Implementation of SVM, Decision Tree, and Random Forest algorithms

ml/svm.v

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+module ml
+
+import math
+
+pub struct SVMConfig {
+pub mut:
+	max_iterations int = 1000
+	learning_rate  f64 = 0.01
+	tolerance      f64 = 1e-6
+}
+
+pub struct DataPoint {
+pub mut:
+	x []f64
+	y int
+}
+
+pub struct SVMModel {
+pub mut:
+	weights []f64
+	bias    f64
+	config  SVMConfig
+}
+
+pub struct SVM {
+pub mut:
+	model  &SVMModel = unsafe { nil }
+	config SVMConfig
+}
+
+pub fn SVM.new(config SVMConfig) &SVM {
+	return &SVM{
+		config: config
+	}
+}
+
+pub fn (mut s SVM) train(data []DataPoint) {
+	s.model = train_svm(data, s.config)
+}
+
+pub fn (s &SVM) predict(x []f64) int {
+	return predict(s.model, x)
+}
+
+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
+}
+
+pub fn train_svm(data []DataPoint, config SVMConfig) &SVMModel {
+	mut model := &SVMModel{
+		weights: []f64{len: data[0].x.len, init: 0.0}
+		bias: 0.0
+		config: config
+	}
+
+	for _ in 0 .. config.max_iterations {
+		mut cost := 0.0
+		for point in data {
+			prediction := vector_dot(model.weights, point.x) + model.bias
+			margin := f64(point.y) * prediction
+
+			if margin < 1 {
+				for i in 0 .. model.weights.len {
+					model.weights[i] += config.learning_rate * (f64(point.y) * point.x[i] - 2 * config.tolerance * model.weights[i])
+				}
+				model.bias += config.learning_rate * f64(point.y)
+				cost += 1 - margin
+			} else {
+				for i in 0 .. model.weights.len {
+					model.weights[i] -= config.learning_rate * 2 * config.tolerance * model.weights[i]
+				}
+			}
+		}
+
+		if cost == 0 {
+			break
+		}
+	}
+
+	return model
+}
+
+pub fn predict(model &SVMModel, x []f64) int {
+	prediction := vector_dot(model.weights, x) + model.bias
+	return if prediction >= 0 { 1 } else { -1 }
+}

ml/svm_test.v

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+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_svm_new() {
+	config := SVMConfig{}
+	svm := SVM.new(config)
+	assert svm.config == config
+}
+
+fn test_svm_train_and_predict() {
+	mut svm := SVM.new(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, config)
+
+	for point in data {
+		prediction := predict(model, point.x)
+		assert prediction == point.y
+	}
+}
+
+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, config)
+
+	for point in data {
+		prediction := predict(model, point.x)
+		assert prediction == point.y
+	}
+}
+
+fn main() {
+	test_vector_dot()
+	test_svm_new()
+	test_svm_train_and_predict()
+	test_train_svm()
+	test_predict()
+	println('All tests passed successfully!')
+}