montassarbz_hw1.ipynb

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      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "class Perceptron(object):\n",
        "    \"\"\"Perceptron classifier.\n",
        "\n",
        "    Parameters\n",
        "    ------------\n",
        "    eta : float\n",
        "        Learning rate (between 0.0 and 1.0)\n",
        "    n_iter : int\n",
        "        Passes over the training dataset.\n",
        "\n",
        "    Attributes\n",
        "    -----------\n",
        "    w_ : 1d-array\n",
        "        Weights after fitting.\n",
        "    errors_ : list\n",
        "        Number of misclassifications (updates) in each epoch.\n",
        "\n",
        "    \"\"\"\n",
        "    def __init__(self, eta=0.01, n_iter=10):\n",
        "        self.eta = eta\n",
        "        self.n_iter = n_iter\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        \"\"\"Fit training data.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        X : {array-like}, shape = [n_samples, n_features]\n",
        "            Training vectors, where n_samples is the number of samples and\n",
        "            n_features is the number of features.\n",
        "        y : array-like, shape = [n_samples]\n",
        "            Target values.\n",
        "\n",
        "        Returns\n",
        "        -------\n",
        "        self : object\n",
        "\n",
        "        \"\"\"\n",
        "        self.w_ = np.zeros(1 + X.shape[1])\n",
        "        self.errors_ = []\n",
        "\n",
        "        for _ in range(self.n_iter):\n",
        "            errors = 0\n",
        "            for xi, target in zip(X, y):\n",
        "                update = self.eta * (target - self.predict(xi))\n",
        "                self.w_[1:] += update * xi\n",
        "                self.w_[0] += update\n",
        "                errors += int(update != 0.0)\n",
        "            self.errors_.append(errors)\n",
        "        return self\n",
        "\n",
        "    def net_input(self, X):\n",
        "        \"\"\"Calculate net input\"\"\"\n",
        "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
        "\n",
        "    def predict(self, X):\n",
        "        \"\"\"Return class label after unit step\"\"\"\n",
        "        return np.where(self.net_input(X) >= 0.0, 1, -1)"
      ]
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        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('https://archive.ics.uci.edu/ml/'\n",
        "        'machine-learning-databases/iris/iris.data', header=None)\n",
        "df.tail()"
      ],
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              "       0    1    2    3               4\n",
              "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
              "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
              "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
              "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
              "149  5.9  3.0  5.1  1.8  Iris-virginica"
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      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# select setosa and versicolor\n",
        "y = df.iloc[0:100, 4].values\n",
        "y = np.where(y == 'Iris-setosa', -1, 1)\n",
        "\n",
        "# extract sepal length and sepal width\n",
        "X = df.iloc[0:100, [0, 1]].values\n",
        "\n",
        "# plot data\n",
        "plt.scatter(X[:50, 0], X[:50, 1],\n",
        "            color='red', marker='o', label='setosa')\n",
        "plt.scatter(X[50:100, 0], X[50:100, 1],\n",
        "            color='blue', marker='x', label='versicolor')\n",
        "\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('sepal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "#plt.savefig('./images/02_06.png', dpi=300)\n",
        "plt.show()"
      ],
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              "<Figure size 640x480 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ppn = Perceptron(eta=0.1, n_iter=1000)\n",
        "\n",
        "ppn.fit(X, y)\n",
        "\n",
        "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Number of updates')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('./perceptron_1.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "ppn1 = Perceptron(eta=0.1, n_iter=500)\n",
        "\n",
        "ppn1.fit(X, y)\n",
        "\n",
        "ppn2 = Perceptron(eta=0.1, n_iter=200)\n",
        "\n",
        "ppn2.fit(X, y)\n",
        "\n",
        "ppn3 = Perceptron(eta=0.1, n_iter=100)\n",
        "\n",
        "ppn3.fit(X, y)\n",
        "\n",
        "ppn4 = Perceptron(eta=0.1, n_iter=50)\n",
        "\n",
        "ppn4.fit(X, y)\n",
        "\n",
        "ppn5 = Perceptron(eta=0.1, n_iter=20)\n",
        "\n",
        "ppn5.fit(X, y)\n",
        "\n",
        "ppn6 = Perceptron(eta=0.1, n_iter=10)\n",
        "\n",
        "ppn6.fit(X, y)\n",
        "#plt.plot(range(1, len(ppn1.errors_) + 1), ppn1.errors_, marker='o')\n",
        "#plt.xlabel('Epochs')\n",
        "#plt.ylabel('Number of updates')\n",
        "\n",
        "#plt.tight_layout()\n",
        "# plt.savefig('./perceptron_1.png', dpi=300)\n",
        "#plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "QFKZRcAD73NQ",
        "outputId": "7852255b-d41e-4e2e-a898-d487552dc7be"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<__main__.Perceptron at 0x7b34bd63bdf0>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "\n",
        "\n",
        "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
        "\n",
        "    # setup marker generator and color map\n",
        "    markers = ('s', 'x', 'o', '^', 'v')\n",
        "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
        "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
        "\n",
        "    # plot the decision surface\n",
        "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
        "                           np.arange(x2_min, x2_max, resolution))\n",
        "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
        "    Z = Z.reshape(xx1.shape)\n",
        "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
        "    plt.xlim(xx1.min(), xx1.max())\n",
        "    plt.ylim(xx2.min(), xx2.max())\n",
        "\n",
        "    # plot class samples\n",
        "    for idx, cl in enumerate(np.unique(y)):\n",
        "        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n",
        "                    alpha=0.8, c=cmap(idx),\n",
        "                    edgecolor='black',\n",
        "                    marker=markers[idx],\n",
        "                    label=cl)"
      ],
      "metadata": {
        "id": "tt4jQCgC78Rd"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(X, y, classifier=ppn)\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('sepal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('./perceptron_2.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n This is the linear function with weights found by our algorithm for \" + str(ppn.n_iter) + \" iterations: : \")\n",
        "print(str(ppn.w_[2]) + \"x1 + \" + str(ppn.w_[1]) + \"x2 + \" + str(ppn.w_[0]))\n",
        "\n",
        "plot_decision_regions(X, y, classifier=ppn1)\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('sepal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('./perceptron_2.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n This is the linear function with weights found by our algorithm for \" + str(ppn1.n_iter) + \" iterations: : \")\n",
        "print(str(ppn1.w_[2]) + \"x1 + \" + str(ppn1.w_[1]) + \"x2 + \" + str(ppn1.w_[0]))\n",
        "plot_decision_regions(X, y, classifier=ppn2)\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('sepal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('./perceptron_2.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n This is the linear function with weights found by our algorithm for \" + str(ppn2.n_iter) + \" iterations: : \")\n",
        "print(str(ppn2.w_[2]) + \"x1 + \" + str(ppn2.w_[1]) + \"x2 + \" + str(ppn2.w_[0]))\n",
        "\n",
        "plot_decision_regions(X, y, classifier=ppn3)\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('sepal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('./perceptron_2.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n This is the linear function with weights found by our algorithm for \" + str(ppn3.n_iter) + \" iterations: : \")\n",
        "print(str(ppn3.w_[2]) + \"x1 + \" + str(ppn3.w_[1]) + \"x2 + \" + str(ppn3.w_[0]))\n",
        "plot_decision_regions(X, y, classifier=ppn4)\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('sepal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('./perceptron_2.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n This is the linear function with weights found by our algorithm for \" + str(ppn4.n_iter) + \" iterations: : \")\n",
        "print(str(ppn4.w_[2]) + \"x1 + \" + str(ppn4.w_[1]) + \"x2 + \" + str(ppn4.w_[0]))\n",
        "\n",
        "plot_decision_regions(X, y, classifier=ppn5)\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('sepal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('./perceptron_2.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n This is the linear function with weights found by our algorithm for \" + str(ppn5.n_iter) + \" iterations: : \")\n",
        "print(str(ppn5.w_[2]) + \"x1 + \" + str(ppn5.w_[1]) + \"x2 + \" + str(ppn5.w_[0]))\n",
        "plot_decision_regions(X, y, classifier=ppn6)\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('sepal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('./perceptron_2.png', dpi=300)\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n This is the linear function with weights found by our algorithm for \" + str(ppn6.n_iter) + \" iterations: \")\n",
        "print(str(ppn6.w_[2]) + \"x1 + \" + str(ppn6.w_[1]) + \"x2 + \" + str(ppn6.w_[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8y_TkFMv8Cus",
        "outputId": "ac9e46d4-e2c6-42a8-e1f1-2fdd28827cf0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-a13da887a678>:24: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "  plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n",
            "<ipython-input-5-a13da887a678>:24: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
            "  plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " This is the linear function with weights found by our algorithm for 1000 iterations: : \n",
            "-20.140000000000235x1 + 15.799999999999814x2 + -24.799999999999944\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " This is the linear function with weights found by our algorithm for 500 iterations: : \n",
            "-18.32000000000015x1 + 14.339999999999842x2 + -20.19999999999996\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " This is the linear function with weights found by our algorithm for 200 iterations: : \n",
            "-16.259999999999945x1 + 10.719999999999924x2 + -8.000000000000004\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " This is the linear function with weights found by our algorithm for 100 iterations: : \n",
            "-11.379999999999967x1 + 8.139999999999986x2 + -4.800000000000002\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " This is the linear function with weights found by our algorithm for 50 iterations: : \n",
            "-7.779999999999998x1 + 5.600000000000004x2 + -2.4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " This is the linear function with weights found by our algorithm for 20 iterations: : \n",
            "-3.2800000000000002x1 + 3.2000000000000015x2 + -0.8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " This is the linear function with weights found by our algorithm for 10 iterations: \n",
            "-1.6599999999999997x1 + 1.680000000000001x2 + -0.4000000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "znxUI0Fo8IX8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}