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it("should make test number 50", function () {
// import la module
var la = require('qminer').la;
// create a new sparse matrix
var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]);
// check the number of rows in sparse matrix
mat.rows;
});
});
it("should make test number 44", function () {
// import la module
var la = require('qminer').la;
// create two sparse matrices
var mat = new la.SparseMatrix([[[0, 1], [3, 2]], [[1, -3]]]);
var mat2 = new la.SparseMatrix([[[0, 3]],[[2, 1]]]);
// get the sum of the two matrices
// returns the sum ( insparse form)
// 4 0
// 0 -3
// 0 1
// 2 0
var sum = mat.plus(mat2);
});
});
it("should make test number 49", function () {
// import la module
var la = require('qminer').la;
// create a new sparse matrix
var mat = new la.SparseMatrix([[[0, 1], [1, 3]], [[0, 2], [1, 4]]]);
// get the frobenious norm of sparse matrix
var norm = mat.frob(); // returns sqrt(30)
});
});
it("should make test number 52", function () {
// import la module
var la = require('qminer').la;
// create a new sparse matrix
var spMat = new la.SparseMatrix([[[0, 1]], [[0, 3], [1, 8]]]);
// print sparse matrix on screen
// each row represents a nonzero element, where first value is row index, second
// value is column index and third value is element value. For this matrix:
// 0 0 1.000000
// 0 1 3.000000
// 1 1 8.000000
spMat.print();
});
});
it("should make test number 45", function () {
// import la module
var la = require('qminer').la;
// create two sparse matrices
var mat = new la.SparseMatrix([[[0, 1], [3, 2]], [[1, -3]]]);
var mat2 = new la.SparseMatrix([[[0, 3]],[[2, 1]]]);
// get the sum of the two matrices
// returns the sum ( insparse form)
// -2 0
// 0 -3
// 0 -1
// 2 0
var diff = mat.minus(mat2);
});
});
it("should make test number 54", function () {
// import the modules
var fs = require('qminer').fs;
var la = require('qminer').la;
// create an empty matrix
var mat = new la.SparseMatrix();
// open a read stream ('mat.dat' was previously created)
var fin = fs.openRead('mat.dat');
// load the matrix
mat.load(fin);
});
});
it("should make test number 55", function () {
// import the modules
var la = require('qminer').la;
// create an empty matrix
var mat = new la.SparseMatrix();
mat.setRowDim(2);
mat.rows // prints 2
});
});
it("should make test number 38", function () {
// import la module
var la = require('qminer').la;
// create a new sparse matrix with array
var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]);
// create a new sparse matrix with specified max rows
var mat2 = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]], 3);
});
});
it('should not throw an exception, sparse matrix', function () {
this.timeout(10000);
var json = { c: 10, maxTime: 12000 };
var onevsall = new analytics.OneVsAll({ model: analytics.SVC, modelParam: json, cats: 2 });
var matrix = new la.Matrix([[1, 2, 1, 1], [2, 1, -3, -4]]);
var vector = new la.Vector([0, 0, 1, 1]);
onevsall.fit(matrix, vector);
var test = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, 1], [1, -3]]]);
assert.doesNotThrow(function () {
var prediction = onevsall.predict(test);
});
})
it('should return an integer vector containing the cluster indeces, sparse matrix', function () {
it('should set the models, sparse matrix', function () {
this.timeout(10000);
var json = { c: 10, maxTime: 12000 };
var onevsall = new analytics.OneVsAll({ model: analytics.SVC, modelParam: json, cats: 2 });
var matrix = new la.SparseMatrix([[[0, 1], [1, 2]], [[0, 2], [1, 1]], [[0, 1], [1, -3]], [[0, 1], [1, -4]]]);
var vector = new la.Vector([0, 0, 1, 1]);
onevsall.fit(matrix, vector);
var param = onevsall.getParams();
assert.equal(param.models.length, 2);
})
it('should throw an exception if there are no parameters given', function () {