From 4f00edc8fc738eb68797c60c6170f9443f81ad85 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Quentin=20Gallou=C3=A9dec?=
<45557362+qgallouedec@users.noreply.github.com>
Date: Wed, 5 Oct 2022 10:47:23 +0200
Subject: [PATCH] numpy and memory note
---
README.md | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 50a519e..fc35bbb 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,
--
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