From 74fcd5c54635a33b07ee751fdc51469183db631f Mon Sep 17 00:00:00 2001
From: Yannick DAYER <yannick.dayer@idiap.ch>
Date: Fri, 26 Nov 2021 16:56:15 +0100
Subject: [PATCH] Remove GMM and Gaussian tests
---
bob/learn/em/__init__.py | 1 -
bob/learn/em/test/test_gaussian.py | 114 ----------
bob/learn/em/test/test_gmm.py | 274 ------------------------
bob/learn/em/test/test_kmeans.py | 14 +-
bob/learn/em/test/test_linearscoring.py | 253 ----------------------
bob/learn/em/test/test_picklability.py | 39 +---
6 files changed, 11 insertions(+), 684 deletions(-)
delete mode 100644 bob/learn/em/test/test_gaussian.py
delete mode 100644 bob/learn/em/test/test_gmm.py
delete mode 100644 bob/learn/em/test/test_linearscoring.py
diff --git a/bob/learn/em/__init__.py b/bob/learn/em/__init__.py
index 472a7d9..212c622 100644
--- a/bob/learn/em/__init__.py
+++ b/bob/learn/em/__init__.py
@@ -1,7 +1,6 @@
import bob.extension
from . import cluster
-from . import mixture
def get_config():
diff --git a/bob/learn/em/test/test_gaussian.py b/bob/learn/em/test/test_gaussian.py
deleted file mode 100644
index db01890..0000000
--- a/bob/learn/em/test/test_gaussian.py
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
-# Thu Feb 16 16:54:45 2012 +0200
-#
-# Copyright (C) 2011-2013 Idiap Research Institute, Martigny, Switzerland
-
-"""Tests the Gaussian machine
-"""
-
-import os
-import tempfile
-
-import numpy
-
-import bob.io.base
-from bob.learn.em import Gaussian
-
-
-def equals(x, y, epsilon):
- return abs(x - y) < epsilon
-
-
-def test_GaussianNormal():
- # Test the likelihood computation of a simple normal Gaussian
- gaussian = Gaussian(2)
- # By default, initialized with zero mean and unit variance
- logLH = gaussian.log_likelihood(numpy.array([0.4, 0.2], "float64"))
- assert equals(logLH, -1.93787706641, 1e-10)
-
-
-def test_GaussianMachine():
- # Test a GaussianMachine more thoroughly
-
- # Initializes a Gaussian with zero mean and unit variance
- g = Gaussian(3)
- assert (g.mean == 0.0).all()
- assert (g.variance == 1.0).all()
- assert g.shape == (3,)
-
- # Set and check mean, variance, variance thresholds
- mean = numpy.array([0, 1, 2], "float64")
- variance = numpy.array([3, 2, 1], "float64")
- g.mean = mean
- g.variance = variance
- g.set_variance_thresholds(0.0005)
- assert (g.mean == mean).all()
- assert (g.variance == variance).all()
- assert (g.variance_thresholds == 0.0005).all()
-
- # Save and read from file
- filename = str(tempfile.mkstemp(".hdf5")[1])
- g.save(bob.io.base.HDF5File(filename, "w"))
- g_loaded = Gaussian(bob.io.base.HDF5File(filename))
- assert g == g_loaded
- assert (g != g_loaded) is False
- assert g.is_similar_to(g_loaded)
-
- # Save and read from file using the keyword argument
- filename = str(tempfile.mkstemp(".hdf5")[1])
- g.save(hdf5=bob.io.base.HDF5File(filename, "w"))
- g_loaded = Gaussian(hdf5=bob.io.base.HDF5File(filename))
- assert g == g_loaded
- assert (g != g_loaded) is False
- assert g.is_similar_to(g_loaded)
-
- # Save and loading from file using the keyword argument
- filename = str(tempfile.mkstemp(".hdf5")[1])
- g.save(bob.io.base.HDF5File(filename, "w"))
- g_loaded = bob.learn.em.Gaussian()
- g_loaded.load(bob.io.base.HDF5File(filename))
- assert g == g_loaded
- assert (g != g_loaded) is False
- assert g.is_similar_to(g_loaded)
-
- # Save and loading from file using the keyword argument
- filename = str(tempfile.mkstemp(".hdf5")[1])
- g.save(bob.io.base.HDF5File(filename, "w"))
- g_loaded = bob.learn.em.Gaussian()
- g_loaded.load(hdf5=bob.io.base.HDF5File(filename))
- assert g == g_loaded
- assert (g != g_loaded) is False
- assert g.is_similar_to(g_loaded)
-
- # Make them different
- g_loaded.set_variance_thresholds(0.001)
- assert (g == g_loaded) is False
- assert g != g_loaded
-
- # Check likelihood computation
- sample1 = numpy.array([0, 1, 2], "float64")
- sample2 = numpy.array([1, 2, 3], "float64")
- sample3 = numpy.array([2, 3, 4], "float64")
- ref1 = -3.652695334228046
- ref2 = -4.569362000894712
- ref3 = -7.319362000894712
- eps = 1e-10
- assert equals(g.log_likelihood(sample1), ref1, eps)
- assert equals(g.log_likelihood(sample2), ref2, eps)
- assert equals(g.log_likelihood(sample3), ref3, eps)
-
- # Check resize and assignment
- g.resize(5)
- assert g.shape == (5,)
- g2 = Gaussian()
- g2 = g
- assert g == g2
- assert (g != g2) is False
- g3 = Gaussian(g)
- assert g == g3
- assert (g != g3) is False
-
- # Clean-up
- os.unlink(filename)
diff --git a/bob/learn/em/test/test_gmm.py b/bob/learn/em/test/test_gmm.py
deleted file mode 100644
index 0d2bd2f..0000000
--- a/bob/learn/em/test/test_gmm.py
+++ /dev/null
@@ -1,274 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
-# Thu Feb 16 17:57:10 2012 +0200
-#
-# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
-
-"""Tests the GMM machine and the GMMStats container
-"""
-
-import os
-import tempfile
-
-import numpy
-
-import bob.io.base
-from bob.io.base.test_utils import datafile
-from bob.learn.em import GMMMachine
-from bob.learn.em import GMMStats
-
-
-def test_GMMStats():
- # Test a GMMStats
- # Initializes a GMMStats
- gs = GMMStats(2, 3)
- log_likelihood = -3.0
- T = 57
- n = numpy.array([4.37, 5.31], "float64")
- sumpx = numpy.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
- sumpxx = numpy.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
- gs.log_likelihood = log_likelihood
- gs.t = T
- gs.n = n
- gs.sum_px = sumpx
- gs.sum_pxx = sumpxx
- assert gs.log_likelihood == log_likelihood
- assert gs.t == T
- assert (gs.n == n).all()
- assert (gs.sum_px == sumpx).all()
- assert (gs.sum_pxx == sumpxx).all()
- assert gs.shape == (2, 3)
-
- # Saves and reads from file
- filename = str(tempfile.mkstemp(".hdf5")[1])
- gs.save(bob.io.base.HDF5File(filename, "w"))
- gs_loaded = GMMStats(bob.io.base.HDF5File(filename))
- assert gs == gs_loaded
- assert (gs != gs_loaded) is False
- assert gs.is_similar_to(gs_loaded)
-
- # Saves and reads from file using the keyword argument
- filename = str(tempfile.mkstemp(".hdf5")[1])
- gs.save(hdf5=bob.io.base.HDF5File(filename, "w"))
- gs_loaded = GMMStats(bob.io.base.HDF5File(filename))
- assert gs == gs_loaded
- assert (gs != gs_loaded) is False
- assert gs.is_similar_to(gs_loaded)
-
- # Saves and load from file using the keyword argument
- filename = str(tempfile.mkstemp(".hdf5")[1])
- gs.save(hdf5=bob.io.base.HDF5File(filename, "w"))
- gs_loaded = GMMStats()
- gs_loaded.load(bob.io.base.HDF5File(filename))
- assert gs == gs_loaded
- assert (gs != gs_loaded) is False
- assert gs.is_similar_to(gs_loaded)
-
- # Saves and load from file using the keyword argument
- filename = str(tempfile.mkstemp(".hdf5")[1])
- gs.save(hdf5=bob.io.base.HDF5File(filename, "w"))
- gs_loaded = GMMStats()
- gs_loaded.load(hdf5=bob.io.base.HDF5File(filename))
- assert gs == gs_loaded
- assert (gs != gs_loaded) is False
- assert gs.is_similar_to(gs_loaded)
-
- # Makes them different
- gs_loaded.t = 58
- assert (gs == gs_loaded) is False
- assert gs != gs_loaded
- assert (gs.is_similar_to(gs_loaded)) is False
- # Accumulates from another GMMStats
- gs2 = GMMStats(2, 3)
- gs2.log_likelihood = log_likelihood
- gs2.t = T
- gs2.n = n
- gs2.sum_px = sumpx
- gs2.sum_pxx = sumpxx
- gs2 += gs
- eps = 1e-8
- assert gs2.log_likelihood == 2 * log_likelihood
- assert gs2.t == 2 * T
- assert numpy.allclose(gs2.n, 2 * n, eps)
- assert numpy.allclose(gs2.sum_px, 2 * sumpx, eps)
- assert numpy.allclose(gs2.sum_pxx, 2 * sumpxx, eps)
-
- # Reinit and checks for zeros
- gs_loaded.init()
- assert gs_loaded.log_likelihood == 0
- assert gs_loaded.t == 0
- assert (gs_loaded.n == 0).all()
- assert (gs_loaded.sum_px == 0).all()
- assert (gs_loaded.sum_pxx == 0).all()
- # Resize and checks size
- assert gs_loaded.shape == (2, 3)
- gs_loaded.resize(4, 5)
- assert gs_loaded.shape == (4, 5)
- assert gs_loaded.sum_px.shape[0] == 4
- assert gs_loaded.sum_px.shape[1] == 5
-
- # Clean-up
- os.unlink(filename)
-
-
-def test_GMMMachine_1():
- # Test a GMMMachine basic features
-
- weights = numpy.array([0.5, 0.5], "float64")
- weights2 = numpy.array([0.6, 0.4], "float64")
- means = numpy.array([[3, 70, 0], [4, 72, 0]], "float64")
- means2 = numpy.array([[3, 7, 0], [4, 72, 0]], "float64")
- variances = numpy.array([[1, 10, 1], [2, 5, 2]], "float64")
- variances2 = numpy.array([[10, 10, 1], [2, 5, 2]], "float64")
- varianceThresholds = numpy.array([[0, 0, 0], [0, 0, 0]], "float64")
- varianceThresholds2 = numpy.array([[0.0005, 0.0005, 0.0005], [0, 0, 0]], "float64")
-
- # Initializes a GMMMachine
- gmm = GMMMachine(2, 3)
- # Sets the weights, means, variances and varianceThresholds and
- # Checks correctness
- gmm.weights = weights
- gmm.means = means
- gmm.variances = variances
- gmm.variance_thresholds = varianceThresholds
- assert gmm.shape == (2, 3)
- assert (gmm.weights == weights).all()
- assert (gmm.means == means).all()
- assert (gmm.variances == variances).all()
- assert (gmm.variance_thresholds == varianceThresholds).all()
-
- # Checks supervector-like accesses
- assert (gmm.mean_supervector == means.reshape(means.size)).all()
- assert (gmm.variance_supervector == variances.reshape(variances.size)).all()
- newMeans = numpy.array([[3, 70, 2], [4, 72, 2]], "float64")
- newVariances = numpy.array([[1, 1, 1], [2, 2, 2]], "float64")
-
- # Checks particular varianceThresholds-related methods
- varianceThresholds1D = numpy.array([0.3, 1, 0.5], "float64")
- gmm.set_variance_thresholds(varianceThresholds1D)
- assert (gmm.variance_thresholds[0, :] == varianceThresholds1D).all()
- assert (gmm.variance_thresholds[1, :] == varianceThresholds1D).all()
-
- gmm.set_variance_thresholds(0.005)
- assert (gmm.variance_thresholds == 0.005).all()
-
- # Checks Gaussians access
- gmm.means = newMeans
- gmm.variances = newVariances
- assert (gmm.get_gaussian(0).mean == newMeans[0, :]).all()
- assert (gmm.get_gaussian(1).mean == newMeans[1, :]).all()
- assert (gmm.get_gaussian(0).variance == newVariances[0, :]).all()
- assert (gmm.get_gaussian(1).variance == newVariances[1, :]).all()
-
- # Checks resize
- gmm.resize(4, 5)
- assert gmm.shape == (4, 5)
-
- # Checks comparison
- gmm2 = GMMMachine(gmm)
- gmm3 = GMMMachine(2, 3)
- gmm3.weights = weights2
- gmm3.means = means
- gmm3.variances = variances
- # gmm3.varianceThresholds = varianceThresholds
- gmm4 = GMMMachine(2, 3)
- gmm4.weights = weights
- gmm4.means = means2
- gmm4.variances = variances
- # gmm4.varianceThresholds = varianceThresholds
- gmm5 = GMMMachine(2, 3)
- gmm5.weights = weights
- gmm5.means = means
- gmm5.variances = variances2
- # gmm5.varianceThresholds = varianceThresholds
- gmm6 = GMMMachine(2, 3)
- gmm6.weights = weights
- gmm6.means = means
- gmm6.variances = variances
- # gmm6.varianceThresholds = varianceThresholds2
-
- assert gmm == gmm2
- assert (gmm != gmm2) is False
- assert gmm.is_similar_to(gmm2)
- assert gmm != gmm3
- assert (gmm == gmm3) is False
- assert gmm.is_similar_to(gmm3) is False
- assert gmm != gmm4
- assert (gmm == gmm4) is False
- assert gmm.is_similar_to(gmm4) is False
- assert gmm != gmm5
- assert (gmm == gmm5) is False
- assert gmm.is_similar_to(gmm5) is False
- assert gmm != gmm6
- assert (gmm == gmm6) is False
- assert gmm.is_similar_to(gmm6) is False
-
-
-def test_GMMMachine_2():
- # Test a GMMMachine (statistics)
-
- arrayset = bob.io.base.load(
- datafile("faithful.torch3_f64.hdf5", __name__, path="../data/")
- )
- gmm = GMMMachine(2, 2)
- gmm.weights = numpy.array([0.5, 0.5], "float64")
- gmm.means = numpy.array([[3, 70], [4, 72]], "float64")
- gmm.variances = numpy.array([[1, 10], [2, 5]], "float64")
- gmm.variance_thresholds = numpy.array([[0, 0], [0, 0]], "float64")
-
- stats = GMMStats(2, 2)
- gmm.acc_statistics(arrayset, stats)
-
- stats_ref = GMMStats(
- bob.io.base.HDF5File(datafile("stats.hdf5", __name__, path="../data/"))
- )
-
- assert stats.t == stats_ref.t
- assert numpy.allclose(stats.n, stats_ref.n, atol=1e-10)
- # assert numpy.array_equal(stats.sumPx, stats_ref.sumPx)
- # Note AA: precision error above
- assert numpy.allclose(stats.sum_px, stats_ref.sum_px, atol=1e-10)
- assert numpy.allclose(stats.sum_pxx, stats_ref.sum_pxx, atol=1e-10)
-
-
-def test_GMMMachine_3():
- # Test a GMMMachine (log-likelihood computation)
-
- data = bob.io.base.load(datafile("data.hdf5", __name__, path="../data/"))
- gmm = GMMMachine(2, 50)
- gmm.weights = bob.io.base.load(datafile("weights.hdf5", __name__, path="../data/"))
- gmm.means = bob.io.base.load(datafile("means.hdf5", __name__, path="../data/"))
- gmm.variances = bob.io.base.load(
- datafile("variances.hdf5", __name__, path="../data/")
- )
-
- # Compare the log-likelihood with the one obtained using Chris Matlab
- # implementation
- matlab_ll_ref = -2.361583051672024e02
- assert abs(gmm(data) - matlab_ll_ref) < 1e-10
-
-
-def test_GMMMachine_4():
-
- import numpy
-
- numpy.random.seed(3) # FIXING A SEED
-
- data = numpy.random.rand(
- 100, 50
- ) # Doesn't matter if it is ramdom. The average of 1D array (in python) MUST output the same result for the 2D array (in C++)
-
- gmm = GMMMachine(2, 50)
- gmm.weights = bob.io.base.load(datafile("weights.hdf5", __name__, path="../data/"))
- gmm.means = bob.io.base.load(datafile("means.hdf5", __name__, path="../data/"))
- gmm.variances = bob.io.base.load(
- datafile("variances.hdf5", __name__, path="../data/")
- )
-
- ll = 0
- for i in range(data.shape[0]):
- ll += gmm(data[i, :])
- ll /= data.shape[0]
-
- assert ll == gmm(data)
diff --git a/bob/learn/em/test/test_kmeans.py b/bob/learn/em/test/test_kmeans.py
index 2bbd1a3..428cc34 100644
--- a/bob/learn/em/test/test_kmeans.py
+++ b/bob/learn/em/test/test_kmeans.py
@@ -56,11 +56,13 @@ def test_kmeans_fit():
data2 = da.random.normal(loc=-1, size=(2000, 3))
data = da.concatenate([data1, data2], axis=0)
machine = KMeansMachine(2, random_state=0).fit(data)
+ centroids = np.array(machine.centroids_) # Silences `argsort not implemented`
+ centroids = centroids[np.argsort(centroids[:,0])]
expected = [
- [1.00426431, 1.00359693, 1.05996704],
[-0.99262315, -1.05226141, -1.00525245],
+ [1.00426431, 1.00359693, 1.05996704],
]
- np.testing.assert_almost_equal(machine.centroids_, expected)
+ np.testing.assert_almost_equal(centroids, expected)
def test_kmeans_fit_init_pp():
@@ -70,11 +72,13 @@ def test_kmeans_fit_init_pp():
data = da.concatenate([data1, data2], axis=0)
trainer = KMeansTrainer(init_method="k-means++", random_state=0)
machine = KMeansMachine(2).fit(data, trainer=trainer)
+ centroids = np.array(machine.centroids_) # Silences `argsort not implemented`
+ centroids = centroids[np.argsort(centroids[:,0])]
expected = [
[-0.99262315, -1.05226141, -1.00525245],
[1.00426431, 1.00359693, 1.05996704],
]
- np.testing.assert_almost_equal(machine.centroids_, expected)
+ np.testing.assert_almost_equal(centroids, expected, decimal=7)
def test_kmeans_fit_init_random():
@@ -84,8 +88,10 @@ def test_kmeans_fit_init_random():
data = da.concatenate([data1, data2], axis=0)
trainer = KMeansTrainer(init_method="random", random_state=0)
machine = KMeansMachine(2).fit(data, trainer=trainer)
+ centroids = np.array(machine.centroids_) # Silences `argsort not implemented`
+ centroids = centroids[np.argsort(centroids[:,0])]
expected = [
[-0.99433738, -1.05561588, -1.01236246],
[0.99800688, 0.99873325, 1.05879539],
]
- np.testing.assert_almost_equal(machine.centroids_, expected)
+ np.testing.assert_almost_equal(centroids, expected, decimal=7)
diff --git a/bob/learn/em/test/test_linearscoring.py b/bob/learn/em/test/test_linearscoring.py
deleted file mode 100644
index eb870ad..0000000
--- a/bob/learn/em/test/test_linearscoring.py
+++ /dev/null
@@ -1,253 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Francois Moulin <Francois.Moulin@idiap.ch>
-# Wed Jul 13 16:00:04 2011 +0200
-#
-# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
-
-"""Tests on the LinearScoring function
-"""
-
-import numpy
-
-from bob.learn.em import GMMMachine
-from bob.learn.em import GMMStats
-from bob.learn.em import linear_scoring
-
-
-def test_LinearScoring():
-
- ubm = GMMMachine(2, 2)
- ubm.weights = numpy.array([0.5, 0.5], "float64")
- ubm.means = numpy.array([[3, 70], [4, 72]], "float64")
- ubm.variances = numpy.array([[1, 10], [2, 5]], "float64")
- ubm.variance_thresholds = numpy.array([[0, 0], [0, 0]], "float64")
-
- model1 = GMMMachine(2, 2)
- model1.weights = numpy.array([0.5, 0.5], "float64")
- model1.means = numpy.array([[1, 2], [3, 4]], "float64")
- model1.variances = numpy.array([[9, 10], [11, 12]], "float64")
- model1.variance_thresholds = numpy.array([[0, 0], [0, 0]], "float64")
-
- model2 = GMMMachine(2, 2)
- model2.weights = numpy.array([0.5, 0.5], "float64")
- model2.means = numpy.array([[5, 6], [7, 8]], "float64")
- model2.variances = numpy.array([[13, 14], [15, 16]], "float64")
- model2.variance_thresholds = numpy.array([[0, 0], [0, 0]], "float64")
-
- stats1 = GMMStats(2, 2)
- stats1.sum_px = numpy.array([[1, 2], [3, 4]], "float64")
- stats1.n = numpy.array([1, 2], "float64")
- stats1.t = 1 + 2
-
- stats2 = GMMStats(2, 2)
- stats2.sum_px = numpy.array([[5, 6], [7, 8]], "float64")
- stats2.n = numpy.array([3, 4], "float64")
- stats2.t = 3 + 4
-
- stats3 = GMMStats(2, 2)
- stats3.sum_px = numpy.array([[5, 6], [7, 3]], "float64")
- stats3.n = numpy.array([3, 4], "float64")
- stats3.t = 3 + 4
-
- test_channeloffset = [
- numpy.array([9, 8, 7, 6], "float64"),
- numpy.array([5, 4, 3, 2], "float64"),
- numpy.array([1, 0, 1, 2], "float64"),
- ]
-
- # Reference scores (from Idiap internal matlab implementation)
- ref_scores_00 = numpy.array(
- [[2372.9, 5207.7, 5275.7], [2215.7, 4868.1, 4932.1]], "float64"
- )
- ref_scores_01 = numpy.array(
- [
- [790.9666666666667, 743.9571428571428, 753.6714285714285],
- [738.5666666666667, 695.4428571428572, 704.5857142857144],
- ],
- "float64",
- )
- ref_scores_10 = numpy.array(
- [[2615.5, 5434.1, 5392.5], [2381.5, 4999.3, 5022.5]], "float64"
- )
- ref_scores_11 = numpy.array(
- [
- [871.8333333333332, 776.3000000000001, 770.3571428571427],
- [793.8333333333333, 714.1857142857143, 717.5000000000000],
- ],
- "float64",
- )
-
- # 1/ Use GMMMachines
- # 1/a/ Without test_channelOffset, without frame-length normalisation
- scores = linear_scoring([model1, model2], ubm, [stats1, stats2, stats3])
- assert (abs(scores - ref_scores_00) < 1e-7).all()
-
- # 1/b/ Without test_channelOffset, with frame-length normalisation
- scores = linear_scoring([model1, model2], ubm, [stats1, stats2, stats3], [], True)
- assert (abs(scores - ref_scores_01) < 1e-7).all()
- # scores = linear_scoring([model1, model2], ubm, [stats1, stats2, stats3], (), True)
- # assert (abs(scores - ref_scores_01) < 1e-7).all()
- # scores = linear_scoring([model1, model2], ubm, [stats1, stats2, stats3], None, True)
- # assert (abs(scores - ref_scores_01) < 1e-7).all()
-
- # 1/c/ With test_channelOffset, without frame-length normalisation
- scores = linear_scoring(
- [model1, model2], ubm, [stats1, stats2, stats3], test_channeloffset
- )
- assert (abs(scores - ref_scores_10) < 1e-7).all()
-
- # 1/d/ With test_channelOffset, with frame-length normalisation
- scores = linear_scoring(
- [model1, model2], ubm, [stats1, stats2, stats3], test_channeloffset, True
- )
- assert (abs(scores - ref_scores_11) < 1e-7).all()
-
- # 2/ Use mean/variance supervectors
- # 2/a/ Without test_channelOffset, without frame-length normalisation
- scores = linear_scoring(
- [model1.mean_supervector, model2.mean_supervector],
- ubm.mean_supervector,
- ubm.variance_supervector,
- [stats1, stats2, stats3],
- )
- assert (abs(scores - ref_scores_00) < 1e-7).all()
-
- # 2/b/ Without test_channelOffset, with frame-length normalisation
- scores = linear_scoring(
- [model1.mean_supervector, model2.mean_supervector],
- ubm.mean_supervector,
- ubm.variance_supervector,
- [stats1, stats2, stats3],
- [],
- True,
- )
- assert (abs(scores - ref_scores_01) < 1e-7).all()
-
- # 2/c/ With test_channelOffset, without frame-length normalisation
- scores = linear_scoring(
- [model1.mean_supervector, model2.mean_supervector],
- ubm.mean_supervector,
- ubm.variance_supervector,
- [stats1, stats2, stats3],
- test_channeloffset,
- )
- assert (abs(scores - ref_scores_10) < 1e-7).all()
-
- # 2/d/ With test_channelOffset, with frame-length normalisation
- scores = linear_scoring(
- [model1.mean_supervector, model2.mean_supervector],
- ubm.mean_supervector,
- ubm.variance_supervector,
- [stats1, stats2, stats3],
- test_channeloffset,
- True,
- )
- assert (abs(scores - ref_scores_11) < 1e-7).all()
-
- # 3/ Using single model/sample
- # 3/a/ without frame-length normalisation
- score = linear_scoring(
- model1.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats1,
- test_channeloffset[0],
- )
- assert abs(score - ref_scores_10[0, 0]) < 1e-7
- score = linear_scoring(
- model1.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats2,
- test_channeloffset[1],
- )
- assert abs(score - ref_scores_10[0, 1]) < 1e-7
- score = linear_scoring(
- model1.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats3,
- test_channeloffset[2],
- )
- assert abs(score - ref_scores_10[0, 2]) < 1e-7
- score = linear_scoring(
- model2.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats1,
- test_channeloffset[0],
- )
- assert abs(score - ref_scores_10[1, 0]) < 1e-7
- score = linear_scoring(
- model2.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats2,
- test_channeloffset[1],
- )
- assert abs(score - ref_scores_10[1, 1]) < 1e-7
- score = linear_scoring(
- model2.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats3,
- test_channeloffset[2],
- )
- assert abs(score - ref_scores_10[1, 2]) < 1e-7
-
- # 3/b/ without frame-length normalisation
- score = linear_scoring(
- model1.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats1,
- test_channeloffset[0],
- True,
- )
- assert abs(score - ref_scores_11[0, 0]) < 1e-7
- score = linear_scoring(
- model1.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats2,
- test_channeloffset[1],
- True,
- )
- assert abs(score - ref_scores_11[0, 1]) < 1e-7
- score = linear_scoring(
- model1.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats3,
- test_channeloffset[2],
- True,
- )
- assert abs(score - ref_scores_11[0, 2]) < 1e-7
- score = linear_scoring(
- model2.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats1,
- test_channeloffset[0],
- True,
- )
- assert abs(score - ref_scores_11[1, 0]) < 1e-7
- score = linear_scoring(
- model2.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats2,
- test_channeloffset[1],
- True,
- )
- assert abs(score - ref_scores_11[1, 1]) < 1e-7
- score = linear_scoring(
- model2.mean_supervector,
- ubm.mean_supervector,
- ubm.variance_supervector,
- stats3,
- test_channeloffset[2],
- True,
- )
- assert abs(score - ref_scores_11[1, 2]) < 1e-7
diff --git a/bob/learn/em/test/test_picklability.py b/bob/learn/em/test/test_picklability.py
index f792a96..f83c8c9 100644
--- a/bob/learn/em/test/test_picklability.py
+++ b/bob/learn/em/test/test_picklability.py
@@ -6,26 +6,7 @@ import pickle
import numpy
-from bob.learn.em import GMMMachine
-from bob.learn.em import GMMStats
-from bob.learn.em import KMeansMachine
-
-
-def test_gmm_machine():
- gmm_machine = GMMMachine(3, 3)
- gmm_machine.means = numpy.arange(9).reshape(3, 3).astype("float")
- gmm_machine_after_pickle = pickle.loads(pickle.dumps(gmm_machine))
-
- assert numpy.allclose(
- gmm_machine_after_pickle.means, gmm_machine_after_pickle.means, 10e-3
- )
- assert numpy.allclose(
- gmm_machine_after_pickle.variances, gmm_machine_after_pickle.variances, 10e-3
- )
- assert numpy.allclose(
- gmm_machine_after_pickle.weights, gmm_machine_after_pickle.weights, 10e-3
- )
-
+from bob.learn.em.cluster import KMeansMachine
def test_kmeans_machine():
# Test a KMeansMachine
@@ -41,21 +22,3 @@ def test_kmeans_machine():
assert numpy.allclose(
kmeans_machine_after_pickle.means, kmeans_machine.means, 10e-3
)
-
-
-def test_gmmstats():
-
- gs = GMMStats(2, 3)
- log_likelihood = -3.0
- T = 1
- n = numpy.array([0.4, 0.6], numpy.float64)
- sumpx = numpy.array([[1.0, 2.0, 3.0], [2.0, 4.0, 3.0]], numpy.float64)
- sumpxx = numpy.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], numpy.float64)
- gs.log_likelihood = log_likelihood
- gs.t = T
- gs.n = n
- gs.sum_px = sumpx
- gs.sum_pxx = sumpxx
-
- gs_after_pickle = pickle.loads(pickle.dumps(gs))
- assert gs == gs_after_pickle
--
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