2024-heraklion-scientific-p.../notebooks/walker/Step_6_loading_parameters_from_file/walker.py
2024-08-27 15:52:41 +03:00

61 lines
2.2 KiB
Python

import numpy as np
class Walker:
""" The Walker knows how to walk at random on a context map. """
def __init__(self, sigma_i, sigma_j, context_map):
self.sigma_i = sigma_i
self.sigma_j = sigma_j
self.size = context_map.shape[0]
# Make sure that the context map is normalized
context_map /= context_map.sum()
self.context_map = context_map
# Pre-compute a 2D grid of coordinates for efficiency
self._grid_ii, self._grid_jj = np.mgrid[0:self.size, 0:self.size]
# --- Walker public interface
def sample_next_step(self, current_i, current_j, random_state=np.random):
""" Sample a new position for the walker. """
# Combine the next-step proposal with the context map to get a
# next-step probability map
next_step_map = self._next_step_proposal(current_i, current_j)
selection_map = self._compute_next_step_probability(next_step_map)
# Draw a new position from the next-step probability map
r = random_state.rand()
cumulative_map = np.cumsum(selection_map)
cumulative_map = cumulative_map.reshape(selection_map.shape)
i_next, j_next = np.argwhere(cumulative_map >= r)[0]
return i_next, j_next
# --- Walker non-public interface
def _next_step_proposal(self, current_i, current_j):
""" Create the 2D proposal map for the next step of the walker. """
# 2D Gaussian distribution , centered at current position,
# and with different standard deviations for i and j
grid_ii, grid_jj = self._grid_ii, self._grid_jj
sigma_i, sigma_j = self.sigma_i, self.sigma_j
rad = (
(((grid_ii - current_i) ** 2) / (sigma_i ** 2))
+ (((grid_jj - current_j) ** 2) / (sigma_j ** 2))
)
p_next_step = np.exp(-(rad / 2.0)) / (2.0 * np.pi * sigma_i * sigma_j)
return p_next_step / p_next_step.sum()
def _compute_next_step_probability(self, next_step_map):
""" Compute the next step probability map from next step proposal and
context map. """
next_step_probability = next_step_map * self.context_map
next_step_probability /= next_step_probability.sum()
return next_step_probability