Removing Gaussians class tests

bob/learn/em/mixture/linear_scoring.py

@@ -59,7 +59,7 @@ def linear_scoring(
     if models_means.ndim == 2:
         models_means = models_means[None, :, :]
 
-    if ubm.traier == "map":
+    if ubm.trainer == "map":
         ubm = ubm.ubm
 
     if isinstance(test_stats, GMMStats):

bob/learn/em/test/test_gaussian.py

-#!/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 class
-"""
-
-import tempfile
-
-import numpy as np
-
-from h5py import File as HDF5File
-
-from bob.learn.em.mixture import Gaussians
-
-
-def equals(x, y, epsilon):
-    return abs(x - y) < epsilon
-
-
-def test_GaussiansObject():
-  """Tests a Gaussians object creation and manipulation."""
-
-  # Initializes a Gaussians with zero mean and unit variance
-  g = Gaussians(means=np.zeros((3,)))
-  np.testing.assert_equal(g["means"], np.zeros((1,3)))
-  np.testing.assert_equal(g["variances"], np.ones((1,3)))
-  np.testing.assert_equal(g.shape, (1,3))
-
-  # Set and check mean, variance, variance thresholds
-  mean     = np.array([[0, 1, 2]], 'float64')
-  variance = np.array([[3, 2, 1]], 'float64')
-  g["means"]     = mean
-  g["variances"] = variance
-  g["variance_thresholds"] = 0.0005
-  np.testing.assert_equal(g["means"], mean)
-  np.testing.assert_equal(g["variances"], variance)
-  np.testing.assert_equal(g["variance_thresholds"], np.full((1, 3), 0.0005))
-
-  # Save and read from file
-  filename = str(tempfile.mkstemp(".hdf5")[1])
-  g.save(HDF5File(filename, 'w'))
-  g_loaded = Gaussians.from_hdf5(HDF5File(filename, "r"))
-  assert g == g_loaded
-  assert (g != g_loaded) is False
-  assert g.is_similar_to(g_loaded)
-
-  # Save and read from file using `from_hdf5`
-  filename = str(tempfile.mkstemp(".hdf5")[1])
-  g.save(hdf5=HDF5File(filename, 'w'))
-  g_loaded = Gaussians.from_hdf5(hdf5=HDF5File(filename, "r"))
-  assert g == g_loaded
-  assert (g != g_loaded) is False
-  assert g.is_similar_to(g_loaded)
-
-  # Save and load from file using `load` into an existing Gaussians
-  filename = str(tempfile.mkstemp(".hdf5")[1])
-  g.save(HDF5File(filename, 'w'))
-  g_loaded = Gaussians(np.zeros(shape=(3,)))  # Dummy fill, same size
-  g_loaded.load(HDF5File(filename, "r"))
-  assert g == g_loaded
-  assert (g != g_loaded) is False
-  assert g.is_similar_to(g_loaded)
-
-  # Make them different
-  g2 = g.copy()
-  g["variance_thresholds"] = 0.001
-  assert (g == g2) is False
-  assert g != g2
-  assert not g.is_similar_to(g_loaded)
-
-  # Check likelihood computation
-  sample1 = np.array([0, 1, 2], 'float64')
-  sample2 = np.array([1, 2, 3], 'float64')
-  sample3 = np.array([2, 3, 4], 'float64')
-  ref1 = -3.652695334228046
-  ref2 = -4.569362000894712
-  ref3 = -7.319362000894712
-  np.testing.assert_almost_equal(g.log_likelihood(sample1), ref1, decimal=10)
-  np.testing.assert_almost_equal(g.log_likelihood(sample2), ref2, decimal=10)
-  np.testing.assert_almost_equal(g.log_likelihood(sample3), ref3, decimal=10)
-
-
-def test_gaussian_variance_threshold():
-    # Creating a Gaussian
-    gaussian = Gaussians(means=[1, 2], variances=[1, 1], variance_thresholds=[1e-5, 1e-5])
-
-    # Testing variance threshold application
-    gaussian["variances"] = np.array([1e-8, 1e-4])
-    np.testing.assert_equal(gaussian["variances"], np.array([[1e-5, 1e-4]]))
-    gaussian["variance_thresholds"] = np.array([1e-7, 1e-3])
-    np.testing.assert_equal(gaussian["variances"], np.array([[1e-5, 1e-3]]))
-    gaussian["variances"] = np.array([0, 1e-8])
-    np.testing.assert_equal(gaussian["variances"], np.array([[1e-7, 1e-3]]))
-    new_gaussian = Gaussians(means=[0,0,0], variances=0, variance_thresholds=1e-5)
-    np.testing.assert_equal(new_gaussian["variances"], np.full((1,3), 1e-5))
-
-
-def test_likelihood():
-    """Tests the likelihood computation of a simple normal Gaussian."""
-    gaussian = Gaussians(means=[0, 0], variances=[1, 1], variance_thresholds=[1e-5, 1e-5])
-    log_likelihood = gaussian.log_likelihood(np.array([[0.4, 0.2]], "float64"))
-    np.testing.assert_almost_equal(log_likelihood, [[-1.93787706641]], decimal=10)
-
-    multi_log_likelihood = gaussian.log_likelihood(
-        np.array([[0.4, 0.2], [0.1, 0.3]], "float64")
-    )
-    expected = np.array([[-1.93787706641, -1.88787706640]])
-    np.testing.assert_almost_equal(multi_log_likelihood, expected, decimal=10)
-
-    # Default settings
-    gaussian_def = Gaussians(means=[1, 2, 3])
-    np.testing.assert_equal(gaussian_def["variances"], np.array([[1.0, 1.0, 1.0]]))
-    eps = np.finfo(float).eps
-    np.testing.assert_equal(
-        gaussian_def["variance_thresholds"], np.array([[eps, eps, eps]])
-    )
-    assert hasattr(gaussian, "log_likelihood")
-    log_likelihood = gaussian.log_likelihood(np.array([1.0, 2.0]))
-    np.testing.assert_almost_equal(log_likelihood, np.array([[-4.33787706641]]), decimal=10)
-
-
-def test_multiple_gaussian():
-    """Tests the capacity to store and work with multiple gaussians."""
-    gaussians = Gaussians(
-        means=np.array([[0, 0],[3, 3]])
-    )
-    expected_means = np.array([[0.0, 0.0], [3.0, 3.0]])
-    expected_variances = np.array([[1.0, 1.0], [1.0, 1.0]])
-    eps = np.finfo(float).eps
-    expected_var_thresholds = np.array([[eps, eps], [eps, eps]])
-    np.testing.assert_equal(gaussians["means"], expected_means)
-    np.testing.assert_equal(gaussians["variances"], expected_variances)
-    np.testing.assert_equal(gaussians["variance_thresholds"], expected_var_thresholds)
-    test_samples = np.array([[0.0, 0.0], [1.0, 2.0], [3.0, 3.0]])
-    log_likelihoods = gaussians.log_likelihood(test_samples)
-    expected_likelihoods = np.array([
-        [-1.8378770664, -4.3378770664, -10.8378770664],  # samples ll On gaussian 0
-        [-10.8378770664, -4.3378770664, -1.8378770664],  # samples ll On gaussian 1
-    ])
-    np.testing.assert_almost_equal(log_likelihoods, expected_likelihoods, decimal=10)
-
-    # Variances threshold application
-    gaussians["variance_thresholds"] = np.array([[1e-5, 1e-5], [1e-3, 1e-3]])
-    expected_variances = np.array([[1, 1], [1, 1]])
-    np.testing.assert_equal(gaussians["variances"], expected_variances)
-    gaussians["variances"] = np.array([[1e-8, 1e-4], [1e-2, 1e-8]])
-    expected_variances = np.array([[1e-5, 1e-4], [1e-2, 1e-3]])
-    np.testing.assert_equal(gaussians["variances"], expected_variances)
-    gaussians["variance_thresholds"] = np.array([[1e-3, 1e-3], [1e-3, 1e-3]])
-    expected_variances = np.array([[1e-3, 1e-3], [1e-2, 1e-3]])
-    np.testing.assert_equal(gaussians["variances"], expected_variances)
-
-    # Initializing with different shapes
-    gaussians = Gaussians(means=np.zeros(shape=(2,3)), variances=0, variance_thresholds=1e-4)
-    np.testing.assert_equal(gaussians["variances"], np.full(shape=(2,3), fill_value=1e-4))
-
-
-def test_gaussians_attributes_copy():
-    """Ensures that the input arrays are correctly copied."""
-    means = np.array([[0, 0], [1, 1], [2, 2]], dtype=float)
-    variances = np.ones_like(means, dtype=float)
-    variance_thresholds = np.full_like(means, 1e-5, dtype=float)
-    gaussians = Gaussians(means, variances, variance_thresholds)
-
-    means[0,0] = np.nan
-    expected_means = np.array([[0, 0], [1, 1], [2, 2]])
-    np.testing.assert_equal(gaussians["means"], expected_means)
-
-    variances[0,0] = np.nan
-    expected_variances = np.ones_like(means, dtype=float)
-    np.testing.assert_equal(gaussians["variances"], expected_variances)
-
-    variance_thresholds[0,0] = np.nan
-    expected_thresholds = np.full_like(means, 1e-5, dtype=float)
-    np.testing.assert_equal(gaussians["variance_thresholds"], expected_thresholds)