algo/IBDT.py
def updateFixationAndSaccadeLikelihood(self) -> None:
        """Queries the model for predicted likelihoods, writes them to cur sample.

        :return:
        """
        if self.cur.base.v < self.fMean:
            self.cur.fixation.likelihood = 1
            self.cur.saccade.likelihood = 0
            return None

        if self.cur.base.v > self.sMean:
            self.cur.fixation.likelihood = 0
            self.cur.saccade.likelihood = 1
            return None


        sample = np.array(self.cur.base.v)
        try:
            likelihoods = self.model.predict( sample )[1][0]
            self.cur.fixation.likelihood = likelihoods[self.fIdx]
            self.cur.saccade.likelihood = likelihoods[self.sIdx]
        #all zeros in sample
        except cv2.error:
            self.cur.fixation.likelihood = 0.0
            self.cur.saccade.likelihood = 0.0
Queries the model for predicted likelihoods, writes them to cur sample.

        :return:
        
Queries the model for predicted likelihoods, writes them to cur sample.
Queries the model for predicted likelihoods writes them to cur sample .
def updateFixationAndSaccadeLikelihood(self) -> None:
        if self.cur.base.v < self.fMean:
            self.cur.fixation.likelihood = 1
            self.cur.saccade.likelihood = 0
            return None
        if self.cur.base.v > self.sMean:
            self.cur.fixation.likelihood = 0
            self.cur.saccade.likelihood = 1
            return None
        sample = np.array(self.cur.base.v)
        try:
            likelihoods = self.model.predict( sample )[1][0]
            self.cur.fixation.likelihood = likelihoods[self.fIdx]
            self.cur.saccade.likelihood = likelihoods[self.sIdx]
        except cv2.error:
            self.cur.fixation.likelihood = 0.0
            self.cur.saccade.likelihood = 0.0
def updateFixationAndSaccadeLikelihood ( self ) -> None : if self . cur . base . v < self . fMean : self . cur . fixation . likelihood = 1 self . cur . saccade . likelihood = 0 return None if self . cur . base . v > self . sMean : self . cur . fixation . likelihood = 0 self . cur . saccade . likelihood = 1 return None sample = np . array ( self . cur . base . v ) try : likelihoods = self . model . predict ( sample ) [ 1 ] [ 0 ] self . cur . fixation . likelihood = likelihoods [ self . fIdx ] self . cur . saccade . likelihood = likelihoods [ self . sIdx ] except cv2 . error : self . cur . fixation . likelihood = 0.0 self . cur . saccade . likelihood = 0.0
Queries the model for predicted likelihoods, writes them to cur sample.
Queries the model for predicted likelihoods writes them to cur sample .
"""Queries the model for predicted likelihoods, writes them to cur sample.

        :return:
        """ #all zeros in sample
None
self