Пример #1
0
    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
Пример #2
0
    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])
Пример #3
0
 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