This repository is used to accomplish my homework in math class.There are six files in all, five for five homeworks and the rest one for the final project.
- 01_curve_fitting.py
- 02_pca.py
- 03_mog_em.py
- 04_lm.py
- 05_svm.py
- final_project.py
- final_project_report.pdf
Goal:
Implement polynomial curve fitting in python.
Requirement:
- Programming lanuage: python
- Plot the results in matplotlib
Experiment result:
- red points stand for 10 training data points generated from function f = sin(x)
- blue curve stands for the true function f = sin(x)
- red curve stands for the predicted curve
Goal:
Represent digits '3' in 2D
- convert data from the UCI Optical Recognition of Handwritten Digits Data Set
- perform PCA over all digit '3' with 2 components
- plot the PCA results as below (also in page #12 of PCA)
Requirements:
- Programming lanuage: python
- Plot the results in matplotlib
Experiment result:
Goal:
implement MOG in 2D case
- Generate 2D Gaussian distribution
- E-M method
Requirements
- Programming lanuage: python
- Plot the results in matplotlib
Experiment result:
Goal:
- Implement the Levenberg-Marquardt method
Requirements:
- Programming lanuage: python
Goal:
Implement (simplified) SVM method
- input 2D data and their label (in two classes)
- implement quadratic programming
- output (and plot) classification results
Requirements:
- Programming lanuage: python
- Plot the results in matplotlib
##06. Final project Paper reading and implement:
ICCV2015 Fast and Effective L0 Gradient Minimization by Region Fusion by RMH Nguyen
Main idea are detailed in the pdf file final_project_report.pdf
Experiment Result:
