util.h

#pragma once
 
#include <math.h>
 
#include <vector>
 
#include "Eigen/Dense"
#include "core/math/geometry/translation2d.h"
 
/**
 * Constrain the input between a minimum and a maximum value
 *
 * @param val  the value to be restrained
 * @param low  the minimum value that will be returned
 * @param high the maximum value that will be returned
 **/
inline constexpr double clamp(double value, double low, double high) {
  return (value < low) ? low : (value > high) ? high : value;
}
 
/**
 * Returns the sign of a number
 * @param x
 *
 * returns the sign +/-1 of x. 0 if x is 0
 **/
inline constexpr double sign(double x) { return (x > 0) - (x < 0); }
 
/**
 * Calculates the variance of  a set of numbers (needed for linear regression)
 * https://en.wikipedia.org/wiki/Variance
 * @param values   the values for which the variance is taken
 * @param mean     the average of values
 */
double variance(std::vector<double> const& values, double mean);
 
/**
 * Calculates the average of a vector of doubles
 * @param values   the list of values for which the average is taken
 */
double mean(std::vector<double> const& values);
 
/**
 * Calculates the covariance of a set of points (needed for linear regression)
 * https://en.wikipedia.org/wiki/Covariance
 * @param points   the points for which the covariance is taken
 * @param meanx    the mean value of all x coordinates in points
 * @param meany    the mean value of all y coordinates in points
 */
double covariance(std::vector<std::pair<double, double>> const& points, double meanx, double meany);
 
/**
 * Calculates the slope and y intercept of the line of best fit for the data
 * @param points the points for the data
 * @return a pair containing the slope and y intercept
 */
std::pair<double, double> calculate_linear_regression(
    std::vector<std::pair<double, double>> const& points
);
 
double estimate_path_length(const std::vector<Translation2d>& points);