def respond_to_demonstration_letter(self,
demonstration,
letter,
mode='midway',
mode_param=0.5):
demo_stroke = Stroke()
demo_stroke.stroke_from_xxyy(
np.reshape(demonstration, len(demonstration)))
demo_stroke.normalize_wrt_max()
if mode == 'midway':
learned_stroke = stroke.midway(demo_stroke,
self.generated_letters[letter],
mode_param)
self.generated_letters[letter] = learned_stroke
save_learned_allograph(self.child_path, letter, learned_stroke)
_, score = stroke.euclidian_distance(demo_stroke,
self.refs[letter])
if mode == 'simple':
learned_stroke = stroke.weigthedSum(demo_stroke,
self.generated_letters[letter],
mode_param)
self.generated_letters[letter] = learned_stroke
save_learned_allograph(self.child_path, letter, learned_stroke)
_, score = stroke.euclidian_distance(demo_stroke,
self.refs[letter])
return self.shape_message(letter), score
def respond_to_demonstration_word(self, demonstrations, mode='midway'): #mutual_modeling will act here
if mode == 'midway':
for letter,stroke in demonstrations:
learned_stroke = stroke.midway(stroke, self.generated_letters[letter])
self.generated_letters[letter] = learned_stroke
save_learned_allograph(self.robot_data, letter, learned_stroke)
score = stroke.euclidian_distance(demo_stroke, self.refs[letter])
def respond_to_demonstration_letter(self, demonstration, letter, grade, mode='midway'):
demo_stroke = Stroke()
demo_stroke.stroke_from_xxyy(np.reshape(demonstration,len(demonstration)))
#demo_stroke.uniformize()
demo_stroke.normalize_wrt_max()
if mode == 'midway':
learned_stroke = stroke.midway(demo_stroke, self.generated_letters[letter], grade)
self.generated_letters[letter] = learned_stroke
save_learned_allograph(self.robot_path, letter, learned_stroke)
_,score = stroke.euclidian_distance(demo_stroke, self.refs[letter])
#if mode = 'PCA'
#if mode = 'sigNorm' (mixture of sigma-log-normal)
#if mode = 'CNN' (1-D convolutionnal neural networks)
return self.shape_message(letter),score