From fef377f47494012c4156ba7fb63621c631523c24 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Wed, 30 Mar 2022 15:52:42 +0200
Subject: [PATCH] Added plotting mechanism

---
 doc/plot/plot_ISV.py | 4 ++++
 doc/plot/plot_JFA.py | 3 +++
 2 files changed, 7 insertions(+)

diff --git a/doc/plot/plot_ISV.py b/doc/plot/plot_ISV.py
index 91b9ff6..fc3f9c1 100644
--- a/doc/plot/plot_ISV.py
+++ b/doc/plot/plot_ISV.py
@@ -57,6 +57,10 @@ ubm = bob.learn.em.GMMMachine(n_gaussians).fit(X)
 gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
 isv_machine = bob.learn.em.ISVMachine(ubm, r_U).fit(gmm_stats, y)
 
+# gmm_stats = [ubm.acc_statistics(x) for x in [setosa, versicolor, virginica]]
+# isv_machine = bob.learn.em.ISVMachine(ubm, r_U).fit(gmm_stats, [0, 1, 2])
+
+
 # isvbase = isv_train([setosa, versicolor, virginica], ubm)
 
 # Variability direction
diff --git a/doc/plot/plot_JFA.py b/doc/plot/plot_JFA.py
index f77e677..cae3e31 100644
--- a/doc/plot/plot_JFA.py
+++ b/doc/plot/plot_JFA.py
@@ -58,6 +58,9 @@ ubm = bob.learn.em.GMMMachine(n_gaussians).fit(X)
 gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
 jfa_machine = bob.learn.em.JFAMachine(ubm, r_U, r_V).fit(gmm_stats, y)
 
+# gmm_stats = [ubm.acc_statistics(x) for x in [setosa, versicolor, virginica]]
+# jfa_machine = bob.learn.em.JFAMachine(ubm, r_U, r_V).fit(gmm_stats, [0, 1, 2])
+
 
 # Variability direction U
 u0 = jfa_machine.U[0:2, 0] / np.linalg.norm(jfa_machine.U[0:2, 0])
-- 
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