Implementation of SVM, Decision Tree, and Random Forest algorithms

ml/svm.v

@@ -0,0 +1,188 @@
+module ml
+
+import math
+import rand
+
+pub struct SVMConfig {
+pub mut:
+	max_iterations int = 1000
+	learning_rate  f64 = 0.01
+	tolerance      f64 = 1e-6
+	c              f64 = 1.0 // Regularization parameter
+}
+
+pub struct DataPoint {
+pub mut:
+	x []f64
+	y int
+}
+
+pub struct SVMModel {
+pub mut:
+	support_vectors []DataPoint
+	alphas          []f64
+	b               f64
+	kernel          KernelFunction @[required]
+	config          SVMConfig
+}
+
+pub struct SVM {
+pub mut:
+	model  &SVMModel = unsafe { nil }
+	kernel KernelFunction @[required]
+	config SVMConfig
+}
+
+type KernelFunction = fn ([]f64, []f64) f64
+
+fn vector_dot(x []f64, y []f64) f64 {
+	mut sum := 0.0
+	for i := 0; i < x.len; i++ {
+		sum += x[i] * y[i]
+	}
+	return sum
+}
+
+fn vector_subtract(x []f64, y []f64) []f64 {
+	mut result := []f64{len: x.len}
+	for i := 0; i < x.len; i++ {
+		result[i] = x[i] - y[i]
+	}
+	return result
+}
+
+pub fn linear_kernel(x []f64, y []f64) f64 {
+	return vector_dot(x, y)
+}
+
+pub fn polynomial_kernel(degree int) KernelFunction {
+	return fn [degree] (x []f64, y []f64) f64 {
+		return math.pow(vector_dot(x, y) + 1.0, f64(degree))
+	}
+}
+
+pub fn rbf_kernel(gamma f64) KernelFunction {
+	return fn [gamma] (x []f64, y []f64) f64 {
+		diff := vector_subtract(x, y)
+		return math.exp(-gamma * vector_dot(diff, diff))
+	}
+}
+
+pub fn SVM.new(kernel KernelFunction, config SVMConfig) &SVM {
+	return &SVM{
+		kernel: kernel
+		config: config
+	}
+}
+
+pub fn (mut s SVM) train(data []DataPoint) {
+	s.model = train_svm(data, s.kernel, s.config)
+}
+
+pub fn (s &SVM) predict(x []f64) int {
+	return predict(s.model, x)
+}
+
+pub fn train_svm(data []DataPoint, kernel KernelFunction, config SVMConfig) &SVMModel {
+	mut model := &SVMModel{
+		support_vectors: []DataPoint{}
+		alphas: []f64{len: data.len, init: 0.0}
+		b: 0.0
+		kernel: kernel
+		config: config
+	}
+
+	mut passes := 0
+	for {
+		mut num_changed_alphas := 0
+		for i in 0 .. data.len {
+			ei := predict_raw(model, data[i].x) - f64(data[i].y)
+			if (data[i].y * ei < -model.config.tolerance && model.alphas[i] < model.config.c)
+				|| (data[i].y * ei > model.config.tolerance && model.alphas[i] > 0) {
+				j := rand.int_in_range(0, data.len - 1) or { panic(err) }
+				ej := predict_raw(model, data[j].x) - f64(data[j].y)
+
+				alpha_i_old := model.alphas[i]
+				alpha_j_old := model.alphas[j]
+
+				mut l, mut h := 0.0, 0.0
+				if data[i].y != data[j].y {
+					l = math.max(0.0, model.alphas[j] - model.alphas[i])
+					h = math.min(model.config.c, model.config.c + model.alphas[j] - model.alphas[i])
+				} else {
+					l = math.max(0.0, model.alphas[i] + model.alphas[j] - model.config.c)
+					h = math.min(model.config.c, model.alphas[i] + model.alphas[j])
+				}
+
+				if l == h {
+					continue
+				}
+
+				eta := 2 * model.kernel(data[i].x, data[j].x) - model.kernel(data[i].x,
+					data[i].x) - model.kernel(data[j].x, data[j].x)
+
+				if eta >= 0 {
+					continue
+				}
+
+				model.alphas[j] = alpha_j_old - f64(data[j].y) * (ei - ej) / eta
+				model.alphas[j] = math.max(l, math.min(h, model.alphas[j]))
+
+				if math.abs(model.alphas[j] - alpha_j_old) < 1e-5 {
+					continue
+				}
+
+				model.alphas[i] = alpha_i_old +
+					f64(data[i].y * data[j].y) * (alpha_j_old - model.alphas[j])
+
+				b1 := model.b - ei - f64(data[i].y) * (model.alphas[i] - alpha_i_old) * model.kernel(data[i].x,
+					data[i].x) - f64(data[j].y) * (model.alphas[j] - alpha_j_old) * model.kernel(data[i].x,
+					data[j].x)
+
+				b2 := model.b - ej - f64(data[i].y) * (model.alphas[i] - alpha_i_old) * model.kernel(data[i].x,
+					data[j].x) - f64(data[j].y) * (model.alphas[j] - alpha_j_old) * model.kernel(data[j].x,
+					data[j].x)
+
+				if 0 < model.alphas[i] && model.alphas[i] < model.config.c {
+					model.b = b1
+				} else if 0 < model.alphas[j] && model.alphas[j] < model.config.c {
+					model.b = b2
+				} else {
+					model.b = (b1 + b2) / 2
+				}
+
+				num_changed_alphas++
+			}
+		}
+
+		if num_changed_alphas == 0 {
+			passes++
+		} else {
+			passes = 0
+		}
+
+		if passes >= model.config.max_iterations {
+			break
+		}
+	}
+
+	for i in 0 .. data.len {
+		if model.alphas[i] > 0 {
+			model.support_vectors << data[i]
+		}
+	}
+
+	return model
+}
+
+fn predict_raw(model &SVMModel, x []f64) f64 {
+	mut sum := 0.0
+	for i, sv in model.support_vectors {
+		sum += model.alphas[i] * f64(sv.y) * model.kernel(x, sv.x)
+	}
+	return sum + model.b
+}
+
+pub fn predict(model &SVMModel, x []f64) int {
+	return if predict_raw(model, x) >= 0 { 1 } else { -1 }
+}

ml/svm_test.v

@@ -0,0 +1,127 @@
+module ml
+
+import math
+
+fn test_vector_dot() {
+	x := [1.0, 2.0, 3.0]
+	y := [4.0, 5.0, 6.0]
+	result := vector_dot(x, y)
+	assert math.abs(result - 32.0) < 1e-6
+}
+
+fn test_vector_subtract() {
+	x := [1.0, 2.0, 3.0]
+	y := [4.0, 5.0, 6.0]
+	result := vector_subtract(x, y)
+	assert result == [-3.0, -3.0, -3.0]
+}
+
+fn test_linear_kernel() {
+	x := [1.0, 2.0, 3.0]
+	y := [4.0, 5.0, 6.0]
+	result := linear_kernel(x, y)
+	assert math.abs(result - 32.0) < 1e-6
+}
+
+fn test_polynomial_kernel() {
+	x := [1.0, 2.0, 3.0]
+	y := [4.0, 5.0, 6.0]
+	kernel := polynomial_kernel(3)
+	result := kernel(x, y)
+	expected := math.pow(32.0 + 1.0, 3)
+	assert math.abs(result - expected) < 1e-6
+}
+
+fn test_rbf_kernel() {
+	x := [1.0, 2.0, 3.0]
+	y := [4.0, 5.0, 6.0]
+	gamma := 0.5
+	kernel := rbf_kernel(gamma)
+	result := kernel(x, y)
+	expected := math.exp(-gamma * 27.0)
+	assert math.abs(result - expected) < 1e-6
+}
+
+fn test_svm_new() {
+	config := SVMConfig{}
+	svm := SVM.new(linear_kernel, config)
+	assert svm.kernel == linear_kernel
+	assert svm.config == config
+}
+
+fn test_svm_train_and_predict() {
+	mut svm := SVM.new(linear_kernel, SVMConfig{})
+	data := [
+		DataPoint{[2.0, 3.0], 1},
+		DataPoint{[1.0, 1.0], -1},
+		DataPoint{[3.0, 4.0], 1},
+		DataPoint{[0.0, 0.0], -1},
+	]
+	svm.train(data)
+
+	for point in data {
+		prediction := svm.predict(point.x)
+		assert prediction == point.y
+	}
+}
+
+fn test_train_svm() {
+	data := [
+		DataPoint{[2.0, 3.0], 1},
+		DataPoint{[1.0, 1.0], -1},
+		DataPoint{[3.0, 4.0], 1},
+		DataPoint{[0.0, 0.0], -1},
+	]
+	config := SVMConfig{}
+	model := train_svm(data, linear_kernel, config)
+
+	for point in data {
+		prediction := predict(model, point.x)
+		assert prediction == point.y
+	}
+}
+
+fn test_predict_raw() {
+	data := [
+		DataPoint{[2.0, 3.0], 1},
+		DataPoint{[1.0, 1.0], -1},
+	]
+	config := SVMConfig{}
+	model := train_svm(data, linear_kernel, config)
+
+	result := predict_raw(model, [2.0, 3.0])
+	assert result > 0
+
+	result2 := predict_raw(model, [1.0, 1.0])
+	assert result2 < 0
+}
+
+fn test_predict() {
+	data := [
+		DataPoint{[2.0, 3.0], 1},
+		DataPoint{[1.0, 1.0], -1},
+		DataPoint{[3.0, 4.0], 1},
+		DataPoint{[0.0, 0.0], -1},
+	]
+	config := SVMConfig{}
+	model := train_svm(data, linear_kernel, config)
+
+	for point in data {
+		prediction := predict(model, point.x)
+		assert prediction == point.y
+	}
+}
+
+fn main() {
+	test_vector_dot()
+	test_vector_subtract()
+	test_linear_kernel()
+	test_polynomial_kernel()
+	test_rbf_kernel()
+	test_svm_new()
+	test_svm_train_and_predict()
+	test_train_svm()
+	test_predict_raw()
+	test_predict()
+	println('All tests passed successfully!')
+}