From cb60405ab2e5762d043206387809ec6e0e8af62d Mon Sep 17 00:00:00 2001
From: linlin <jajupmochi@gmail.com>
Date: Mon, 5 Oct 2020 16:38:23 +0200
Subject: [PATCH] New translations compute_distance_in_kernel_space.py (French)

---
 .../kernels/compute_distance_in_kernel_space.py    | 73 ++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 lang/fr/gklearn/examples/kernels/compute_distance_in_kernel_space.py

diff --git a/lang/fr/gklearn/examples/kernels/compute_distance_in_kernel_space.py b/lang/fr/gklearn/examples/kernels/compute_distance_in_kernel_space.py
new file mode 100644
index 0000000..76c7494
--- /dev/null
+++ b/lang/fr/gklearn/examples/kernels/compute_distance_in_kernel_space.py
@@ -0,0 +1,73 @@
+# -*- coding: utf-8 -*-
+"""compute_distance_in_kernel_space.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/17tZP6IrineQmzo9sRtfZOnHpHx6HnlMA
+
+**This script demonstrates how to compute distance in kernel space between the image of a graph and the mean of images of a group of graphs.**
+---
+
+**0.   Install `graphkit-learn`.**
+"""
+
+"""**1.   Get dataset.**"""
+
+from gklearn.utils import Dataset
+
+# Predefined dataset name, use dataset "MUTAG".
+ds_name = 'MUTAG'
+
+# Initialize a Dataset.
+dataset = Dataset()
+# Load predefined dataset "MUTAG".
+dataset.load_predefined_dataset(ds_name)
+len(dataset.graphs)
+
+"""**2.  Compute graph kernel.**"""
+
+from gklearn.kernels import PathUpToH
+import multiprocessing
+
+# Initailize parameters for graph kernel computation.
+kernel_options = {'depth': 3,
+				  'k_func': 'MinMax',
+				  'compute_method': 'trie'
+				  }
+
+# Initialize graph kernel.
+graph_kernel = PathUpToH(node_labels=dataset.node_labels, # list of node label names.
+						 edge_labels=dataset.edge_labels, # list of edge label names.
+						 ds_infos=dataset.get_dataset_infos(keys=['directed']), # dataset information required for computation.
+						 **kernel_options, # options for computation.
+						 )
+
+# Compute Gram matrix.
+gram_matrix, run_time = graph_kernel.compute(dataset.graphs,
+											 parallel='imap_unordered', # or None.
+											 n_jobs=multiprocessing.cpu_count(), # number of parallel jobs.
+											 normalize=True, # whether to return normalized Gram matrix.
+											 verbose=2 # whether to print out results.
+                                            )
+
+"""**3.   Compute distance in kernel space.**
+
+Given a dataset $\mathcal{G}_N$, compute the distance in kernel space between the image of $G_1 \in \mathcal{G}_N$ and the mean of images of $\mathcal{G}_k \subset \mathcal{G}_N$.
+"""
+
+from gklearn.preimage.utils import compute_k_dis
+
+# Index of $G_1$.
+idx_1 = 10
+# Indices of graphs in $\mathcal{G}_k$.
+idx_graphs = range(0, 10)
+
+# Compute the distance in kernel space.
+dis_k = compute_k_dis(idx_1,
+                      idx_graphs,
+					  [1 / len(idx_graphs)] * len(idx_graphs), # weights for images of graphs in $\mathcal{G}_k$; all equal when computing the mean.
+					  gram_matrix, # gram matrix of al graphs.
+					  withterm3=False
+					  )
+print(dis_k)
\ No newline at end of file