def rotation_s(x, plot=False):
flip = np.random.choice([-1], size=(1, x.shape[1]))
rotate_axis = np.arange(x.shape[1])
np.random.shuffle(rotate_axis)
x_ = flip[:, :] * x[:, rotate_axis]
if plot:
hlp.plot1d(x, x_, save_file='aug_examples/rotation_s.png')
return x_
def scaling_s(x, sigma=0.1, plot=False):
# https://arxiv.org/pdf/1706.00527.pdf
factor = np.random.normal(loc=1., scale=sigma, size=(1, x.shape[1]))
x_ = np.multiply(x, factor[:, :])
if plot:
hlp.plot1d(x, x_, save_file='aug_examples/scal.png')
return x_
def time_warp_s(x, sigma=0.2, knot=4, plot=False):
from scipy.interpolate import CubicSpline
orig_steps = np.arange(x.shape[0])
random_warps = np.random.normal(loc=1.0,
scale=sigma,
size=(1, knot + 2, x.shape[1]))
warp_steps = (np.ones(
(x.shape[1], 1)) * (np.linspace(0, x.shape[0] - 1., num=knot + 2))).T
ret = np.zeros_like(x)
for dim in range(x.shape[1]):
time_warp = CubicSpline(warp_steps[:, dim], warp_steps[:, dim] *
random_warps[0, :, dim])(orig_steps)
scale = (x.shape[0] - 1) / time_warp[-1]
ret[:, dim] = np.interp(orig_steps,
np.clip(scale * time_warp, 0, x.shape[0] - 1),
x[:, dim]).T
if plot:
hlp.plot1d(x, ret, save_file='aug_examples/time_warp_s.png')
return ret
def magnitude_warp_s(x, sigma=0.2, knot=4, plot=False):
from scipy.interpolate import CubicSpline
orig_steps = np.arange(x.shape[0])
random_warps = np.random.normal(loc=1.0,
scale=sigma,
size=(1, knot + 2, x.shape[1]))
warp_steps = (np.ones(
(x.shape[1], 1)) * (np.linspace(0, x.shape[0] - 1., num=knot + 2))).T
li = []
for dim in range(x.shape[1]):
li.append(
CubicSpline(warp_steps[:, dim], random_warps[0, :,
dim])(orig_steps))
warper = np.array(li).T
x_ = x * warper
if plot:
hlp.plot1d(x, x_, save_file='aug_examples/magnitude_warp_s.png')
return x_