From 4c5907459c212e43577db67bacd029ade9b685dc Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Fri, 22 Apr 2022 16:23:56 +0200
Subject: [PATCH] [doc] fix doctests for ISV and JFA
---
doc/guide.rst | 52 ++++++++++++++-------------------------------------
1 file changed, 14 insertions(+), 38 deletions(-)
diff --git a/doc/guide.rst b/doc/guide.rst
index 537d6be..c445a32 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -329,33 +329,21 @@ The snippet bellow shows how to:
>>> data_class2 = np.random.normal(-0.2, 0.2, (10, 3))
>>> X = np.vstack((data_class1, data_class2))
>>> y = np.hstack((np.zeros(10, dtype=int), np.ones(10, dtype=int)))
- >>> # Training an UBM with 2 gaussians
- >>> ubm = bob.learn.em.GMMMachine(2).fit(X)
-
- >>> # The input the the ISV Training is the statistics of the GMM
- >>> # Here we are creating a GMMStats for each datapoints, which is NOT usual,
- >>> # but it is done for testing purposes
- >>> gmm_stats = [ubm.transform(x[np.newaxis]) for x in X]
-
- >>> # Finally doing the ISV training with U subspace with dimension of 2
- >>> isv_machine = bob.learn.em.ISVMachine(ubm, r_U=2).fit(gmm_stats, y)
- >>> print(isv_machine.U)
- [[-0.079 -0.011]
- [ 0.078 0.039]
- [ 0.129 0.018]
- [ 0.175 0.254]
- [ 0.019 0.027]
- [-0.132 -0.191]]
+ >>> # Create an ISV machine with a UBM of 2 gaussians
+ >>> isv_machine = bob.learn.em.ISVMachine(r_U=2, ubm_kwargs=dict(n_gaussians=2))
+ >>> _ = isv_machine.fit(X, y) # DOCTEST: +SKIP_
+ >>> isv_machine.U
+ array(...)
>>> # Enrolling a subject
>>> enroll_data = np.array([[1.2, 0.1, 1.4], [0.5, 0.2, 0.3]])
- >>> model = isv_machine.enroll_with_array(enroll_data)
+ >>> model = isv_machine.enroll(enroll_data)
>>> print(model)
[[ 0.54 0.246 0.505 1.617 -0.791 0.746]]
>>> # Probing
>>> probe_data = np.array([[1.2, 0.1, 1.4], [0.5, 0.2, 0.3]])
- >>> score = isv_machine.score_with_array(model, probe_data)
+ >>> score = isv_machine.score(model, probe_data)
>>> print(score)
[2.754]
@@ -404,31 +392,19 @@ such session variability model.
>>> data_class2 = np.random.normal(-0.2, 0.2, (10, 3))
>>> X = np.vstack((data_class1, data_class2))
>>> y = np.hstack((np.zeros(10, dtype=int), np.ones(10, dtype=int)))
- >>> # Training an UBM with 2 gaussians
- >>> ubm = bob.learn.em.GMMMachine(2).fit(X)
-
- >>> # The input the the JFA Training is the statistics of the GMM
- >>> # Here we are creating a GMMStats for each datapoints, which is NOT usual,
- >>> # but it is done for testing purposes
- >>> gmm_stats = [ubm.transform(x[np.newaxis]) for x in X]
-
- >>> # Finally doing the JFA training with U and V subspaces with dimension of 2
- >>> jfa_machine = bob.learn.em.JFAMachine(ubm, r_U=2, r_V=2).fit(gmm_stats, y)
- >>> print(jfa_machine.U)
- [[-0.069 -0.029]
- [ 0.079 0.039]
- [ 0.123 0.042]
- [ 0.17 0.255]
- [ 0.018 0.027]
- [-0.128 -0.192]]
+ >>> # Create a JFA machine with a UBM of 2 gaussians
+ >>> jfa_machine = bob.learn.em.JFAMachine(r_U=2, r_V=2, ubm_kwargs=dict(n_gaussians=2))
+ >>> _ = jfa_machine.fit(X, y)
+ >>> jfa_machine.U
+ array(...)
>>> enroll_data = np.array([[1.2, 0.1, 1.4], [0.5, 0.2, 0.3]])
- >>> model = jfa_machine.enroll_with_array(enroll_data)
+ >>> model = jfa_machine.enroll(enroll_data)
>>> print(model)
(array([0.634, 0.165]), array([ 0., 0., 0., 0., -0., 0.]))
>>> probe_data = np.array([[1.2, 0.1, 1.4], [0.5, 0.2, 0.3]])
- >>> score = jfa_machine.score_with_array(model, probe_data)
+ >>> score = jfa_machine.score(model, probe_data)
>>> print(score)
[0.471]
--
GitLab