qminer
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A C++ based data analytics platform for processing large-scale real-time streams containing structured and unstructured data
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JavaScript
/**
* Copyright (c) 2015, Jozef Stefan Institute, Quintelligence d.o.o. and contributors
* All rights reserved.
*
* This source code is licensed under the FreeBSD license found in the
* LICENSE file in the root directory of this source tree.
*/
// JavaScript source code
var la = require('../../index.js').la;
var analytics = require('../../index.js').analytics;
var assert = require("../../src/nodejs/scripts/assert.js");
//Unit test for LIBSVM SVR
describe("LIBSVM SVR test", function () {
describe("Constructor test", function () {
it("It should return a default constructor", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 1);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 0.001);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, false);
});
it("It should return a SVR created by Json", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 5, eps: 5, batchSize: 5, maxIterations: 5, maxTime: 1, minDiff: 1e-10, verbose: true });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 5);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 5);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, true);
});
it("It should return a SVR created by Json, not all key values are given", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 5, batchSize: 5, maxTime: 1, verbose: true });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 5);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 0.001);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, true);
});
it("It should return a SVR created by an empty Json", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 1);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 0.001);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, false);
});
it("It should return a SVR created by Json, with added key values", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM", alpha: 5, beta: 10, s: 3, batchSize: 10000, verbose: true });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 1);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 0.001);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, true);
});
});
describe("GetParams tests", function () {
it("should return the parameters of the default SVR model as Json", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 1);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 0.001);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, false);
})
it("should return the parameters of the default SVR model as Json, without some key values", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 3, eps: 2, maxTime: 1 });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 3);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 2);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, false);
})
it("should return the parameters of the default SVR model as Json, with added key values", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM", alpha: 3, beta: 3, z: 3 });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 1);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 0.001);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, false);
})
});
describe("SetParams tests", function () {
it("should return the existing SVR with the changed values", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
SVR.setParams({ eps: 3, maxTime: 2 });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 1);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 3);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, false);
})
it("should return the existing SVR with the changed, added values", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
SVR.setParams({ eps: 3, maxTime: 2, alpha: 5, z: 10 });
var SVRjSon = SVR.getParams();
assert.strictEqual(SVRjSon.kernel, "LINEAR");
assert.strictEqual(SVRjSon.svmType, "default");
assert.strictEqual(SVRjSon.c, 1);
assert.strictEqual(SVRjSon.j, 1);
assert.strictEqual(SVRjSon.eps, 3);
assert.strictEqual(SVRjSon.gamma, 1);
assert.strictEqual(SVRjSon.p, 0.1);
assert.strictEqual(SVRjSon.degree, 1);
assert.strictEqual(SVRjSon.nu, 0.01);
assert.strictEqual(SVRjSon.coef0, 1);
assert.strictEqual(SVRjSon.cacheSize, 100);
assert.strictEqual(SVRjSon.verbose, false);
})
it("should throw an exception if the argument is not Json", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
assert.throws(function () {
SVR.setParams(1);
});
})
it("should throw an exception if there is no given argument", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
assert.throws(function () {
SVR.setParams();
});
})
});
describe("Weights tests", function () {
it("should return an empty vector", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
var Vec = SVR.weights;
assert.strictEqual(Vec.length, 0);
})
it("should return an empty vector even if the parameters have been changed", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
SVR.setParams({ j: 3, maxTime: 2 });
var Vec = SVR.weights;
assert.strictEqual(Vec.length, 0);
})
});
describe("Bias tests", function () {
it("should return zero", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
var num = SVR.bias;
assert.strictEqual(num, 0);
})
it("should return zero even if the parameters have been changed", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
SVR.setParams({ j: 3, maxTime: 2 });
var num = SVR.bias;
assert.strictEqual(num, 0);
})
});
describe("getModel tests", function () {
it("should return parameters of the model", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
var Model = SVR.getModel();
assert.strictEqual(Model.weights.length, 0);
assert.strictEqual(Model.bias, 0);
})
it("should ignore extra parameters given to the function", function () {
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
var Model = SVR.getModel(1);
assert.strictEqual(Model.weights.length, 0);
assert.strictEqual(Model.bias, 0);
})
});
describe("Fit Tests", function () {
it("should not throw an exception for correct values", function () {
var matrix = new la.Matrix([[1, -1], [1, -1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
assert.doesNotThrow(function () {
SVR.fit(matrix, vector);
});
})
//degenerated example
it("should return a fitted model for the degenerated example", function () {
var matrix = new la.Matrix([[1, -1], [1, -1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
SVR.fit(matrix, vector);
var model = SVR.getModel();
assert.eqtol(model.weights[0], 0, 1e-2);
assert.eqtol(model.weights[1], 0, 1e-2);
})
//the tolerance in the test is equal to epsilon in SVR
it("should return a fitted model", function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var model = SVR.getModel();
assert.eqtol(model.weights[0], 0, 1e-1);
assert.eqtol(model.weights[1], 0, 1e-1);
})
//not passing
it('should make a model from one-dimensional vectors', function () {
var matrix = new la.Matrix([[1, -1]]);
var vector = new la.Vector([1, -1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var weights = SVR.weights;
assert.eqtol(weights[0], 1, 3e-1);
})
it('should throw an exception if the number of matrix cols and the vector length aren\'t equal', function () {
var matrix = new la.Matrix([[1, -1, 2], [1, -2, 3]]);
var vector = new la.Vector([1, -1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
assert.throws(function () {
SVR.fit(matrix, vector);
});
})
//not passing
it('should forget the previous model', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 100 });
SVR.fit(matrix, vector);
var weights = SVR.weights;
assert.eqtol(weights[0], 0, 1e-1);
assert.eqtol(weights[1], 0, 1e-1);
var matrix2 = new la.Matrix([[1, -1]]);
var vec2 = new la.Vector([1, -1]);
SVR.fit(matrix2, vec2);
weights = SVR.weights;
assert.eqtol(weights[0], 1, 1e-1);
})
// test for setParams
it('should not change the model', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var model = SVR.getModel();
assert.eqtol(model.weights[0], 0, 1e-1);
assert.eqtol(model.weights[1], 0, 1e-1);
// changing the parameters
SVR.setParams({ c: 100, maxTime: 12000 });
var weights = SVR.weights;
assert.eqtol(weights[0], 0, 1e-1);
assert.eqtol(weights[1], 0, 1e-1);
})
});
describe('Predict Tests', function () {
it('should not throw an exception for giving the correct values', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var vec2 = new la.Vector([2, 3]);
assert.doesNotThrow(function () {
SVR.predict(vec2);
});
})
it('should return the prediction of the vector [2, 3]', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
// Note that since all targets are 1, it suffices to set w=0 and b=-1 to solve w*x-b = y.
var vec2 = new la.Vector([2, 3]);
var prediction = SVR.predict(vec2);
assert.eqtol(prediction, 1, 5e-1);
})
it('should throw an exception if the given vector is to long', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
assert.throws(function () {
SVR.predict(new la.Vector([1, 2, 3]));
});
})
it('should throw an exception if the given vector is to short', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
assert.throws(function () {
SVR.predict(new la.Vector([1]));
});
})
it('should throw an exception if the given vector is empty', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
assert.throws(function () {
SVR.predict(new la.Vector());
});
})
//not passing
it('should return the prediction of the one-dimensional vector', function () {
var matrix = new la.Matrix([[1, -1]]);
var vector = new la.Vector([1, -1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var vec2 = new la.Vector([3]);
var prediction = SVR.predict(vec2);
assert.eqtol(prediction, 3, 5e-1);
})
// if fit is not previously used
it('should throw an exception if the fit is not used', function () {
var SVR = new analytics.SVR({ c: 10 });
var vector = new la.Vector([1, 1]);
assert.throws(function () {
SVR.predict(vector);
});
})
it('should return the predictions for the matrix', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var mat2 = new la.Matrix([[2, -3, 5], [1, -4, 0.3]]);
var prediction = SVR.predict(mat2);
assert.eqtol(prediction[0], 1, 5e-1);
assert.eqtol(prediction[1], 1, 5e-1);
assert.eqtol(prediction[2], 1, 5e-1);
})
it('should throw an exception if there are too many rows in the matrix', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var mat2 = new la.Matrix([[2, -3, 5], [1, -4, 0.3], [0, 0, 1]]);
assert.throws(function () {
SVR.predict(mat2);
});
})
it('should throw an exception if there are too lesser rows in the matrix', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var mat2 = new la.Matrix([[2, -3, 5]]);
assert.throws(function () {
SVR.predict(mat2);
});
})
});
describe('DecisionFunction Tests', function () {
it('should not return an exception for the given correct values', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var vec2 = new la.Vector([2, 3]);
assert.doesNotThrow(function () {
SVR.decisionFunction(vec2);
})
})
it('should return the distance from the model', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var vec2 = new la.Vector([2, 3]);
var distance = SVR.decisionFunction(vec2);
assert.eqtol(distance, 1, 5e-1);
})
//not passing
it('should return the distance of the one-dimensional vector from the model', function () {
var matrix = new la.Matrix([[1, -1]]);
var vector = new la.Vector([1, -1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var vec2 = new la.Vector([0.5]);
var distance = SVR.decisionFunction(vec2);
assert.eqtol(distance, 0.5, 1e-1);
})
it('should return the distance of the matrix from the model', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var matrix2 = new la.Matrix([[1, 2, 3], [-2, 3, -0.5]]);
var distance = SVR.decisionFunction(matrix2);
assert.eqtol(distance[0], 1, 5e-1);
assert.eqtol(distance[1], 1, 5e-1);
assert.eqtol(distance[2], 1, 5e-1);
})
it('should throw an exception if the vector is too long', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var vec2 = new la.Vector([2, 3, 4]);
assert.throws(function () {
SVR.decisionFunction(vec2);
});
})
it('should throw an exception if the vector is too short', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var vec2 = new la.Vector([2]);
assert.throws(function () {
SVR.decisionFunction(vec2);
});
})
it('should throw an exception if the matrix has to many rows', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var matrix2 = new la.Matrix([[1, 2, 3], [2, 3, -4], [-1, 0, 0]]);
assert.throws(function () {
SVR.decisionFunction(matrix2);
})
})
it('should throw an exception if the matrix has too lesser of rows', function () {
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM", c: 10 });
SVR.fit(matrix, vector);
var matrix2 = new la.Matrix([[1, 2, 3]]);
assert.throws(function () {
SVR.decisionFunction(matrix2);
});
})
});
describe('Serialization Tests', function () {
it('should serialize and deserialize', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVR = new analytics.SVR({ algorithm: "LIBSVM" });
SVR.fit(matrix, vec);
SVR.save(require('../../index.js').fs.openWrite('svc_test.bin')).close();
var SVR2 = new analytics.SVR(require('../../index.js').fs.openRead('svc_test.bin'));
assert.deepEqual(SVR.getParams(), SVR2.getParams());
assert.eqtol(SVR.weights.minus(SVR2.weights).norm(), 0, 1e-8);
})
});
describe('Kernel tests', function () {
it('should find a fit with polynomial kernel', function () {
X = [[-3],
[-2],
[-1],
[0],
[1],
[2],
[3]];
var y = [9, 4, 1, 0, 4, 1, 9];
var matrix = new la.Matrix(X);
matrix = matrix.transpose();
var vec = new la.Vector(y);
// unbalance: positive examples are 1000 times more important
var SVR = new analytics.SVR({ algorithm: "LIBSVM", kernel: "POLY", degree: 2, p: 10e-3 });
SVR.fit(matrix, vec);
assert.eqtol(SVR.predict(matrix).minus(new la.Vector([9, 4, 1, 0, 1, 4, 9])).norm(), 0, 1e-1);
});
it('should find a fit with RBF kernel', function () {
X = [[1, 0],
[0, 1],
[-1, 0],
[0, -1],
[2, 0],
[0, 2],
[-2, 0],
[0, -2]];
var y = [1, 1, 1, 1, 2, 2, 2, 2];
var matrix = new la.Matrix(X);
matrix = matrix.transpose();
var vec = new la.Vector(y);
// unbalance: positive examples are 1000 times more important
var SVR = new analytics.SVR({ algorithm: "LIBSVM", kernel: "RBF", p: 10e-3 });
SVR.fit(matrix, vec);
assert.eqtol(SVR.predict(matrix).minus(new la.Vector([1, 1, 1, 1, 2, 2, 2, 2])).norm(), 0, 1e-1);
});
});
})