Cocalc Sd109 Tutorial Ipynb

Tutorial made for the Virtual Global Sage Days 109 We will introduce some of the fundamental concepts in Python and SageMath. Learn how to run Sage command line or notebook Basic expressions and variables in Python and SageMath Introduce some basic packages for plotting etc. This tutorial will show you how to use the included Simulation class to simulate node dynamics by specifying a graph, the initial state of the nodes, and node state transitions.

Included with these tutorials is a simulation.py file that contains a Simulation class. This class will allow us to skip the details of writing a simulation, allowing us to concentrate on the interesting parts. A simulation is made up of three main parts: The graph on which to simulate node dynamics This one is easy, we just need a NetworkX Graph instance. For this example we'll use a random graph.

You can run the following cell multiple times if you wish, until you get a graph that looks good: By the end of this lecture you will be able to: Define variables and differentiate between global and local variables. Identify and use different object types in python. Use some of the python's default functions and define your own functions. Introduction to numpy and matplotlib libraries

This page represents a collection of notes and tutorials for common functions, methods, definitions, etc. used in our class. This is not comprehensive and is intended as an aid for the course. For additional help, use the SAGE cheat sheet, SAGE tutorial, or stackoverflow. There are many ways to define functions in SAGE. The two main methods are:

defining a function explictly in terms of variables or defining a variable and then using it in a function formula. Note that the show command simply produces a nicely formated output using LaTeX.\LaTeX.LATE​X. Assume the following situation. From an experiment we have gathered following data: We want to use the data as an input to a simulation. However, as visible, the data is noisy and thus may lead to instability of our simulation.

First we will load modules supporting this tutorial. Note that you should install matplotlib first if not already happenend, as only this tutorial needs matplotlib. For usage of ebcpy, you don't need it. Let's specify the path to our measurement data and load it. If you're familiar with python and DataFrames, you will ask yourself: Why do I need the TimeSeriesData-Class? We implemented this class to combine the powerful pandas.DataFrame class with new functions for an easy usage in the context of Building Energy Systems for three main reasons:

Most data in our case is Time-Dependent, therefore functions for easy conversion between seconds (for simulation) and Timestamps (for measurements) is needed Let's look at the avocado data, which we looked at in week 3, and try to use the small hass volumes of avocados to predict their large hass volumes. To reduce the size of the dataset, let's also narrow our observations to only include avocados from 2015. We can measure the quality of our regression model using the RMSPE value—just like how we used accuracy to evaluate our knn classification models. In the readings, we looked at both RMSE and RMSPE and their differences. RMSE refers to the root mean squared error, or predicting and evaluating prediction quality on the training data.

RMSPE refers to the root mean squared prediction error, or the error in our predictions made about the actual testing data. We look at this property when we evaluate the quality of our final predictions. Recognize situations where a simple regression analysis would be appropriate for making predictions. Explain the kkk-nearest neighbour (kkk-nn) regression algorithm and describe how it differs from k-nn classification. Interpret the output of a kkk-nn regression. In a dataset with two variables, perform kkk-nearest neighbour regression in R using tidymodels to predict the values for a test dataset.

Using R, execute cross-validation in R to choose the number of neighbours. All material moved to the more comprehensive CoCalc Manual CoCalc is a cloud-based service that provides infrastructure and services that are useful for running courses based on Jupyter Notebooks. It is used for teaching by Universities around the world. All material moved to the more comprehensive CoCalc Manual For a list of authors see the contributors section.

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