{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Abnormal component analysis\n\nSample usage of abnormal component analysis for dimension reduction.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from depth.model.DepthEucl import DepthEucl \nimport numpy as np\nfrom matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "## Creating dataset and samples\nnp.random.seed(2801)\nmat1=[[1, 0, 0, 0, 0],[0, 1, 0, 0, 0],[0, 0, 1, 0, 0],[0, 0, 0, 1, 0],[0, 0, 0, 0, 1]]\nmat2=[[0.25, 0, 0, 0, 0],[0, 0.25, 0, 0, 0],[0, 0, 0.25, 0, 0],[0, 0, 0, 0.25, 0],[0, 0, 0, 0, 0.25]]\nmat3=[[0.5, 0, 0, 0, 0],[0, 0.5, 0, 0, 0],[0, 0, 0.5, 0, 0],[0, 0, 0, 0.5, 0],[0, 0, 0, 0, 0.5]]\ndata1 = np.random.multivariate_normal([0,0,0,0,0], mat1, 980)\ndata2 = np.random.multivariate_normal([7.5,7.5,0,0,0], mat2, 10)\ndata3 = np.random.multivariate_normal([0,0,0,5,5], mat3, 10)\ndataset=np.concatenate((data1,data2,data3),axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create model and load dataset for depth computation \n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "model=DepthEucl().load_dataset(dataset)\nbaseACA=model.ACA(dim=2,) # evaluate the dataset itself"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "datasetACA=np.dot(dataset,baseACA)\nfig=plt.figure()\nplt.scatter(datasetACA[:,0],datasetACA[:,1], c=\"blue\",label=\"Dataset\")\nplt.xlabel(\"First abnormal component (AC1)\")\nplt.ylabel(\"Second abnormal component (AC2)\")\nplt.title(\"Dataset visualization using abnormal component analysis\")\nplt.legend()\nplt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.13.4"
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