{ "cells": [ { "cell_type": "markdown", "id": "11174c4d-dcb5-49d8-96fc-b83ad35a6193", "metadata": {}, "source": [ "
\n", "\n", "
\n", "Chair of Cyber-Physical-Systems, Austria" ] }, { "cell_type": "markdown", "id": "a04ead7b-4fce-4ea2-9501-d60a112aadcb", "metadata": {}, "source": [ "\n", "\n", "| Credentials | |\n", "|----|---|\n", "|Host | Montanuniversitaet Leoben |\n", "|Web | https://cps.unileoben.ac.at |\n", "|Mail | cps@unileoben.ac.at |\n", "|Authors | Melanie Neubauer & Elmar Rückert|\n", "|Corresponding Authors | melanie.neubauer@unileoben.ac.at, rueckert@unileoben.ac.at |\n", "|Last edited | 07.06.2024 |\n" ] }, { "cell_type": "markdown", "id": "35a0d7a0", "metadata": {}, "source": [ "# Final Exam" ] }, { "cell_type": "raw", "id": "3dfb754b-ee67-4090-aa8c-394880ede449", "metadata": {}, "source": [ "Name:\n", "Mat Number:" ] }, { "cell_type": "raw", "id": "36aa65d0-1304-413f-88e9-04a98e2add1a", "metadata": {}, "source": [ "Point overview:\n", "- Import Section /2\n", "- Q1 Descr. Testing /4\n", "- Q2 write function /4\n", "- Q3 answer questions /2\n", "- Q4 databases /6\n", "- Q5 write class /4\n", "- Q6 descr. code and answer /4\n", "- Q7 linear reg basics /4\n", "- Q8 linear reg coding /4\n", "- Q9 gaussian processes /6\n", "\n", "You reached /40 Points!" ] }, { "cell_type": "markdown", "id": "4b05e025-9843-410e-80cd-f2db24148da8", "metadata": {}, "source": [ "
\n", "Please read the following tasks carefully. Good luck :)" ] }, { "cell_type": "markdown", "id": "480d04cd-83c9-43e7-827f-2cf3d357ab7e", "metadata": {}, "source": [ "### Install Section" ] }, { "cell_type": "code", "execution_count": 56, "id": "9c4525f3-aeab-4dcb-9cc5-eb79ac3e04a2", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.8.3)\n", "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.2.0)\n", "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.50.0)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n", "Requirement already satisfied: numpy<2,>=1.21 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.26.4)\n", "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (23.2)\n", "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (10.2.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.1.2)\n", "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n", "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install matplotlib" ] }, { "cell_type": "code", "execution_count": 57, "id": "0e5aad9c-bbba-429a-b541-36a36591b68a", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (2.2.2)\n", "Requirement already satisfied: numpy>=1.22.4 in /opt/conda/lib/python3.10/site-packages (from pandas) (1.26.4)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas) (2023.3)\n", "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas) (2024.1)\n", "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install pandas" ] }, { "cell_type": "markdown", "id": "e807646d-e717-4825-a3d3-9d4a652d4851", "metadata": {}, "source": [ "_________________________" ] }, { "cell_type": "markdown", "id": "9208862e-7b8b-435a-8ef5-4bbf09fe9feb", "metadata": {}, "source": [ "### Import Section (2 Points)\n", "Import all the needed libaries here in the beginning! Do not import libaries below this section!" ] }, { "cell_type": "code", "execution_count": 1, "id": "34bbdd51-4cd9-4dd3-a27e-3809b3705f60", "metadata": { "tags": [] }, "outputs": [], "source": [ "# import all necessary libaries here!\n", "import os\n", "#add code" ] }, { "cell_type": "markdown", "id": "c7cac7dd-43b1-4c10-a186-950f3f826700", "metadata": {}, "source": [ "__________________________________" ] }, { "cell_type": "markdown", "id": "3b371f61-68f9-4f46-a624-c243f559027c", "metadata": {}, "source": [ "### Question 1: Describing and Testing the Code (4 Points)\n", "\n", "Break down and explain each line of the provided code snippet. Additionally, describe the role of the '%' symbol in this context. Why is it necessary within this code?" ] }, { "cell_type": "code", "execution_count": 60, "id": "6458bfad-fab3-48aa-b42c-b67329fc8332", "metadata": { "tags": [] }, "outputs": [], "source": [ "def calc_median(dataset):\n", " tmp_dataset = dataset.copy()\n", " tmp_dataset.sort()\n", " if len(tmp_dataset) % 2 == 0:\n", " median = (tmp_dataset[len(tmp_dataset) // 2] + tmp_dataset[len(tmp_dataset) // 2 - 1]) / 2\n", " else:\n", " median = tmp_dataset[len(tmp_dataset) // 2]\n", " return median\n", "\n", "def get_dataset(path):\n", " with open(path, 'r') as f:\n", " reader = csv.reader(f)\n", " dataset = list(reader)\n", " dataset = [float(x[0]) for x in dataset]\n", " return dataset" ] }, { "cell_type": "markdown", "id": "afa185ff-bd11-4c2b-ae55-277217c63bf8", "metadata": {}, "source": [ "Test the above code on the *data.csv* and print the result." ] }, { "cell_type": "code", "execution_count": 61, "id": "5d7a7ac6-5a5c-422c-b310-82b2779cd7fe", "metadata": { "tags": [] }, "outputs": [], "source": [ "# your code" ] }, { "cell_type": "markdown", "id": "5b407d76-41d0-4834-bdc5-a71526d2f416", "metadata": {}, "source": [ "___________________________" ] }, { "cell_type": "markdown", "id": "686678ee-16ab-4dd9-a9a6-dddeb74ae0f9", "metadata": {}, "source": [ "### Question 2: Write a short Function (4 Points)\n", "Write a short function which calculates the variance of a dataset and returns it. (hint: to perform x² use ** instead of ^, Pandas is not allowed)" ] }, { "cell_type": "markdown", "id": "399589fd-dc12-4c8f-8902-9bfe374add4a", "metadata": { "tags": [] }, "source": [ "The variance of a dataset can be calculated using the formula:\n", "\n", "$ \\text{mean} = \\frac{1}{n} \\sum_{i=1}^{n} x_i$\n", "\n", "$ \\text{variance} = \\frac{\\sum_{i=1}^{n} (x_i - \\text{mean})^2}{n}$\n", "\n", "\n", "where:\n", "- $n$ is the number of elements in the dataset,\n", "- $x_i$ represents each individual value in the dataset, and\n", "- $\\text{mean}$ is the mean of the dataset." ] }, { "cell_type": "code", "execution_count": 72, "id": "8df48b65-79be-4a7f-8f47-cdecaa11d390", "metadata": { "tags": [] }, "outputs": [], "source": [ "# your function" ] }, { "cell_type": "markdown", "id": "c7ff344b-f9d2-4ee8-8ad9-ce3be8ecbb33", "metadata": {}, "source": [ "Test your code on the data.csv table below and print the results:" ] }, { "cell_type": "code", "execution_count": 69, "id": "705b0c3b-72a0-438d-a9b7-dbb5600aa9c3", "metadata": { "tags": [] }, "outputs": [], "source": [ "# your test code" ] }, { "cell_type": "markdown", "id": "6eb9cbcb-7944-4779-a0f8-4666a08fba0e", "metadata": {}, "source": [ "____________________________________________" ] }, { "cell_type": "markdown", "id": "8058570f-45f7-447d-b956-ee2fd63042f3", "metadata": {}, "source": [ "### Question 3: Answer the following Questions (2 Points)\n", "a) Why is it necessary to preprocess the data? " ] }, { "cell_type": "raw", "id": "36d73f9a-2716-4e39-81e8-bb906380f250", "metadata": {}, "source": [ "Your Answer:" ] }, { "cell_type": "markdown", "id": "d1ceee7e-8269-4cc0-af46-90886fb27cae", "metadata": {}, "source": [ "b) How would you preprocess your data? Describe the steps." ] }, { "cell_type": "raw", "id": "61969d27-0b98-410c-bffc-16a1de912972", "metadata": {}, "source": [ "Your Answer:" ] }, { "cell_type": "markdown", "id": "aae08c0a-5ffe-482f-8463-e67be8d93e41", "metadata": {}, "source": [ "_____________________________" ] }, { "cell_type": "markdown", "id": "e6bf5855-09f5-4bee-a42c-5360593945fb", "metadata": {}, "source": [ "### Question 4: Databases (6 Points)\n", "Put the following code into the correct order and run it. Describe each line with comments:" ] }, { "cell_type": "code", "execution_count": null, "id": "df3b243b-e660-4bb2-b1c8-9c556401ddbc", "metadata": { "tags": [] }, "outputs": [], "source": [ "for i in my_text:\n", " cur.execute(\"INSERT INTO finalExam VALUES (?)\", (i,))" ] }, { "cell_type": "code", "execution_count": null, "id": "d10e84ab-8ae3-4bb5-a7f6-223e1026e443", "metadata": { "tags": [] }, "outputs": [], "source": [ "conn.commit()" ] }, { "cell_type": "code", "execution_count": null, "id": "f7d0ed77-eb9f-47af-8213-876036175b0d", "metadata": { "tags": [] }, "outputs": [], "source": [ "cur.execute(\"DELETE FROM finalExam WHERE w = 'Bad'\")" ] }, { "cell_type": "code", "execution_count": null, "id": "9f3abf3c-c1b6-4fe1-a5ba-c4f42acd5279", "metadata": { "tags": [] }, "outputs": [], "source": [ "cur = conn.cursor()" ] }, { "cell_type": "code", "execution_count": null, "id": "866766f6-6791-4b48-9162-cd0a27c78c51", "metadata": { "tags": [] }, "outputs": [], "source": [ "cur.execute(\"INSERT INTO finalExam VALUES (?)\", ('Nice',))" ] }, { "cell_type": "code", "execution_count": null, "id": "31cf9cf5-2bef-4133-989e-c0f9e6db6b1d", "metadata": { "tags": [] }, "outputs": [], "source": [ "cur.execute(\"SELECT * FROM finalExam\")" ] }, { "cell_type": "code", "execution_count": null, "id": "5eb267d8-b049-4a26-ad4f-556c842921b3", "metadata": { "tags": [] }, "outputs": [], "source": [ "print(cur.fetchall())\n", "conn.close()\n", "os.remove('new.db')" ] }, { "cell_type": "code", "execution_count": null, "id": "f43304ba-6b54-4615-896e-68c8c7250667", "metadata": { "tags": [] }, "outputs": [], "source": [ "conn = sqlite3.connect('new.db')" ] }, { "cell_type": "code", "execution_count": null, "id": "003e239e-a23f-4de5-92d1-f2130c43b500", "metadata": { "tags": [] }, "outputs": [], "source": [ "cur.execute(\"SELECT COUNT(*) FROM finalExam\")" ] }, { "cell_type": "code", "execution_count": null, "id": "8ec6794f-5645-4fae-b31c-2c4403407470", "metadata": { "tags": [] }, "outputs": [], "source": [ "my_text = 'My Final Exam Is Bad'\n", "my_text = my_text.split(' ')" ] }, { "cell_type": "code", "execution_count": null, "id": "19b1b7a0-f0fd-4405-88ef-e2c106f1b6f1", "metadata": { "tags": [] }, "outputs": [], "source": [ "cur.execute('''CREATE TABLE IF NOT EXISTS finalExam\n", " (w Text)''')" ] }, { "cell_type": "code", "execution_count": null, "id": "69c2daf9-ec2e-40c6-9d11-8dab7ef31982", "metadata": { "tags": [] }, "outputs": [], "source": [ "print(cur.fetchone())" ] }, { "cell_type": "markdown", "id": "6f401683-92a3-4f14-8a34-b5c451fa79a7", "metadata": {}, "source": [ "________________________________________________" ] }, { "cell_type": "markdown", "id": "29a762ac-a1af-44e4-9eef-5e9617ec2ab2", "metadata": {}, "source": [ "### Question 5: Data Analyzer (4 Points)\n", "Design a Python class, DataAnalyzer, to facilitate the analysis and visualization of data stored in a database. The class should provide methods to create a SQLite database from a DataFrame and plot the data using Matplotlib." ] }, { "cell_type": "markdown", "id": "e1f08f56-dd0c-4df3-95b8-cffed58b1d07", "metadata": {}, "source": [ "The class should be initialized with the following parameters:\n", "- database_name: A string representing the name of the SQLite database.\n", "- x_values: A list with x-values.\n", "- y_values: A list with y-values.\n", "\n", "Method: create_database\n", "- Connect to the SQLite database specified by database_name.\n", "- Create a table and insert the x and y values.\n", "- Print the number of entries in the database after creating.\n", "\n", "Method: plot_values\n", "- Plot the data with Matplotlib.\n", "- Include axis labels and title for better visualization." ] }, { "cell_type": "code", "execution_count": 1, "id": "cdd18556-9fd8-441d-9c1f-8f40bbd0ad01", "metadata": { "tags": [] }, "outputs": [], "source": [ "# your Class:\n", "class DataAnalyzer:\n", " def __init__(self, database_name, x_values, y_values):\n", " self.database_name = database_name\n", " self.x_values = x_values\n", " self.y_values = y_values\n", "\n", " def create_database(self):\n", " # Your Code\n", " pass\n", "\n", " def plot_values(self):\n", " \"\"\"\n", " Plots the X and Y values using Matplotlib.\n", " \"\"\"\n", " plt.plot(self.x_values, self.y_values, marker='o', linestyle='')\n", " plt.title('Data Plot')\n", " plt.xlabel('X')\n", " plt.ylabel('Y')\n", " plt.grid(True)\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "30605de1-8eb0-4403-a36b-d22418341e9e", "metadata": {}, "source": [ "Test your code below on *coordinates.csv*. The name of the database should be your *matr. nr.* You should test both functions! (for import use pandas or csv)" ] }, { "cell_type": "code", "execution_count": 10, "id": "6ea4c395-1246-4530-994b-9c764a5c57ed", "metadata": { "tags": [] }, "outputs": [], "source": [ "# your test code:" ] }, { "cell_type": "markdown", "id": "8eec9931-ddac-4128-9b42-dbd349186c10", "metadata": {}, "source": [ "____________________________" ] }, { "cell_type": "markdown", "id": "7fccb26e-6d90-480b-b449-82d8d3c24a8e", "metadata": { "tags": [] }, "source": [ "### Question 6: Describe the code and answer the questions (4 Points)" ] }, { "cell_type": "markdown", "id": "f110bc78-13dc-4251-af7a-32bff74ce5bc", "metadata": {}, "source": [ "a) Describe the following code with comments." ] }, { "cell_type": "code", "execution_count": 47, "id": "3f6d6805-5e97-4fbd-b202-4020e9485643", "metadata": { "tags": [] }, "outputs": [], "source": [ "def function(X, y, factor=0.2):\n", " idx = np.arange(X.shape[0])\n", " np.random.shuffle(idx)\n", " X = X[idx]\n", " y = y[idx]\n", " split = int((1 - factor) * X.shape[0])\n", " X_train, X_test = X[:split], X[split:]\n", " y_train, y_test = y[:split], y[split:]\n", " return X_train, X_test, y_train, y_test" ] }, { "cell_type": "code", "execution_count": null, "id": "d99078f3-a670-4d04-bb7a-f93e9054b50d", "metadata": {}, "outputs": [], "source": [ "X = normalized_data.drop(target_column, axis=1).values\n", "y = normalized_data[target_column].values\n", "X_train, X_test, y_train, y_test = function(X, y, factor=0.2)" ] }, { "cell_type": "markdown", "id": "c9dbe438-5c74-4a02-8ea3-efc0e966e483", "metadata": {}, "source": [ "b) Why do we need the above code?" ] }, { "cell_type": "raw", "id": "161a52a4-79e2-4401-ba8a-6f680d47771e", "metadata": {}, "source": [ "Your Answer:" ] }, { "cell_type": "markdown", "id": "f117c05a-8d98-4dbd-a4c4-6c252bb10fb3", "metadata": { "tags": [] }, "source": [ "c) Why do we drop the target_column? " ] }, { "cell_type": "raw", "id": "e998c866-416e-457d-aee8-d1034ae21f84", "metadata": {}, "source": [ "Your Answer:" ] }, { "cell_type": "markdown", "id": "1a5e037a-f9d7-4e13-88c0-3f88241e4398", "metadata": {}, "source": [ "d) What would happen if we change the factor to 0.4?" ] }, { "cell_type": "raw", "id": "8d3ec404-4508-413c-9d00-9ce1f24c492f", "metadata": {}, "source": [ "Your Answer:" ] }, { "cell_type": "markdown", "id": "7585983a-1ec5-48e2-930a-e8316604fc97", "metadata": {}, "source": [ "_______________________" ] }, { "cell_type": "markdown", "id": "99b839a4-c249-43c0-a035-0a240e1bb350", "metadata": {}, "source": [ "### Question 7: Linear Regression Basics (4 Points)" ] }, { "cell_type": "code", "execution_count": 48, "id": "fb9fef79-41bb-4be3-8c40-57095e133c47", "metadata": { "tags": [] }, "outputs": [], "source": [ "class LinearRegression:\n", " def __init__(self):\n", " self.coefficients = None\n", "\n", " def train(self, X, y):\n", " X = np.hstack((np.ones((X.shape[0], 1)), X))\n", " self.coefficients = np.linalg.inv(X.T @ X) @ X.T @ y\n", "\n", " def predict(self, X):\n", " X = np.hstack((np.ones((X.shape[0], 1)), X))\n", " return X @ self.coefficients" ] }, { "cell_type": "markdown", "id": "d67a67c9-b129-412c-b29b-2869892fcf88", "metadata": {}, "source": [ "a) Why do we need linear regression?" ] }, { "cell_type": "raw", "id": "29d16a49-69d4-4c59-921a-33201fa04d95", "metadata": {}, "source": [ "Your Answer:" ] }, { "cell_type": "markdown", "id": "43ef5af3-9da0-4dc0-84e1-4731f5687040", "metadata": {}, "source": [ "b) What is the difference of using scikit-learn for linear regression and your own method?" ] }, { "cell_type": "raw", "id": "b6bcafd8-8e89-4fa6-888f-f30a93b019ec", "metadata": {}, "source": [ "Your Answer:" ] }, { "cell_type": "markdown", "id": "fdb3bd28-59ca-47ec-a750-79652cbc7d74", "metadata": {}, "source": [ "c) Write down the function of the mean squared error? When do you use it?" ] }, { "cell_type": "raw", "id": "791be49f-75ea-43a0-825a-0567ecf72aa1", "metadata": {}, "source": [ "Your Answer:" ] }, { "cell_type": "markdown", "id": "24b784d2-005e-4537-b0ce-c534f0303ced", "metadata": {}, "source": [ "d) Describe step per step how you would perform linear regression on a given dataset. (more detailed answer)" ] }, { "cell_type": "raw", "id": "c6b02c04-2ad1-48f2-9719-14df04846d8e", "metadata": {}, "source": [ "Your Answer:" ] }, { "cell_type": "markdown", "id": "43b95d6a-44ea-4631-bdad-524754d1d11a", "metadata": {}, "source": [ "_________" ] }, { "cell_type": "markdown", "id": "484c2dc0-67a1-48ab-87b4-db41ebfb6d02", "metadata": {}, "source": [ "### Question 8: Linear Regression Coding (4 Points)\n", "Perform linear regression on the *winequality-red.csv* with the target *alcohol* (pandas is allowed). Calculate the mean squared error and print it. Perform the task without preprocessing your data. It is necessary to split the data. " ] }, { "cell_type": "code", "execution_count": null, "id": "8f36613b-ecb5-40b0-9918-4119cb8943e2", "metadata": {}, "outputs": [], "source": [ "#your Code" ] }, { "cell_type": "markdown", "id": "bda2c424-b619-4e88-8431-c2232fb7babc", "metadata": {}, "source": [ "_______________" ] }, { "cell_type": "markdown", "id": "ea23296e-ef5b-4268-9398-e36190b8ea96", "metadata": {}, "source": [ "### Question 9: Gaussian Processes (6 Points)\n", "Fit the names to the different kernels below:" ] }, { "cell_type": "markdown", "id": "e3680ed3-eb35-41a5-aa35-4a1b5bf45ec3", "metadata": { "tags": [] }, "source": [ "Add the Names:\n", "\n", "1. **... Kernel**: $k(x, x') = \\exp \\left( -2 \\sin^2 \\left( \\frac{\\pi ||x - x'||}{p} \\right) \\right) / \\sigma^2 $\n", "2. **... Kernel**: $k(x, x') = x^T x' $\n", "3. **... Kernel**: $k(x, x') = \\exp \\left( - \\frac{||x - x'||^2}{2\\sigma^2} \\right)$\n", "4. **... Kernel**: $k(x, x') = (x^T x' + 1)^d $" ] }, { "cell_type": "markdown", "id": "1759762b-5b0a-4e6f-9651-277bc4643fb4", "metadata": {}, "source": [ "Fit the above numbers to the code below" ] }, { "cell_type": "raw", "id": "0c6ddc30-f804-472c-92d1-8a03d3c3b020", "metadata": {}, "source": [ "Your Answer:\n", " function1 = ...\n", " function2 = ...\n", " function3 = ...\n", " function4 = ..." ] }, { "cell_type": "code", "execution_count": null, "id": "530bc9da-a454-4de1-ba07-d4b62a4fae1e", "metadata": {}, "outputs": [], "source": [ "class function1:\n", " def __init__(self, theta=1.0):\n", " self.theta = theta\n", " self.bounds = ((1e-5, 1e5),)\n", " self.num_params = 1\n", "\n", " def __call__(self, X1, X2):\n", " return self.theta * np.dot(X1, X2.T)\n", "\n", " def set_params(self, params):\n", " self.theta = params[0]" ] }, { "cell_type": "code", "execution_count": null, "id": "c7518ccc-f5f5-4401-8d1d-d9167cc6dde8", "metadata": {}, "outputs": [], "source": [ "class function2:\n", " def __init__(self, theta=1.0, d=3):\n", " self.theta = theta\n", " self.d = d\n", " self.bounds = ((1e-5, 1e5), (1, 10))\n", " self.num_params = 2\n", "\n", " def __call__(self, X1, X2):\n", " return (self.theta * np.dot(X1, X2.T) + 1) ** self.d\n", "\n", " def set_params(self, params):\n", " self.theta = params[0]\n", " self.d = params[1]\n", "\n", " def gradient(self, X):\n", " return np.array(\n", " [\n", " self.d * self.theta * np.dot(X, X.T) ** (self.d - 1),\n", " self.theta * np.dot(X, X.T) ** self.d * np.log(np.dot(X, X.T)),\n", " ]\n", " )" ] }, { "cell_type": "code", "execution_count": null, "id": "ce47fa6e-038c-488f-b05e-a5ddddbc4e90", "metadata": {}, "outputs": [], "source": [ "class function3:\n", " def __init__(self, theta=1.0):\n", " self.theta = theta\n", " self.bounds = ((1e-5, 1e5),)\n", " self.num_params = 1\n", "\n", " def __call__(self, X1, X2):\n", " return np.exp(-2 * np.sin(np.pi * cdist(X1, X2) / self.theta) ** 2)\n", "\n", " def set_params(self, params):\n", " self.theta = params[0]" ] }, { "cell_type": "code", "execution_count": null, "id": "07bad441-5c3c-42c2-8507-b884d827acf0", "metadata": {}, "outputs": [], "source": [ "class function4:\n", " def __init__(self, theta=1.0):\n", " self.theta = theta\n", " self.bounds = ((1e-5, 1e5),)\n", " self.num_params = 1\n", "\n", " def __call__(self, X1, X2):\n", " return np.exp(-self.theta * cdist(X1, X2) ** 2)\n", "\n", " def set_params(self, params):\n", " self.theta = params[0]" ] }, { "cell_type": "markdown", "id": "df5f819b-98d9-4e76-bc42-d9c71ca7b076", "metadata": {}, "source": [ "___________________________" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.11" } }, "nbformat": 4, "nbformat_minor": 5 }