9/8/2023 0 Comments Matlab for machine learningWrite a test script that calls the myknnEnsemblePredict function. For more details, see Change Default Compiler. You can use mex -setup to view and change the default compiler. MATLAB Coder locates and uses a supported, installed compiler. To generate C/C++ code, you must have access to a C/C++ compiler that is configured properly. This folder includes the entry-point function file ( myknnEnsemblePredict.m) and the test file ( test_myknnEnsemblePredict.m, described later on). Note: If you click the button located in the upper-right section of this page and open this example in MATLAB, then MATLAB opens the example folder. See Check Code with the Code Analyzer (MATLAB Coder). Adding this directive instructs the MATLAB Code Analyzer to help you diagnose and fix violations that would result in errors during code generation. = predict(CompactMdl,X,varargin) Īdd the %#codegen compiler directive (or pragma) to the entry-point function after the function signature to indicate that you intend to generate code for the MATLAB algorithm. In order to run the demo of your choice you should move to the chosen folder (i.e.Function = myknnEnsemblePredict(X,fileName,varargin) %#codegenĬompactMdl = loadLearnerForCoder(fileName) In case of MatLab you may also use its web-version. Thus in order to launch demos you need either Octave or MatLab to be installed on you local machine. This repository contains *.m scripts that are intended to be run in Octave or MatLab. The source of the following machine learning topics map is this wonderful blog post How to Use This Repository Install Octave or MatLab □ Neural Network: Multilayer Perceptron (MLP) - example: handwritten digits recognition. Usage examples: as a substitute of all other algorithms in general, image recognition, voice recognition, image processing (applying specific style), language translation, etc. The neural network itself isn't an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. □ Anomaly Detection using Gaussian distribution - example: detect overloaded server. Usage examples: intrusion detection, fraud detection, system health monitoring, removing anomalous data from the dataset etc. Anomaly DetectionĪnomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. □ K-means algorithm - example: split data into three clusters. Usage examples: market segmentation, social networks analysis, organize computing clusters, astronomical data analysis, image compression, etc. The algorithm itself decides what characteristic to use for splitting. In clustering problems we split the training examples by unknown characteristics. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. □ Logistic Regression - examples: microchip fitness detection, handwritten digits recognitions using one-vs-all approach. Usage examples: spam-filters, language detection, finding similar documents, handwritten letters recognition, etc. In classification problems we split input examples by certain characteristic. □ Linear Regression - example: house prices prediction. Usage examples: stock price forecast, sales analysis, dependency of any number, etc. Basically we try to draw a line/plane/n-dimensional plane along the training examples. In regression problems we do real value predictions. The ultimate purpose is to find such model parameters that will successfully continue correct input→output mapping (predictions) even for new input examples. Then we're training our model (machine learning algorithm parameters) to map the input to the output correctly (to do correct prediction). In supervised learning we have a set of training data as an input and a set of labels or "correct answers" for each training set as an output. In most cases the explanations are based on this great machine learning course. The purpose of this repository was not to implement machine learning algorithms using 3 rd party libraries or Octave/MatLab "one-liners" but rather to practice and to better understand the mathematics behind each algorithm. This repository contains MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics behind them being explained. For Python/Jupyter version of this repository please check homemade-machine-learning project.
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