diff --git a/bob/learn/em/__init__.py b/bob/learn/em/__init__.py
index 0c1ebcb69060792fa85da77ac7572330821e6133..879f33b9d854ad026bea3dad175dd52599a93a3e 100644
--- a/bob/learn/em/__init__.py
+++ b/bob/learn/em/__init__.py
@@ -1,4 +1,5 @@
import bob.extension
+from .mixture import linear_scoring
def get_config():
diff --git a/bob/learn/em/cluster/k_means.py b/bob/learn/em/cluster/k_means.py
index 2969aa6b2dcc7467ee5f0240d625cd75fa57e0a2..72da1f18a9527fffede1fddfa43eb5862f618629 100644
--- a/bob/learn/em/cluster/k_means.py
+++ b/bob/learn/em/cluster/k_means.py
@@ -157,12 +157,13 @@ class KMeansMachine(BaseEstimator):
weights = weights_count / weights_count.sum()
# Accumulate
+ dask_compatible_eye = np.eye(n_cluster) * np.array(1, like=data)
means_sum = np.sum(
- np.eye(n_cluster)[closest_centroid_indices][:, :, None] * data[:, None],
+ dask_compatible_eye[closest_centroid_indices][:, :, None] * data[:, None],
axis=0,
)
variances_sum = np.sum(
- np.eye(n_cluster)[closest_centroid_indices][:, :, None]
+ dask_compatible_eye[closest_centroid_indices][:, :, None]
* (data[:, None] ** 2),
axis=0,
)
diff --git a/bob/learn/em/test/test_kmeans.py b/bob/learn/em/test/test_kmeans.py
index b6a63ba15cf3868c84fe17215b4dc864c09727d3..426b3e540da9990859155f9bcb671d31a6858f3a 100644
--- a/bob/learn/em/test/test_kmeans.py
+++ b/bob/learn/em/test/test_kmeans.py
@@ -14,55 +14,82 @@ import numpy as np
from bob.learn.em.cluster import KMeansMachine
+def to_numpy(*args):
+ result = []
+ for x in args:
+ result.append(np.array(x))
+ if len(result) == 1:
+ return result[0]
+ return result
+
+
+def to_dask_array(*args):
+ result = []
+ for x in args:
+ result.append(da.from_array(np.array(x)))
+ if len(result) == 1:
+ return result[0]
+ return result
+
+
def test_KMeansMachine():
# Test a KMeansMachine
means = np.array([[3, 70, 0], [4, 72, 0]], "float64")
mean = np.array([3, 70, 1], "float64")
- # Initializes a KMeansMachine
- km = KMeansMachine(2)
- km.centroids_ = means
+ for transform in (to_numpy, to_dask_array):
+ means, mean = transform(means, mean)
+
+ # Initializes a KMeansMachine
+ km = KMeansMachine(2)
+ km.centroids_ = means
- # Distance and closest mean
- np.testing.assert_almost_equal(km.transform(mean)[0], 1)
- np.testing.assert_almost_equal(km.transform(mean)[1], 6)
+ # Distance and closest mean
+ np.testing.assert_almost_equal(km.transform(mean)[0], 1)
+ np.testing.assert_almost_equal(km.transform(mean)[1], 6)
- (index, dist) = km.get_closest_centroid(mean)
+ (index, dist) = km.get_closest_centroid(mean)
- assert index == 0, index
- np.testing.assert_almost_equal(dist, 1.0)
- np.testing.assert_almost_equal(km.get_min_distance(mean), 1)
+ assert index == 0, index
+ np.testing.assert_almost_equal(dist, 1.0)
+ np.testing.assert_almost_equal(km.get_min_distance(mean), 1)
def test_KMeansMachine_var_and_weight():
- kmeans = KMeansMachine(2)
- kmeans.centroids_ = np.array([[1.2, 1.3], [0.2, -0.3]])
+ for transform in (to_numpy, to_dask_array):
+ kmeans = KMeansMachine(2)
+ kmeans.centroids_ = transform(np.array([[1.2, 1.3], [0.2, -0.3]]))
- data = np.array([[1.0, 1], [1.2, 3], [0, 0], [0.3, 0.2], [0.2, 0]])
- variances, weights = kmeans.get_variances_and_weights_for_each_cluster(data)
+ data = np.array([[1.0, 1], [1.2, 3], [0, 0], [0.3, 0.2], [0.2, 0]])
+ data = transform(data)
+ variances, weights = kmeans.get_variances_and_weights_for_each_cluster(data)
- variances_result = np.array([[0.01, 1.0], [0.01555556, 0.00888889]])
- weights_result = np.array([0.4, 0.6])
+ variances_result = np.array([[0.01, 1.0], [0.01555556, 0.00888889]])
+ weights_result = np.array([0.4, 0.6])
+
+ np.testing.assert_almost_equal(variances, variances_result)
+ np.testing.assert_almost_equal(weights, weights_result)
- np.testing.assert_almost_equal(variances, variances_result)
- np.testing.assert_almost_equal(weights, weights_result)
np.set_printoptions(precision=9)
+
def test_kmeans_fit():
np.random.seed(0)
data1 = np.random.normal(loc=1, size=(2000, 3))
data2 = np.random.normal(loc=-1, size=(2000, 3))
data = np.concatenate([data1, data2], axis=0)
- machine = KMeansMachine(2, random_state=0).fit(data)
- centroids = machine.centroids_[np.argsort(machine.centroids_[:,0])]
- expected = [
- [-1.07173464, -1.06200356, -1.00724920],
- [ 0.99479125, 0.99665564, 0.97689017],
- ]
- print(centroids)
- np.testing.assert_almost_equal(centroids, expected)
+
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ machine = KMeansMachine(2, random_state=0).fit(data)
+ centroids = machine.centroids_[np.argsort(machine.centroids_[:, 0])]
+ expected = [
+ [-1.07173464, -1.06200356, -1.00724920],
+ [0.99479125, 0.99665564, 0.97689017],
+ ]
+ np.testing.assert_almost_equal(centroids, expected)
def test_kmeans_fit_init_pp():
@@ -70,14 +97,16 @@ def test_kmeans_fit_init_pp():
data1 = np.random.normal(loc=1, size=(2000, 3))
data2 = np.random.normal(loc=-1, size=(2000, 3))
data = np.concatenate([data1, data2], axis=0)
- machine = KMeansMachine(2, init_method="k-means++", random_state=0).fit(data)
- centroids = machine.centroids_[np.argsort(machine.centroids_[:,0])]
- expected = [
- [-1.07173464, -1.06200356, -1.00724920],
- [ 0.99479125, 0.99665564, 0.97689017],
- ]
- print(centroids)
- np.testing.assert_almost_equal(centroids, expected, decimal=7)
+
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ machine = KMeansMachine(2, init_method="k-means++", random_state=0).fit(data)
+ centroids = machine.centroids_[np.argsort(machine.centroids_[:, 0])]
+ expected = [
+ [-1.07173464, -1.06200356, -1.00724920],
+ [0.99479125, 0.99665564, 0.97689017],
+ ]
+ np.testing.assert_almost_equal(centroids, expected, decimal=7)
def test_kmeans_fit_init_random():
@@ -85,11 +114,12 @@ def test_kmeans_fit_init_random():
data1 = np.random.normal(loc=1, size=(2000, 3))
data2 = np.random.normal(loc=-1, size=(2000, 3))
data = np.concatenate([data1, data2], axis=0)
- machine = KMeansMachine(2, init_method="random", random_state=0).fit(data)
- centroids = machine.centroids_[np.argsort(machine.centroids_[:,0])]
- expected = [
- [-1.07329460, -1.06207104, -1.00714365],
- [ 0.99529015, 0.99570570, 0.97580858],
- ]
- print(centroids)
- np.testing.assert_almost_equal(centroids, expected, decimal=7)
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ machine = KMeansMachine(2, init_method="random", random_state=0).fit(data)
+ centroids = machine.centroids_[np.argsort(machine.centroids_[:, 0])]
+ expected = [
+ [-1.07329460, -1.06207104, -1.00714365],
+ [0.99529015, 0.99570570, 0.97580858],
+ ]
+ np.testing.assert_almost_equal(centroids, expected, decimal=7)