diff --git a/bob/learn/em/__init__.py b/bob/learn/em/__init__.py
index 99d4c77a77bedb426a1ff588e3bd2c7b24edd79e..472a7d9d350af332a54eb1ea7d6ade9d473c7a89 100644
--- a/bob/learn/em/__init__.py
+++ b/bob/learn/em/__init__.py
@@ -1,7 +1,8 @@
+import bob.extension
+
from . import cluster
from . import mixture
-import bob.extension
def get_config():
"""Returns a string containing the configuration information."""
@@ -10,4 +11,3 @@ def get_config():
# gets sphinx autodoc done right - don't remove it
__all__ = [_ for _ in dir() if not _.startswith("_")]
-
diff --git a/bob/learn/em/cluster/k_means.py b/bob/learn/em/cluster/k_means.py
index fcf105040abd93cd82e6c1b5e59ecbd5da179e93..98418b442881d04719e0c1a2083ec9950d97179e 100644
--- a/bob/learn/em/cluster/k_means.py
+++ b/bob/learn/em/cluster/k_means.py
@@ -3,11 +3,11 @@
# @date: Tue 27 Jul 2021 11:04:10 UTC+02
import logging
-from typing import Union
from typing import Tuple
+from typing import Union
-import numpy as np
import dask.array as da
+import numpy as np
from dask_ml.cluster.k_means import k_init
from sklearn.base import BaseEstimator
@@ -152,7 +152,9 @@ class KMeansMachine(BaseEstimator):
"""
if trainer is None:
logger.info("Using default k-means trainer.")
- trainer = KMeansTrainer(init_method="k-means||", random_state=self.random_state)
+ trainer = KMeansTrainer(
+ init_method="k-means||", random_state=self.random_state
+ )
logger.debug(f"Initializing trainer.")
trainer.initialize(
diff --git a/bob/learn/em/test/test_gaussian.py b/bob/learn/em/test/test_gaussian.py
index 0e64705d0c74d2445d7887e8b10461edaee1d14a..db018907bdefe6bdd6fefc43524020e167a0a7eb 100644
--- a/bob/learn/em/test/test_gaussian.py
+++ b/bob/learn/em/test/test_gaussian.py
@@ -9,104 +9,106 @@
"""
import os
-import numpy
import tempfile
-import bob.io.base
+import numpy
+import bob.io.base
from bob.learn.em import Gaussian
+
def equals(x, y, epsilon):
- return (abs(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)
+ # 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)
+ # 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
index 1733c6692104c6ab40b6e4df7f4ee8367b9fa80d..0d2bd2f6cf788972c8f7211306f707d319b7b001 100644
--- a/bob/learn/em/test/test_gmm.py
+++ b/bob/learn/em/test/test_gmm.py
@@ -9,255 +9,266 @@
"""
import os
-import numpy
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
-from bob.learn.em import GMMStats, GMMMachine
def test_GMMStats():
- # Test a GMMStats
- # Initializes a GMMStats
- gs = GMMStats(2,3)
- log_likelihood = -3.
- T = 57
- n = numpy.array([4.37, 5.31], 'float64')
- sumpx = numpy.array([[1., 2., 3.], [4., 5., 6.]], 'float64')
- sumpxx = numpy.array([[10., 20., 30.], [40., 50., 60.]], '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)
+ # 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
+ # 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)
+ # 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")
- 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 = GMMStats(2, 2)
- gmm.acc_statistics(arrayset, stats)
+ stats_ref = GMMStats(
+ bob.io.base.HDF5File(datafile("stats.hdf5", __name__, path="../data/"))
+ )
- 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)
- 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 = -2.361583051672024e+02
- assert abs(gmm(data) - matlab_ll_ref) < 1e-10
-
-
+ # 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)
-
-
+ 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 0cadba354a4e40f288c124b92f7d953830a189dd..2bbd1a361efc09213b5d3875714cd6fcda1894bd 100644
--- a/bob/learn/em/test/test_kmeans.py
+++ b/bob/learn/em/test/test_kmeans.py
@@ -8,13 +8,12 @@
"""Tests the KMeans machine
"""
+import dask.array as da
import numpy as np
from bob.learn.em.cluster import KMeansMachine
from bob.learn.em.cluster import KMeansTrainer
-import dask.array as da
-
def test_KMeansMachine():
# Test a KMeansMachine
@@ -57,7 +56,10 @@ 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)
- expected = [[1.00426431, 1.00359693, 1.05996704], [-0.99262315, -1.05226141, -1.00525245]]
+ expected = [
+ [1.00426431, 1.00359693, 1.05996704],
+ [-0.99262315, -1.05226141, -1.00525245],
+ ]
np.testing.assert_almost_equal(machine.centroids_, expected)
@@ -68,7 +70,10 @@ 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)
- expected = [[-0.99262315, -1.05226141, -1.00525245], [1.00426431, 1.00359693, 1.05996704]]
+ expected = [
+ [-0.99262315, -1.05226141, -1.00525245],
+ [1.00426431, 1.00359693, 1.05996704],
+ ]
np.testing.assert_almost_equal(machine.centroids_, expected)
@@ -79,5 +84,8 @@ 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)
- expected = [[-0.99433738, -1.05561588, -1.01236246], [0.99800688, 0.99873325, 1.05879539]]
+ expected = [
+ [-0.99433738, -1.05561588, -1.01236246],
+ [0.99800688, 0.99873325, 1.05879539],
+ ]
np.testing.assert_almost_equal(machine.centroids_, expected)
diff --git a/bob/learn/em/test/test_linearscoring.py b/bob/learn/em/test/test_linearscoring.py
index 9958e20bd2e91c09d7adc9fb655897ba9e749ff6..eb870adeee7f4cf2c8aeaba0e7d005dfa486bc6b 100644
--- a/bob/learn/em/test/test_linearscoring.py
+++ b/bob/learn/em/test/test_linearscoring.py
@@ -10,119 +10,244 @@
import numpy
-from bob.learn.em import GMMMachine, GMMStats, linear_scoring
+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
+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 033bfe1ac6410a96703b37c384ed9c6fb777f323..f792a961a9c28cb9b1aad9c46c11f471cc6115c4 100644
--- a/bob/learn/em/test/test_picklability.py
+++ b/bob/learn/em/test/test_picklability.py
@@ -2,12 +2,13 @@
# vim: set fileencoding=utf-8 :
# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-from bob.learn.em import GMMMachine
-from bob.learn.em import KMeansMachine
-from bob.learn.em import GMMStats
+import pickle
import numpy
-import pickle
+
+from bob.learn.em import GMMMachine
+from bob.learn.em import GMMStats
+from bob.learn.em import KMeansMachine
def test_gmm_machine():