|-|
├──|code
| |
| ├── calibration.py (Library Files)
| ├── classifiers.py (Library Files)
| ├── dist.py (Generates Feature Distribution Plot)
| ├── FinalEvaluation.py (Generates Evaluation Data for GMMs, Tied Covariance, RBF SVM, ...)
| ├── GMMOptimization.py (Generates Optimized Parameters for GMMs)
| ├── KernelSVM_Normalized.py (Generates Optimized Parameters for Normalized RBF SVM)
| ├── KernelSVM.py (Generates Optimized Parameters for RBF SVM)
| ├── LinearSVM.py (Generates Optimized Parameters for Linear SVM)
| ├── LogReg.py (Generates Optimzed Parameters for Logistic Regression)
| ├── LogRegQuad.py (Generates Optimzed Parameters for Quadratic Logistic Regression)
| ├── model_evaluation.py (Generates evalation data for logistic regressions and linear SVM)
| ├── model_validation.py (Process generated data for optimized parameters)
| ├── MVG-FC-Optimization.py (Generates Optimized Parameters for MVG Classifier)
| ├── MVG-NB-Optimization.py (Generates Optimized Parameters for MVG Classifier)
| ├── MVG.py (Generates Optimized PCA for MVG Classifier)
| ├── MVG-TC-Optimization.py (Generates Optimized Parameters for MVG Classifier)
| ├── PCA_Tests.py (Tests to select good PCA values)
| ├── PolySVM_Normalized.py (Generates Optimzed Parameters for Normalized Polynomial SVM)
| ├── PolySVM.py (Generates Optimzed Parameters for Polynomial SVM)
| ├── PolySVM_Whitened.py (Generates Optimzed Parameters for WhitenedPolynomial SVM)
| ├── scatter.py (Generates scatter plots)
| └── utils.py (Library files)
|
├── data (Datasets and scores computed by validating for GMMs, Kernel SVMs, Gaussian Classifiers)
├── img (Images for report)
├── report (Report source folder)
└── trained (Scores computed by validating for linear SVMs and Logistic Regressions)