Relintai/MLPP
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Reordered functions in Stat.hpp, added Stat.range, Stat.midrange, cleaned up code for LinAlg.max, LinAlg.min

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This commit is contained in:
novak_99 2021-06-04 18:04:51 -07:00
parent 6dfa1cafe3
commit 0753ffe261
5 changed files with 46 additions and 37 deletions
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12
MLPP/LinAlg/LinAlg.cpp
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@ -391,19 +391,11 @@ namespace MLPP{
}
double LinAlg::max(std::vector<std::vector<double>> A){
std::vector<double> max_elements;
for(int i = 0; i < A.size(); i++){
max_elements.push_back(max(A[i]));
}
return max(max_elements);
return max(flatten(A));
}
double LinAlg::min(std::vector<std::vector<double>> A){
std::vector<double> max_elements;
for(int i = 0; i < A.size(); i++){
max_elements.push_back(min(A[i]));
}
return min(max_elements);
return min(flatten(A));
}
std::vector<std::vector<double>> LinAlg::round(std::vector<std::vector<double>> A){

44
MLPP/Stat/Stat.cpp
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@ -6,6 +6,7 @@
#include "Stat.hpp"
#include "Activation/Activation.hpp"
#include "LinAlg/LinAlg.hpp"
#include "Data/Data.hpp"
#include <algorithm>
#include <map>
@ -66,6 +67,27 @@ namespace MLPP{
return modes;
}
double Stat::range(std::vector<double> x){
LinAlg alg;
return alg.max(x) - alg.min(x);
}
double Stat::midrange(std::vector<double> x){
return range(x)/2;
}
double Stat::absAvgDeviation(std::vector<double> x){
double sum = 0;
for(int i = 0; i < x.size(); i++){
sum += std::abs(x[i] - mean(x));
}
return sum / x.size();
}
double Stat::standardDeviation(std::vector<double> x){
return std::sqrt(variance(x));
}
double Stat::variance(std::vector<double> x){
double sum = 0;
for(int i = 0; i < x.size(); i++){
@ -90,6 +112,11 @@ namespace MLPP{
return correlation(x, y) * correlation(x, y);
}
double Stat::chebyshevIneq(double k){
// X may or may not belong to a Gaussian Distribution
return 1 - 1 / (k * k);
}
double Stat::weightedMean(std::vector<double> x, std::vector<double> weights){
double sum = 0;
double weights_sum = 0;
@ -189,21 +216,4 @@ namespace MLPP{
}
return (y - x) / (log(y) - log(x));
}
double Stat::standardDeviation(std::vector<double> x){
return std::sqrt(variance(x));
}
double Stat::absAvgDeviation(std::vector<double> x){
double sum = 0;
for(int i = 0; i < x.size(); i++){
sum += std::abs(x[i] - mean(x));
}
return sum / x.size();
}
double Stat::chebyshevIneq(double k){
//Pr(|X - mu| >= k * sigma) <= 1/k^2, X may or may not belong to a Gaussian Distribution
return 1 - 1 / (k * k);
}
}

9
MLPP/Stat/Stat.hpp
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@ -20,10 +20,16 @@ namespace MLPP{
double mean(std::vector <double> x);
double median(std::vector<double> x);
std::vector<double> mode(std::vector<double> x);
double range(std::vector<double> x);
double midrange(std::vector<double> x);
double absAvgDeviation(std::vector <double> x);
double standardDeviation(std::vector <double> x);
double variance(std::vector <double> x);
double covariance(std::vector <double> x, std::vector <double> y);
double correlation(std::vector <double> x, std::vector<double> y);
double R2(std::vector <double> x, std::vector<double> y);
double chebyshevIneq(double k);
// Extras
double weightedMean(std::vector<double> x, std::vector<double> weights);
@ -40,9 +46,6 @@ namespace MLPP{
double stolarskyMean(double x, double y, double p);
double identricMean(double x, double y);
double logMean(double x, double y);
double standardDeviation(std::vector <double> x);
double absAvgDeviation(std::vector <double> x);
double chebyshevIneq(double k);
private:

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18
main.cpp
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@ -78,17 +78,23 @@ int main() {
// std::vector<double> outputSet = {0,1,1,0};
// // STATISTICS
// std::vector<double> x = {1,2,3,4,5,6,7,8,9,1};
// std::vector<double> x = {1,2,3,4,5,6,7,8,9,10};
// std::vector<double> y = {10,9,8,7,6,5,4,3,2,1};
// std::vector<double> w = {0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1};
// std::cout << "Arithmetic Mean: " << stat.mean(x) << std::endl;
// std::cout << "Median: " << stat.median(x) << std::endl;
// alg.printVector(stat.mode(x));
// std::cout << "Range: " << stat.range(x) << std::endl;
// std::cout << "Midrange: " << stat.midrange(x) << std::endl;
// std::cout << "Absolute Average Deviation: " << stat.absAvgDeviation(x) << std::endl;
// std::cout << "Standard Deviation: " << stat.standardDeviation(x) << std::endl;
// std::cout << "Variance: " << stat.variance(x) << std::endl;
// std::cout << "Covariance: " << stat.covariance(x, y) << std::endl;
// std::cout << "Correlation: " << stat.correlation(x, y) << std::endl;
// std::cout << "R^2: " << stat.R2(x, y) << std::endl;
// // Returns 1 - (1/k^2)
// std::cout << "Chebyshev Inequality: " << stat.chebyshevIneq(2) << std::endl;
// std::cout << "Weighted Mean: " << stat.weightedMean(x, w) << std::endl;
// std::cout << "Geometric Mean: " << stat.geometricMean(x) << std::endl;
// std::cout << "Harmonic Mean: " << stat.harmonicMean(x) << std::endl;
@ -103,10 +109,7 @@ int main() {
// std::cout << "Stolarsky Mean (p = 5): " << stat.stolarskyMean(1, 10, 5) << std::endl;
// std::cout << "Identric Mean: " << stat.identricMean(1, 10) << std::endl;
// std::cout << "Logarithmic Mean: " << stat.logMean(1, 10) << std::endl;
// std::cout << "Standard Deviation: " << stat.standardDeviation(x) << std::endl;
// std::cout << "Absolute Average Deviation: " << stat.absAvgDeviation(x) << std::endl;
// // Returns 1 - (1/k^2)
// std::cout << "Chebyshev Inequality: " << stat.chebyshevIneq(2) << std::endl;
// // LINEAR ALGEBRA
// std::vector<std::vector<double>> A = {
@ -144,12 +147,12 @@ int main() {
// alg.printVector(model.modelSetTest((alg.transpose(inputSet))));
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
// LOGISTIC REGRESSION
// // LOGISTIC REGRESSION
// std::vector<std::vector<double>> inputSet;
// std::vector<double> outputSet;
// data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet);
// LogReg model(inputSet, outputSet);
// model.gradientDescent(0.0001, 100000, 0);
// model.SGD(0.001, 100000, 0);
// model.MLE(0.1, 10000, 0);
// alg.printVector(model.modelSetTest(inputSet));
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
@ -188,7 +191,6 @@ int main() {
// std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
// // SOFTMAX REGRESSION
// time_t start = time(0);
// std::vector<std::vector<double>> inputSet;
// std::vector<double> tempOutputSet;
// data.setData(4, "/Users/marcmelikyan/Desktop/Data/Iris.csv", inputSet, tempOutputSet);
@ -374,6 +376,8 @@ int main() {
// std::vector<double> vectorOfCubes = {1,2,64,27};
// alg.printMatrix(alg.cbrt(matrixOfCubes));
// alg.printVector(alg.cbrt(vectorOfCubes));
// std::cout << alg.max({{1,2,3,4,5}, {6,5,3,4,1}, {9,9,9,9,9}}) << std::endl;
// std::cout << alg.min({{1,2,3,4,5}, {6,5,3,4,1}, {9,9,9,9,9}}) << std::endl;
return 0;
}