Commit 32411da0 authored by Guillaume HEUSCH's avatar Guillaume HEUSCH

[algorithm] fixed docstrings in MLP

parent cf4bfbe9
Pipeline #21739 failed with stage
in 12 minutes and 12 seconds
...@@ -19,7 +19,7 @@ class MLP(Algorithm): ...@@ -19,7 +19,7 @@ class MLP(Algorithm):
Attributes Attributes
---------- ----------
hidden_units : :py:obj:`tuple` of int hidden_units : :py:obj:`tuple` of :any:`int`
The number of hidden units in each hidden layer The number of hidden units in each hidden layer
max_iter : int max_iter : int
The maximum number of training iterations The maximum number of training iterations
...@@ -54,10 +54,10 @@ class MLP(Algorithm): ...@@ -54,10 +54,10 @@ class MLP(Algorithm):
def train_projector(self, training_features, projector_file): def train_projector(self, training_features, projector_file):
"""Trains the MLP """Trains the MLP
Parameters: Parameters
----------- ----------
training_features : :py:obj:`list` of :py:class:`numpy.ndarray` or :py:class:`bob.bio.video.utils.FrameContainer` training_features : :py:obj:`list` of :py:class:`numpy.ndarray`
Data used to train the MLP. The real data are in training_features[0] and the attacks are in training_features[1] Data used to train the MLP. The real attempts are in training_features[0] and the attacks are in training_features[1]
projector_file : str projector_file : str
Filename where to save the trained model. Filename where to save the trained model.
...@@ -71,11 +71,6 @@ class MLP(Algorithm): ...@@ -71,11 +71,6 @@ class MLP(Algorithm):
label_attack = numpy.zeros((len(training_features[1]), 2), dtype='float64') label_attack = numpy.zeros((len(training_features[1]), 2), dtype='float64')
label_attack[:, 1] = 0 label_attack[:, 1] = 0
#if isinstance(training_features[0][0], FrameContainer):
# real = convert_frame_cont_to_array(training_features[0])
#if isinstance(training_features[1][0], FrameContainer):
# attack = convert_frame_cont_to_array(training_features[1])
real = numpy.array(training_features[0]) real = numpy.array(training_features[0])
attack = numpy.array(training_features[1]) attack = numpy.array(training_features[1])
X = numpy.vstack([real, attack]) X = numpy.vstack([real, attack])
...@@ -115,7 +110,8 @@ class MLP(Algorithm): ...@@ -115,7 +110,8 @@ class MLP(Algorithm):
Parameters Parameters
---------- ----------
feature : :py:class:`numpy.ndarray` or :py:class:`bob.bio.video.utils.FrameContainer` feature : :py:class:`numpy.ndarray`
The feature to classify
Returns Returns
------- -------
......
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