How to use the qminer.analytics.NearestNeighborAD function in qminer

it("should make test number 33", function () {

	 // import analytics module
	 var analytics = require('qminer').analytics;
	 // create a new NearestNeighborAD object
	 var neighbor = new analytics.NearestNeighborAD();
	 // set it's parameters to rate: 0.1
	 neighbor.setParams({ rate: 0.1 });
	
});
});

it("should make test number 35", function () {

	  // import modules
	  var analytics = require('qminer').analytics;
	  var la = require('qminer').la;
	  var fs = require('qminer').fs;
	  // create a new NearestNeighborAD object
	  var neighbor = new analytics.NearestNeighborAD();
	  // create a new sparse matrix
	  var matrix = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, -2], [1, 3]], [[0, 0], [1, 1]]]);
	  // fit the model with the matrix
	  neighbor.fit(matrix);
	  // create an output stream object and save the model
	  var fout = fs.openWrite('neighbor_example.bin');
	  neighbor.save(fout);
	  fout.close();
	  // create a new Nearest Neighbor Anomaly model by loading the model
	  var fin = fs.openRead('neighbor_example.bin');
	  var neighbor2 = new analytics.NearestNeighborAD(fin);
     
});
});

var analytics = require('qminer').analytics;
	  var la = require('qminer').la;
	  var fs = require('qminer').fs;
	  // create a new NearestNeighborAD object
	  var neighbor = new analytics.NearestNeighborAD();
	  // create a new sparse matrix
	  var matrix = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, -2], [1, 3]], [[0, 0], [1, 1]]]);
	  // fit the model with the matrix
	  neighbor.fit(matrix);
	  // create an output stream object and save the model
	  var fout = fs.openWrite('neighbor_example.bin');
	  neighbor.save(fout);
	  fout.close();
	  // create a new Nearest Neighbor Anomaly model by loading the model
	  var fin = fs.openRead('neighbor_example.bin');
	  var neighbor2 = new analytics.NearestNeighborAD(fin);
     
});
});

it("should make test number 37", function () {

	 // import modules
	 var analytics = require('qminer').analytics;
	 var la = require('qminer').la;
	 // create a new NearestNeighborAD object
	 var neighbor = new analytics.NearestNeighborAD();
	 // create a new sparse matrix
	 var matrix = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, -2], [1, 3]], [[0, 0], [1, 1]]]);
	 // fit the model with the matrix
	 neighbor.fit(matrix);
	 // create a new sparse vector
	 var vector = new la.SparseVector([[0, 2], [1, 5]]);
	 // update the model with the vector
	 neighbor.partialFit(vector);
	
});
});

it("should make test number 40", function () {

	 // import modules
	 var analytics = require('qminer').analytics;
	 var la = require('qminer').la;
	 // create a new NearestNeighborAD object
	 var neighbor = new analytics.NearestNeighborAD();
	 // create a new sparse matrix
	 var matrix = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, -2], [1, 3]], [[0, 0], [1, 1]]]);
	 // fit the model with the matrix
	 neighbor.fit(matrix);
	 // create a new sparse vector
	 var vector = new la.SparseVector([[0, 4], [1, 0]]);
	 // check if the vector is an anomaly
	 var prediction = neighbor.predict(vector); // returns 1
	
});
});

it('should get the parameters of the default object', function () {
            var neighbor = new analytics.NearestNeighborAD();
            var params = neighbor.getParams();
            assert.equal(params.rate[0], [0.05]);
        })
        it('should get the parameters of the object', function () {

it('should explain differences for the given vector', function () {
            var neighbor = new analytics.NearestNeighborAD({ windowSize: 3 });
            var matrix = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, -2], [1, 3]], [[0, 0], [1, 1]]]);
            var idVec = new la.IntVector([1, 2, 3]);
            neighbor.fit(matrix, idVec);

            var vector = new la.SparseVector([[1, 4]]);
            var explain = neighbor.explain(vector);
            assert.equal(explain.nearestDat, 1);
            assert.equal(explain.distance, 5);
            assert.equal(explain.features[0].id, 0);
            assert.equal(explain.features[0].val, 0);
            assert.equal(explain.features[0].nearVal, 1);
            assert.equal(explain.features[0].contribution, 0.2);
            assert.equal(explain.features[1].id, 1);
            assert.equal(explain.features[1].val, 4);
            assert.equal(explain.features[1].nearVal, 2);
            assert.equal(explain.features[1].contribution, 0.8);

it('should not throw an exception', function () {
            var neighbor = new analytics.NearestNeighborAD({ windowSize: 2 });
            var matrix = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, -2], [1, 3]]]);
            neighbor.fit(matrix);

            var vector = new la.SparseVector([[0, 0], [1, 1]]);
            assert.doesNotThrow(function () {
                neighbor.partialFit(vector);
            });
        })
        it('should update the model and return the threshold equal to 8, vector input', function () {

it('should set the parameters of the default object', function () {
            var neighbor = new analytics.NearestNeighborAD();
            neighbor.setParams({ rate: [0.1] });
            var params = neighbor.getParams();
            assert.equal(params.rate[0], 0.1);
        })
        it('should set the parameters of the object', function () {

it('should update the model and return the threshold equal to 8, vector input', function () {
            var neighbor = new analytics.NearestNeighborAD({ windowSize: 2 });
            var matrix = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, -2], [1, 3]]]);
            neighbor.fit(matrix);

            var vector = new la.SparseVector([[0, 0], [1, 1]]);
            neighbor.partialFit(vector);
            var model = neighbor.getModel();

            assert.eqtol(model.threshold, 8);
        })
    });