{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Cuda application for halfspace, projection and asymmectric projection depth \n\nSample usage of halfspace depth computation.\nIt will plot samples and dataset based on halfspace depth values.\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, 2]]\nmat2=[[1, 0],[0, 1]]\nX = np.random.multivariate_normal([1,1], mat2, 50)\ndataset = np.random.multivariate_normal([0,0], mat1, 5000)\n\n## visualize points\nfig=plt.figure()\nplt.scatter(dataset[:,0],dataset[:,1], c=\"blue\",label=\"Dataset\")\nplt.scatter(X[:,0],X[:,1], c=\"red\",label=\"New points\")\nplt.xlabel(\"First component\")\nplt.ylabel(\"Second component\")\nplt.title(\"Dataset and Sample visualization\")\nplt.legend()\nplt.show()"
      ]
    },
    {
      "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,CUDA=True)\nmodel.halfspace(X,exact=False,solver=\"refinedrandom\",NRandom= 1000,n_refinements=10,CUDA=True) # Compute X Depth w.r.t. the dataset  \nmodel.projection(X,solver=\"refinedrandom\",NRandom= 1000,n_refinements=10,CUDA=True) # Compute X Depth w.r.t. the dataset  \nmodel.aprojection(X,solver=\"refinedrandom\",NRandom= 1000,n_refinements=10,CUDA=True) # Compute X Depth w.r.t. the dataset  \nplt.subplots(1,3,figsize=(15,5))\nplt.subplot(1,3,1)\nplt.scatter(model.halfspaceDepth,model.projectionDepth)\nplt.xlabel(\"Halfspace depth\")\nplt.ylabel(\"Projection depth\")\nplt.subplot(1,3,2)\nplt.scatter(model.halfspaceDepth,model.aprojectionDepth)\nplt.xlabel(\"Halfspace depth\")\nplt.ylabel(\"Asymmetric projection depth\")\nplt.subplot(1,3,3)\nplt.scatter(model.projectionDepth,model.aprojectionDepth)\nplt.xlabel(\"Projection depth\")\nplt.ylabel(\"Asymmetric projection depth\")\nplt.suptitle(\"Depth results using CUDA application\")\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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}