├── src/
│ ├── knn_experiment.ipynb
│ ├── decisiontree_experiment.ipynb
| ├── experiment.ipynb
| ├── img_preprocessing.ipynb
├── MNIST/
├── Caltech10/
Assuming that your system is in python 2.7 which includes these packages
- opencv-python
- scikit-learn
- matplot-lib
- pillow
- scipy
- numpy
- jupyter
which can be easily installed through pip
.
Please make sure the folders structure are similar with data included in MNIST and Caltech10 folders for the code to run smoothly.
- Run
python jupyter notebook
command line in the root folder - Click on the Kernel button to run all on file
img_preprocecssing.ipynb
- Click on the Kernel button to run all on file
knn_experiment.ipynb
for implementation and experimentation results of KNN algorithm on both datasets - Click on the Kernel button to run all on file
decisiontree_experiment.ipynb
for implementation and experimentation results of decision tree algorithm on both datasets - Feel free to edit the call on the pickle_operating when loading the binarized version of the preprocessing dataset between with and without PCA transformation applied.
Notes:
Image preprocessing + Loading the dataset is done in the img_preprocecssing.ipynb
file
MNIST_data_1.pickle
andCaltech_data_1.pickle
are datasets without PCA preprocessingMNIST_data_2.pickle
andCaltech_data_2.pickle
are datasets with PCA preprocessing