135 lines
3.7 KiB
Plaintext
135 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "google",
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"metadata": {},
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"source": [
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"##### Copyright 2023 Google LLC."
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]
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},
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{
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"cell_type": "markdown",
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"id": "apache",
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"metadata": {},
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"source": [
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"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"you may not use this file except in compliance with the License.\n",
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"You may obtain a copy of the License at\n",
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"\n",
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" http://www.apache.org/licenses/LICENSE-2.0\n",
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"\n",
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"Unless required by applicable law or agreed to in writing, software\n",
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"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"See the License for the specific language governing permissions and\n",
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"limitations under the License.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "basename",
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"metadata": {},
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"source": [
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"# clone_model_sample_sat"
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]
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},
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{
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"cell_type": "markdown",
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"id": "link",
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"metadata": {},
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"source": [
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"<table align=\"left\">\n",
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"<td>\n",
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"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/sat/clone_model_sample_sat.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
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"</td>\n",
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"<td>\n",
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"<a href=\"https://github.com/google/or-tools/blob/main/ortools/sat/samples/clone_model_sample_sat.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\"/>View source on GitHub</a>\n",
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"</td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "doc",
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"metadata": {},
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"source": [
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"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
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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": "install",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install ortools"
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]
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},
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{
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"cell_type": "markdown",
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"id": "description",
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"metadata": {},
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"source": [
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"\n",
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"Showcases deep copying of a model.\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": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ortools.sat.python import cp_model\n",
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"\n",
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"\n",
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"def clone_model_sample_sat():\n",
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" \"\"\"Showcases cloning a model.\"\"\"\n",
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" # Creates the model.\n",
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" model = cp_model.CpModel()\n",
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"\n",
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" # Creates the variables.\n",
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" num_vals = 3\n",
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" x = model.new_int_var(0, num_vals - 1, \"x\")\n",
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" y = model.new_int_var(0, num_vals - 1, \"y\")\n",
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" z = model.new_int_var(0, num_vals - 1, \"z\")\n",
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"\n",
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" # Creates the constraints.\n",
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" model.add(x != y)\n",
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"\n",
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" model.maximize(x + 2 * y + 3 * z)\n",
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"\n",
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" # Creates a solver and solves.\n",
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" solver = cp_model.CpSolver()\n",
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" status = solver.solve(model)\n",
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"\n",
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" if status == cp_model.OPTIMAL:\n",
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" print(\"Optimal value of the original model: {}\".format(solver.objective_value))\n",
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"\n",
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" # Clones the model.\n",
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" copy = model.clone()\n",
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"\n",
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" copy_x = copy.get_int_var_from_proto_index(x.index)\n",
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" copy_y = copy.get_int_var_from_proto_index(y.index)\n",
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"\n",
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" copy.add(copy_x + copy_y <= 1)\n",
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"\n",
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" status = solver.solve(copy)\n",
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"\n",
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" if status == cp_model.OPTIMAL:\n",
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" print(\"Optimal value of the modified model: {}\".format(solver.objective_value))\n",
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"\n",
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"\n",
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"clone_model_sample_sat()\n",
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"\n"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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