Delete ReadMe.md

ReadMe.md

-# Image classification
-
-Legrand Frédéric - Project for the Deep Learning Course at ECL
-
-# Usage
-
-To download and run the project, open a terminal in the desired folder and run these commands, they will:
-- Download the project
-- Enter the project folder
-- Add the data folder in the project folder
-- Install the necessary dependancies
-- Enter test folder
-- Test that the code is functionnal with pytest (All 8 tests should run without issues)
-
-```
-git clone https://gitlab.ec-lyon.fr/flegrand/image-classification.git
-cd image-classification 
-pip install -r requirements.txt
-cd tests
-pytest
-```
-
-If all tests are succesful, the project has all the necessary dependancies and everything should run normally.
-
-# Description
-
-This section of the readme goes through all the code and explains it.
-
-## Prepare the CIFAR dataset
-
-All the code for this section is in the read_cifar.py file.
-Unit tests have been created in tests/test_read_cifar.py
-Running pytest in tests folder will provide proof that algorithms work as expected.
-
-### 1- 
-
-Data folder contains the cifar-10-batches-py folder with the relevant data for the image classification.
-We focus on the cifar image dataset.
-
-### 2-
-
-read_cifar_batch takes in a file path and returns the corresponding data and labels
-We import the data as float32 values to be able to use numpy. 
-Since values are between 0 and 256 for the data and between 0-10 for the labels we can import the labels as int64.
-
-```rb
-def read_cifar_batch(filePath: str):
-    with open(filePath, 'rb') as fo:
-        dict = pickle.load(fo, encoding='bytes')
-    return (np.array(dict[b'data']).astype('float64'), np.array(dict[b'labels']).astype('int64'))
-```
-
-### 3-
-
-read_cifar takes in a folder path and returns the corresponding data and labels by making use of the previous read_cifar_batch function.
-
-```rb
-def read_cifar(folderPath: str):
-    data, labels = read_cifar_batch(folderPath+"/test_batch")
-    for i in range(1,6):
-        tempData, tempLabels = read_cifar_batch(folderPath+"/data_batch_"+str(i))
-        labels = np.concatenate((labels,tempLabels), axis=0)
-        data = np.concatenate((data, tempData), axis=0)
-    return data, labels
-```
-
-### 4-
-
-split_dataset takes in some numpy array that represents the data, some corresponding labels and a split pourcentage.
-
-The function finds a random permutation of these values with np.random.shuffle.
-We then mix both data and labels in the same way.
-
-We return the first elements so that we reach the split pourcentage we wanted.
-
-```rb
-def split_dataset(data: np.array, labels: list, split: float):
-    if not 0 < split < 1:
-        raise ValueError('Split is not a float between 0 and 1')
-    
-    # Generate random indices for shuffling the data
-    num_samples = len(labels)
-    indices = np.arange(num_samples)
-    np.random.shuffle(indices)
-    
-    # Shuffle the data and labels using the random indices
-    shuffled_data = data[indices]
-    shuffled_labels = np.array(labels)[indices]
-    
-    # Calculate the split index based on the given split ratio
-    split_index = int(split * num_samples)
-    
-    # Split the data and labels into training and testing sets
-    data_train, data_test = shuffled_data[:split_index], shuffled_data[split_index:]
-    labels_train, labels_test = shuffled_labels[:split_index], shuffled_labels[split_index:]
-
-    return data_train, data_test, labels_train, labels_test
-```
-
-## k-nearest neighbors
-
-All the code for this section is in the knn.py file.
-Functions have unit tests in the tests folder
-Running pytest in tests folder will provide proof that algorithms work as expected.
-
-### 1-
-
-distance_matrix takes in two matrices which represents pictures.
-It returns the L2 Euclidian distance of each image in the first set to each image in the second set.
-
-Because of this the result is a numpy array of size: number_of_items_in_the_first_set * number_of_items_in_the_second_set
-
-```rb
-def distance_matrix(matrix1 : np.array, matrix2 : np.array):
-    sum_of_squares_matrix1 = np.sum(np.square(matrix1), axis=1, keepdims=True)
-    sum_of_squares_matrix2 = np.sum(np.square(matrix2), axis=1, keepdims=True)
-
-    dot_product = np.dot(matrix1, matrix2.T)
-    
-    dists = np.sqrt(sum_of_squares_matrix1 + sum_of_squares_matrix2.T - 2 * dot_product)
-    return dists