Residuals, MAE, MSE, and RMSE

/**
 * Fintech Math Bootcamp · Lesson 088 of 120
 * Residuals, MAE, MSE, and RMSE
 * Module 09: Dependence, Regression, and Model Foundations
 *
 * Scenario: Choosing an error metric that reflects the cost of mistakes
 * Rule:     MAE=mean|e|; MSE=mean(e²); RMSE=√MSE
 *
 * Try it:   Why does RMSE have the original output units while MSE does not?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/residuals-mae-mse-and-rmse/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

export function lesson088() {
  const actual=[2,4,5,4,5],predicted=[2.8,3.4,4,4.6,5.2];
  const residuals=actual.map((v,i)=>v-predicted[i]);
  const mae=residuals.reduce((s,e)=>s+Math.abs(e),0)/residuals.length;
  const mse=residuals.reduce((s,e)=>s+e*e,0)/residuals.length;
  const result={residuals,mae,mse,rmse:Math.sqrt(mse)};
  return result;
}

export const checkedResult = {"residuals":[-0.7999999999999998,0.6000000000000001,1,-0.5999999999999996,-0.20000000000000018],"mae":0.6399999999999999,"mse":0.47999999999999987,"rmse":0.6928203230275508};

// Run this file directly: npx tsx lessons/09-dependence-regression-and-model-foundations/088-residuals-mae-mse-and-rmse.ts
if (process.argv[1] && import.meta.url.endsWith(process.argv[1].replace(/\\/g, "/").split("/").pop()!)) {
  console.log(JSON.stringify(lesson088(), null, 2));
}

"""
Fintech Math Bootcamp · Lesson 088 of 120
Residuals, MAE, MSE, and RMSE
Module 09: Dependence, Regression, and Model Foundations

Scenario: Choosing an error metric that reflects the cost of mistakes
Rule:     MAE=mean|e|; MSE=mean(e²); RMSE=√MSE

Try it:   Why does RMSE have the original output units while MSE does not?

Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/residuals-mae-mse-and-rmse/
Free course:    https://courses.thefintechbuilder.com
Synthetic teaching example, not financial advice or a production library.
"""

import json
import math


def lesson_088():
    actual, predicted = [2, 4, 5, 4, 5], [2.8, 3.4, 4, 4.6, 5.2]
    residuals = [a - p for a, p in zip(actual, predicted)]
    absolute = 0
    squared = 0
    for e in residuals:
        absolute += abs(e)
        squared += e * e
    mae = absolute / len(residuals)
    mse = squared / len(residuals)
    return {"residuals": residuals, "mae": mae, "mse": mse, "rmse": math.sqrt(mse)}


if __name__ == "__main__":
    print(json.dumps(lesson_088(), indent=2))

// Fintech Math Bootcamp - Lesson 088 of 120
// Residuals, MAE, MSE, and RMSE
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Choosing an error metric that reflects the cost of mistakes
// Rule:     MAE=mean|e|; MSE=mean(e^2); RMSE=sqrt(MSE)
//
// Try it:   Why does RMSE have the original output units while MSE does not?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/residuals-mae-mse-and-rmse/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
import java.util.ArrayList;
import java.util.Arrays;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

public class Main {
    static Map<String, Object> lesson088() {
        double[] actual = {2, 4, 5, 4, 5}, predicted = {2.8, 3.4, 4, 4.6, 5.2};
        double[] residuals = new double[actual.length];
        double absolute = 0, squared = 0;
        for (int i = 0; i < actual.length; i++) {
            double e = actual[i] - predicted[i];
            residuals[i] = e;
            absolute += Math.abs(e);
            squared += e * e;
        }
        double mae = absolute / residuals.length, mse = squared / residuals.length;
        Map<String, Object> result = new LinkedHashMap<String, Object>();
        result.put("residuals", residuals);
        result.put("mae", mae);
        result.put("mse", mse);
        result.put("rmse", Math.sqrt(mse));
        return result;
    }

    public static void main(String[] args) {
        System.out.println(toJson(lesson088(), ""));
    }

    // Minimal JSON writer: two-space indent, whole numbers without a decimal point, NaN as null.
    static String toJson(Object value, String indent) {
        if (value == null) return "null";
        if (value instanceof Boolean) return value.toString();
        if (value instanceof Number) return formatNumber(((Number) value).doubleValue());
        if (value instanceof String) return quote((String) value);
        if (value instanceof double[]) {
            List<Object> boxed = new ArrayList<Object>();
            for (double d : (double[]) value) boxed.add(d);
            return toJson(boxed, indent);
        }
        if (value instanceof Object[]) return toJson(Arrays.asList((Object[]) value), indent);
        String inner = indent + "  ";
        StringBuilder out = new StringBuilder();
        if (value instanceof Map) {
            Map<?, ?> map = (Map<?, ?>) value;
            if (map.isEmpty()) return "{}";
            out.append("{\n");
            int i = 0;
            for (Map.Entry<?, ?> entry : map.entrySet()) {
                out.append(inner).append(quote(entry.getKey().toString())).append(": ")
                   .append(toJson(entry.getValue(), inner));
                out.append(++i < map.size() ? ",\n" : "\n");
            }
            return out.append(indent).append("}").toString();
        }
        List<?> list = (List<?>) value;
        if (list.isEmpty()) return "[]";
        out.append("[\n");
        for (int i = 0; i < list.size(); i++) {
            out.append(inner).append(toJson(list.get(i), inner));
            out.append(i + 1 < list.size() ? ",\n" : "\n");
        }
        return out.append(indent).append("]").toString();
    }

    static String formatNumber(double x) {
        if (Double.isNaN(x) || Double.isInfinite(x)) return "null";
        if (x == Math.rint(x) && Math.abs(x) < 1e15) return Long.toString((long) x);
        return Double.toString(x);
    }

    static String quote(String s) {
        StringBuilder out = new StringBuilder("\"");
        for (char c : s.toCharArray()) {
            if (c == '"' || c == '\\') out.append('\\').append(c);
            else if (c == '\n') out.append("\\n");
            else if (c < 0x20) out.append(String.format("\\u%04x", (int) c));
            else out.append(c);
        }
        return out.append('"').toString();
    }
}

// Fintech Math Bootcamp · Lesson 088 of 120
// Residuals, MAE, MSE, and RMSE
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Choosing an error metric that reflects the cost of mistakes
// Rule:     MAE=mean|e|; MSE=mean(e²); RMSE=√MSE
//
// Try it:   Why does RMSE have the original output units while MSE does not?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/residuals-mae-mse-and-rmse/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
package main

import (
	"encoding/json"
	"fmt"
	"math"
)

type Lesson088Result struct {
	Residuals []float64 `json:"residuals"`
	MAE       float64   `json:"mae"`
	MSE       float64   `json:"mse"`
	RMSE      float64   `json:"rmse"`
}

func lesson088() Lesson088Result {
	actual := []float64{2, 4, 5, 4, 5}
	predicted := []float64{2.8, 3.4, 4, 4.6, 5.2}
	residuals := make([]float64, len(actual))
	absolute, squared := 0.0, 0.0
	for i, v := range actual {
		e := v - predicted[i]
		residuals[i] = e
		absolute += math.Abs(e)
		squared += e * e
	}
	n := float64(len(residuals))
	mse := squared / n
	return Lesson088Result{Residuals: residuals, MAE: absolute / n, MSE: mse, RMSE: math.Sqrt(mse)}
}

func main() {
	out, err := json.MarshalIndent(lesson088(), "", "  ")
	if err != nil {
		panic(err)
	}
	fmt.Println(string(out))
}

// Fintech Math Bootcamp · Lesson 088 of 120
// Residuals, MAE, MSE, and RMSE
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Choosing an error metric that reflects the cost of mistakes
// Rule:     MAE=mean|e|; MSE=mean(e²); RMSE=√MSE
//
// Try it:   Why does RMSE have the original output units while MSE does not?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/residuals-mae-mse-and-rmse/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <iostream>
#include <optional>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>

// A minimal JSON value, enough to print this lesson's result.
struct Json {
    enum class Kind { Null, Bool, Number, String, Array, Object };
    Kind kind = Kind::Null;
    bool flag = false;
    double number = 0.0;
    std::string text;
    std::vector<std::string> keys;  // object keys, parallel to items
    std::vector<Json> items;        // array elements or object values

    Json() = default;
    Json(bool value) : kind(Kind::Bool), flag(value) {}
    Json(int value) : kind(Kind::Number), number(value) {}
    Json(double value) : kind(Kind::Number), number(value) {}
    Json(const char* value) : kind(Kind::String), text(value) {}
    Json(const std::string& value) : kind(Kind::String), text(value) {}
    Json(const std::vector<double>& values) : kind(Kind::Array) {
        for (double v : values) items.push_back(Json(v));
    }
};

Json jsonArray(const std::vector<Json>& values) {
    Json array;
    array.kind = Json::Kind::Array;
    array.items = values;
    return array;
}

Json jsonObject(const std::vector<std::pair<std::string, Json>>& fields) {
    Json object;
    object.kind = Json::Kind::Object;
    for (const auto& field : fields) {
        object.keys.push_back(field.first);
        object.items.push_back(field.second);
    }
    return object;
}

// Shortest decimal form that reads back as the same double.
std::string formatNumber(double x) {
    if (!std::isfinite(x)) return "null";
    char buffer[32];
    if (x == std::floor(x) && std::fabs(x) < 1e15) {
        std::snprintf(buffer, sizeof buffer, "%.0f", x);
        return buffer;
    }
    for (int precision = 1; precision <= 17; ++precision) {
        std::snprintf(buffer, sizeof buffer, "%.*g", precision, x);
        if (std::strtod(buffer, nullptr) == x) break;
    }
    return buffer;
}

std::string quote(const std::string& s) {
    std::string out = "\"";
    for (char c : s) {
        if (c == '"' || c == '\\') { out += '\\'; out += c; }
        else if (c == '\n') out += "\\n";
        else out += c;
    }
    return out + "\"";
}

std::string toJson(const Json& value, const std::string& indent = "") {
    switch (value.kind) {
        case Json::Kind::Null: return "null";
        case Json::Kind::Bool: return value.flag ? "true" : "false";
        case Json::Kind::Number: return formatNumber(value.number);
        case Json::Kind::String: return quote(value.text);
        default: break;
    }
    const bool isObject = value.kind == Json::Kind::Object;
    if (value.items.empty()) return isObject ? "{}" : "[]";
    const std::string inner = indent + "  ";
    std::string out = isObject ? "{\n" : "[\n";
    for (std::size_t i = 0; i < value.items.size(); ++i) {
        out += inner;
        if (isObject) out += quote(value.keys[i]) + ": ";
        out += toJson(value.items[i], inner);
        out += i + 1 < value.items.size() ? ",\n" : "\n";
    }
    return out + indent + (isObject ? "}" : "]");
}

Json lesson088() {
    const std::vector<double> actual = {2, 4, 5, 4, 5}, predicted = {2.8, 3.4, 4, 4.6, 5.2};
    std::vector<double> residuals;
    double absolute = 0.0, squared = 0.0;
    for (std::size_t i = 0; i < actual.size(); ++i) {
        const double e = actual[i] - predicted[i];
        residuals.push_back(e);
        absolute += std::fabs(e);
        squared += e * e;
    }
    const double n = static_cast<double>(residuals.size());
    const double mae = absolute / n, mse = squared / n;
    return jsonObject({
        {"residuals", residuals},
        {"mae", mae},
        {"mse", mse},
        {"rmse", std::sqrt(mse)},
    });
}

int main() {
    std::cout << toJson(lesson088()) << '\n';
    return 0;
}

// Fintech Math Bootcamp · Lesson 088 of 120
// Residuals, MAE, MSE, and RMSE
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Choosing an error metric that reflects the cost of mistakes
// Rule:     MAE=mean|e|; MSE=mean(e²); RMSE=√MSE
//
// Try it:   Why does RMSE have the original output units while MSE does not?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/residuals-mae-mse-and-rmse/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.

/// A minimal JSON value, enough to print this lesson's result.
#[allow(dead_code)]
enum Json {
    Null,
    Bool(bool),
    Num(f64),
    Str(String),
    Arr(Vec<Json>),
    Obj(Vec<(String, Json)>),
}

#[allow(dead_code)]
impl Json {
    fn obj(fields: Vec<(&str, Json)>) -> Json {
        Json::Obj(fields.into_iter().map(|(k, v)| (k.to_string(), v)).collect())
    }

    fn nums(values: &[f64]) -> Json {
        Json::Arr(values.iter().map(|&v| Json::Num(v)).collect())
    }

    /// Pretty-prints with two-space indentation.
    fn pretty(&self, indent: &str) -> String {
        let inner = format!("{}  ", indent);
        match self {
            Json::Null => "null".to_string(),
            Json::Bool(b) => b.to_string(),
            Json::Num(x) => format_number(*x),
            Json::Str(s) => quote(s),
            Json::Arr(items) if items.is_empty() => "[]".to_string(),
            Json::Obj(fields) if fields.is_empty() => "{}".to_string(),
            Json::Arr(items) => {
                let body: Vec<String> = items
                    .iter()
                    .map(|v| format!("{}{}", inner, v.pretty(&inner)))
                    .collect();
                format!("[\n{}\n{}]", body.join(",\n"), indent)
            }
            Json::Obj(fields) => {
                let body: Vec<String> = fields
                    .iter()
                    .map(|(k, v)| format!("{}{}: {}", inner, quote(k), v.pretty(&inner)))
                    .collect();
                format!("{{\n{}\n{}}}", body.join(",\n"), indent)
            }
        }
    }
}

fn format_number(x: f64) -> String {
    if !x.is_finite() {
        "null".to_string()
    } else if x == x.trunc() && x.abs() < 1e15 {
        format!("{}", x as i64)
    } else {
        format!("{}", x)
    }
}

fn quote(s: &str) -> String {
    let mut out = String::from("\"");
    for c in s.chars() {
        match c {
            '"' => out.push_str("\\\""),
            '\\' => out.push_str("\\\\"),
            '\n' => out.push_str("\\n"),
            c => out.push(c),
        }
    }
    out.push('"');
    out
}

fn lesson_088() -> Json {
    let actual = [2.0_f64, 4.0, 5.0, 4.0, 5.0];
    let predicted = [2.8_f64, 3.4, 4.0, 4.6, 5.2];
    let residuals: Vec<f64> = actual.iter().zip(predicted.iter()).map(|(a, p)| a - p).collect();
    let n = residuals.len() as f64;
    let mae = residuals.iter().fold(0.0_f64, |s, e: &f64| s + e.abs()) / n;
    let mse: f64 = residuals.iter().fold(0.0_f64, |s, e| s + e * e) / n;
    Json::obj(vec![
        ("residuals", Json::nums(&residuals)),
        ("mae", Json::Num(mae)),
        ("mse", Json::Num(mse)),
        ("rmse", Json::Num(mse.sqrt())),
    ])
}

fn main() {
    println!("{}", lesson_088().pretty(""));
}

// Fintech Math Bootcamp · Lesson 088 of 120
// Residuals, MAE, MSE, and RMSE
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Choosing an error metric that reflects the cost of mistakes
// Rule:     MAE=mean|e|; MSE=mean(e²); RMSE=√MSE
//
// Try it:   Why does RMSE have the original output units while MSE does not?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/residuals-mae-mse-and-rmse/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text.Json;

var options = new JsonSerializerOptions { WriteIndented = true };
Console.WriteLine(JsonSerializer.Serialize(Lesson088(), options));

static object Lesson088()
{
    double[] actual = { 2, 4, 5, 4, 5 }, predicted = { 2.8, 3.4, 4, 4.6, 5.2 };
    double[] residuals = actual.Select((v, i) => v - predicted[i]).ToArray();
    double mae = residuals.Aggregate(0.0, (s, e) => s + Math.Abs(e)) / residuals.Length;
    double mse = residuals.Aggregate(0.0, (s, e) => s + e * e) / residuals.Length;
    return new
    {
        residuals,
        mae,
        mse,
        rmse = Math.Sqrt(mse),
    };
}