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Commit 4f00edc8 authored by Quentin Gallouédec's avatar Quentin Gallouédec
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numpy and memory note

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......@@ -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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