metrics Namespace Reference

losses namespace that contains a couple of useful losses in machine learning More...

Functions
double	recall (const std::vector< double > &y, const std::vector< double > &y_pred)
	recall function[tp / tp + fn]
double	accuracy_score (const std::vector< double > &y, const std::vector< double > &y_pred)
	accuracy score function[(tp + tn) / (tp + tn + fp + fn)]
double	precision (const std::vector< double > &y, const std::vector< double > &y_pred)
	precision function[tp / tp + fp]
double	f1_score (const std::vector< double > &y, const std::vector< double > &y_pred)
	f1 score function: [2 * precision * recall / precision + recall]
double	euclidean_distance (const std::vector< double > &x, const std::vector< double > &y)
	euclidean distance function
double	manhattan_distance (const std::vector< double > &x, const std::vector< double > &y)
	manhattan distance function
double	minkowski_distance (const std::vector< double > &x, const std::vector< double > &y, const double p)
	minkowski distance

Detailed Description

losses namespace that contains a couple of useful losses in machine learning

Function Documentation

accuracy_score()

double metrics::accuracy_score ( const std::vector< double > & y, const std::vector< double > & y_pred )

inline

accuracy score function[(tp + tn) / (tp + tn + fp + fn)]

Returns

double

euclidean_distance()

double metrics::euclidean_distance ( const std::vector< double > & x, const std::vector< double > & y )

inline

euclidean distance function

Parameters

x(vector<double>)	the first passed vector
y(vector<double>)	the second passed vector

Returns

double

f1_score()

double metrics::f1_score ( const std::vector< double > & y, const std::vector< double > & y_pred )

inline

f1 score function: [2 * precision * recall / precision + recall]

Returns

double

manhattan_distance()

double metrics::manhattan_distance ( const std::vector< double > & x, const std::vector< double > & y )

inline

manhattan distance function

Parameters

x(vector<double>)	the first passed vector
y(vector<double>)	the secoond passed vector

Returns

double

minkowski_distance()

double metrics::minkowski_distance ( const std::vector< double > & x, const std::vector< double > & y, const double p )

inline

minkowski distance

Parameters

x(vector<double>)	the first passed vector
y(vector<double>)	the second passed vector
p(double)	The order of the norm of the difference

Returns

double

precision()

double metrics::precision ( const std::vector< double > & y, const std::vector< double > & y_pred )

inline

precision function[tp / tp + fp]

Returns

double

recall()

double metrics::recall ( const std::vector< double > & y, const std::vector< double > & y_pred )

inline

recall function[tp / tp + fn]

Returns

double