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46 lines
1.6 KiB
JavaScript
46 lines
1.6 KiB
JavaScript
import assert from 'assert'
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import math from '../../../../src/bundleAny'
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describe('kldivergence', function () {
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it('should return 0, cause distributions is equals', function () {
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const q = [0.1, 0.4, 0.5, 0.2]
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assert.strictEqual(math.kldivergence(q, q), 0)
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assert.strictEqual(math.kldivergence(math.matrix(q), q), 0)
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assert.strictEqual(math.kldivergence(q, math.matrix(q)), 0)
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assert.strictEqual(math.kldivergence(math.matrix(q), math.matrix(q)), 0)
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})
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it('should return distance between two distrubutions', function () {
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const q = [0.5, 0.6, 0.7]
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const p = [0.4, 0.5, 0.6]
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assert.strictEqual(math.kldivergence(q, p), 0.00038410187968898266)
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const q2 = [0.9, 0.2, 0.8, 0.4]
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const p2 = [0.1, 0.8, 0.7, 0.6]
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assert.strictEqual(math.kldivergence(q2, p2), 0.6707144627487189)
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})
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it('should return normalized distance between two distributions', function () {
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const q = [1, 2, 3, 4, 5, 6, 7, 8]
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const p = [2, 3, 4, 5, 6, 7, 8, 9]
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assert.strictEqual(math.kldivergence(q, p), 0.006970870019248255)
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})
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it('should return infinity', function () {
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const q = [1, 2]
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const p = [0, 1]
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assert.strictEqual(math.kldivergence(q, p), Infinity)
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})
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it('should return NaN', function () {
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const q = [-1, 2]
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const p = [0.4, 1]
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assert.strictEqual(isNaN(parseFloat(math.kldivergence(q, p))), true)
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})
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it('should return bignumber', function () {
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const result = math.kldivergence([math.bignumber(4), math.bignumber(7)], [math.bignumber(1), math.bignumber(4)])
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assert.strictEqual(result.toString(), '0.0717688178200499468328227075658945850681301640503275280115029999')
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})
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})
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