Merge
This commit is contained in:
HorlogeSkynet
parent
371f2d33d9
commit
a9ffa62585
@ -7,20 +7,19 @@
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#include "../Modules/Graphics/graphics.h"
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using namespace std;
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#define ALPHA 0.001
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#define NBPOINTS 250
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#define NBITERATIONS 1000
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#define INTERNALWIDTH 5
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double leadingCoeficient[] = {1, 2};
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void getRandomCoordinates(double[], double[]);
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void getGradient(double[], double[], double[], double[]);
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void getGradient(const double[], const double[], const double[], double[]);
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@ -61,7 +60,7 @@ int main(int argc, char **argv)
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deltaGrad[1] = grad[1] - gradOld[1];
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alpha = (-alpha * (gradOld[0] * deltaGrad[0] + gradOld[1] * deltaGrad[1]))
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/ ((deltaGrad[0] * deltaGrad[0]) + (deltaGrad[1] * deltaGrad[1]));
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/ ((deltaGrad[0] * deltaGrad[0]) + (deltaGrad[1] * deltaGrad[1]));
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}
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else
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@ -74,13 +73,12 @@ int main(int argc, char **argv)
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}
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cout << "Theta: [" << theta[0] << "; " << theta[1] << "]" << endl;
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cout << "Done in " << i << " iterations." << endl;
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cout << "| Done in " << i << " iterations. |" << endl;
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/* This function will display the input coordinates, and the computed regression */
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glutDisplay(&argc, argv, "Affine Regression (C++)", x, y, (const int)NBPOINTS, theta, sizeof(leadingCoeficient) / sizeof(*leadingCoeficient));
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/* This function will display the input coordinates,
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and the computed regression, and will be waiting while the window stay open */
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sfmlDisplay("Affine Regression (C++)", x, y, (const int)NBPOINTS, theta, NBELEMENTS(leadingCoeficient));
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/* Now, waiting for user closing the window */
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glutMainLoop();
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return 0;
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}
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@ -97,10 +95,11 @@ void getRandomCoordinates(double x[], double y[])
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}
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void getGradient(double x[], double y[], double theta[], double gradient[])
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void getGradient(const double x[], const double y[], const double theta[], double gradient[])
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{
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gradient[0]=0;
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gradient[1]=0;
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gradient[0] = 0;
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gradient[1] = 0;
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for(int i(0); i < NBPOINTS; i++)
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{
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gradient[0] += (theta[1] * x[i] + theta[0]) - y[i];
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@ -5,19 +5,19 @@
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#include <cmath>
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#include <ctime>
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#include "../Modules/Graphics/graphics.h"
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using namespace std;
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#define ALPHA 0.001
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#define NBPOINTS 25
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#define NBPOINTS 500
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#define NBITERATIONS 1000
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#define INTERNALWIDTH 5
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#define LEADINGCOEFICIENT 2
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void getRandomCoordinates(double[], double[]);
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double getGradient(double[], double[], double);
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double getGradient(const double[], const double[], const double[]);
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@ -28,35 +28,42 @@ int main(int argc, char const *argv[])
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srand(time(0));
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double x[NBPOINTS] = {0};
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double y[NBPOINTS] = {0};
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double x[NBPOINTS] = {0.0};
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double y[NBPOINTS] = {0.0};
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/* Coordinates generation */
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getRandomCoordinates(x, y);
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double theta(0);
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double theta[1] = {0.0};
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double alpha(ALPHA);
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double gradOld(getGradient(x, y, theta));
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double grad;
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double i;
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/* Training ! */
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for(i=0; i < NBITERATIONS; i++)
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int i;
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for(i = 0; i < NBITERATIONS; i++)
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{
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theta -= alpha * gradOld;
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theta[0] -= alpha * gradOld;
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grad = getGradient(x, y, theta);
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if (grad!=gradOld)
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if(grad != gradOld)
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{
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alpha = -alpha * gradOld / (grad - gradOld);
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}
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else
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{
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break;
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}
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gradOld = grad;
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}
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cout << "Theta: " << theta << endl;
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cout << "Done in " << i << " iterations." << endl;
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cout << "Theta: " << theta[0] << endl;
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cout << "| Done in " << i << " iterations. |" << endl;
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sfmlDisplay("Linear Regression (C++)", x, y, (const int)NBPOINTS, theta, NBELEMENTS(theta));
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return 0;
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}
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@ -73,13 +80,13 @@ void getRandomCoordinates(double x[], double y[])
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}
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double getGradient(double x[], double y[], double theta)
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double getGradient(const double x[], const double y[], const double theta[])
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{
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double value = 0;
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double value = 0.0;
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for(short int i(0); i < NBPOINTS; i++)
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{
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value += ((theta * x[i]) - y[i]) * x[i];
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value += ((theta[0] * x[i]) - y[i]) * x[i];
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}
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return value / NBPOINTS;
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25
Makefile
25
Makefile
@ -1,21 +1,24 @@
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# Makefile for: MINDLE
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CXX=g++
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GFLAGS=-lGLU -lGL -lglut
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GFLAGS=-lsfml-graphics -lsfml-window -lsfml-system
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LIN=LinearRegression
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MAT=Modules/Matrix
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GRAPH=Modules/Graphics
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ifdef DEBUG
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CXXFLAGS = -g -ansi -std=c++11 -Wall -Wextra -Wpedantic -Wshadow -Wno-missing-field-initializers -Wstrict-overflow
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CXXFLAGS=-g -ansi -std=c++11 -Wall -Wextra -Wpedantic -Wshadow -Wno-missing-field-initializers -Wstrict-overflow
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LDFLAGS=-g -ansi -std=c++11 -Wall -Wextra -Wpedantic -Wshadow -Wno-missing-field-initializers -Wstrict-overflow
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else
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CXXFLAGS = -O3 -ansi -std=c++11 -Wall -Wextra -Wpedantic -Wshadow -Wno-missing-field-initializers -Wstrict-overflow
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CXXFLAGS=-O3 -ansi -std=c++11 -Wall -Wextra -Wpedantic -Wshadow -Wno-missing-field-initializers -Wstrict-overflow
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LDFLAGS=-O3 -ansi -std=c++11 -Wall -Wextra -Wpedantic -Wshadow -Wno-missing-field-initializers -Wstrict-overflow
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endif
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all: linearRegression.bin affineRegression.bin linearRegressionVectors.bin
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# Linear Regression
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linearRegression.bin: LinearRegression/linearRegression.cpp
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$(CXX) -o $@ $^ $(LDFLAGS)
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@ -28,21 +31,27 @@ affineRegression.bin: $(LIN)/affineRegression.o $(GRAPH)/graphics.o
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linearRegressionVectors.bin: $(LIN)/linearRegressionVectors.o $(MAT)/Matrix.o
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$(CXX) -o $@ $^ $(LDFLAGS)
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# Object files
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affineRegression.o: $(LIN)/affineRegression.cpp $(GRAPH)/Graphics.o
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$(CXX) -o $@ -c $^ $(CXXFLAGS)
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$(CXX) -c -o $@ $^ $(CXXFLAGS)
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linearRegressionVectors.o: $(LIN)/linearRegressionVectors.cpp
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$(CXX) -c -o $@ $^ $(CXXFLAGS)
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# Matrix module
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$(MAT)/Matrix.o: $(MAT)/Matrix.cpp $(MAT)/Matrix.h
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$(CXX) -c -o $@ $< $(CXXFLAGS)
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$(CXX) -c -o $@ $< $(CXXFLAGS)
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# Graphics module
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$(GRAPH)/Graphics.o: $(GRAPH)/Graphics.cpp $(GRAPH)/Graphics.h
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$(CXX) -c -o $@ $< $(CXXFLAGS)
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$(CXX) -c -o $@ $< $(CXXFLAGS)
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#.SILENT:
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.SILENT:
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.PHONY: clean
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clean:
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find . -name '*.bin' -delete
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find . -name '*.o' -delete
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find . -name '*.o' -delete
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@ -1,70 +1,95 @@
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#include "graphics.h"
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void glutDisplay(int *argc, char *argv[], const char *const windowName, double x[], double y[],
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const int nbPoints, double theta[], const size_t nbDimensions)
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using namespace sf;
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void sfmlDisplay(const char* const string, const double x[], const double y[], const int nbPoints, const double theta[], const int nbDimensions)
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{
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glutInit(argc, argv);
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glutInitDisplayMode(GLUT_RGBA | GLUT_DOUBLE);
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glutInitWindowSize(SIZE_X, SIZE_Y);
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glutInitWindowPosition(POSITION_X, POSITION_Y);
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glutCreateWindow(windowName);
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/* Let's create a new window ! */
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RenderWindow window(VideoMode(SIZE_X, SIZE_Y), string);
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int window_width = glutGet(GLUT_WINDOW_WIDTH);
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int window_height = glutGet(GLUT_WINDOW_HEIGHT);
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/* One shape for each points of the coordinates, and for each point of our computed regression */
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CircleShape *shape = new CircleShape[2 * nbPoints];
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/* Let's display some stuff over here */
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glClearColor(255, 255, 255, 255);
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glClear(GL_COLOR_BUFFER_BIT);
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glMatrixMode(GL_PROJECTION);
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glLoadIdentity();
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gluOrtho2D(0, window_width, 0, window_height);
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glLineWidth(2.0);
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glMatrixMode(GL_MODELVIEW);
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glLoadIdentity();
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/* Input coordinates */
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glBegin(GL_LINES);
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double ratio_x = window_width / (double)nbPoints;
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double ratio_y = window_height / abs(getMax(y, nbPoints) - getMin(y, nbPoints));
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glVertex2f(0.0, 0.0);
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glColor3f(0.0, 0.0, 0.0);
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for(int i(0); i < nbPoints; i++)
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if(shape == nullptr)
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{
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glVertex2f(x[i] * ratio_x, y[i] * ratio_y);
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std::cout << "Error during allocation of the shapes array for display result." << std::endl;
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exit(EXIT_FAILURE);
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}
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glEnd();
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const int window_width = window.getSize().x;
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const int window_height = window.getSize().y;
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/* What the regression has found out */
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glBegin(GL_LINES);
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const double ratio_x = window_width / (double)nbPoints;
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const double ratio_y = window_height / std::abs(getMax(y, nbPoints) - getMin(y, nbPoints));
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double temp_y = 0.0;
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glVertex2f(0.0, 0.0);
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glColor3f(0.75, 0.5, 0.5);
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for(int i(0); i < nbPoints; i++)
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/* Dots for generated coordinates */
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int i;
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for(i = 0; i < nbPoints; i++)
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{
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for(int j(0); j < (int)nbDimensions; j++)
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/* The dot will be red, and with a radius of 10 ! */
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shape[i].setRadius(2);
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shape[i].setFillColor(Color(255, 0, 0));
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shape[i].setPosition(x[i] * ratio_x, window_height - y[i] * ratio_y);
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}
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/* Dots for our computed regression */
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for(double temp_y = 0.0; i < 2 * nbPoints; i++, temp_y = 0.0)
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{
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/* The dot will be blue, and with a radius of 20 ! */
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shape[i].setRadius(2);
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shape[i].setFillColor(Color(0, 0, 255));
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/* Condition if we're doing a simple linear regression (y = a * x), or not */
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if(nbDimensions > 1)
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{
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temp_y += pow(x[i], (double)j) * theta[j];
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for(int j(0); j < (int)nbDimensions; j++)
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{
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temp_y += pow(x[i - nbPoints - 1], (double)j) * theta[j];
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}
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}
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glVertex2f(x[i] * ratio_x, temp_y * ratio_y);
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temp_y = 0.0;
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else
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{
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temp_y = x[i - nbPoints - 1] * theta[0];
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}
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shape[i].setPosition(x[i - nbPoints - 1] * ratio_x, window_height - temp_y * ratio_y);
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}
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glEnd();
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/* Set a low frame rate limit in order to deal with CPU-usage while in the loop below */
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window.setFramerateLimit(25);
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glFlush();
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glutSwapBuffers();
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/* Infinite loop waiting for closing action */
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while(window.isOpen())
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{
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Event event;
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while(window.pollEvent(event))
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{
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if(event.type == Event::Closed)
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{
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window.close();
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}
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}
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window.clear(Color(255, 255, 255, 255));
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for(i = 0; i < 2 * nbPoints; i++)
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{
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window.draw(shape[i]);
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}
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window.display();
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}
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delete[] shape;
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}
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double getMin(double tab[], const int n)
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double getMin(const double tab[], const int n)
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{
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double minimum = tab[0];
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@ -80,7 +105,7 @@ double getMin(double tab[], const int n)
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}
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double getMax(double tab[], const int n)
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double getMax(const double tab[], const int n)
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{
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double maximum = tab[0];
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@ -1,19 +1,17 @@
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#pragma once
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#include <iostream>
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#include <cmath>
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#include <cstring>
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#include <GL/gl.h>
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#include <GL/glu.h>
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#include <GL/glut.h>
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#include <SFML/Graphics.hpp>
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#define POSITION_X 764
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#define POSITION_Y 256
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#define NBELEMENTS(x) (int)(sizeof(x) / sizeof(*x))
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#define SIZE_X 1024
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#define SIZE_Y 1024
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void glutDisplay(int*, char**, const char *const, double[], double[], const int, double[], const size_t);
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void sfmlDisplay(const char* const, const double[], const double[], const int, const double[], const int);
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double getMin(double[], const int);
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double getMax(double[], const int);
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double getMin(const double[], const int);
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double getMax(const double[], const int);
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10
README.md
10
README.md
@ -1,10 +1,10 @@
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# MINDLE Is Not a Deep Learning Experimentation
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# MINDLE Is Not a Deep Learning Experimentation
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Requirements (for C++ codes):
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- g++ 6.2.0 (<-- Because, of course, we won't upload any binaries here)
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- freeglut3-dev (<-- Glut library is being used at the moment)
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- g++ >= 4.9 (<-- Because, of course, we won't upload any binaries here)
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- libsfml-dev (for SFML library)
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Old requirements (for Python codes):
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@ -18,7 +18,7 @@ Old requirements (for Python codes):
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Notes to read:
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1. Some words about the frameworks we used with Python in the past: Theano & TensorFlow gave us a too high-level abstraction for our needs. We couldn't manage to get something to work, and this is not about getting computations on the GPU; that was just impossible, and we nearly destroy our computers many times... Now, we start from scratch with C++. At least, we would understand the errors throwed.
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1. Some words about the frameworks we used with Python in the past: Theano & TensorFlow gave us a too high-level abstraction for our needs. We couldn't manage to get something to work, and this is not about getting computations on the GPU; that was just impossible, and we nearly destroy our computers many times... Now, we start from scratch with C++. At least, we would understand the errors thrown.
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2. Based on [this "tutorial"](https://github.com/Newmu/Theano-Tutorials), you'll be able to perform digits classification by getting the return of [loadMNIST.py](https://github.com/HorlogeSkynet/MachineLearning/tree/master/dev_null/MNIST/loadMNIST.py). If you run it, the code will download and read the training examples automatically.
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Reference in New Issue
Block a user