Improve tests

tests/lib/model/abod.test.js

@@ -58,4 +58,27 @@ describe('anomaly detection', () => {
 			expect(y[i]).toBe(true)
 		}
 	})
+
+	test('LB-ABOD many outliers', () => {
+		const model = new LBABOD(10, 2)
+		const x = [
+			[-0.5, 0],
+			[-1.0, 0.5],
+			[0.5, -0.1],
+			[0.4, -0.2],
+			[-0.6, -0.8],
+			[10, 10],
+			[-10, 10],
+			[10, -10],
+		]
+		const y = model.predict(x)
+		for (let i = 0; i < y.length - 3; i++) {
+			expect(y[i]).toBe(false)
+		}
+		let c = 0
+		for (let i = y.length - 3; i < y.length; i++) {
+			if (y[i]) c++
+		}
+		expect(c).toBe(2)
+	})
 })

tests/lib/model/adamenn.test.js

@@ -17,3 +17,18 @@ test('predict', () => {
 	const acc = accuracy(y, t)
 	expect(acc).toBeGreaterThan(0.9)
 })
+
+test('specify params', () => {
+	const model = new ADAMENN(7, 3, 10, 3, 2, 0.5)
+	const x = Matrix.concat(Matrix.randn(50, 2, 0, 0.2), Matrix.randn(50, 2, 5, 0.2)).toArray()
+	const t = []
+	for (let i = 0; i < x.length; i++) {
+		t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / 50))
+	}
+
+	model.fit(x, t)
+	const y = model.predict(x)
+	expect(y).toHaveLength(x.length)
+	const acc = accuracy(y, t)
+	expect(acc).toBeGreaterThan(0.9)
+})

tests/lib/model/association_analysis.test.js

@@ -16,6 +16,7 @@ test('items', () => {
 	const items = [...model.items()].flat()
 	items.sort()
 	expect(items).toEqual(['c', 'data', 'image', 'java', 'net', 'web'])
+	expect(model.items(6)).toHaveLength(0)
 })
 
 test('items large support', () => {
@@ -51,6 +52,7 @@ test('support', () => {
 	model.fit(data)
 	expect(model.support('data')).toBeCloseTo(4 / 7)
 	expect(model.support('data', 'image')).toBeCloseTo(2 / 7)
+	expect(model.support('hoge')).toBe(0)
 })
 
 test('confidence', () => {
@@ -67,6 +69,8 @@ test('confidence', () => {
 	]
 	model.fit(data)
 	expect(model.confidence('java', 'c')).toBeCloseTo(0.4)
+	expect(model.confidence('net', 'web')).toBe(0)
+	expect(model.confidence('hoge', 'data')).toBe(0)
 })
 
 test('lift', () => {

tests/lib/model/automatic_thresholding.test.js

@@ -8,7 +8,9 @@ test('clustering', () => {
 	const n = 50
 	const x = Matrix.concat(Matrix.randn(n, 1, 0, 0.1), Matrix.randn(n, 1, 5, 0.1)).value
 
-	model.fit(x)
+	for (let i = 0; i < 2; i++) {
+		model.fit(x)
+	}
 	const y = model.predict(x)
 	expect(y).toHaveLength(x.length)
 

tests/lib/model/average_shifted_histogram.test.js

@@ -3,29 +3,49 @@ import AverageShiftedHistogram from '../../../lib/model/average_shifted_histogra
 
 import { correlation } from '../../../lib/evaluate/regression.js'
 
-test('density estimation', () => {
-	const model = new AverageShiftedHistogram({ size: 0.1 }, 10)
-	const n = 500
-	const x = Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)).toArray()
-	model.fit(x)
-	const y = model.predict(x)
-	expect(y).toHaveLength(x.length)
+describe('density estimation', () => {
+	test('size', () => {
+		const model = new AverageShiftedHistogram({ size: 0.1 }, 10)
+		const n = 500
+		const x = Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)).toArray()
+		model.fit(x)
+		const y = model.predict(x)
+		expect(y).toHaveLength(x.length)
 
-	const p = []
-	for (let i = 0; i < x.length; i++) {
-		const p1 = Math.exp(-x[i].reduce((s, v) => s + v ** 2, 0) / (2 * 0.1)) / (2 * Math.PI * 0.1)
-		const p2 = Math.exp(-x[i].reduce((s, v) => s + (v - 5) ** 2, 0) / (2 * 0.1)) / (2 * Math.PI * 0.1)
-		p[i] = (p1 + p2) / 2
-	}
-	const corr = correlation(y, p)
-	expect(corr).toBeGreaterThan(0.9)
-})
+		const p = []
+		for (let i = 0; i < x.length; i++) {
+			const p1 = Math.exp(-x[i].reduce((s, v) => s + v ** 2, 0) / (2 * 0.1)) / (2 * Math.PI * 0.1)
+			const p2 = Math.exp(-x[i].reduce((s, v) => s + (v - 5) ** 2, 0) / (2 * 0.1)) / (2 * Math.PI * 0.1)
+			p[i] = (p1 + p2) / 2
+		}
+		const corr = correlation(y, p)
+		expect(corr).toBeGreaterThan(0.9)
+	})
+
+	test('domain', () => {
+		const model = new AverageShiftedHistogram(
+			{
+				domain: [
+					[-1, 1],
+					[-1, 1],
+				],
+				size: 0.1,
+			},
+			10
+		)
+		const n = 5000
+		const x = Matrix.random(n, 2, -1, 1).toArray()
+		model.fit(x)
+		const y = model.predict(x)
+		expect(y).toHaveLength(x.length)
+	})
 
-test('density estimation outsize', () => {
-	const model = new AverageShiftedHistogram({ size: 0.1 }, 10)
-	const n = 500
-	const x = Matrix.randn(n, 2, 0, 0.1).toArray()
-	model.fit(x)
-	const y = model.predict([[-10, -10]])
-	expect(y[0]).toBe(0)
+	test('outsize', () => {
+		const model = new AverageShiftedHistogram({ size: 0.1 }, 10)
+		const n = 500
+		const x = Matrix.randn(n, 2, 0, 0.1).toArray()
+		model.fit(x)
+		const y = model.predict([[-10, -10]])
+		expect(y[0]).toBe(0)
+	})
 })

tests/lib/model/cast.test.js

@@ -23,3 +23,24 @@ test('cast', () => {
 	const ri = randIndex(y, t)
 	expect(ri).toBeGreaterThan(0.9)
 })
+
+test('large t', () => {
+	const model = new CAST(0.9)
+	const n = 50
+	const x = Matrix.concat(
+		Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)),
+		Matrix.randn(n, 2, [0, 5], 0.1)
+	).toArray()
+
+	model.fit(x)
+	expect(model.size).toBeGreaterThanOrEqual(3)
+	const y = model.predict(x)
+	expect(y).toHaveLength(x.length)
+
+	const t = []
+	for (let i = 0; i < x.length; i++) {
+		t[i] = Math.floor(i / n)
+	}
+	const ri = randIndex(y, t)
+	expect(ri).toBeGreaterThan(0.7)
+})

tests/lib/model/ensemble_binary.test.js

@@ -36,6 +36,38 @@ describe('oneone', () => {
 		expect(acc).toBeGreaterThan(0.95)
 	})
 
+	test('predict returns 1d array', () => {
+		const model = new EnsembleBinaryModel(function () {
+			this.init = (x, y) => {
+				this.x = Matrix.fromArray(x)
+				this.y = Matrix.fromArray(y)
+			}
+			this.fit = () => {
+				this.w = this.x.tDot(this.x).solve(this.x.tDot(this.y))
+				this.b = Matrix.sub(this.x.dot(this.w), this.y).mean(0)
+			}
+			this.predict = x => {
+				const p = Matrix.fromArray(x).dot(this.w)
+				p.sub(this.b)
+				return p.value
+			}
+		}, 'oneone')
+		const n = 100
+		const x = Matrix.concat(
+			Matrix.concat(Matrix.randn(n, 2, 0, 0.2), Matrix.randn(n, 2, 5, 0.2)),
+			Matrix.randn(n, 2, [-1, 4], 0.2)
+		).toArray()
+		const t = []
+		for (let i = 0; i < x.length; i++) {
+			t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / n))
+		}
+		model.init(x, t)
+		model.fit()
+		const y = model.predict(x)
+		const acc = accuracy(y, t)
+		expect(acc).toBeGreaterThan(0.95)
+	})
+
 	test('construct with classes', () => {
 		const model = new EnsembleBinaryModel(
 			function () {
@@ -202,6 +234,38 @@ describe('onerest', () => {
 		expect(acc).toBeGreaterThan(0.95)
 	})
 
+	test('predict returns 1d array', () => {
+		const model = new EnsembleBinaryModel(function () {
+			this.init = (x, y) => {
+				this.x = Matrix.fromArray(x)
+				this.y = Matrix.fromArray(y)
+			}
+			this.fit = () => {
+				this.w = this.x.tDot(this.x).solve(this.x.tDot(this.y))
+				this.b = Matrix.sub(this.x.dot(this.w), this.y).mean(0)
+			}
+			this.predict = x => {
+				const p = Matrix.fromArray(x).dot(this.w)
+				p.sub(this.b)
+				return p.value
+			}
+		}, 'onerest')
+		const n = 100
+		const x = Matrix.concat(
+			Matrix.concat(Matrix.randn(n, 2, 0, 0.2), Matrix.randn(n, 2, 5, 0.2)),
+			Matrix.randn(n, 2, [-1, 4], 0.2)
+		).toArray()
+		const t = []
+		for (let i = 0; i < x.length; i++) {
+			t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / n))
+		}
+		model.init(x, t)
+		model.fit()
+		const y = model.predict(x)
+		const acc = accuracy(y, t)
+		expect(acc).toBeGreaterThan(0.95)
+	})
+
 	test('constructo with classes', () => {
 		const model = new EnsembleBinaryModel(
 			function () {

tests/lib/model/huber_regression.test.js

@@ -4,7 +4,7 @@ import HuberRegression from '../../../lib/model/huber_regression.js'
 import { rmse } from '../../../lib/evaluate/regression.js'
 
 describe.each([undefined, 'rls', 'gd'])('fit %s', method => {
-	test.each([undefined, 1])('e: %p', e => {
+	test.each([undefined, 1.0e-5])('e: %p', e => {
 		const model = new HuberRegression(e, method)
 		const x = Matrix.randn(50, 2, 0, 5).toArray()
 		const t = []

tests/lib/model/isodata.test.js

@@ -31,6 +31,54 @@ test('clustering', () => {
 	expect(ri).toBeGreaterThan(0.9)
 })
 
+test('large init k', () => {
+	const model = new ISODATA(100, 1, 20, 10, 1, 0.8)
+	const n = 50
+	const x = Matrix.concat(
+		Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)),
+		Matrix.randn(n, 2, [-2, 5], 0.1)
+	).toArray()
+
+	model.init(x)
+	for (let i = 0; i < 100; i++) {
+		model.fit(x)
+	}
+	expect(model.size).toBeGreaterThanOrEqual(3)
+	const y = model.predict(x)
+	expect(y).toHaveLength(x.length)
+
+	const t = []
+	for (let i = 0; i < x.length; i++) {
+		t[i] = Math.floor(i / n)
+	}
+	const ri = randIndex(y, t)
+	expect(ri).toBeGreaterThan(0.75)
+})
+
+test('small init k', () => {
+	const model = new ISODATA(1, 3, 20, 10, 0.1, 0.8)
+	const n = 50
+	const x = Matrix.concat(
+		Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)),
+		Matrix.randn(n, 2, [-2, 5], 0.1)
+	).toArray()
+
+	model.init(x)
+	for (let i = 0; i < 10; i++) {
+		model.fit(x)
+	}
+	expect(model.size).toBeGreaterThanOrEqual(3)
+	const y = model.predict(x)
+	expect(y).toHaveLength(x.length)
+
+	const t = []
+	for (let i = 0; i < x.length; i++) {
+		t[i] = Math.floor(i / n)
+	}
+	const ri = randIndex(y, t)
+	expect(ri).toBeGreaterThan(0.75)
+})
+
 test('predict before fit', () => {
 	const model = new ISODATA(5, 1, 20, 10, 1, 0.8)
 	const x = Matrix.randn(50, 2, 0, 0.1).toArray()

tests/lib/model/kmeans.test.js

@@ -30,6 +30,13 @@ describe.each([KMeans, KMeanspp, KMedoids, KMedians])('%p', methodCls => {
 		expect(ri).toBeGreaterThan(0.9)
 	})
 
+	test('fit before init', () => {
+		const model = new methodCls()
+		const x = Matrix.randn(50, 2, 0, 0.1).toArray()
+		const d = model.fit(x)
+		expect(d).toBe(0)
+	})
+
 	test('predict before fit', () => {
 		const model = new methodCls()
 		const x = Matrix.randn(50, 2, 0, 0.1).toArray()
@@ -61,6 +68,13 @@ describe('semi-classifier', () => {
 		expect(acc).toBeGreaterThan(0.95)
 	})
 
+	test('fit before init', () => {
+		const model = new SemiSupervisedKMeansModel()
+		const x = Matrix.randn(50, 2, 0, 0.1).toArray()
+		const d = model.fit(x, Array(50).fill(0))
+		expect(d).toBe(0)
+	})
+
 	test('predict before fit', () => {
 		const model = new SemiSupervisedKMeansModel()
 		const x = Matrix.randn(50, 2, 0, 0.1).toArray()

tests/lib/model/kmodes.test.js

@@ -63,6 +63,21 @@ describe('predict', () => {
 		expect(model.size).toBe(0)
 	})
 
+	test('fit before init', () => {
+		const model = new KModes()
+		const x = []
+		for (let i = 0; i < 50n; i++) {
+			const xi = []
+			for (let k = 0; k < 5; k++) {
+				const r = Math.floor(Math.random() * 10)
+				xi[k] = String.fromCharCode('a'.charCodeAt(0) + r)
+			}
+			x.push(xi)
+		}
+		const d = model.fit(x)
+		expect(d).toBe(0)
+	})
+
 	test('before fit', () => {
 		const model = new KModes()
 		const x = []