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"""Nearest Neighbor Index module.
This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
this program. If not, see <http://www.gnu.org/licenses/>.
"""
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "GPLv3"
from argaze import GazeFeatures, DataFeatures
import numpy
from scipy.spatial.distance import cdist
class ScanPathAnalyzer(GazeFeatures.ScanPathAnalyzer):
"""Implementation of Nearest Neighbor Index algorithm as described in:
**Di Nocera F., Terenzi M., Camilli M. (2006).**
*Another look at scanpath: distance to nearest neighbour as a measure of mental workload.*
Developments in Human Factors in Transportation, Design, and Evaluation.
[https://www.researchgate.net](https://www.researchgate.net/publication/239470608_Another_look_at_scanpath_distance_to_nearest_neighbour_as_a_measure_of_mental_workload)
"""
@DataFeatures.PipelineStepInit
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.__size = (0, 0)
self.__nearest_neighbor_index = 0
@property
def size(self) -> tuple[float, float]:
"""Frame dimension."""
return self.__size
@size.setter
def size(self, size: tuple[float, float]):
self.__size = size
@DataFeatures.PipelineStepMethod
def analyze(self, scan_path: GazeFeatures.ScanPathType):
assert(len(scan_path) > 1)
# Gather fixations focus points
fixations_focus = []
for step in scan_path:
fixations_focus.append(step.first_fixation.focus)
# Compute inter fixation distances
distances = cdist(fixations_focus, fixations_focus)
# Find minimal distances between each fixations
minimums = numpy.apply_along_axis(lambda row: numpy.min(row[numpy.nonzero(row)]), 1, distances)
# Average of minimun distances
dNN = numpy.sum(minimums / len(fixations_focus))
# Mean random distance
dran = 0.5 * numpy.sqrt(self.size[0] * self.size[1] / len(fixations_focus))
self.__nearest_neighbor_index = dNN / dran
@property
def nearest_neighbor_index(self) -> float:
"""Nearest Neighbor Index."""
return self.__nearest_neighbor_index
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