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

it("should make test number 46", function () {

	 // import analytics module
	 var analytics = require('qminer').analytics;
	 // create a new Recursive Linear Regression model
	 var linreg = new analytics.RecLinReg({ dim: 10 });
	 // set the parameters of the model
	 linreg.setParams({ dim: 3, recFact: 1e2, forgetFact: 0.5 });
	
});
});

it("should make test number 47", function () {

	 // import analytics module
	 var analytics = require('qminer').analytics;
	 // create a new Recursive Linear Regression model
	 var linreg = new analytics.RecLinReg({ dim: 10 });
	 // get the parameters of the model
	 var params = linreg.getParams(); // returns { dim: 10, recFact: 1.0, forgetFact: 1.0 }
	
});
});

it('should return the model', function () {
            var linreg = new analytics.RecLinReg({ dim: 2, regFact: 1e-10, forgetFact: 1.0 });
            var X = new la.Matrix([[1, 2], [1, -1]]);
            var y = new la.Vector([3, 3]);

            linreg.fit(X, y);
            var model = linreg.getModel();
            assert.eqtol(model.weights.length, 2);
            assert.eqtol(model.weights[0], 2);
            assert.eqtol(model.weights[1], 1);
        })
    });

it('should not throw an exception', function () {
            var linreg = new analytics.RecLinReg({ dim: 10, regFact: 2.0, forgetFact: 15.0 });
            assert.doesNotThrow(function () {
                linreg.setParams({ dim: 1.0 });
            });
        })
        it('should change some of the parameters', function () {

it('should serialize and deserialize', function () {
            var linreg = new analytics.RecLinReg({ dim: 2, regFact: 1e-10, forgetFact: 1.0 });
            var X = new la.Matrix([[1, 2], [1, -1]]);
            var y = new la.Vector([3, 3]);
            linreg.fit(X, y);
            linreg.save(require('qminer').fs.openWrite('reclinreg_test.bin')).close();
            var linreg2 = new analytics.RecLinReg(require('qminer').fs.openRead('reclinreg_test.bin'));
            assert.deepEqual(linreg.getParams(), linreg2.getParams());
            assert.eqtol(linreg.weights.minus(linreg2.weights).norm(), 0, 1e-8);
        })
    });

it('should make a new model by fitting the matrix', function () {
            var linreg = new analytics.RecLinReg({ dim: 2, regFact: 1e-10, forgetFact: 1.0 });
            var X = new la.Matrix([[1, 2], [1, -1]]);
            var y = new la.Vector([3, 3]);

            linreg.fit(X, y);
            var weights = linreg.weights;
            assert.eqtol(weights[0], 2);
            assert.eqtol(weights[1], 1);
        })
        it('should throw an exception if the number of rows exceed the dimension', function () {

it('should serialize and deserialize', function () {
            var linreg = new analytics.RecLinReg({ dim: 2, regFact: 1e-10, forgetFact: 1.0 });
            var X = new la.Matrix([[1, 2], [1, -1]]);
            var y = new la.Vector([3, 3]);
            linreg.fit(X, y);
            linreg.save(require('qminer').fs.openWrite('reclinreg_test.bin')).close();
            var linreg2 = new analytics.RecLinReg(require('qminer').fs.openRead('reclinreg_test.bin'));
            assert.deepEqual(linreg.getParams(), linreg2.getParams());
            assert.eqtol(linreg.weights.minus(linreg2.weights).norm(), 0, 1e-8);
        })
    });

it('should throw an exception if there are no parameters given', function () {
            var linreg = new analytics.RecLinReg({ dim: 2, regFact: 1e-10, forgetFact: 1.0 });
            var X = new la.Matrix([[1, 2], [1, -1]]);
            var y = new la.Vector([3, 3]);
            linreg.fit(X, y);
            assert.throws(function () {
                linreg.predict();
            });
        })
    });

it('should throw an exception if the number of rows exceed the dimension', function () {
            var linreg = new analytics.RecLinReg({ dim: 2, regFact: 1e-10, forgetFact: 1.0 });
            var X = new la.Matrix([[1, 2], [1, -1], [1, 1]]);
            var y = new la.Vector([3, 3]);

            assert.throws(function () {
                linreg.fit(X, y);
            });
        })
        it('should throw an exception if the number of rows is less than the dimension', function () {

it('should not throw an exception', function () {
            var linreg = new analytics.RecLinReg({ dim: 2, regFact: 1e-10, forgetFact: 1.0 });
            assert.doesNotThrow(function () {
                var model = linreg.getModel();
            });
        })
        it('should return the default values of the model', function () {