Example #1
0
func init() {

	// create the /tmp/.goml/ dir for persistance testing
	// if it doesn't already exist!
	err := os.MkdirAll("/tmp/.goml", os.ModePerm)
	if err != nil {
		panic(fmt.Sprintf("You should be able to create the directory for goml model persistance testing.\n\tError returned: %v\n", err.Error()))
	}

	// the line y=3
	flatX = [][]float64{}
	flatY = []float64{}
	for i := -10; i < 10; i++ {
		for j := -10; j < 10; j++ {
			for k := -10; k < 10; k++ {
				flatX = append(flatX, []float64{float64(i), float64(j), float64(k)})
				flatY = append(flatY, 3.0)
			}
		}
	}

	// the line y=x
	increasingX = [][]float64{}
	increasingY = []float64{}
	for i := -10; i < 10; i++ {
		increasingX = append(increasingX, []float64{float64(i)})
		increasingY = append(increasingY, float64(i))
	}

	threeDLineX = [][]float64{}
	threeDLineY = []float64{}

	normX = [][]float64{}
	normY = []float64{}
	// the line z = 10 + (x/10) + (y/5)
	for i := -10; i < 10; i++ {
		for j := -10; j < 10; j++ {
			threeDLineX = append(threeDLineX, []float64{float64(i), float64(j)})
			threeDLineY = append(threeDLineY, 10+float64(i)/10+float64(j)/5)

			normX = append(normX, []float64{float64(i), float64(j)})
		}
	}

	base.Normalize(normX)
	for i := range normX {
		normY = append(normY, 10+float64(normX[i][0])/10+float64(normX[i][1])/5)
	}

	// noisy x has random noise embedded
	rand.Seed(42)
	noisyX = [][]float64{}
	noisyY = []float64{}
	for i := 256.0; i < 1024; i += 2 {
		noisyX = append(noisyX, []float64{i + (rand.Float64()-0.5)*3})
		noisyY = append(noisyY, 0.5*i+rand.NormFloat64()*25)
	}
	// save the random data to make some nice plots!
	base.SaveDataToCSV("/tmp/.goml/noisy_linear.csv", noisyX, noisyY, true)
}
Example #2
0
func init() {

	// create the /tmp/.goml/ dir for persistance testing
	// if it doesn't already exist!
	err := os.MkdirAll("/tmp/.goml", os.ModePerm)
	if err != nil {
		panic(fmt.Sprintf("You should be able to create the directory for goml model persistance testing.\n\tError returned: %v\n", err.Error()))
	}

	// the line y=3
	flatX = [][]float64{}
	flatY = []float64{}
	for i := -10; i < 10; i++ {
		for j := -10; j < 10; j++ {
			for k := -10; k < 10; k++ {
				flatX = append(flatX, []float64{float64(i), float64(j), float64(k)})
				flatY = append(flatY, 3.0)
			}
		}
	}

	// the line y=x
	increasingX = [][]float64{}
	increasingY = []float64{}
	for i := -10; i < 10; i++ {
		increasingX = append(increasingX, []float64{float64(i)})
		increasingY = append(increasingY, float64(i))
	}

	threeDLineX = [][]float64{}
	threeDLineY = []float64{}

	normX = [][]float64{}
	normY = []float64{}
	// the line z = 10 + (x/10) + (y/5)
	for i := -10; i < 10; i++ {
		for j := -10; j < 10; j++ {
			threeDLineX = append(threeDLineX, []float64{float64(i), float64(j)})
			threeDLineY = append(threeDLineY, 10+float64(i)/10+float64(j)/5)

			normX = append(normX, []float64{float64(i), float64(j)})
		}
	}

	base.Normalize(normX)
	for i := range normX {
		normY = append(normY, 10+float64(normX[i][0])/10+float64(normX[i][1])/5)
	}
}
Example #3
0
// use normalized data
func TestTriangleKMeansShouldPass2(t *testing.T) {
	norm := append([][]float64{}, circles...)
	base.Normalize(norm)
	model := NewTriangleKMeans(4, 2, norm)

	assert.Nil(t, model.Learn(), "Learning error should be nil")

	// now predict with the same training set and
	// make sure the classes are the same within
	// each block
	c1, err := model.Predict([]float64{-10, -10}, true)
	assert.Nil(t, err, "Prediction error should be nil")

	c2, err := model.Predict([]float64{-10, 10}, true)
	assert.Nil(t, err, "Prediction error should be nil")

	c3, err := model.Predict([]float64{10, -10}, true)
	assert.Nil(t, err, "Prediction error should be nil")

	c4, err := model.Predict([]float64{10, 10}, true)
	assert.Nil(t, err, "Prediction error should be nil")

	var count int
	var wrong int

	for i := -12.0; i < -8; i += 0.2 {
		for j := -12.0; j < -8; j += 0.2 {
			guess, err := model.Predict([]float64{i, j}, true)
			assert.Nil(t, err, "Prediction error should be nil")

			if c1[0] != guess[0] {
				wrong++
			}
			count++
		}

		for j := 8.0; j < 12; j += 0.2 {
			guess, err := model.Predict([]float64{i, j}, true)
			assert.Nil(t, err, "Prediction error should be nil")

			if c2[0] != guess[0] {
				wrong++
			}
			count++
		}
	}

	for i := 8.0; i < 12; i += 0.2 {
		for j := -12.0; j < -8; j += 0.2 {
			guess, err := model.Predict([]float64{i, j}, true)
			assert.Nil(t, err, "Prediction error should be nil")

			if c3[0] != guess[0] {
				wrong++
			}
			count++
		}

		for j := 8.0; j < 12; j += 0.2 {
			guess, err := model.Predict([]float64{i, j}, true)
			assert.Nil(t, err, "Prediction error should be nil")

			if c4[0] != guess[0] {
				wrong++
			}
			count++
		}
	}

	accuracy := 100 * (1 - float64(wrong)/float64(count))
	assert.True(t, accuracy > 87, "Accuracy (%v) should be greater than 87 percent", accuracy)
	fmt.Printf("Accuracy: %v percent\n\tPoints Tested: %v\n\tMisclassifications: %v\n\tClasses: %v\n", accuracy, count, wrong, []float64{c1[0], c2[0], c3[0], c4[0]})
}
Example #4
0
// use normalized data
func TestKNNShouldPass2(t *testing.T) {
	norm := append([][]float64{}, fourClusters...)
	base.Normalize(norm)
	model := NewKNN(3, norm, fourClustersY, base.EuclideanDistance)

	var count int
	var wrong int

	duration := time.Duration(0)
	for i := -12.0; i < -8; i += 0.5 {
		for j := -12.0; j < -8; j += 0.5 {
			now := time.Now()
			guess, err := model.Predict([]float64{i, j}, true)
			duration += time.Now().Sub(now)
			assert.Nil(t, err, "Prediction error should be nil")

			if 0.0 != guess[0] {
				wrong++
			}
			count++
		}

		for j := 8.0; j < 12; j += 0.5 {
			now := time.Now()
			guess, err := model.Predict([]float64{i, j}, true)
			duration += time.Now().Sub(now)
			assert.Nil(t, err, "Prediction error should be nil")

			if 1.0 != guess[0] {
				wrong++
			}
			count++
		}
	}

	for i := 8.0; i < 12; i += 0.5 {
		for j := -12.0; j < -8; j += 0.5 {
			now := time.Now()
			guess, err := model.Predict([]float64{i, j}, true)
			duration += time.Now().Sub(now)
			assert.Nil(t, err, "Prediction error should be nil")

			if 2.0 != guess[0] {
				wrong++
			}
			count++
		}

		for j := 8.0; j < 12; j += 0.5 {
			now := time.Now()
			guess, err := model.Predict([]float64{i, j}, true)
			duration += time.Now().Sub(now)
			assert.Nil(t, err, "Prediction error should be nil")

			if 3.0 != guess[0] {
				wrong++
			}
			count++
		}
	}

	accuracy := 100 * (1 - float64(wrong)/float64(count))
	assert.True(t, accuracy > 95, "Accuracy (%v) should be greater than 95 percent", accuracy)
	fmt.Printf("Accuracy: %v percent\n\tPoints Tested: %v\n\tMisclassifications: %v\n\tAverage Prediction Time: %v\n", accuracy, count, wrong, duration/time.Duration(count))
}
Example #5
0
// tests basically make a bunch of planes where
// when the input is above the plane the resultant
// output is 1.0, else 0.0
func init() {

	// create the /tmp/.goml/ dir for persistance testing
	// if it doesn't already exist!
	err := os.MkdirAll("/tmp/.goml", os.ModePerm)
	if err != nil {
		panic(fmt.Sprintf("You should be able to create the directory for goml model persistance testing.\n\tError returned: %v\n", err.Error()))
	}

	// 1 when ( 10*i + j/20 + k ) > 0
	fourDX = [][]float64{}
	fourDY = []float64{}
	for i := -40; i < 40; i += 4 {
		for j := -40; j < 40; j += 4 {
			for k := -40; k < 40; k += 4 {
				fourDX = append(fourDX, []float64{float64(i), float64(j), float64(k)})
				if 10*i+j/20+k > 0 {
					fourDY = append(fourDY, 1.0)
				} else {
					fourDY = append(fourDY, 0.0)
				}
			}
		}
	}

	// 1 when i > 0
	twoDX = [][]float64{}
	twoDY = []float64{}
	for i := -40.0; i < 40.0; i += 0.15 {
		twoDX = append(twoDX, []float64{i})
		if i/2+10 > 0 {
			twoDY = append(twoDY, 1.0)
		} else {
			twoDY = append(twoDY, 0.0)
		}
	}

	threeDX = [][]float64{}
	threeDY = []float64{}

	nX = [][]float64{}
	nY = []float64{}
	// 1 when i+j > 5
	for i := -10; i < 10; i++ {
		for j := -10; j < 10; j++ {
			threeDX = append(threeDX, []float64{float64(i), float64(j)})
			nX = append(nX, []float64{float64(i), float64(j)})

			if i+j > 5 {
				threeDY = append(threeDY, 1.0)
			} else {
				threeDY = append(threeDY, 0.0)
			}
		}
	}

	base.Normalize(nX)

	for i := range nX {
		if nX[i][0]+nX[i][1] > 5 {
			nY = append(nY, 1.0)
		} else {
			nY = append(nY, 0.0)
		}
	}
}
Example #6
0
// tests basically make a bunch of planes where
// when the input is above the plane the resultant
// output is 1.0, else 0.0
func init() {

	// create the /tmp/.goml/ dir for persistance testing
	// if it doesn't already exist!
	err := os.MkdirAll("/tmp/.goml", os.ModePerm)
	if err != nil {
		panic(fmt.Sprintf("You should be able to create the directory for goml model persistance testing.\n\tError returned: %v\n", err.Error()))
	}

	// 1 when ( 10*i + j/20 + k ) > 0
	fourDX = [][]float64{}
	fourDY = []float64{}
	for i := -40; i < 40; i += 4 {
		for j := -40; j < 40; j += 4 {
			for k := -40; k < 40; k += 4 {
				fourDX = append(fourDX, []float64{float64(i), float64(j), float64(k)})
				if 10*i+j/20+k > 0 {
					fourDY = append(fourDY, 1.0)
				} else {
					fourDY = append(fourDY, 0.0)
				}
			}
		}
	}

	// 1 when i > 0
	twoDX = [][]float64{}
	twoDY = []float64{}
	for i := -40.0; i < 40.0; i += 0.15 {
		twoDX = append(twoDX, []float64{i})
		if i/2+10 > 0 {
			twoDY = append(twoDY, 1.0)
		} else {
			twoDY = append(twoDY, 0.0)
		}
	}

	threeDX = [][]float64{}
	threeDY = []float64{}

	nX = [][]float64{}
	nY = []float64{}
	// 1 when i+j > 5
	for i := -10; i < 10; i++ {
		for j := -10; j < 10; j++ {
			threeDX = append(threeDX, []float64{float64(i), float64(j)})
			nX = append(nX, []float64{float64(i), float64(j)})

			if i+j > 5 {
				threeDY = append(threeDY, 1.0)
			} else {
				threeDY = append(threeDY, 0.0)
			}
		}
	}

	base.Normalize(nX)

	for i := range nX {
		if nX[i][0]+nX[i][1] > 5 {
			nY = append(nY, 1.0)
		} else {
			nY = append(nY, 0.0)
		}
	}

	// now make gaussian clusters for cool plots!
	rand.Seed(42)
	gaussianX = [][]float64{}
	gaussianY = []float64{}

	for i := 0; i < 100; i++ {
		gaussianX = append(gaussianX, []float64{
			rand.NormFloat64()*3 + 10,
			rand.NormFloat64()*3 + 10,
		})
		gaussianY = append(gaussianY, 1.0)
	}
	for i := 0; i < 100; i++ {
		gaussianX = append(gaussianX, []float64{
			rand.NormFloat64() * 5,
			rand.NormFloat64() * 5,
		})
		gaussianY = append(gaussianY, 0.0)
	}
	base.SaveDataToCSV("/tmp/.goml/gaussian_clusters.csv", gaussianX, gaussianY, true)
}