discrete cosine transform

MLPP/Transforms/Transforms.cpp

@@ -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;
+    }
+}

MLPP/Transforms/Transforms.hpp

@@ -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 */

README.md

@@ -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***
+15. ***Mathematical Transforms***
-16. ***Statistics Module***
+    1. Discrete Cosine Transform
-17. ***Data Processing Module***
+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 

buildSO.sh

@@ -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

main.cpp

@@ -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; 
+    Transforms trans; 
-    tensorSet.push_back(input);
-    tensorSet.push_back(input);
-    tensorSet.push_back(input);
 
-    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.