224 lines
6.7 KiB
Plaintext
224 lines
6.7 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8ad9fe94",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Walker:\n",
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" # ...\n",
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" \n",
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" def sample_next_step(self, current_i, current_j, random_state=np.random):\n",
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" \"\"\" Sample a new position for the walker. \"\"\"\n",
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" # Combine the next-step proposal with the context map to get a\n",
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" # next-step probability map\n",
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" next_step_map = self._next_step_proposal(current_i, current_j)\n",
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" selection_map = self._compute_next_step_probability(next_step_map)\n",
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"\n",
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" # Draw a new position from the next-step probability map\n",
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" r = random_state.rand()\n",
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" cumulative_map = np.cumsum(selection_map)\n",
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" cumulative_map = cumulative_map.reshape(selection_map.shape)\n",
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" i_next, j_next = np.argwhere(cumulative_map >= r)[0]\n",
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"\n",
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" return i_next, j_next\n",
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"\n",
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" def _next_step_proposal(self, current_i, current_j):\n",
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" \"\"\" Create the 2D proposal map for the next step of the walker. \"\"\"\n",
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" # 2D Gaussian distribution , centered at current position,\n",
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" # and with different standard deviations for i and j\n",
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" grid_ii, grid_jj = self._grid_ii, self._grid_jj\n",
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" sigma_i, sigma_j = self.sigma_i, self.sigma_j\n",
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"\n",
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" rad = (\n",
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" (((grid_ii - current_i) ** 2) / (sigma_i ** 2))\n",
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" + (((grid_jj - current_j) ** 2) / (sigma_j ** 2))\n",
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" )\n",
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"\n",
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" p_next_step = np.exp(-(rad / 2.0)) / (2.0 * np.pi * sigma_i * sigma_j)\n",
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" return p_next_step / p_next_step.sum()\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "95148e45",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Walker:\n",
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" # ...\n",
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"\n",
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" def _next_step_proposal(self, current_i, current_j):\n",
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" \"\"\" Create the 2D proposal map for the next step of the walker. \"\"\"\n",
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" raise NotImplementedError(\"`_next_step_proposal` not implemented\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "da34226f",
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"metadata": {},
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"outputs": [],
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"source": [
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"class GaussianWalker(Walker):\n",
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" # ...\n",
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"\n",
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" def _next_step_proposal(self, current_i, current_j):\n",
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" \n",
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" \n",
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" \n",
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" \n",
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"class RectangularWalker(Walker):\n",
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" # ...\n",
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"\n",
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" def _next_step_proposal(self, current_i, current_j):\n",
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"\n",
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" \n",
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" \n",
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" \n",
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"class JumpingWalker(Walker):\n",
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" # ...\n",
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"\n",
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" def _next_step_proposal(self, current_i, current_j):\n",
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" \n",
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" \n",
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" \n",
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" \n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cfd90d16",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Walker:\n",
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" # ...\n",
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"\n",
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"\n",
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" def _compute_next_step_probability(self, next_step_map):\n",
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" \"\"\" Compute the next step probability map from next step proposal and\n",
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" context map. \"\"\"\n",
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" next_step_probability = next_step_map * self.context_map\n",
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" next_step_probability /= next_step_probability.sum()\n",
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" return next_step_probability\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "72041675",
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"metadata": {},
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"outputs": [],
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"source": [
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"class GaussianWalkerWithProductInteraction(Walker):\n",
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" def _next_step_proposal(self, current_i, current_j):\n",
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" # ...\n",
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" def _compute_next_step_probability(self, next_step_map):\n",
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" # ...\n",
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"\n",
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" \n",
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"class GaussianWalkerWithSumInteraction(Walker):\n",
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" def _next_step_proposal(self, current_i, current_j):\n",
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" # ...\n",
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" def _compute_next_step_probability(self, next_step_map):\n",
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" # ...\n",
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"\n",
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" \n",
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"class RectangularWalkerWithProductInteraction(Walker):\n",
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" def _next_step_proposal(self, current_i, current_j):\n",
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" # ...\n",
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" def _compute_next_step_probability(self, next_step_map):\n",
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" # ...\n",
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"\n",
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" \n",
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"class RectangularWalkerWithSumInteraction(Walker):\n",
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" def _next_step_proposal(self, current_i, current_j):\n",
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" # ...\n",
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" def _compute_next_step_probability(self, next_step_map):\n",
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" # ...\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5ee2e200",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Walker:\n",
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" def __init__(self, size, context_map, next_step_proposal, next_step_proposal_arguments):\n",
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" self.next_step_proposal = next_step_proposal\n",
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" # ...\n",
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"\n",
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" \n",
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" def sample_next_step(self, current_i, current_j, random_state=np.random):\n",
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" \"\"\" Sample a new position for the walker. \"\"\"\n",
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" # Combine the next-step proposal with the context map to get a\n",
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" # next-step probability map\n",
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" next_step_map = self.next_step_proposal(current_i, current_j, **next_step_proposal_arguments)\n",
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" selection_map = self._compute_next_step_probability(next_step_map)\n",
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"\n",
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" # Draw a new position from the next-step probability map\n",
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" r = random_state.rand()\n",
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" cumulative_map = np.cumsum(selection_map)\n",
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" cumulative_map = cumulative_map.reshape(selection_map.shape)\n",
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" i_next, j_next = np.argwhere(cumulative_map >= r)[0]\n",
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"\n",
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" return i_next, j_next\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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