add decision tree model to trashbins, which are generated on map
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109
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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109
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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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": 9,
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"id": "fac14368",
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = pandas.read_csv('/Users/mac/Desktop/tree_dataset.csv', sep=\";\")\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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"cell_type": "code",
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"execution_count": null,
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"id": "4d25d809",
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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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"id": "663eaf94",
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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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"id": "f3c9b416",
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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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"id": "bcfb233e",
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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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"id": "11f0c1a2",
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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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"id": "b1a93eb4",
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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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"id": "528f6ade",
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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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"id": "efb466fe",
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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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"id": "5dbe234e",
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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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"metadata": {
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"kernelspec": {
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"display_name": "env",
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"language": "python",
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"name": "env"
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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": "3.8.5"
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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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126
Untitled.ipynb
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126
Untitled.ipynb
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@ -0,0 +1,126 @@
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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": 24,
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"id": "fac14368",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1]\n"
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]
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}
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],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree\n",
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"dataset = pandas.read_csv('/Users/mac/Desktop/tree_dataset.csv', sep=\";\")\n",
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"decisions = [\"decision\"]\n",
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"attributes = [\"season\", \"trash_type\", \"mass\", \"space\", \"trash_mass\"]\n",
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"\n",
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"x = dataset[attributes]\n",
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"y = dataset[decisions]\n",
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"decision_tree = DecisionTreeClassifier()\n",
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"decision_tree.fit(x.values, y.values)\n",
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"decision = decision_tree.predict(\n",
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" [[5, 3 , 3, 1, 2]])\n",
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"print(decision)"
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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": "04bbec40",
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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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"id": "9c1d0193",
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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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"id": "8e40a924",
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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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"id": "b430f8b8",
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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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"id": "58ce5faa",
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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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"id": "a4b72d3d",
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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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"id": "b4e0aae9",
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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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"id": "74fb263f",
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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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"id": "5dbe234e",
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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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"metadata": {
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"kernelspec": {
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"display_name": "env",
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"language": "python",
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"name": "env"
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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": "3.8.5"
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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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@ -3,37 +3,26 @@ import matplotlib.pyplot as plt
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import pandas
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from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree
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'''
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atrybuty w pliku csv muszą być integerami, wstępnie ustaliłem:
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season = {"wiosna": 1, "lato": 2, "jesien":3, "zima":4}
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enough_space_in_trashmaster = { "no": 1, "yes":2}
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time_since_flush = [1,2,3,4,5,6,7,8,9,10]
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type_of_trash = {"bio":1, "szklo":2, "plastik":3, "papier":4, "mieszane":5}
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access_to_bin = { "no":1, "yes":2}
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distance = [1,2,3,4,5,6,7,8,9,10]
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decision = [0,1] - decyzje zostaną zmienione z tych z wagami na zero jedynkowe ze względu na pewne trudności w dalszej pracy
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'''
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decisions = ["decision"]
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attributes = ["season", "enough_space_in_trashmaster", "time_since_flush", "type_of_trash", "access_to_bin", "distance"]
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attributes = ["season", "trash_type", "mass", "space", "trash_mass"]
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# return tree made from attributes
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def tree():
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dataset = pandas.read_csv('./decision_tree/drzewo_decyzyjne.csv')
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dataset = pandas.read_csv('/Users/mac/Desktop/tree_dataset.csv', sep=";")
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x = dataset[attributes]
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y = dataset[decisions]
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decision_tree = DecisionTreeClassifier()
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decision_tree = decision_tree.fit(x, y)
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decision_tree = decision_tree.fit(x.values, y.values)
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return decision_tree
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# return decision made from tree and attributes
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def decision(decision_tree, season, enough_space_in_trashmaster, time_since_flush, type_of_trash, access_to_bin,
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distance):
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def decision(decision_tree, season, trash_type, mass, space, trash_mass):
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decision = decision_tree.predict(
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[[season, enough_space_in_trashmaster, time_since_flush, type_of_trash, access_to_bin, distance]])
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[[season, trash_type , mass, space, trash_mass]])
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return decision
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@ -1,94 +1,91 @@
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|--- feature_4 <= 1.50
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| |--- feature_5 <= 1.50
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| | |--- feature_2 <= 6.50
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|--- feature_2 <= 3.50
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| |--- feature_4 <= 3.50
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| | |--- feature_0 <= 1.50
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| | | |--- class: 0
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| | |--- feature_2 > 6.50
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| | | |--- feature_0 <= 3.00
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| | | | |--- class: 1
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| | | |--- feature_0 > 3.00
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| | | | |--- class: 0
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| |--- feature_5 > 1.50
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| | |--- class: 0
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|--- feature_4 > 1.50
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| |--- feature_1 <= 1.50
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| | |--- feature_0 <= 3.50
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| | | |--- class: 0
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| | |--- feature_0 > 3.50
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| | | |--- class: 1
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| |--- feature_1 > 1.50
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| | |--- feature_0 <= 3.50
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| | | |--- feature_0 <= 1.50
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| | | | |--- feature_3 <= 2.50
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| | |--- feature_0 > 1.50
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| | | |--- feature_3 <= 3.50
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| | | | |--- feature_2 <= 2.50
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| | | | | |--- class: 1
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| | | | |--- feature_3 > 2.50
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| | | | | |--- feature_3 <= 3.50
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| | | | | | |--- feature_5 <= 7.50
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| | | | | | | |--- feature_2 <= 5.50
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| | | | | | | | |--- class: 0
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| | | | | | | |--- feature_2 > 5.50
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| | | | | | | | |--- class: 1
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| | | | | | |--- feature_5 > 7.50
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| | | | | | | |--- class: 0
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| | | | | |--- feature_3 > 3.50
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| | | | |--- feature_2 > 2.50
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| | | | | |--- feature_4 <= 2.50
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| | | | | | |--- class: 1
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| | | |--- feature_0 > 1.50
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| | | | |--- feature_3 <= 1.50
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| | | | | |--- feature_5 <= 2.50
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| | | | | | |--- feature_5 <= 1.50
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| | | | | | | |--- class: 1
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| | | | | | |--- feature_5 > 1.50
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| | | | | |--- feature_4 > 2.50
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| | | | | | |--- class: 0
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| | | |--- feature_3 > 3.50
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| | | | |--- feature_3 <= 4.50
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| | | | | |--- feature_1 <= 2.50
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| | | | | | |--- feature_0 <= 2.50
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| | | | | | | |--- feature_1 <= 1.50
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| | | | | | | | |--- feature_4 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_4 > 2.50
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| | | | | | | | | |--- feature_2 <= 2.00
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- feature_2 > 2.00
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| | | | | | | | | | |--- class: 0
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| | | | | | | |--- feature_1 > 1.50
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| | | | | | | | |--- class: 0
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| | | | | | |--- feature_0 > 2.50
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| | | | | | | |--- feature_4 <= 2.50
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| | | | | | | | |--- class: 1
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| | | | | | | |--- feature_4 > 2.50
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| | | | | | | | |--- feature_2 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_2 > 2.50
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| | | | | | | | | |--- class: 0
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| | | | | |--- feature_1 > 2.50
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| | | | | | |--- feature_0 <= 3.50
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| | | | | | | |--- class: 0
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| | | | | | |--- feature_0 > 3.50
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| | | | | | | |--- feature_1 <= 3.50
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| | | | | | | | |--- feature_2 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_2 > 2.50
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| | | | | | | | | |--- feature_4 <= 2.00
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- feature_4 > 2.00
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| | | | | | | | | | |--- class: 0
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| | | | | | | |--- feature_1 > 3.50
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| | | | | | | | |--- class: 0
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| | | | |--- feature_3 > 4.50
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| | | | | |--- class: 0
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| |--- feature_4 > 3.50
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| | |--- feature_2 <= 1.50
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| | | |--- feature_4 <= 4.50
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| | | | |--- feature_3 <= 3.50
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| | | | | |--- feature_0 <= 1.50
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| | | | | | |--- class: 0
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| | | | | |--- feature_0 > 1.50
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| | | | | | |--- class: 1
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| | | | |--- feature_3 > 3.50
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| | | | | |--- feature_1 <= 2.50
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| | | | | | |--- feature_3 <= 4.50
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| | | | | | | |--- feature_0 <= 2.50
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| | | | | | | | |--- class: 0
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| | | | | | | |--- feature_0 > 2.50
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| | | | | | | | |--- class: 1
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| | | | | |--- feature_5 > 2.50
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| | | | | | |--- class: 1
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| | | | |--- feature_3 > 1.50
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| | | | | |--- feature_2 <= 3.50
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| | | | | | |--- feature_0 <= 2.50
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| | | | | | | |--- feature_5 <= 2.00
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| | | | | | | | |--- class: 1
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| | | | | | | |--- feature_5 > 2.00
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| | | | | | | | |--- feature_5 <= 4.00
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| | | | | | | | | |--- class: 0
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| | | | | | | | |--- feature_5 > 4.00
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| | | | | | | | | |--- feature_3 <= 2.50
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| | | | | | | | | | |--- feature_5 <= 7.00
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| | | | | | | | | | | |--- class: 0
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| | | | | | | | | | |--- feature_5 > 7.00
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| | | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- feature_3 > 2.50
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| | | | | | | | | | |--- class: 1
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| | | | | | |--- feature_0 > 2.50
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| | | | | | | |--- class: 1
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| | | | | |--- feature_2 > 3.50
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| | | | | | |--- feature_5 <= 1.50
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| | | | | | |--- feature_3 > 4.50
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| | | | | | | |--- class: 0
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| | | | | | |--- feature_5 > 1.50
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| | | | | | | |--- feature_3 <= 2.50
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| | | | | | | | |--- feature_5 <= 5.00
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_5 > 5.00
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| | | | | | | | | |--- class: 0
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| | | | | | | |--- feature_3 > 2.50
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| | | | | | | | |--- feature_5 <= 5.50
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| | | | | | | | | |--- feature_0 <= 2.50
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| | | | | | | | | | |--- feature_2 <= 4.50
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| | | | | | | | | | | |--- class: 0
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| | | | | | | | | | |--- feature_2 > 4.50
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| | | | | | | | | | | |--- class: 0
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| | | | | | | | | |--- feature_0 > 2.50
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| | | | | | | | | | |--- feature_2 <= 4.50
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| | | | | | | | | | | |--- class: 0
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| | | | | | | | | | |--- feature_2 > 4.50
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| | | | | | | | | | | |--- truncated branch of depth 2
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| | | | | | | | |--- feature_5 > 5.50
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| | | | | | | | | |--- feature_5 <= 6.50
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- feature_5 > 6.50
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| | | | | | | | | | |--- feature_2 <= 8.50
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| | | | | | | | | | | |--- truncated branch of depth 3
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| | | | | | | | | | |--- feature_2 > 8.50
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| | | | | | | | | | | |--- truncated branch of depth 4
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| | |--- feature_0 > 3.50
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| | | |--- class: 1
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| | | | | |--- feature_1 > 2.50
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| | | | | | |--- class: 0
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| | | |--- feature_4 > 4.50
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| | | | |--- class: 0
|
||||
| | |--- feature_2 > 1.50
|
||||
| | | |--- class: 0
|
||||
|--- feature_2 > 3.50
|
||||
| |--- feature_1 <= 1.50
|
||||
| | |--- feature_4 <= 1.50
|
||||
| | | |--- feature_2 <= 4.50
|
||||
| | | | |--- feature_3 <= 4.50
|
||||
| | | | | |--- feature_0 <= 1.50
|
||||
| | | | | | |--- class: 0
|
||||
| | | | | |--- feature_0 > 1.50
|
||||
| | | | | | |--- class: 1
|
||||
| | | | |--- feature_3 > 4.50
|
||||
| | | | | |--- class: 0
|
||||
| | | |--- feature_2 > 4.50
|
||||
| | | | |--- class: 0
|
||||
| | |--- feature_4 > 1.50
|
||||
| | | |--- class: 0
|
||||
| |--- feature_1 > 1.50
|
||||
| | |--- class: 0
|
||||
|
Binary file not shown.
@ -1,23 +1,23 @@
|
||||
import pygame as pg
|
||||
from enum import Enum
|
||||
|
||||
from random import randrange
|
||||
from map.tile import Tile
|
||||
|
||||
class Waste_Type(Enum):
|
||||
BIO = 0
|
||||
GLASS = 1
|
||||
PLASTIC = 2
|
||||
PAPER = 3
|
||||
MIX = 4
|
||||
|
||||
def __int__(self):
|
||||
return self.value
|
||||
class Trashbin(Tile):
|
||||
def __init__(self, img, x, y, width, height, waste_type: Waste_Type):
|
||||
def __init__(self, img, x, y, width, height, waste_type):
|
||||
super().__init__(img, x, y, width, height)
|
||||
# dis_dump dis_trash mass space trash_mass trash_space
|
||||
self.x = x
|
||||
self.y = y
|
||||
|
||||
self.waste_type = waste_type
|
||||
self.days_after_pickup = 0
|
||||
self.max_capacity = 100
|
||||
self.used_capacity = 0
|
||||
self.access = True
|
||||
self.season = randrange(4)
|
||||
self.trash_type = randrange(5)
|
||||
self.mass = randrange(5)
|
||||
self.space = randrange(5)
|
||||
self.trash_mass = randrange(5)
|
||||
|
||||
|
||||
def get_coords(self):
|
||||
return (self.x, self.y)
|
||||
|
||||
def get_attributes(self):
|
||||
return (self.season, self.trash_type, self.mass, self.space, self.trash_mass)
|
||||
|
81
main.py
81
main.py
@ -13,17 +13,22 @@ from path_search_algorthms import a_star, a_star_utils
|
||||
from decision_tree import decisionTree
|
||||
|
||||
from game_objects import aiPlayer
|
||||
import itertools
|
||||
|
||||
|
||||
def printTree():
|
||||
def getTree():
|
||||
tree = decisionTree.tree()
|
||||
decisionTree.tree_as_txt(tree)
|
||||
decisionTree.tree_to_png(tree)
|
||||
# decisionTree.tree_to_png(tree)
|
||||
decisionTree.tree_to_structure(tree)
|
||||
drzewo = decisionTree.tree_from_structure('./decision_tree/tree_model')
|
||||
print("Dla losowych danych predykcja czy wziąć kosz to: ")
|
||||
dec = decisionTree.decision(drzewo, 4, 2, 7, 4, 2, 3)
|
||||
print(dec)
|
||||
# print("Dla losowych danych predykcja czy wziąć kosz to: ")
|
||||
# dec = decisionTree.decision(drzewo, *(4,1,1,1))
|
||||
# print('---')
|
||||
# print(f"decision is{dec}")
|
||||
# print('---')
|
||||
|
||||
return drzewo
|
||||
|
||||
|
||||
class Game():
|
||||
@ -31,6 +36,7 @@ class Game():
|
||||
def __init__(self):
|
||||
pg.init()
|
||||
self.clock = pg.time.Clock()
|
||||
self.dt = self.clock.tick(FPS) / 333.0
|
||||
self.screen = pg.display.set_mode((WIDTH, HEIGHT))
|
||||
pg.display.set_caption("Trashmaster")
|
||||
self.load_data()
|
||||
@ -38,8 +44,17 @@ class Game():
|
||||
# because dont work without data.txt
|
||||
# self.init_bfs()
|
||||
# self.init_a_star()
|
||||
self.t = aiPlayer.aiPlayer(self.player, game=self)
|
||||
|
||||
self.dt = self.clock.tick(FPS) / 1000.0
|
||||
|
||||
def get_actions_by_coords(self,x,y):
|
||||
pos = (x,y)
|
||||
offset_x, offset_y = self.camera.offset()
|
||||
clicked_coords = [math.floor(pos[0] / TILESIZE) - offset_x, math.floor(pos[1] / TILESIZE) - offset_y]
|
||||
actions = a_star.search_path(math.floor(self.player.pos[0] / TILESIZE),
|
||||
math.floor(self.player.pos[1] / TILESIZE), self.player.rotation(),
|
||||
clicked_coords[0], clicked_coords[1], self.mapArray)
|
||||
return actions
|
||||
|
||||
def init_game(self):
|
||||
# initialize all variables and do all the setup for a new game
|
||||
@ -47,16 +62,15 @@ class Game():
|
||||
# sprite groups and map array for calculations
|
||||
(self.roadTiles, self.wallTiles, self.trashbinTiles), self.mapArray = map.get_tiles()
|
||||
self.agentSprites = pg.sprite.Group()
|
||||
|
||||
# player obj
|
||||
self.player = Player(self, 32, 32)
|
||||
|
||||
# camera obj
|
||||
self.camera = map_utils.Camera(MAP_WIDTH_PX, MAP_HEIGHT_PX)
|
||||
|
||||
# other
|
||||
self.debug_mode = False
|
||||
|
||||
|
||||
def init_bfs(self):
|
||||
start_node = (0, 0)
|
||||
target_node = (18, 18)
|
||||
@ -81,6 +95,38 @@ class Game():
|
||||
path = a_star.search_path(start_x, start_y, target_x, target_y, self.mapArray)
|
||||
print(path)
|
||||
|
||||
def init_decision_tree(self):
|
||||
# logika pracy z drzewem
|
||||
self.positive_decision = []
|
||||
self.negative_decision = []
|
||||
|
||||
self.positive_actions = []
|
||||
self.negative_actions = []
|
||||
for i in self.trashbinTiles:
|
||||
atrrs_container = i.get_attributes()
|
||||
x, y = i.get_coords()
|
||||
dec = decisionTree.decision(getTree(), *atrrs_container)
|
||||
if dec[0] == 1:
|
||||
self.positive_decision.append(i)
|
||||
self.positive_actions.append(self.get_actions_by_coords(x, y))
|
||||
else:
|
||||
self.negative_decision.append(i)
|
||||
self.negative_actions.append(i)
|
||||
|
||||
# j = 0
|
||||
# for i in self.positive_actions:
|
||||
|
||||
# print(f"step {j} actions is : {i}")
|
||||
# j+=1
|
||||
|
||||
# vec = pg.math.Vector2
|
||||
# for i in self.positive_actions:
|
||||
# self.t.startAiController(i)
|
||||
# self.player.pos = vec(32, 32)
|
||||
|
||||
self.t.startAiController(self.positive_actions[0])
|
||||
|
||||
|
||||
def load_data(self):
|
||||
game_folder = path.dirname(__file__)
|
||||
img_folder = path.join(game_folder, 'resources/textures')
|
||||
@ -91,7 +137,7 @@ class Game():
|
||||
def run(self):
|
||||
# game loop - set self.playing = False to end the game
|
||||
self.playing = True
|
||||
|
||||
self.init_decision_tree()
|
||||
while self.playing:
|
||||
self.dt = self.clock.tick(FPS) / 1000.0
|
||||
self.events()
|
||||
@ -142,24 +188,17 @@ class Game():
|
||||
actions = a_star.search_path(math.floor(self.player.pos[0] / TILESIZE),
|
||||
math.floor(self.player.pos[1] / TILESIZE), self.player.rotation(),
|
||||
clicked_coords[0], clicked_coords[1], self.mapArray)
|
||||
print(actions)
|
||||
# print(actions)
|
||||
|
||||
if (actions != None):
|
||||
t = aiPlayer.aiPlayer(self.player, game=self)
|
||||
t.startAiController(actions)
|
||||
|
||||
def show_start_screen(self):
|
||||
pass
|
||||
|
||||
def show_go_screen(self):
|
||||
pass
|
||||
self.t.startAiController(actions)
|
||||
|
||||
|
||||
|
||||
# create the game object
|
||||
|
||||
if __name__ == "__main__":
|
||||
g = Game()
|
||||
g.show_start_screen()
|
||||
printTree()
|
||||
|
||||
|
||||
g.run()
|
||||
g.show_go_screen()
|
@ -21,13 +21,15 @@ def generate_map():
|
||||
map[y][x] = 1
|
||||
|
||||
# generowanie smietnikow
|
||||
for i in range(0, 5):
|
||||
for i in range(0, 10):
|
||||
x = random.randint(0, MAP_WIDTH-1)
|
||||
y = random.randint(0, MAP_HEIGHT-1)
|
||||
map[y][x] = 2
|
||||
|
||||
return map
|
||||
|
||||
trashbins =[]
|
||||
|
||||
# tworzenie grup sprite'ow
|
||||
def get_sprites(map, pattern):
|
||||
roadTiles = pg.sprite.Group()
|
||||
@ -54,6 +56,7 @@ def get_sprites(map, pattern):
|
||||
trashbin = Trashbin(trashbin_pattern[trashbinId], offsetX, offsetY, 32, 30, trashbinId)
|
||||
roadTiles.add(tile)
|
||||
trashbinTiles.add(trashbin)
|
||||
trashbins.append(trashbin)
|
||||
|
||||
return roadTiles, wallTiles, trashbinTiles
|
||||
|
||||
|
Loading…
Reference in New Issue
Block a user