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savitzky-golay-generalized\n\nGeneral Least-Squares Smoothing and Differentiation by the Convolution (Savitzky-Golay) Method Peter A. Gorry\n\nPretty much the same as the savitzky-golay method, but without border problems, and without inventing points.\nIt can be maybe merged into the savitzky-golay project but by the now this is the version used in the last GSD project.\n\nI'll try an automatic parameter tunning based on the SNR or in the entropy of the signal.\n\n# Usage\n\n```js\nnpm i ml-savitzky-golay-generalized\nconst {sgg} = require(\"ml-savitzky-golay-generalized\");\nsgg(dataY, deltaX|X, options)\n```\n\n## Parameters\n\n### dataY\n\nThe data to be filtered.\n\n### deltaX | X\n\ndeltaX specifies the difference between 2 consecutive points of the independent: deltaX = X[i+1] - X[i]. Specficiying a deltaX suppose that all your points are equally spaced on the independent variable.\nIf your points are not equally spaced in the ordinate variable, then you have to provide explicitly your X values. The algorithm will use the average deltaX within each bin of 'windowSize' points to approximate the derivatives. This fast approximation only works if the X is almost locally equally spaced.\n\n### options\n\n#### windowSize:\n\nThe odd number of points to approximate the regression polynomial. Default 9\n\n#### derivative:\n\nThe grade of the derivative. 0 by default (Smoothing)\n\n#### polynomial:\n\nThe order of the regression polynomial. Default 3\n\n## Examples\n\n```js\nconst { sgg } = require('ml-savitzky-golay-generalized');\n```\n\n### Smoothing example\n\n```js\nconst options = {\n  windowSize: 15,\n  derivative: 0,\n  polynomial: 3,\n};\n\nconst noiseLevel = 0.1;\nconst data = new Array(200);\nfor (let i = 0; i \u003c data.length; i++)\n  data[i] =\n    Math.sin((i * Math.PI * 2) / data.length) +\n    (Math.random() - 0.5) * noiseLevel;\nconst answer = sgg(data, (Math.PI * 2) / data.length, options);\nconsole.log(answer);\n```\n\n### First derivative test (Equally spaced x)\n\n```js\nconst options = {\n  windowSize: 45,\n  derivative: 1,\n  polynomial: 3,\n};\n\nconst noiseLevel = 0.1;\nconst data = new Array(200);\nfor (let i = 0; i \u003c data.length; i++) {\n  data[i] =\n    Math.sin((i * Math.PI * 2) / data.length) +\n    (Math.random() - 0.5) * noiseLevel;\n}\nconst answer = sgg(data, (Math.PI * 2) / data.length, options);\nconsole.log(answer);\n```\n\n### First derivative test x as vector(It could be non-equally spaced!!)\n\n```js\nconst options = {\n  windowSize: 47,\n  derivative: 1,\n  polynomial: 3,\n};\n\nconst noiseLevel = 0.1;\nconst data = new Array(200);\nconst x = new Array(200);\nfor (let i = 0; i \u003c data.length; i++) {\n  data[i] =\n    Math.sin((i * Math.PI * 2) / data.length) +\n    (Math.random() - 0.5) * noiseLevel;\n  x[i] = (i * Math.PI * 2) / data.length;\n}\n\nconst ans = sgg(data, (Math.PI * 2) / data.length, options);\nconst ans2 = sgg(data, x, options);\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmljs%2Fsavitzky-golay-generalized","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmljs%2Fsavitzky-golay-generalized","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmljs%2Fsavitzky-golay-generalized/lists"}