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It implements the following methods:\n+* <br>1. {@link module:qm.StreamAggr#getInteger} returns the severity of the alarm.\n+* <br>2. {@link module:qm.StreamAggr#getTimestamp} returns the timestamp of the latest alarm.\n+* <br>3. {@link module:qm.StreamAggr#saveJson} returns the Json with the description and explanation of the alarm.\n+* @property {string} name - The given name for the stream aggregator.\n+* @property {string} type - The type of the stream aggregator. <b>Important:</b> It must be equal to `'nnAnomalyDetector'`.\n+* @property {string} store - The name of the store from which it takes the data.\n+* @property {string} inAggr - The name of the stream aggregator to which it connects and gets data.\n+* It <b>cannot</b> be connect to the {@link module:qm~StreamAggrTimeSeriesWindow}.\n+\n+* @example\n+* // import the qm module\n+* var qm = require('qminer');\n+* // create a base with a simple store named Cars with 4 fields\n+* let base = new qm.Base({\n+*     mode: 'createClean',\n+*     schema: [{\n+*         name: 'Cars',\n+*         fields: [\n+*             { name: 'NumberOfCars', type: 'float' },\n+*             { name: 'Temperature', type: 'float' },\n+*             { name: 'Precipitation', type: 'float' },\n+*             { name: 'Time', type: 'datetime' }\n+*         ]\n+*     }]\n+* });\n+* // create the store\n+* let store = base.store('Cars');\n+* //define a feature space aggregator on the Cars store which needs at least 2 records to be initialized. Use three of the \n+* four fields of the store to create feature vectors with normalized 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