![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Tutorial notebook for the Fall 2024 Course \"Machine Learning for Materials Science\", University of Tennessee Knoxville, Department of Materials Science and Engineering.\n",
+ "\n",
+ "Instructor Sergei V. Kalinin\n",
+ "\n",
+ "These examples and visualization functions are from the Sebastian Rashka book, Chapter 1\n",
+ "\n",
+ "https://subscription.packtpub.com/book/data/9781801819312/pref/preflvl1sec03/what-this-book-covers"
+ ],
+ "metadata": {
+ "id": "I-r__b7llk7E"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Simple Perceptron"
+ ],
+ "metadata": {
+ "id": "_yj2ALv4IgJp"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "First, let's have a look at a simple perceptron. Note:\n",
+ "- how the weights and bias are initialized\n",
+ "- what happens during the fit\n",
+ "- what happens during predict"
+ ],
+ "metadata": {
+ "id": "GA4PG59dGPVi"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "class Perceptron:\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",
+ " random_state : int\n",
+ " Random number generator seed for random weight\n",
+ " initialization.\n",
+ "\n",
+ " Attributes\n",
+ " -----------\n",
+ " w_ : 1d-array\n",
+ " Weights after fitting.\n",
+ " b_ : Scalar\n",
+ " Bias unit after fitting.\n",
+ " errors_ : list\n",
+ " Number of misclassifications (updates) in each epoch.\n",
+ "\n",
+ " \"\"\"\n",
+ " def __init__(self, eta=0.01, n_iter=50, random_state=1):\n",
+ " self.eta = eta\n",
+ " self.n_iter = n_iter\n",
+ " self.random_state = random_state\n",
+ "\n",
+ " def fit(self, X, y):\n",
+ " \"\"\"Fit training data.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : {array-like}, shape = [n_examples, n_features]\n",
+ " Training vectors, where n_examples is the number of\n",
+ " examples and n_features is the number of features.\n",
+ " y : array-like, shape = [n_examples]\n",
+ " Target values.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " self : object\n",
+ "\n",
+ " \"\"\"\n",
+ " rgen = np.random.RandomState(self.random_state)\n",
+ " self.w_ = rgen.normal(loc=0.0, scale=0.01,\n",
+ " size=X.shape[1])\n",
+ " self.b_ = np.float_(0.)\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_ += update * xi\n",
+ " self.b_ += 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_) + self.b_\n",
+ "\n",
+ " def predict(self, X):\n",
+ " \"\"\"Return class label after unit step\"\"\"\n",
+ " return np.where(self.net_input(X) >= 0.0, 1, 0)\n"
+ ],
+ "metadata": {
+ "id": "eF4Y9YcyzkdW"
+ },
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's import the Iris data set"
+ ],
+ "metadata": {
+ "id": "9fcHBv1LGhRa"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "import pandas as pd\n",
+ "\n",
+ "s = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
+ "print('From URL:', s)\n",
+ "\n",
+ "df = pd.read_csv(s,header=None,encoding='utf-8')\n",
+ "df.tail()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 223
+ },
+ "id": "G-8h8Cwr4qWK",
+ "outputId": "bd766af2-b03d-458a-cdb8-1a5e6e423f27"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "From URL: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " 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"
+ ],
+ "text/html": [
+ "\n",
+ " | \n", + " | 0 | \n", + "1 | \n", + "2 | \n", + "3 | \n", + "4 | \n", + "
|---|---|---|---|---|---|
| 145 | \n", + "6.7 | \n", + "3.0 | \n", + "5.2 | \n", + "2.3 | \n", + "Iris-virginica | \n", + "
| 146 | \n", + "6.3 | \n", + "2.5 | \n", + "5.0 | \n", + "1.9 | \n", + "Iris-virginica | \n", + "
| 147 | \n", + "6.5 | \n", + "3.0 | \n", + "5.2 | \n", + "2.0 | \n", + "Iris-virginica | \n", + "
| 148 | \n", + "6.2 | \n", + "3.4 | \n", + "5.4 | \n", + "2.3 | \n", + "Iris-virginica | \n", + "
| 149 | \n", + "5.9 | \n", + "3.0 | \n", + "5.1 | \n", + "1.8 | \n", + "Iris-virginica | \n", + "
Perceptron(eta0=0.1, random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Perceptron(eta0=0.1, random_state=1)