discrete cosine transform

This commit is contained in:
novak_99 2022-02-12 01:32:46 -08:00
parent c21c750727
commit 4cc61e4c1e
8 changed files with 107 additions and 12 deletions

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@ -0,0 +1,58 @@
//
// Transforms.cpp
//
// Created by Marc Melikyan on 11/13/20.
//
#include "Transforms.hpp"
#include "LinAlg/LinAlg.hpp"
#include <iostream>
#include <string>
#include <cmath>
namespace MLPP{
// DCT ii.
std::vector<std::vector<double>> Transforms::discreteCosineTransform(std::vector<std::vector<double>> A){
LinAlg alg;
A = alg.scalarAdd(-128, A); // Center around 0.
std::vector<std::vector<double>> B;
B.resize(A.size());
for(int i = 0; i < B.size(); i++){
B[i].resize(A[i].size());
}
int M = A.size();
for(int i = 0; i < B.size(); i++){
for(int j = 0; j < B[i].size(); j++){
double sum = 0;
double alphaI;
if(i == 0){
alphaI = 1/std::sqrt(M);
}
else{
alphaI = std::sqrt(double(2)/double(M));
}
double alphaJ;
if(j == 0){
alphaJ = 1/std::sqrt(M);
}
else{
alphaJ = std::sqrt(double(2)/double(M));
}
for(int k = 0; k < B.size(); k++){
for(int f = 0; f < B[k].size(); f++){
sum += A[k][f] * std::cos( (M_PI * i * (2 * k + 1)) / (2 * M)) * std::cos( (M_PI * j * (2 * f + 1)) / (2 * M));
}
}
B[i][j] = sum;
B[i][j] *= alphaI * alphaJ;
}
}
return B;
}
}

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@ -0,0 +1,20 @@
//
// Transforms.hpp
//
//
#ifndef Transforms_hpp
#define Transforms_hpp
#include <vector>
#include <string>
namespace MLPP{
class Transforms{
public:
std::vector<std::vector<double>> discreteCosineTransform(std::vector<std::vector<double>> A);
};
}
#endif /* Transforms_hpp */

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@ -190,9 +190,11 @@ The result will be the model's predictions for the entire dataset.
5. Diffrential Equations Solvers
- Euler's Method
- Growth Method
15. ***Linear Algebra Module***
16. ***Statistics Module***
17. ***Data Processing Module***
15. ***Mathematical Transforms***
1. Discrete Cosine Transform
16. ***Linear Algebra Module***
17. ***Statistics Module***
18. ***Data Processing Module***
1. Setting and Printing Datasets
2. Feature Scaling
3. Mean Normalization
@ -204,7 +206,7 @@ The result will be the model's predictions for the entire dataset.
- RGB to YCbCr
- RGB to XYZ
- XYZ to RGB
18. ***Utilities***
19. ***Utilities***
1. TP, FP, TN, FN function
2. Precision
3. Recall

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@ -1,4 +1,4 @@
g++ -I MLPP -c -fPIC main.cpp MLPP/Stat/Stat.cpp MLPP/LinAlg/LinAlg.cpp MLPP/Regularization/Reg.cpp MLPP/Activation/Activation.cpp MLPP/Utilities/Utilities.cpp MLPP/Data/Data.cpp MLPP/Cost/Cost.cpp MLPP/ANN/ANN.cpp MLPP/HiddenLayer/HiddenLayer.cpp MLPP/OutputLayer/OutputLayer.cpp MLPP/MLP/MLP.cpp MLPP/LinReg/LinReg.cpp MLPP/LogReg/LogReg.cpp MLPP/UniLinReg/UniLinReg.cpp MLPP/CLogLogReg/CLogLogReg.cpp MLPP/ExpReg/ExpReg.cpp MLPP/ProbitReg/ProbitReg.cpp MLPP/SoftmaxReg/SoftmaxReg.cpp MLPP/TanhReg/TanhReg.cpp MLPP/SoftmaxNet/SoftmaxNet.cpp MLPP/Convolutions/Convolutions.cpp MLPP/AutoEncoder/AutoEncoder.cpp MLPP/MultinomialNB/MultinomialNB.cpp MLPP/BernoulliNB/BernoulliNB.cpp MLPP/GaussianNB/GaussianNB.cpp MLPP/KMeans/KMeans.cpp MLPP/kNN/kNN.cpp MLPP/PCA/PCA.cpp MLPP/OutlierFinder/OutlierFinder.cpp MLPP/MANN/MANN.cpp MLPP/MultiOutputLayer/MultiOutputLayer.cpp MLPP/SVC/SVC.cpp MLPP/NumericalAnalysis/NumericalAnalysis.cpp MLPP/DualSVC/DualSVC.cpp --std=c++17
g++ -I MLPP -c -fPIC main.cpp MLPP/Stat/Stat.cpp MLPP/LinAlg/LinAlg.cpp MLPP/Regularization/Reg.cpp MLPP/Activation/Activation.cpp MLPP/Utilities/Utilities.cpp MLPP/Data/Data.cpp MLPP/Cost/Cost.cpp MLPP/ANN/ANN.cpp MLPP/HiddenLayer/HiddenLayer.cpp MLPP/OutputLayer/OutputLayer.cpp MLPP/MLP/MLP.cpp MLPP/LinReg/LinReg.cpp MLPP/LogReg/LogReg.cpp MLPP/UniLinReg/UniLinReg.cpp MLPP/CLogLogReg/CLogLogReg.cpp MLPP/ExpReg/ExpReg.cpp MLPP/ProbitReg/ProbitReg.cpp MLPP/SoftmaxReg/SoftmaxReg.cpp MLPP/TanhReg/TanhReg.cpp MLPP/SoftmaxNet/SoftmaxNet.cpp MLPP/Convolutions/Convolutions.cpp MLPP/AutoEncoder/AutoEncoder.cpp MLPP/MultinomialNB/MultinomialNB.cpp MLPP/BernoulliNB/BernoulliNB.cpp MLPP/GaussianNB/GaussianNB.cpp MLPP/KMeans/KMeans.cpp MLPP/kNN/kNN.cpp MLPP/PCA/PCA.cpp MLPP/OutlierFinder/OutlierFinder.cpp MLPP/MANN/MANN.cpp MLPP/MultiOutputLayer/MultiOutputLayer.cpp MLPP/SVC/SVC.cpp MLPP/NumericalAnalysis/NumericalAnalysis.cpp MLPP/DualSVC/DualSVC.cpp MLPP/Transforms/Transforms.cpp --std=c++17
g++ -shared -o MLPP.so Reg.o LinAlg.o Stat.o Activation.o LinReg.o Utilities.o Cost.o LogReg.o ProbitReg.o ExpReg.o CLogLogReg.o SoftmaxReg.o TanhReg.o kNN.o KMeans.o UniLinReg.o SoftmaxNet.o MLP.o AutoEncoder.o HiddenLayer.o OutputLayer.o ANN.o BernoulliNB.o GaussianNB.o MultinomialNB.o Convolutions.o OutlierFinder.o Data.o MultiOutputLayer.o MANN.o SVC.o NumericalAnalysis.o DualSVC.o
sudo mv MLPP.so /usr/local/lib

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@ -48,7 +48,7 @@
#include "MLPP/NumericalAnalysis/NumericalAnalysis.hpp"
#include "MLPP/DualSVC/DualSVC.hpp"
#include "MLPP/GAN/GAN.hpp"
#include "MLPP/Transforms/Transforms.hpp"
using namespace MLPP;
@ -466,16 +466,31 @@ int main() {
// // CONVOLUTION, POOLING, ETC..
// std::vector<std::vector<double>> input = {
// {1},
// };
// std::vector<std::vector<std::vector<double>>> tensorSet;
// tensorSet.push_back(input);
// tensorSet.push_back(input);
// tensorSet.push_back(input);
// alg.printTensor(data.rgb2xyz(tensorSet));
std::vector<std::vector<double>> input = {
{1},
{62,55,55,54,49,48,47,55},
{62,57,54,52,48,47,48,53},
{61,60,52,49,48,47,49,54},
{63,61,60,60,63,65,68,65},
{67,67,70,74,79,85,91,92},
{82,95,101,106,114,115,112,117},
{96,111,115,119,128,128,130,127},
{109,121,127,133,139,141,140,133},
};
std::vector<std::vector<std::vector<double>>> tensorSet;
tensorSet.push_back(input);
tensorSet.push_back(input);
tensorSet.push_back(input);
Transforms trans;
alg.printTensor(data.rgb2xyz(tensorSet));
alg.printMatrix(trans.discreteCosineTransform(input));
// alg.printMatrix(conv.convolve(input, conv.getPrewittVertical(), 1)); // Can use padding
// alg.printMatrix(conv.pool(input, 4, 4, "Max")); // Can use Max, Min, or Average pooling.