82 lines
1.9 KiB
JavaScript
82 lines
1.9 KiB
JavaScript
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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2018 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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/**
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* Compute an unbiased sample covariance matrix incrementally.
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*
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* @module @stdlib/stats/incr/covmat
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*
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* @example
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* var Float64Array = require( '@stdlib/array/float64' );
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* var ndarray = require( '@stdlib/ndarray/ctor' );
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* var incrcovmat = require( '@stdlib/stats/incr/covmat' );
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*
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* // Create an output covariance matrix:
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* var buffer = new Float64Array( 4 );
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* var shape = [ 2, 2 ];
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* var strides = [ 2, 1 ];
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* var offset = 0;
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* var order = 'row-major';
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*
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* var cov = ndarray( 'float64', buffer, shape, strides, offset, order );
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*
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* // Create a covariance matrix accumulator:
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* var accumulator = incrcovmat( cov );
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*
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* var out = accumulator();
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* // returns null
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*
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* // Create a data vector:
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* buffer = new Float64Array( 2 );
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* shape = [ 2 ];
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* strides = [ 1 ];
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*
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* var vec = ndarray( 'float64', buffer, shape, strides, offset, order );
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*
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* // Provide data to the accumulator:
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* vec.set( 0, 2.0 );
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* vec.set( 1, 1.0 );
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*
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* out = accumulator( vec );
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* // returns <ndarray>
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*
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* var bool = ( out === cov );
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* // returns true
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*
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* vec.set( 0, -5.0 );
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* vec.set( 1, 3.14 );
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*
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* out = accumulator( vec );
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* // returns <ndarray>
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*
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* // Retrieve the covariance matrix:
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* out = accumulator();
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* // returns <ndarray>
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*/
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// MODULES //
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var incrcovmat = require( './main.js' );
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// EXPORTS //
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module.exports = incrcovmat;
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