reshaped = f.args_from_vector(x_opt)
if len(x_reshaped) == 1:
x_reshaped = x_reshaped[0]
return x_reshaped
def fmin_l_bfgs_b(fn,
init_args=None,
init_kwargs=None,
scalar_bounds=None,
return_info=False,
**scipy_kwargs):
"""
Minimize a scalar valued function using SciPy's L-BFGS-B algorithm. The
initial parameter guess is 'init_args'.
"""
init_args = utils.as_seq(init_args, tuple)
init_kwargs = utils.as_seq(init_kwargs, dict)
f_df = VectorArg(fn,
init_args=init_args,
init_kwargs=init_kwargs,
compile_fn=True,
compile_grad=True)
x0 = f_df.vector_from_args(init_args, init_kwargs)
if 'approx_grad' in scipy_kwargs:
raise TypeError('duplicate argument: approx_grad')
if scalar_bounds is not None:
lb, ub = scalar_bounds
bounds = np.empty((len(x0), 2))
bounds[:, 0] = lb
bounds[:, 1] = ub
if 'bounds' in scipy_kwargs:
raise TypeError('duplicate argument: bounds')
scipy_kwargs['bounds'] = bounds
x_opt, f_opt, info = scipy.optimize.fmin_l_bfgs_b(
func=f_df,
x0=x0,
approx_grad=False,
**scipy_kwargs)
x_reshaped = f_df.args_from_vector(x_opt)
if len(x_reshaped) == 1:
x_reshaped = x_reshaped[0]
if return_info:
return x_reshaped, {'f_opt': f_opt, 'info': info}
else:
return x_reshaped
def fmin_l_bfgs_b(fn,
init_args=None,
init_kwargs=None,
scalar_bounds=None,
return_info=False,
**scipy_kwargs):
"""
Minimize a scalar valued function using SciPy's L-BFGS-B algorithm. The
initial parameter guess is 'init_args'.
"""
init_args = utils.as_seq(init_args, tuple)
init_kwargs = utils.as_seq(init_kwargs, dict)
f_df = VectorArg(fn,
init_args=init_args,
init_kwargs=init_kwargs,
function=True,
gradient=True)
x0 = f_df.vector_from_args(init_args, init_kwargs)
if 'approx_grad' in scipy_kwargs:
raise TypeError('duplicate argument: approx_grad')
if scalar_bounds is not None:
lb, ub = scalar_bounds
bounds = np.empty((len(x0), 2))
bounds[:, 0] = lb
bounds[:, 1] = ub
if 'bounds' in scipy_kwargs:
raise TypeError('duplicate argument: bounds')
scipy_kwargs['bounds'] = bounds
x_opt, f_opt, info = scipy.optimize.fmin_l_bfgs_b(
func=f_df,
x0=x0,
approx_grad=False,
**scipy_kwargs)
x_reshaped = f_df.args_from_vector(x_opt)
if len(x_reshaped) == 1:
x_reshaped = x_reshaped[0]
if return_info:
return x_reshaped, {'f_opt': f_opt, 'info': info}
else:
return x_reshaped
def test_vectorarg(self):
def f(x):
bar = np.ones(x.shape)
bar[1, 1] = 0
return (x * 3 * bar).sum()
x = np.ones((2, 4))
v = VectorArg(f, [x], function=True, gradient=True)
result = v(x.flat)
self.assertTrue(np.allclose(result[0], 21))
expected_grad = np.ones(x.size) * 3
expected_grad[6] = 0
self.assertTrue =(np.allclose(result[1], expected_grad))
def fmin_ncg(fn, init_args=None, init_kwargs=None, **scipy_kwargs):
"""
Minimize a scalar valued function using SciPy's Newton-CG algorithm. The
initial parameter guess is 'init_args'.
"""
init_args = utils.as_seq(init_args, tuple)
init_kwargs = utils.as_seq(init_kwargs, dict)
f = VectorArg(fn,
init_args=init_args,
init_kwargs=init_kwargs,
function=True)
fprime = VectorArg(fn,
init_args=init_args,
init_kwargs=init_kwargs,
gradient=True)
fhess_p = VectorArg(fn,
init_args=init_args,
init_kwargs=init_kwargs,
hessian_vector=True)
x0 = f.vector_from_args(init_args, init_kwargs)
x_opt = scipy.optimize.fmin_ncg(
f=f,
x0=x0,
fprime=fprime,
fhess_p=fhess_p,
full_output=False,
**scipy_kwargs)
x_reshaped = f.args_from_vector(x_opt)
if len(x_reshaped) == 1:
x_reshaped = x_reshaped[0]
return x_reshaped
