From 90d7585c12c0ef45b104be2573ae8bda0c267786 Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Fri, 22 Apr 2022 13:58:25 +0200
Subject: [PATCH] [doc] fix JFA plot script
---
doc/plot/plot_JFA.py | 43 ++-----------------------------------------
1 file changed, 2 insertions(+), 41 deletions(-)
diff --git a/doc/plot/plot_JFA.py b/doc/plot/plot_JFA.py
index 6cfa4bc..a56ab32 100644
--- a/doc/plot/plot_JFA.py
+++ b/doc/plot/plot_JFA.py
@@ -8,37 +8,6 @@ import bob.learn.em
np.random.seed(2) # FIXING A SEED
-def isv_train(features, ubm):
- """
- Train U matrix
-
- **Parameters**
- features: List of :py:class:`bob.learn.em.GMMStats` organized by class
-
- n_gaussians: UBM (:py:class:`bob.learn.em.GMMMachine`)
-
- """
-
- stats = []
- for user in features:
- user_stats = []
- for f in user:
- s = bob.learn.em.GMMStats(ubm.shape[0], ubm.shape[1])
- ubm.transform(f, s)
- user_stats.append(s)
- stats.append(user_stats)
-
- relevance_factor = 4
- subspace_dimension_of_u = 1
-
- isvbase = bob.learn.em.ISVBase(ubm, subspace_dimension_of_u)
- trainer = bob.learn.em.ISVTrainer(relevance_factor)
- # trainer.rng = bob.core.random.mt19937(int(self.init_seed))
- bob.learn.em.train(trainer, isvbase, stats, max_iterations=50)
-
- return isvbase
-
-
# GENERATING DATA
iris_data = load_iris()
X = np.column_stack((iris_data.data[:, 0], iris_data.data[:, 3]))
@@ -73,10 +42,8 @@ ubm.variances = np.array(
)
ubm.weights = np.array([0.36, 0.36, 0.28])
-# .fit(X)
-gmm_stats = [ubm.transform(x[np.newaxis]) for x in X]
-jfa_machine = bob.learn.em.JFAMachine(ubm, r_U, r_V, em_iterations=50)
+jfa_machine = bob.learn.em.JFAMachine(r_U, r_V, ubm=ubm, em_iterations=50)
# Initializing with old bob initialization
jfa_machine.U = np.array(
@@ -89,13 +56,7 @@ jfa_machine.Y = np.array(
jfa_machine.D = np.array(
[0.732467, 0.281321, 0.543212, -0.512974, 1.04108, 0.835224]
)
-jfa_machine = jfa_machine.fit(gmm_stats, y)
-
-
-# .fit(gmm_stats, y)
-
-# gmm_stats = [ubm.transform(x) for x in [setosa, versicolor, virginica]]
-# jfa_machine = bob.learn.em.JFAMachine(ubm, r_U, r_V).fit(gmm_stats, [0, 1, 2])
+jfa_machine = jfa_machine.fit(X, y)
# Variability direction U
--
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