[pre-commit.ci] auto fixes from pre-commit.com hooks

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nnaisense · Aug 19, 2024 · cd792cc · cd792cc
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diff --git a/examples/notebooks/Functional_API/functional_ops.ipynb b/examples/notebooks/Functional_API/functional_ops.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "05a384ac-fa4d-434e-a8c0-367350dec224",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Genetic algorithm with the help of functional operators\n",
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f9f42f4-43ad-4766-84ee-8a4fe2a9fe2b",
+   "id": "1",
    "metadata": {},
    "source": [
     "---\n",
@@ -47,7 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3dd28c51-e08b-43b3-8cf9-efecdce49203",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8379cbbb-084c-461e-889a-2b334d52c138",
+   "id": "3",
    "metadata": {},
    "source": [
     "Below, we have the implementations for the fitness functions `rastrigin` and `sphere`.\n",
@@ -71,7 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5f1d45aa-50a4-4bc8-90a9-ed75b1bb1fbd",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ea8ddfeb-48de-4eab-9296-9e7862950d77",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd1fe365-ae1b-4898-91d0-cb7517ef5f84",
+   "id": "6",
    "metadata": {},
    "source": [
     "In this notebook, the variable `f` points to the fitness function whose value we want to minimize:"
@@ -106,7 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fc6d3958-61e7-4838-a9c8-0c9c3d0e343c",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da899fd2-5bfe-41bd-8c76-20d8b5c2bf71",
+   "id": "8",
    "metadata": {},
    "source": [
     "Various hyperparameters and problem settings:"
@@ -125,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6625c172-0ec2-492e-9851-0a60594d9086",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +146,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36782df7-9728-44e2-ab24-312a0e83b0d2",
+   "id": "10",
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    "outputs": [],
    "source": [
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84b24feb-1e84-4de6-ae24-ca277f5f5f4d",
+   "id": "11",
    "metadata": {},
    "source": [
     "Decision values of the final population:"
@@ -226,7 +226,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "21898601-e05d-489f-8cc1-3beef8bdd33b",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a067340a-5c87-4406-82f4-9cdcccaa3349",
+   "id": "13",
    "metadata": {},
    "source": [
     "Best solution of the final population:"
@@ -244,7 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28fccbc1-c605-4b3f-9545-9e4de01b7f07",
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [

diff --git a/examples/notebooks/Functional_API/multiobj_batched_ops.ipynb b/examples/notebooks/Functional_API/multiobj_batched_ops.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3a0bd5c3-661c-4b5d-9960-6f679ac6e1c7",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Multiobjective optimization via functional operators API\n",
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f37a843a-5d3e-4bdd-a743-244c156d1408",
+   "id": "1",
    "metadata": {},
    "source": [
     "---\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fda7ae3a-970b-4803-a222-35cf0550bf19",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3019da0f-4545-4cbb-bf28-ee0fc5ea2f62",
+   "id": "3",
    "metadata": {},
    "source": [
     "Below, we implement Kursawe's function.\n",
@@ -50,7 +50,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0455f2df-c438-470c-a9e2-c35086c8eed3",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b904d3b-8008-4782-9d8d-b9be3f7ec770",
+   "id": "5",
    "metadata": {},
    "source": [
     "Below, we have the constants regarding the problem, and hyperparameters:"
@@ -82,7 +82,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9c634129-8b1e-4050-a618-d5cce7b6843f",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b29688fc-73ae-4a07-8377-b2c7ddeb9640",
+   "id": "7",
    "metadata": {},
    "source": [
     "Initialize a population, and store it via the variable `population`:"
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ab53d77a-c30a-4ed3-ab05-6969a9933280",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c7b7c69-d728-4a29-8b5e-6dbb26b4f7b8",
+   "id": "9",
    "metadata": {},
    "source": [
     "Evaluate the initial population, and store the evaluation results within the variable `evals`:"
@@ -128,7 +128,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a3d63e29-6849-4593-bc18-49d200a1298a",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8022fda7-0d73-432e-afe3-d5c1c2ca7d12",
+   "id": "11",
    "metadata": {},
    "source": [
     "Main loop of the optimization:"
@@ -147,7 +147,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "378e59ee-31f7-41e0-b3e1-a9059e4c6aef",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fae1133-c0a5-4867-ad65-58bd4d25cb77",
+   "id": "13",
    "metadata": {},
    "source": [
     "Considering that `evals` now stores the evaluation results of the latest population, we can take the best solutions belonging to the best pareto-front as follows:"
@@ -215,7 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2a26ede1-490d-4925-914a-475c731b119c",
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,7 +239,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4349eb26-1d33-403b-8f59-b9a3c506f4dc",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -248,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6027aa8a-cc0a-433a-b0de-2956137becaa",
+   "id": "16",
    "metadata": {},
    "source": [
     "Plot the fitnesses of the best solutions:"
@@ -257,7 +257,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f2e29903-a3e5-4f2a-9f35-2e73fd5988ec",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d5be444-bad7-42eb-be8f-3bdb57f358c0",
+   "id": "18",
    "metadata": {},
    "source": [
     "---\n",
@@ -283,7 +283,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8f38b144-9bf3-4d4e-a266-14efbe909905",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1c82431e-e9f4-4d6e-9dba-b4a711139480",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +328,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "24f5a201-7d33-4bee-a569-dbfa97d0efd3",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66418331-30da-48b0-a2ea-425ba8b30730",
+   "id": "22",
    "metadata": {},
    "source": [
     "For each solution within each population, compute the domination count:"
@@ -382,7 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4f7cd61d-0396-4e52-a4a2-e793791991c7",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -392,7 +392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "403568ab-5463-4c4a-bccc-3dd918eb60a5",
+   "id": "24",
    "metadata": {},
    "source": [
     "From each population, take the best pareto-front, and plot the fitnesses belonging to that pareto-front:"
@@ -401,7 +401,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ccc9ffcc-06ff-44b0-998f-5874468c2383",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [