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[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-09-19 UTC."],[[["\u003cp\u003eImplements the Learning Vector Quantization (LVQ) algorithm for clustering data.\u003c/p\u003e\n"],["\u003cp\u003eUsers can specify the desired number of clusters, learning rate, training epochs, and input normalization.\u003c/p\u003e\n"],["\u003cp\u003eBased on the Kohonen's work as described in "The Handbook of Brain Theory and Neural Networks".\u003c/p\u003e\n"],["\u003cp\u003eThe algorithm learns by adjusting cluster prototypes based on the input data during training epochs.\u003c/p\u003e\n"],["\u003cp\u003eIt returns a Clusterer object that can be used to predict the cluster assignments for new data points.\u003c/p\u003e\n"]]],["The `ee.Clusterer.wekaLVQ` function implements the Learning Vector Quantization algorithm for clustering. It requires specifying the number of clusters (`numClusters`, default 7), the learning rate (`learningRate`, default 1, between 0 and 1), the number of training epochs (`epochs`, default 1000, at least 1), and whether to normalize the input attributes (`normalizeInput`, default false). The function returns a Clusterer object. The algorithm's details are described in a specific paper by T. Kohonen.\n"],null,[]]