diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..d44086f2550ddf318e30c29d156b10bee1d43c02
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+/data
+/__pycache__
+
diff --git a/LICENCE b/LICENCE
new file mode 100644
index 0000000000000000000000000000000000000000..1232db2da35c4deb2d8872b7f9f96ae933c93215
--- /dev/null
+++ b/LICENCE
@@ -0,0 +1,201 @@
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diff --git a/knn.py b/knn.py
new file mode 100644
index 0000000000000000000000000000000000000000..9da45fbe8f17a5a97342fd64518f68b64367d091
--- /dev/null
+++ b/knn.py
@@ -0,0 +1,162 @@
+# K-nearest-neighbors
+
+# 1. function distance_matrix
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.optimize.slsqp import concatenate
+
+from read_cifar import read_cifar, split_dataset
+
+path = "data/cifar-10-batches-py/"
+
+
+# first we write the function that calculate the distance between two matrix
+# we will use this function to compute the distance between two matrix with equal shape
+def equal_shape_distance_matrix(X, V):
+    """
+    compute the Euclidean distance between two matrix with equal shape
+
+    :param
+       X: first matrix
+       V: second matrix
+    :return:
+        the Euclidean distance between X and V
+    """
+    return (
+        np.dot(X, X.transpose())
+        + np.dot(V, V.transpose())
+        - 2 * np.dot(X, V.transpose())
+    )
+
+
+# distance_matrix function between two matrix of any shape
+
+
+def distance_matrix(data_train, data_test):
+
+    """
+    compute the Euclidean distance between two matrix
+
+    :param
+       data_train: the data_train matrix that contains the training data
+       data_test: the data_test matrix that contains the test data
+    :return:
+        dist: the Euclidean distance between data_train and data_test as a matrix
+    """
+    # we compute the first distance with equal shapes
+    dist = equal_shape_distance_matrix(data_train[: data_test.shape[0]], data_test)
+
+    # we compute the distance between the test set and the p part of the training data
+    p = int(data_train.shape[0] / data_test.shape[0])
+    for i in range(1, p):
+        sub_dist = equal_shape_distance_matrix(
+            data_train[data_test.shape[0] * i : data_test.shape[0] * (i + 1)], data_test
+        )
+        dist = np.concatenate((dist, sub_dist), axis=1)
+    return dist
+
+
+# 2. the function Knn_predict
+
+
+def knn_predict(labels_train, dists, k):
+
+    """
+    compute the predicted labels for the data_test
+
+    :param
+       labels_train: the labels of the training_data with whom we will compare the predicted labels
+       dists: the distance matrix that contains the euclidean distances between the data_train and the test_train
+       k = number of neighbors
+    :return:
+        lables_predicted: the predicted labels for the data_test
+
+    """
+    # we initialize the matrix of predicted labels
+    num_test = dists.shape[0]
+    lables_predicted = np.zeros(num_test)
+    for i in range(num_test):
+        closest_labels = []
+
+        # list des indices des plus petites distances
+        sorted_dist = np.argsort(dists[i])
+
+        # les k premiers labels qui correspondent au data_train qui ont la plus petite distance avec les data_test
+        closest_labels = list(labels_train[sorted_dist[0:k]])
+
+        pass
+        # les labels prédits pour les data_tets
+        lables_predicted[i] = np.argmax(np.bincount(closest_labels))
+
+        pass
+    return lables_predicted
+
+
+# 4. evaluate_knn
+
+
+def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
+
+    """
+    evaluate the accuracy of our prediction model
+
+    :param
+       data_train: the data of the training set
+       labels_train: the labels of the data_train
+       data_test: the data of the test set
+       labels_test: the actual labels (true labels) for the test set
+       k = number of neighbors
+    :return:
+        accuracy: the accuracy of the model
+    """
+
+    # we call for distance_matrix and knn_predict
+    dists = distance_matrix(data_train, data_test)
+    y_test_pred = knn_predict(labels_train, dists, k)
+
+    # total number of predictions
+    num_test = dists.shape[0]
+    # number of correct predictions
+    correct = np.sum(y_test_pred == labels_test)
+    # accuracy
+    accuracy = float(correct) / num_test
+    print("Got %d / %d correct, accuracy is : %f" % (correct, num_test, accuracy))
+    return accuracy
+
+
+if __name__ == "__main__":
+
+    # load data and split it into train and test
+
+    data, labels = read_cifar(path)
+
+    # we choose the split factor 0.9
+    data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
+
+    print(data_test.shape)
+
+    # we reduce the shape of the test to prevent memory issues
+
+    num_test = 2000
+    mask = list(range(num_test))
+    data_test = data_test[mask]
+    labels_test = labels_test[mask]
+
+    # we calcul the accuracy for k from 1 to 20
+
+    Ks = []
+    accuracies = []
+
+    for k in range(1, 20):
+
+        accuracy = evaluate_knn(data_train, labels_train, data_test, labels_test, k)
+        Ks.append(k)
+        accuracies.append(accuracy)
+
+    # we plot the variation of the accuracy as a function of k and save it as knn.png
+
+    plt.plot(Ks, accuracies, "o")
+    plt.title("Accuracy vs K")
+    plt.savefig("knn.png", bbox_inches="tight")
+    plt.show()
diff --git a/read_cifar.py b/read_cifar.py
new file mode 100644
index 0000000000000000000000000000000000000000..b324313cae738fdeafd63b6fd5ef53cb7fb10578
--- /dev/null
+++ b/read_cifar.py
@@ -0,0 +1,123 @@
+# prepare the CIFAR dataset
+
+# 1. we create a folder data in wich we will move the downloaded cifar-10-batches-py folder
+#
+# 2.  function read_cifar_batch
+
+import os
+import pickle
+
+import numpy as np
+
+path = "data/cifar-10-batches-py/"
+
+
+def read_cifar_batch(file):
+
+    """
+    load single batch of cifar.
+
+    :param
+        file: the path of the single batch.
+
+    :return:
+        data: a matrix that contains the data of the batch.
+        labels: a vector whose values correspond to the class code of the data of the same index in data.
+
+    """
+
+    with open(file, "rb") as fo:
+
+        dict = pickle.load(fo, encoding="bytes")
+        data = dict[b"data"]
+        labels = dict[b"labels"]
+        data = data.reshape(10000, 3072)
+        labels = np.array(labels)
+
+    return data, labels
+
+
+# 3. the function read_cifar
+
+
+def read_cifar(path):
+
+    """
+    load all batches of cifar including the test batch.
+
+    :param
+        path: the path of the directory containing the six batches (five data_batch and one test_batch).
+
+    :return:
+        data: a matrix that contains all data of the batches
+        labels: a vector whose values correspond to the class code of the data of the same index in data
+
+    """
+
+    XT = []
+    YT = []
+
+    for i in range(1, 6):
+
+        f = os.path.join(path, "data_batch_%d" % (i,))
+        X, Y = read_cifar_batch(f)
+        XT.append(X)
+        YT.append(Y)
+
+    T, W = read_cifar_batch(path + "test_batch")
+    XT.append(T)
+    YT.append(W)
+
+    data = np.concatenate(XT)
+    labels = np.concatenate(YT)
+
+    del X, Y
+
+    return data, labels
+
+
+# 4. the function split_dataset
+
+from sklearn.model_selection import train_test_split
+
+
+def split_dataset(data, labels, split):
+
+    """
+    Split the dataset into a training set and a test set
+
+    :param
+        data: the dataset
+        labels: the labels corresponding to the dataset
+        split: a float between 0 and 1 which determines the split factor of the training set with respect to the test set.
+    :return:
+        data_train: a matrix that contains the data of the training set
+        data_test: a matrix that contains the data of the test set
+        labels_train: a vector whose values correspond to the class code of the data of the same index in data_train
+        labels_train: a vector whose values correspond to the class code of the data of the same index in data_test
+    """
+
+    # shuffle = True means that the data must be shuffled, so that two successive calls shouldn't give the same output.
+    data_train, data_test, labels_train, labels_test = train_test_split(
+        data, labels, test_size=split, shuffle=True
+    )
+
+    return data_train, data_test, labels_train, labels_test
+
+
+if __name__ == "__main__":
+
+    data, labels = read_cifar_batch(path + "data_batch_1")
+    print(data.shape)
+    print(labels.shape)
+
+    data, labels = read_cifar(path)
+    print(data.shape)
+    print(labels.shape)
+
+    data_train, data_test, labels_train, labels_test = split_dataset(
+        data, labels, split=0.2
+    )
+
+    print(data_train.shape)
+    print(data_test.shape)