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"""Nearest Neighbor Index module.
"""
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "BSD"
from typing import TypeVar, Tuple, Any
from dataclasses import dataclass, field
from argaze import GazeFeatures, DataFeatures
import numpy
from scipy.spatial.distance import cdist
@dataclass
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)
"""
size: tuple[float, float]
"""Frame dimension."""
def __post_init__(self):
super().__init__()
self.__nearest_neighbor_index = 0
@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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