barebones_tsvm_clean_moons.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "barebones.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPp6/7eKD2iR7Tuv0n8I++h",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/foxtrotmike/CS909/blob/master/barebones_tsvm_clean_moons.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TG-En3BVlBGG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "outputId": "d13cfdd8-e5be-4006-e2e7-e2a85c9e1007"
      },
      "source": [
        "\"\"\"\n",
        "Author: Fayyaz Minhas\n",
        "\"\"\"\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.nn.modules as nn\n",
        "import torch.optim as optim\n",
        "from sklearn.datasets import make_moons, make_circles;  \n",
        "#Let's generate some data\n",
        "\n",
        "X,y = make_circles(n_samples=800,  shuffle=True, noise=0.05, random_state=None, factor = 0.5)\n",
        "Xp = X[y==1]; Xn = X[y!=1]\n",
        "#Xp = 1.0+np.random.randn(100,2); Xn = -1.0-np.random.randn(100,2);\n",
        "inputs = np.vstack((Xp,Xn))\t\n",
        "#labels\n",
        "targets = np.hstack((np.ones(len(Xp)),-np.ones(len(Xn))))\n",
        "ttargets = targets+0 #copy of true labels\n",
        "targets[5:-5]=np.nan; #setting labels to unknown\n",
        "#targets[:3]=1.0;targets[-3:]=-1.0;#assume we only know two labels\n",
        "#kernel \n",
        "from sklearn.metrics.pairwise import rbf_kernel\n",
        "K = (np.dot(inputs,inputs.T)+1)**2\n",
        "K = rbf_kernel(inputs,gamma=2.0)\n",
        "K = torch.from_numpy(K).float() #kernel\n",
        "\n",
        "device = torch.device('cpu')\n",
        "#device = torch.device('cuda') # Uncomment this to run on GPU\n",
        "x = torch.from_numpy(inputs).float()\n",
        "y = torch.from_numpy(targets).float()\n",
        "N, D_in,D_out = x.shape[0], x.shape[1], 1\n",
        "# Loss function implementation\n",
        "def L1(z):\n",
        "    g = 10\n",
        "    zz = torch.zeros((2,len(z)))\n",
        "    zz[1] = 1-z    \n",
        "    closs = torch.logsumexp(g*zz,0)/g\n",
        "    #closs = torch.max(torch.Tensor([0]) , 1 - z) \n",
        "    return closs\n",
        "def L2(z):\n",
        "    #g = 10\n",
        "    #zz = torch.zeros((2,len(z)))\n",
        "    #zz[1] = z\n",
        "    #az = (torch.logsumexp(-g*zz,0)+torch.logsumexp(g*zz,0))/g #smooth approximation of abs https://math.stackexchange.com/questions/728094/approximate-x-with-a-smooth-function\n",
        "    #az = torch.logsumexp(g*zz,0)*2/g-z-(2/g)*torch.log(torch.tensor(2)) #smooth approximation of abs \n",
        "    #zz[1] = 1-az    \n",
        "    #closs= torch.logsumexp(g*zz,0)/g #approx of max(0,1-abs(z))\n",
        "    closs = torch.exp(-3*(z**2)) #original approximation used in the paper \n",
        "    #closs = torch.exp(-0.5*(z**2)) #original approximation used in the paper \"LARGE SCALE TRANSDUCTIVE SVMS\"  by Collobert 2006    \n",
        "    #closs= torch.max(torch.Tensor([0]) , 1 - torch.abs(z)) \n",
        "    s = -0.3\n",
        "    #closs = torch.min(torch.tensor(1+s),torch.max(torch.tensor(0),1-torch.abs(z)))\n",
        "    return closs\n",
        "\n",
        "eps = 1e-6\n",
        "# Create random Tensors for weights; setting requires_grad=True means that we\n",
        "# want to compute gradients for these Tensors during the backward pass.\n",
        "#wb = torch.randn(N, device=device, requires_grad=True)\n",
        "#wb = torch.tensor(torch.empty(N).normal_(mean=0.1,std=1.0), requires_grad=True)\n",
        "\n",
        "# Parameters\n",
        "learning_rate = 1e-2\n",
        "lambda_w = 0.01\n",
        "lambda_u = 1.0\n",
        "\n",
        "cratio = np.mean(ttargets)\n",
        "#Training \n",
        "\n",
        "uidx, pidx, nidx = torch.isnan(y), y==1, y==-1\n",
        "n_unsupv, n_pos, n_neg = torch.sum(uidx), torch.sum(pidx), torch.sum(nidx)\n",
        "lower, upper = -(1.0 / np.sqrt(N)), (1.0 / np.sqrt(N)) #xavier weight init\n",
        "wb = lower + torch.rand(N) * (upper - lower)\n",
        "wb[pidx]=torch.abs(wb[pidx])\n",
        "wb[nidx]=-torch.abs(wb[nidx])\n",
        "\n",
        "wb = torch.tensor(wb,requires_grad=True)\n",
        "\n",
        "plt.hist(wb.detach().numpy());plt.title('Sample Weighths')\n",
        "optimizer = optim.Adam([wb], lr=learning_rate)\n",
        "L = [] #history of losses\n",
        "for epoch in range(2000):\n",
        "      \"\"\"\n",
        "      # REPRESENTATION\n",
        "      \"\"\"\n",
        "      y_pred = wb@K\n",
        "      berr = torch.mean(y_pred[uidx])-cratio\n",
        "      y_pred-=berr\n",
        "      \"\"\"\n",
        "      # EVALUATION\n",
        "      \"\"\"      \n",
        "      loss_s, loss_u = 0.0,0.0\n",
        "      if n_pos:\n",
        "        loss_s = torch.mean(L1(y_pred[pidx]))\n",
        "      if n_neg:\n",
        "        loss_s+=torch.mean(L1(-1*y_pred[nidx]))\n",
        "      if n_unsupv:\n",
        "        loss_u = torch.mean(L2(y_pred[uidx]))/L2(torch.tensor([0]))\n",
        "      \n",
        "      #bloss = 1-torch.exp(-3*(berr**2))\n",
        "      #bloss = torch.abs(berr)\n",
        "      obj = loss_s+lambda_u*loss_u+lambda_w*wb@K@wb#+lambda_b*bloss #empirical loss + regularization     \n",
        "\n",
        "      \"\"\"\n",
        "      #OPTIMIZATION\n",
        "      \"\"\"    \n",
        "      obj.backward()\n",
        "      # Using built-in optimizer\n",
        "      optimizer.step()\n",
        "      optimizer.zero_grad()\n",
        "      L.append(obj.detach().numpy()) #save for history and plotting\n",
        "\n",
        "wbn = wb.detach().numpy()\n",
        "#plt.close(\"all\")\n",
        "plt.figure()\n",
        "plt.plot(L);plt.yscale('log')\n",
        "\n",
        "plt.grid(); plt.xlabel('Epochs'); plt.ylabel('value');plt.legend(['Loss','Objective'])\n",
        "\"\"\"\n",
        "def clf(inputs): \n",
        "  return inputs@wb\n",
        "\n",
        "print(\"Predictions: \",clf(inputs))\n",
        "print(\"Weights: \",wbn)\n",
        "plt.figure()\n",
        "\n",
        "\n",
        "plotit(inputs,targets,clf=clf,conts=[0],extent = [-5,+5,-5,+5])\n",
        "\"\"\""
      ],
      "execution_count": 1905,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:76: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\ndef clf(inputs): \\n  return inputs@wb\\n\\nprint(\"Predictions: \",clf(inputs))\\nprint(\"Weights: \",wbn)\\nplt.figure()\\n\\n\\nplotit(inputs,targets,clf=clf,conts=[0],extent = [-5,+5,-5,+5])\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1905
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = wb@K\n",
        "berr = torch.mean(y_pred[uidx])-cratio\n",
        "z = (y_pred-berr).detach().numpy()\n",
        "print(\"Accuracy\",np.mean(z*ttargets>0))\n",
        "from sklearn.metrics import roc_auc_score\n",
        "print(\"AUROC\",roc_auc_score(ttargets, z))\n",
        "print(\"AUROC\",roc_auc_score(ttargets, wbn))"
      ],
      "metadata": {
        "id": "ERafpLRRK0Dm",
        "outputId": "44e3834f-7089-4477-816e-3ee47bff6e91",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1906,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 1.0\n",
            "AUROC 1.0\n",
            "AUROC 0.8587625000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(z);plt.title('Prediction Scores')"
      ],
      "metadata": {
        "id": "S5XtmMsjNVab",
        "outputId": "9f9d13d7-9b5b-45fc-fbba-928489b92511",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "execution_count": 1907,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Prediction Scores')"
            ]
          },
          "metadata": {},
          "execution_count": 1907
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(wbn);plt.title('Sample Weighths')"
      ],
      "metadata": {
        "id": "hOgJslRuN2mb",
        "outputId": "7d6c6e9f-e8ad-45a7-940e-a0a803bccaf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "execution_count": 1908,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Sample Weighths')"
            ]
          },
          "metadata": {},
          "execution_count": 1908
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "UPkKJ1WCOW0K",
        "outputId": "2448d64c-39ce-42ad-c128-3c070d7f7229",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1909,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 1.,  1.,  1.,  1.,  1., nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
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              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -1., -1., -1.,\n",
              "        -1., -1.])"
            ]
          },
          "metadata": {},
          "execution_count": 1909
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ttargets"
      ],
      "metadata": {
        "id": "Sd-mQyAfbHSx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5f0a61a-7eaf-4e5c-c942-7e00e8281c1f"
      },
      "execution_count": 1910,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(z[~uidx]),cratio"
      ],
      "metadata": {
        "id": "8g0YTvhjkSGp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81c52adf-2d24-43c4-c052-5e7cffd47928"
      },
      "execution_count": 1911,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.036840238, 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 1911
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.random import randn #importing randn\n",
        "import numpy as np #importing numpy\n",
        "import matplotlib.pyplot as plt #importing plotting module\n",
        "import itertools\n",
        "import warnings\n",
        "\n",
        "def plotit(X,Y=None,clf=None,  conts = None, ccolors = ('b','k','r'), colors = ('c','y'), markers = ('s','o'), hold = False, transform = None,extent = None,**kwargs):\n",
        "    \"\"\"\n",
        "    A function for showing data scatter plot and classification boundary\n",
        "    of a classifier for 2D data\n",
        "        X: nxd  matrix of data points\n",
        "        Y: (optional) n vector of class labels\n",
        "        clf: (optional) classification/discriminant function handle\n",
        "        conts: (optional) contours (if None, contours are drawn for each class boundary)\n",
        "        ccolors: (optional) colors for contours   \n",
        "        colors: (optional) colors for each class (sorted wrt class id)\n",
        "            can be 'scaled' or 'random' or a list/tuple of color ids\n",
        "        markers: (optional) markers for each class (sorted wrt class id)\n",
        "        hold: Whether to hold the plot or not for overlay (default: False).\n",
        "        transform: (optional) a function handle for transforming data before passing to clf\n",
        "        kwargs: any keyword arguments to be passed to clf (if any)        \n",
        "    \"\"\"\n",
        "    if clf is not None and X.shape[1]!=2:\n",
        "        warnings.warn(\"Data Dimensionality is not 2. Unable to plot.\")\n",
        "        return\n",
        "    if markers is None:\n",
        "        markers = ('.',)\n",
        "    eps=1e-6\n",
        "    d0,d1 = (0,1)\n",
        "    if extent is None:\n",
        "        minx, maxx = np.min(X[:,d0])-eps, np.max(X[:,d0])+eps\n",
        "        miny, maxy = np.min(X[:,d1])-eps, np.max(X[:,d1])+eps\n",
        "        extent = [minx,maxx,miny,maxy]\n",
        "    else:\n",
        "        [minx,maxx,miny,maxy] = extent\n",
        "    if Y is not None:\n",
        "        classes = sorted(set(Y))\n",
        "        if conts is None or len(conts)<2:\n",
        "            #conts = list(classes)\n",
        "            vmin,vmax = classes[0]-eps,classes[-1]+eps\n",
        "        else:            \n",
        "            vmin,vmax= np.min(conts)-eps,np.max(conts)+eps\n",
        "        \n",
        "    else:\n",
        "        vmin,vmax=-2-eps,2+eps\n",
        "        if conts is None or len(conts)<2:            \n",
        "            conts = sorted([-1+eps,0,1-eps])\n",
        "        else:\n",
        "            vmin,vmax= np.min(conts)-eps,np.max(conts)+eps\n",
        "        \n",
        "    if clf is not None:\n",
        "        npts = 150\n",
        "        x = np.linspace(minx,maxx,npts)\n",
        "        y = np.linspace(miny,maxy,npts)\n",
        "        t = np.array(list(itertools.product(x,y)))\n",
        "        if transform is not None:\n",
        "            t = transform(t)\n",
        "        z = clf(t,**kwargs)\n",
        "        \n",
        "        z = np.reshape(z,(npts,npts)).T        \n",
        "        \n",
        "        \n",
        "        plt.contour(x,y,z,conts,linewidths = [2],colors=ccolors,extent=extent, label='f(x)=0')\n",
        "        #plt.imshow(np.flipud(z), extent = extent, cmap=plt.cm.Purples, vmin = -2, vmax = +2); plt.colorbar()\n",
        "        plt.pcolormesh(x, y, z,cmap=plt.cm.Purples,vmin=vmin,vmax=vmax);plt.colorbar()\n",
        "        plt.axis(extent)\n",
        "    \n",
        "    if Y is not None:        \n",
        "        for i,y in enumerate(classes):\n",
        "            \n",
        "            if colors is None or colors=='scaled':\n",
        "                cc = np.array([[i,i,i]])/float(len(classes))\n",
        "            elif colors =='random':\n",
        "                cc = np.array([[np.random.rand(),np.random.rand(),np.random.rand()]])\n",
        "            else:\n",
        "                cc = colors[i%len(colors)]\n",
        "            \n",
        "            mm = markers[i%len(markers)]\n",
        "            plt.scatter(X[Y==y,d0],X[Y==y,d1], marker = mm,c = cc, s = 50)     \n",
        "         \n",
        "    else:\n",
        "        plt.scatter(X[:,d0],X[:,d1],marker = markers[0], c = 'k', s = 5)\n",
        "    plt.xlabel('$x_1$')\n",
        "    plt.ylabel('$x_2$')   \n",
        "    if not hold:\n",
        "        plt.grid()        \n",
        "        plt.show()\n",
        "    return extent"
      ],
      "metadata": {
        "id": "9XfH2Z-Whwxv"
      },
      "execution_count": 1912,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Labeled Examples\");plotit(inputs,(2*((pidx+nidx)>0)-1).numpy()); plotit(inputs,2*(z>0)-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "1oMt8gKZzREX",
        "outputId": "29327b98-c708-4be0-964b-802535debad0"
      },
      "execution_count": 1913,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-1.1009880480129286,\n",
              " 1.1004721571212055,\n",
              " -1.1080998034988936,\n",
              " 1.0917939737671714]"
            ]
          },
          "metadata": {},
          "execution_count": 1913
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotit(inputs,2*(wbn>0)-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "WOamUUsgvwLO",
        "outputId": "ea47af4a-1fec-4d4b-ea8d-2901bc2eef37"
      },
      "execution_count": 1914,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-1.1009880480129286,\n",
              " 1.1004721571212055,\n",
              " -1.1080998034988936,\n",
              " 1.0917939737671714]"
            ]
          },
          "metadata": {},
          "execution_count": 1914
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotit(inputs,2*((z>0)!=(wbn>0))-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "zRNSe1w_xuW_",
        "outputId": "59cb09e4-ace2-4d8b-c98f-ea245849a531"
      },
      "execution_count": 1915,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-1.1009880480129286,\n",
              " 1.1004721571212055,\n",
              " -1.1080998034988936,\n",
              " 1.0917939737671714]"
            ]
          },
          "metadata": {},
          "execution_count": 1915
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KmiqymxRz2Nf"
      },
      "execution_count": 1915,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "vE_so2vv0B2Q"
      },
      "execution_count": 1915,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNiJM_TC1IEf",
        "outputId": "98661acc-8c9c-49b2-85b8-0d1ffd31af83"
      },
      "execution_count": 1916,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.,  1.,  1.,  1.,  1., nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, -1., -1., -1., -1., -1.])"
            ]
          },
          "metadata": {},
          "execution_count": 1916
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "YYUOkXUr4dDk"
      },
      "execution_count": 1916,
      "outputs": []
    }
  ]
}