diff --git a/bob/learn/em/factor_analysis.py b/bob/learn/em/factor_analysis.py
index 6e8fdefbec11a2f6fcf9176a80bfa2a95901c9ad..b65da8263eb6190b0a55e038ac7837cfba347ebd 100644
--- a/bob/learn/em/factor_analysis.py
+++ b/bob/learn/em/factor_analysis.py
@@ -180,12 +180,7 @@ class FactorAnalysisBase(BaseEstimator):
@U.setter
def U(self, value):
- U_shape = (self.supervector_dimension, self.r_U)
- if value.shape != U_shape:
- raise ValueError(
- f"U must be a numpy array of shape {U_shape}, but a matrix of shape {value.shape} was provided."
- )
- self._U = value
+ self._U = np.array(value)
@property
def D(self):
@@ -194,12 +189,7 @@ class FactorAnalysisBase(BaseEstimator):
@D.setter
def D(self, value):
- D_shape = (self.supervector_dimension,)
- if value.shape != D_shape:
- raise ValueError(
- f"D must be a numpy array of shape {D_shape}, but a matrix of shape {value.shape} was provided."
- )
- self._D = value
+ self._D = np.array(value)
@property
def V(self):
@@ -208,12 +198,7 @@ class FactorAnalysisBase(BaseEstimator):
@V.setter
def V(self, value):
- V_shape = (self.supervector_dimension, self.r_V)
- if value.shape != V_shape:
- raise ValueError(
- f"V must be a numpy array of shape {V_shape}, but a matrix of shape {value.shape} was provided."
- )
- self._V = value
+ self._V = np.array(value)
def estimate_number_of_classes(self, y):
"""
diff --git a/bob/learn/em/test/test_factor_analysis.py b/bob/learn/em/test/test_factor_analysis.py
index 524ad18e42c504767cbe0ca80793352624f56a9c..d2399b26bfaa4f0711c67bea7c34340a8921b5a7 100644
--- a/bob/learn/em/test/test_factor_analysis.py
+++ b/bob/learn/em/test/test_factor_analysis.py
@@ -467,6 +467,32 @@ def test_ISV_JFA_fit():
means = np.vstack(
(np.random.normal(0, 0.5, (1, 3)), np.random.normal(1, 0.5, (1, 3)))
)
+ prior_U = [
+ [-0.150035, -0.44441],
+ [-1.67812, 2.47621],
+ [-0.52885, 0.659141],
+ [-0.538446, 1.67376],
+ [-0.111288, 2.06948],
+ [1.39563, -1.65004],
+ ]
+
+ prior_V = [
+ [0.732467, 0.281321],
+ [0.543212, -0.512974],
+ [1.04108, 0.835224],
+ [-0.363719, -0.324688],
+ [-1.21579, -0.905314],
+ [-0.993204, -0.121991],
+ ]
+
+ prior_D = [
+ 0.943986,
+ -0.0900599,
+ -0.528103,
+ 0.541502,
+ -0.717824,
+ 0.463729,
+ ]
for prior, machine_type, ref in [
(
@@ -478,53 +504,37 @@ def test_ISV_JFA_fit():
True,
"isv",
[
- [-0.02361267, 0.0157274],
- [-0.00372588, 0.00248165],
- [0.06517179, -0.04340818],
- [0.02694231, -0.01794513],
- [0.07560949, -0.05036029],
- [0.10668997, -0.07106169],
+ [-0.01018673, -0.0266506],
+ [-0.00160621, -0.00420217],
+ [0.02811705, 0.07356008],
+ [0.011624, 0.0304108],
+ [0.03261831, 0.08533629],
+ [0.04602191, 0.12040291],
],
- # TODO(tiago): The reference used to be the values below but are different now
- # [
- # [-0.01, -0.027],
- # [-0.002, -0.004],
- # [0.028, 0.074],
- # [0.012, 0.03],
- # [0.033, 0.085],
- # [0.046, 0.12],
- # ]
),
(
None,
"jfa",
[
- [-0.04687046, -0.06302095],
- [-0.04380423, -0.05889816],
- [-0.02083793, -0.0280182],
- [-0.04728452, -0.06357768],
- [-0.04371283, -0.05877527],
- [-0.0203464, -0.0273573],
+ [-0.05673845, -0.0543068],
+ [-0.05302666, -0.05075409],
+ [-0.02522509, -0.02414402],
+ [-0.05723968, -0.05478655],
+ [-0.05291602, -0.05064819],
+ [-0.02463007, -0.0235745],
],
),
(
True,
"jfa",
[
- [6.54547662e-03, 1.98699266e-04],
- [9.48510389e-02, 2.87936736e-03],
- [-5.98879972e-01, -1.81800375e-02],
- [-5.76350228e-01, -1.74961082e-02],
- [-8.86302168e-01, -2.69052355e-02],
- [-8.25011907e-01, -2.50446636e-02],
+ [0.002881, -0.00584225],
+ [0.04143539, -0.08402497],
+ [-0.26149924, 0.53028251],
+ [-0.25156832, 0.51014406],
+ [-0.38687765, 0.78453174],
+ [-0.36015821, 0.73034858],
],
- # TODO(tiago): The reference used to be the values below but are different now
- # [[ 0.003 -0.006]
- # [ 0.041 -0.084]
- # [-0.261 0.53 ]
- # [-0.252 0.51 ]
- # [-0.387 0.785]
- # [-0.36 0.73 ]]
),
]:
ref = np.asarray(ref)
@@ -535,6 +545,8 @@ def test_ISV_JFA_fit():
if prior is None:
ubm = None
+ # we still provide an initial UBM because KMeans training is not
+ # determenistic depending on inputting numpy or dask arrays
ubm_kwargs = dict(n_gaussians=2, ubm=_create_ubm_prior(means))
else:
ubm = _create_ubm_prior(means)
@@ -550,9 +562,13 @@ def test_ISV_JFA_fit():
if machine_type == "isv":
machine = ISVMachine(2, **machine_kwargs)
+ machine.U = prior_U
test_attr = "U"
else:
machine = JFAMachine(2, 2, **machine_kwargs)
+ machine.U = prior_U
+ machine.V = prior_V
+ machine.D = prior_D
test_attr = "V"
err_msg = f"Test failed with prior={prior} and machine_type={machine_type} and transform={transform}"