diff --git a/bob/learn/em/factor_analysis.py b/bob/learn/em/factor_analysis.py
index 820a1ee786f42a9cf66fb7419ea43343edf4c887..1d0b81edff2f27fdfa0e74ec2d7c05913b6c2896 100644
--- a/bob/learn/em/factor_analysis.py
+++ b/bob/learn/em/factor_analysis.py
@@ -362,10 +362,9 @@ class FactorAnalysisBase(BaseEstimator):
E[y_i] for class `i`
"""
-
f_i = x_i.sum_px
n_i = x_i.n
- n_ic = np.repeat(n_i, self.supervector_dimension // 2)
+ n_ic = np.repeat(n_i, self.feature_dimension)
V = self._V
## N_ih*( m + D*z)
@@ -644,7 +643,7 @@ class FactorAnalysisBase(BaseEstimator):
"""
- tmp_CD = np.repeat(n_acc_i, self.supervector_dimension // 2)
+ tmp_CD = np.repeat(n_acc_i, self.feature_dimension)
id_plus_d_prod = np.ones(tmp_CD.shape) + dt_inv_sigma_d * tmp_CD
return 1 / id_plus_d_prod
@@ -664,7 +663,7 @@ class FactorAnalysisBase(BaseEstimator):
m = self.mean_supervector
- tmp_CD = np.repeat(n_acc_i, self.supervector_dimension // 2)
+ tmp_CD = np.repeat(n_acc_i, self.feature_dimension)
## JFA session part
V_dot_v = V @ latent_y_i if latent_y_i is not None else 0
@@ -675,7 +674,7 @@ class FactorAnalysisBase(BaseEstimator):
# Looping over the sessions
for session_id in range(len(X_i)):
n_i = X_i[session_id].n
- tmp_CD = np.repeat(n_i, self.supervector_dimension // 2)
+ tmp_CD = np.repeat(n_i, self.feature_dimension)
x_i_h = latent_x_i[:, session_id]
fn_z_i -= tmp_CD * (U @ x_i_h)
@@ -757,7 +756,7 @@ class FactorAnalysisBase(BaseEstimator):
X_i, latent_x_i, latent_y_i, n_acc[y_i], f_acc[y_i]
)
- tmp_CD = np.repeat(n_acc[y_i], self.supervector_dimension // 2)
+ tmp_CD = np.repeat(n_acc[y_i], self.feature_dimension)
acc_D_A1 += (
id_plus_d_prod + latent_z[y_i] * latent_z[y_i]
) * tmp_CD
@@ -1024,7 +1023,7 @@ class FactorAnalysisBase(BaseEstimator):
# y = self.y[i] # Not doing JFA
- tmp_CD = np.repeat(n_acc_i, self.supervector_dimension // 2)
+ tmp_CD = np.repeat(n_acc_i, self.feature_dimension)
fn_y_i = f_acc_i.flatten() - tmp_CD * (
m - D * latent_z_i
@@ -1036,7 +1035,7 @@ class FactorAnalysisBase(BaseEstimator):
for session_id in range(len(X_i)):
n_i = X_i[session_id].n
U_dot_x = U @ latent_x_i[:, session_id]
- tmp_CD = np.repeat(n_i, self.supervector_dimension // 2)
+ tmp_CD = np.repeat(n_i, self.feature_dimension)
fn_y_i -= tmp_CD * U_dot_x
return fn_y_i
@@ -1130,7 +1129,9 @@ class ISVMachine(FactorAnalysisBase):
"""
- def __init__(self, ubm, r_U, em_iterations=10, relevance_factor=4.0, seed=0):
+ def __init__(
+ self, ubm, r_U, em_iterations=10, relevance_factor=4.0, seed=0
+ ):
super(ISVMachine, self).__init__(
ubm,
r_U=r_U,
@@ -1201,6 +1202,8 @@ class ISVMachine(FactorAnalysisBase):
if not hasattr(self, "_U") or not hasattr(self, "_D"):
self.create_UVD()
+ y = y.tolist() if not isinstance(y, list) else y
+
# TODO: Point of parallelism
n_acc, f_acc = self.initialize(X, y)
for i in range(self.em_iterations):
@@ -1675,6 +1678,8 @@ class JFAMachine(FactorAnalysisBase):
):
self.create_UVD()
+ y = y.tolist() if not isinstance(y, list) else y
+
# TODO: Point of parallelism
n_acc, f_acc = self.initialize(X, y)
diff --git a/doc/plot/plot_ISV.py b/doc/plot/plot_ISV.py
new file mode 100644
index 0000000000000000000000000000000000000000..91b9ff61fdee5cc709c3797f3d5d753ddaf6780d
--- /dev/null
+++ b/doc/plot/plot_ISV.py
@@ -0,0 +1,141 @@
+from sklearn.datasets import load_iris
+
+import bob.learn.em
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(2) # FIXING A SEED
+
+
+def isv_train(features, ubm):
+ """
+ Train U matrix
+
+ **Parameters**
+ features: List of :py:class:`bob.learn.em.GMMStats` organized by class
+
+ n_gaussians: UBM (:py:class:`bob.learn.em.GMMMachine`)
+
+ """
+
+ stats = []
+ for user in features:
+ user_stats = []
+ for f in user:
+ s = bob.learn.em.GMMStats(ubm.shape[0], ubm.shape[1])
+ ubm.acc_statistics(f, s)
+ user_stats.append(s)
+ stats.append(user_stats)
+
+ relevance_factor = 4
+ subspace_dimension_of_u = 1
+
+ isvbase = bob.learn.em.ISVBase(ubm, subspace_dimension_of_u)
+ trainer = bob.learn.em.ISVTrainer(relevance_factor)
+ # trainer.rng = bob.core.random.mt19937(int(self.init_seed))
+ bob.learn.em.train(trainer, isvbase, stats, max_iterations=50)
+
+ return isvbase
+
+
+# GENERATING DATA
+iris_data = load_iris()
+X = iris_data.data
+y = iris_data.target
+
+setosa = X[iris_data.target == 0]
+versicolor = X[iris_data.target == 1]
+virginica = X[iris_data.target == 2]
+
+n_gaussians = 3
+r_U = 1
+
+
+# TRAINING THE PRIOR
+ubm = bob.learn.em.GMMMachine(n_gaussians).fit(X)
+
+gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
+isv_machine = bob.learn.em.ISVMachine(ubm, r_U).fit(gmm_stats, y)
+
+# isvbase = isv_train([setosa, versicolor, virginica], ubm)
+
+# Variability direction
+u0 = isv_machine.U[0:2, 0] / np.linalg.norm(isv_machine.U[0:2, 0])
+u1 = isv_machine.U[2:4, 0] / np.linalg.norm(isv_machine.U[2:4, 0])
+u2 = isv_machine.U[4:6, 0] / np.linalg.norm(isv_machine.U[4:6, 0])
+
+figure, ax = plt.subplots()
+plt.scatter(setosa[:, 0], setosa[:, 1], c="darkcyan", label="setosa")
+plt.scatter(
+ versicolor[:, 0], versicolor[:, 1], c="goldenrod", label="versicolor"
+)
+plt.scatter(virginica[:, 0], virginica[:, 1], c="dimgrey", label="virginica")
+
+plt.scatter(
+ ubm.means[:, 0],
+ ubm.means[:, 1],
+ c="blue",
+ marker="x",
+ label="centroids - mle",
+)
+# plt.scatter(ubm.means[:, 0], ubm.means[:, 1], c="blue",
+# marker=".", label="within class varibility", s=0.01)
+
+ax.arrow(
+ ubm.means[0, 0],
+ ubm.means[0, 1],
+ u0[0],
+ u0[1],
+ fc="k",
+ ec="k",
+ head_width=0.05,
+ head_length=0.1,
+)
+ax.arrow(
+ ubm.means[1, 0],
+ ubm.means[1, 1],
+ u1[0],
+ u1[1],
+ fc="k",
+ ec="k",
+ head_width=0.05,
+ head_length=0.1,
+)
+ax.arrow(
+ ubm.means[2, 0],
+ ubm.means[2, 1],
+ u2[0],
+ u2[1],
+ fc="k",
+ ec="k",
+ head_width=0.05,
+ head_length=0.1,
+)
+plt.text(
+ ubm.means[0, 0] + u0[0],
+ ubm.means[0, 1] + u0[1] - 0.1,
+ r"$\mathbf{U}_1$",
+ fontsize=15,
+)
+plt.text(
+ ubm.means[1, 0] + u1[0],
+ ubm.means[1, 1] + u1[1] - 0.1,
+ r"$\mathbf{U}_2$",
+ fontsize=15,
+)
+plt.text(
+ ubm.means[2, 0] + u2[0],
+ ubm.means[2, 1] + u2[1] - 0.1,
+ r"$\mathbf{U}_3$",
+ fontsize=15,
+)
+
+plt.xticks([], [])
+plt.yticks([], [])
+
+# plt.grid(True)
+plt.xlabel("Sepal length")
+plt.ylabel("Petal width")
+plt.legend()
+plt.tight_layout()
+plt.show()
diff --git a/doc/plot/plot_JFA.py b/doc/plot/plot_JFA.py
new file mode 100644
index 0000000000000000000000000000000000000000..f77e6779c9f6f9b52ac78e72d76b498b8ec61d92
--- /dev/null
+++ b/doc/plot/plot_JFA.py
@@ -0,0 +1,201 @@
+from sklearn.datasets import load_iris
+
+import bob.learn.em
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(2) # FIXING A SEED
+
+
+def isv_train(features, ubm):
+ """
+ Train U matrix
+
+ **Parameters**
+ features: List of :py:class:`bob.learn.em.GMMStats` organized by class
+
+ n_gaussians: UBM (:py:class:`bob.learn.em.GMMMachine`)
+
+ """
+
+ stats = []
+ for user in features:
+ user_stats = []
+ for f in user:
+ s = bob.learn.em.GMMStats(ubm.shape[0], ubm.shape[1])
+ ubm.acc_statistics(f, s)
+ user_stats.append(s)
+ stats.append(user_stats)
+
+ relevance_factor = 4
+ subspace_dimension_of_u = 1
+
+ isvbase = bob.learn.em.ISVBase(ubm, subspace_dimension_of_u)
+ trainer = bob.learn.em.ISVTrainer(relevance_factor)
+ # trainer.rng = bob.core.random.mt19937(int(self.init_seed))
+ bob.learn.em.train(trainer, isvbase, stats, max_iterations=50)
+
+ return isvbase
+
+
+# GENERATING DATA
+iris_data = load_iris()
+X = iris_data.data
+y = iris_data.target
+
+setosa = X[iris_data.target == 0]
+versicolor = X[iris_data.target == 1]
+virginica = X[iris_data.target == 2]
+
+n_gaussians = 3
+r_U = 1
+r_V = 1
+
+
+# TRAINING THE PRIOR
+ubm = bob.learn.em.GMMMachine(n_gaussians).fit(X)
+
+gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
+jfa_machine = bob.learn.em.JFAMachine(ubm, r_U, r_V).fit(gmm_stats, y)
+
+
+# Variability direction U
+u0 = jfa_machine.U[0:2, 0] / np.linalg.norm(jfa_machine.U[0:2, 0])
+u1 = jfa_machine.U[2:4, 0] / np.linalg.norm(jfa_machine.U[2:4, 0])
+u2 = jfa_machine.U[4:6, 0] / np.linalg.norm(jfa_machine.U[4:6, 0])
+
+
+# Variability direction V
+v0 = jfa_machine.V[0:2, 0] / np.linalg.norm(jfa_machine.V[0:2, 0])
+v1 = jfa_machine.V[2:4, 0] / np.linalg.norm(jfa_machine.V[2:4, 0])
+v2 = jfa_machine.V[4:6, 0] / np.linalg.norm(jfa_machine.V[4:6, 0])
+
+
+figure, ax = plt.subplots()
+plt.scatter(setosa[:, 0], setosa[:, 1], c="darkcyan", label="setosa")
+plt.scatter(
+ versicolor[:, 0], versicolor[:, 1], c="goldenrod", label="versicolor"
+)
+plt.scatter(virginica[:, 0], virginica[:, 1], c="dimgrey", label="virginica")
+
+plt.scatter(
+ ubm.means[:, 0],
+ ubm.means[:, 1],
+ c="blue",
+ marker="x",
+ label="centroids - mle",
+)
+# plt.scatter(ubm.means[:, 0], ubm.means[:, 1], c="blue",
+# marker=".", label="within class varibility", s=0.01)
+
+# U
+ax.arrow(
+ ubm.means[0, 0],
+ ubm.means[0, 1],
+ u0[0],
+ u0[1],
+ fc="k",
+ ec="k",
+ head_width=0.05,
+ head_length=0.1,
+)
+ax.arrow(
+ ubm.means[1, 0],
+ ubm.means[1, 1],
+ u1[0],
+ u1[1],
+ fc="k",
+ ec="k",
+ head_width=0.05,
+ head_length=0.1,
+)
+ax.arrow(
+ ubm.means[2, 0],
+ ubm.means[2, 1],
+ u2[0],
+ u2[1],
+ fc="k",
+ ec="k",
+ head_width=0.05,
+ head_length=0.1,
+)
+plt.text(
+ ubm.means[0, 0] + u0[0],
+ ubm.means[0, 1] + u0[1] - 0.1,
+ r"$\mathbf{U}_1$",
+ fontsize=15,
+)
+plt.text(
+ ubm.means[1, 0] + u1[0],
+ ubm.means[1, 1] + u1[1] - 0.1,
+ r"$\mathbf{U}_2$",
+ fontsize=15,
+)
+plt.text(
+ ubm.means[2, 0] + u2[0],
+ ubm.means[2, 1] + u2[1] - 0.1,
+ r"$\mathbf{U}_3$",
+ fontsize=15,
+)
+
+# V
+ax.arrow(
+ ubm.means[0, 0],
+ ubm.means[0, 1],
+ v0[0],
+ v0[1],
+ fc="k",
+ ec="k",
+ head_width=0.05,
+ head_length=0.1,
+)
+ax.arrow(
+ ubm.means[1, 0],
+ ubm.means[1, 1],
+ v1[0],
+ v1[1],
+ fc="k",
+ ec="k",
+ head_width=0.05,
+ head_length=0.1,
+)
+ax.arrow(
+ ubm.means[2, 0],
+ ubm.means[2, 1],
+ v2[0],
+ v2[1],
+ fc="k",
+ ec="k",
+ head_width=0.05,
+ head_length=0.1,
+)
+plt.text(
+ ubm.means[0, 0] + v0[0],
+ ubm.means[0, 1] + v0[1] - 0.1,
+ r"$\mathbf{V}_1$",
+ fontsize=15,
+)
+plt.text(
+ ubm.means[1, 0] + v1[0],
+ ubm.means[1, 1] + v1[1] - 0.1,
+ r"$\mathbf{V}_2$",
+ fontsize=15,
+)
+plt.text(
+ ubm.means[2, 0] + v2[0],
+ ubm.means[2, 1] + v2[1] - 0.1,
+ r"$\mathbf{V}_3$",
+ fontsize=15,
+)
+
+plt.xticks([], [])
+plt.yticks([], [])
+
+# plt.grid(True)
+plt.xlabel("Sepal length")
+plt.ylabel("Petal width")
+plt.legend(loc=2)
+# plt.ylim([-1, 3.5])
+
+plt.tight_layout()
+plt.show()