103 lines
2.5 KiB
Plaintext
103 lines
2.5 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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"metadata": {},
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"outputs": [],
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"source": [
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"from model import *\n",
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"from similarity import *\n",
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"import csv"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"case_base = CaseBase.from_csv(\n",
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" \"data/SIM_001.csv\",\n",
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" problem_fields = (\"v\", \"v_left\", \"v_front\", \"d_left\", \"d_front\", \"type_left\", \"type_front\", \"radius_curve(m)\", \"slope_street\", \"street_type\", \"time\", \"weather\", \"type_vehicle\", \"speed_limit(km/h)\"),\n",
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" solution_fields = (\"action\"),\n",
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" encoding = \"utf-8\",\n",
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" delimiter = \";\",\n",
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" set_int = True\n",
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")\n",
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"print(case_base)\n",
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"case_base[:3]"
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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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"metadata": {},
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"outputs": [],
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"source": []
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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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"metadata": {},
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"outputs": [],
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"source": [
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"query = Query.from_problems(\n",
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" v = 28,\n",
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" v_left = 37,\n",
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" v_front = 22.5,\n",
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" d_left = -20,\n",
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" d_front = 51,\n",
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")\n",
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"# sim_funcs: manhattan_sim, euclid_sim\n",
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"\n",
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"retrieved = case_base.retrieve(\n",
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" query,\n",
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" v_left = euclid_sim,\n",
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" v_front = euclid_sim,\n",
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" d_left = euclid_sim,\n",
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" d_front = euclid_sim,\n",
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")\n",
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"\n",
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"print(\"Your Query:\")\n",
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"for k, v in query.problem.items():\n",
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" print(f\" - {k} = {v}\")\n",
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"print()\n",
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"print(\"I recommend you this car:\")\n",
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"print(\" \".join(retrieved.solution.values()).capitalize())\n",
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"print()\n",
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"print(\"Explanation:\")\n",
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"for field, sim_val in retrieved.sim_per_field.items():\n",
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" print(f\" - {field} =\", retrieved.problem[field], f\"(similarity: {sim_val:.2f})\")"
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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",
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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": "3.11.0"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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