diff --git a/README.md b/README.md index 50a519e125c6d8e5b3864dffd421e46697038d82..fc35bbb7064f1ff41f56710130c1ed8b3fc65bba 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Two classification models will be successively developed and tested: k-nearest n ### Before you start -In this tutorial we use Python 3.7 or above. Make sure it is properly installed. +In this tutorial we use Python 3.7 or higher. Make sure it is properly installed. Make sure `numpy` is installed. We assume that `git` is installed, and that you are familiar with the basic `git` commands. (Optionnaly, you can use GitHub Desktop.) We also assume that you have access to the [ECL GitLab](https://gitlab.ec-lyon.fr/). If necessary, please consult [this tutorial](https://gitlab.ec-lyon.fr/edelland/inf_tc2/-/blob/main/Tutoriel_gitlab/tutoriel_gitlab.md). @@ -73,6 +73,8 @@ This database can be obtained at the address https://www.cs.toronto.edu/~kriz/ci - `labels_train` the training labels, and - `k` the number of of neighbors. This function must return the predicted labels for the elements of `data_train`. + **Note:** if the memory occupation is too important, you can use several batches for the calculation of the distance matrix (loop on sub-batches of test data). + {: .note} 3. Write the function `evaluate_knn` taking as parameters: - `data_train` the training data, - `labels_train` the corresponding labels,