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Commit 74fcd5c5 authored by Yannick DAYER's avatar Yannick DAYER :face_in_clouds:
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Remove GMM and Gaussian tests

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2 merge requests!43Remove C++ code, tests, dependencies, and build scripts,!40Transition to a pure python implementation
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bob/learn/em/__init__.py
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import bob.extension
from . import cluster
from . import mixture
def get_config():
......
bob/learn/em/test/test_gaussian.py deleted 100644 → 0
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#!/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)
bob/learn/em/test/test_gmm.py deleted 100644 → 0
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#!/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)
bob/learn/em/test/test_kmeans.py
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......@@ -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)
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#!/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
bob/learn/em/test/test_picklability.py
+ 1
− 38
View file @ 74fcd5c5
......@@ -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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