Intercepts, Slopes, Coefficients, and Predictions

/**
 * Fintech Math Bootcamp · Lesson 087 of 120
 * Intercepts, Slopes, Coefficients, and Predictions
 * Module 09: Dependence, Regression, and Model Foundations
 *
 * Scenario: Interpreting a coefficient before using it for extrapolation
 * Rule:     prediction = intercept + slope·input
 *
 * Try it:   Is the prediction at x=10 validated by fitting on x=1 through 5?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/intercepts-slopes-coefficients-and-predictions/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

export function lesson087() {
  const intercept=2.2,slope=.6,xMin=1,xMax=5; // fitted on x in [1, 5]
  const predict=(x:number)=>intercept+slope*x;
  const outsideFit=(x:number)=>x<xMin||x>xMax; // note: x=0 is outside too
  const result={at3:predict(3),at0:predict(0),at10:predict(10),
    extrapolatedAt10:outsideFit(10)};
  return result;
}

export const checkedResult = {"at3":4,"at0":2.2,"at10":8.2,"extrapolatedAt10":true};

// Run this file directly: npx tsx lessons/09-dependence-regression-and-model-foundations/087-intercepts-slopes-coefficients-and-predictions.ts
if (process.argv[1] && import.meta.url.endsWith(process.argv[1].replace(/\\/g, "/").split("/").pop()!)) {
  console.log(JSON.stringify(lesson087(), null, 2));
}

"""
Fintech Math Bootcamp · Lesson 087 of 120
Intercepts, Slopes, Coefficients, and Predictions
Module 09: Dependence, Regression, and Model Foundations

Scenario: Interpreting a coefficient before using it for extrapolation
Rule:     prediction = intercept + slope·input

Try it:   Is the prediction at x=10 validated by fitting on x=1 through 5?

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

import json


def lesson_087():
    intercept, slope, x_min, x_max = 2.2, 0.6, 1, 5  # fitted on x in [1, 5]

    def predict(x):
        return intercept + slope * x

    def outside_fit(x):
        return x < x_min or x > x_max  # note: x=0 is outside too

    return {
        "at3": predict(3),
        "at0": predict(0),
        "at10": predict(10),
        "extrapolatedAt10": outside_fit(10),
    }


if __name__ == "__main__":
    print(json.dumps(lesson_087(), indent=2))

// Fintech Math Bootcamp - Lesson 087 of 120
// Intercepts, Slopes, Coefficients, and Predictions
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Interpreting a coefficient before using it for extrapolation
// Rule:     prediction = intercept + slope-input
//
// Try it:   Is the prediction at x=10 validated by fitting on x=1 through 5?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/intercepts-slopes-coefficients-and-predictions/
// 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 final double INTERCEPT = 2.2, SLOPE = 0.6, X_MIN = 1, X_MAX = 5; // fitted on x in [1, 5]

    static double predict(double x) {
        return INTERCEPT + SLOPE * x;
    }

    static boolean outsideFit(double x) {
        return x < X_MIN || x > X_MAX; // note: x=0 is outside too
    }

    static Map<String, Object> lesson087() {
        Map<String, Object> result = new LinkedHashMap<String, Object>();
        result.put("at3", predict(3));
        result.put("at0", predict(0));
        result.put("at10", predict(10));
        result.put("extrapolatedAt10", outsideFit(10));
        return result;
    }

    public static void main(String[] args) {
        System.out.println(toJson(lesson087(), ""));
    }

    // 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 087 of 120
// Intercepts, Slopes, Coefficients, and Predictions
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Interpreting a coefficient before using it for extrapolation
// Rule:     prediction = intercept + slope·input
//
// Try it:   Is the prediction at x=10 validated by fitting on x=1 through 5?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/intercepts-slopes-coefficients-and-predictions/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
package main

import (
	"encoding/json"
	"fmt"
)

type Lesson087Result struct {
	At3              float64 `json:"at3"`
	At0              float64 `json:"at0"`
	At10             float64 `json:"at10"`
	ExtrapolatedAt10 bool    `json:"extrapolatedAt10"`
}

func lesson087() Lesson087Result {
	intercept, slope, xMin, xMax := 2.2, 0.6, 1.0, 5.0 // fitted on x in [1, 5]
	predict := func(x float64) float64 { return intercept + slope*x }
	outsideFit := func(x float64) bool { return x < xMin || x > xMax } // note: x=0 is outside too
	return Lesson087Result{
		At3:              predict(3),
		At0:              predict(0),
		At10:             predict(10),
		ExtrapolatedAt10: outsideFit(10),
	}
}

func main() {
	out, err := json.MarshalIndent(lesson087(), "", "  ")
	if err != nil {
		panic(err)
	}
	fmt.Println(string(out))
}

// Fintech Math Bootcamp · Lesson 087 of 120
// Intercepts, Slopes, Coefficients, and Predictions
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Interpreting a coefficient before using it for extrapolation
// Rule:     prediction = intercept + slope·input
//
// Try it:   Is the prediction at x=10 validated by fitting on x=1 through 5?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/intercepts-slopes-coefficients-and-predictions/
// 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 lesson087() {
    const double intercept = 2.2, slope = 0.6, xMin = 1, xMax = 5;  // fitted on x in [1, 5]
    auto predict = [=](double x) { return intercept + slope * x; };
    auto outsideFit = [=](double x) { return x < xMin || x > xMax; };  // note: x=0 is outside too
    return jsonObject({
        {"at3", predict(3)},
        {"at0", predict(0)},
        {"at10", predict(10)},
        {"extrapolatedAt10", outsideFit(10)},
    });
}

int main() {
    std::cout << toJson(lesson087()) << '\n';
    return 0;
}

// Fintech Math Bootcamp · Lesson 087 of 120
// Intercepts, Slopes, Coefficients, and Predictions
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Interpreting a coefficient before using it for extrapolation
// Rule:     prediction = intercept + slope·input
//
// Try it:   Is the prediction at x=10 validated by fitting on x=1 through 5?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/intercepts-slopes-coefficients-and-predictions/
// 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_087() -> Json {
    let (intercept, slope, x_min, x_max) = (2.2_f64, 0.6_f64, 1.0_f64, 5.0_f64); // fitted on x in [1, 5]
    let predict = |x: f64| intercept + slope * x;
    let outside_fit = |x: f64| x < x_min || x > x_max; // note: x=0 is outside too
    Json::obj(vec![
        ("at3", Json::Num(predict(3.0))),
        ("at0", Json::Num(predict(0.0))),
        ("at10", Json::Num(predict(10.0))),
        ("extrapolatedAt10", Json::Bool(outside_fit(10.0))),
    ])
}

fn main() {
    println!("{}", lesson_087().pretty(""));
}

// Fintech Math Bootcamp · Lesson 087 of 120
// Intercepts, Slopes, Coefficients, and Predictions
// Module 09: Dependence, Regression, and Model Foundations
//
// Scenario: Interpreting a coefficient before using it for extrapolation
// Rule:     prediction = intercept + slope·input
//
// Try it:   Is the prediction at x=10 validated by fitting on x=1 through 5?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/intercepts-slopes-coefficients-and-predictions/
// 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(Lesson087(), options));

static object Lesson087()
{
    double intercept = 2.2, slope = 0.6, xMin = 1, xMax = 5; // fitted on x in [1, 5]
    double Predict(double x) => intercept + slope * x;
    bool OutsideFit(double x) => x < xMin || x > xMax; // note: x=0 is outside too
    return new
    {
        at3 = Predict(3),
        at0 = Predict(0),
        at10 = Predict(10),
        extrapolatedAt10 = OutsideFit(10),
    };
}