Commit 93d9b59e authored by Manuel Günther's avatar Manuel Günther
Browse files

Cosmetic corrections of documentation

parent ffbbf993
...@@ -188,7 +188,8 @@ static auto train = bob::extension::FunctionDoc( ...@@ -188,7 +188,8 @@ static auto train = bob::extension::FunctionDoc(
"To accomplish this, either prepare a list with all your class observations organized in 2D arrays or pass a 3D array in which the first dimension (depth) contains as many elements as classes you want to discriminate.\n\n" "To accomplish this, either prepare a list with all your class observations organized in 2D arrays or pass a 3D array in which the first dimension (depth) contains as many elements as classes you want to discriminate.\n\n"
".. note::\n\n" ".. note::\n\n"
" We set at most :py:meth:`output_size` eigen-values and vectors on the passed machine.\n" " We set at most :py:meth:`output_size` eigen-values and vectors on the passed machine.\n"
" You can compress the machine output further using :py:meth:`Machine.resize` if necessary." " You can compress the machine output further using :py:meth:`Machine.resize` if necessary.",
true
) )
.add_prototype("X, [machine]", "machine, eigen_values") .add_prototype("X, [machine]", "machine, eigen_values")
.add_parameter("X", "[array_like(2D, floats)] or array_like(3D, floats)", "The input data, separated to contain the training data per class in the first dimension") .add_parameter("X", "[array_like(2D, floats)] or array_like(3D, floats)", "The input data, separated to contain the training data per class in the first dimension")
...@@ -282,7 +283,8 @@ static auto output_size = bob::extension::FunctionDoc( ...@@ -282,7 +283,8 @@ static auto output_size = bob::extension::FunctionDoc(
"This method should be used to setup linear machines and input vectors prior to feeding them into this trainer.\n\n" "This method should be used to setup linear machines and input vectors prior to feeding them into this trainer.\n\n"
"The value of ``X`` should be a sequence over as many 2D 64-bit floating point number arrays as classes in the problem. " "The value of ``X`` should be a sequence over as many 2D 64-bit floating point number arrays as classes in the problem. "
"All arrays will be checked for conformance (identical number of columns). " "All arrays will be checked for conformance (identical number of columns). "
"To accomplish this, either prepare a list with all your class observations organized in 2D arrays or pass a 3D array in which the first dimension (depth) contains as many elements as classes you want to discriminate." "To accomplish this, either prepare a list with all your class observations organized in 2D arrays or pass a 3D array in which the first dimension (depth) contains as many elements as classes you want to discriminate.",
true
) )
.add_prototype("X","size") .add_prototype("X","size")
.add_parameter("X", "[array_like(2D, floats)] or array_like(3D, floats)", "The input data, separated to contain the training data per class in the first dimension") .add_parameter("X", "[array_like(2D, floats)] or array_like(3D, floats)", "The input data, separated to contain the training data per class in the first dimension")
......
...@@ -143,7 +143,8 @@ static auto train = bob::extension::FunctionDoc( ...@@ -143,7 +143,8 @@ static auto train = bob::extension::FunctionDoc(
"train", "train",
"Trains a linear machine to perform linear logistic regression", "Trains a linear machine to perform linear logistic regression",
"The resulting machine will have the same number of inputs as columns in ``negatives`` and ``positives`` and a single output. " "The resulting machine will have the same number of inputs as columns in ``negatives`` and ``positives`` and a single output. "
"This method always returns a machine, which will be identical to the one provided (if the user passed one) or a new one allocated internally." "This method always returns a machine, which will be identical to the one provided (if the user passed one) or a new one allocated internally.",
true
) )
.add_prototype("negatives, positives, [machine]", "machine") .add_prototype("negatives, positives, [machine]", "machine")
.add_parameter("negatives, positives", "array_like(2D, float)", "``negatives`` and ``positives`` should be arrays organized in such a way that every row corresponds to a new observation of the phenomena (i.e., a new sample) and every column corresponds to a different feature") .add_parameter("negatives, positives", "array_like(2D, float)", "``negatives`` and ``positives`` should be arrays organized in such a way that every row corresponds to a new observation of the phenomena (i.e., a new sample) and every column corresponds to a different feature")
......
...@@ -575,7 +575,8 @@ static auto forward = bob::extension::FunctionDoc( ...@@ -575,7 +575,8 @@ static auto forward = bob::extension::FunctionDoc(
"If one provides a 1D array, the ``output`` array, if provided, should also be 1D, matching the output size of this machine. " "If one provides a 1D array, the ``output`` array, if provided, should also be 1D, matching the output size of this machine. "
"If one provides a 2D array, it is considered a set of vertically stacked 1D arrays (one input per row) and a 2D array is produced or expected in ``output``. " "If one provides a 2D array, it is considered a set of vertically stacked 1D arrays (one input per row) and a 2D array is produced or expected in ``output``. "
"The ``output`` array in this case shall have the same number of rows as the ``input`` array and as many columns as the output size for this machine.\n\n" "The ``output`` array in this case shall have the same number of rows as the ``input`` array and as many columns as the output size for this machine.\n\n"
".. note:: The :py:meth:`__call__` function is an alias for this method." ".. note:: The :py:meth:`__call__` function is an alias for this method.",
true
) )
.add_prototype("input, [output]", "output") .add_prototype("input, [output]", "output")
.add_parameter("input", "array_like(1D or 2D, float)", "The array that should be projected; must be compatible with :py:attr:`shape` [0]") .add_parameter("input", "array_like(1D or 2D, float)", "The array that should be projected; must be compatible with :py:attr:`shape` [0]")
...@@ -725,7 +726,8 @@ BOB_CATCH_MEMBER("save", 0) ...@@ -725,7 +726,8 @@ BOB_CATCH_MEMBER("save", 0)
static auto is_similar_to = bob::extension::FunctionDoc( static auto is_similar_to = bob::extension::FunctionDoc(
"is_similar_to", "is_similar_to",
"Compares this LinearMachine with the ``other`` one to be approximately the same", "Compares this LinearMachine with the ``other`` one to be approximately the same",
"The optional values ``r_epsilon`` and ``a_epsilon`` refer to the relative and absolute precision for the :py:attr:`weights`, :py:attr:`biases` and any other values internal to this machine." "The optional values ``r_epsilon`` and ``a_epsilon`` refer to the relative and absolute precision for the :py:attr:`weights`, :py:attr:`biases` and any other values internal to this machine.",
true
) )
.add_prototype("other, [r_epsilon], [a_epsilon]", "similar") .add_prototype("other, [r_epsilon], [a_epsilon]", "similar")
.add_parameter("other", ":py:class:`Machine`", "The other machine to compare with") .add_parameter("other", ":py:class:`Machine`", "The other machine to compare with")
...@@ -764,7 +766,8 @@ static auto resize = bob::extension::FunctionDoc( ...@@ -764,7 +766,8 @@ static auto resize = bob::extension::FunctionDoc(
".. note::\n\n" ".. note::\n\n"
" Use this method to force data compression.\n" " Use this method to force data compression.\n"
" All will work out given most relevant factors to be preserved are organized on the top of the weight matrix.\n" " All will work out given most relevant factors to be preserved are organized on the top of the weight matrix.\n"
" In this way, reducing the system size will suppress less relevant projections." " In this way, reducing the system size will suppress less relevant projections.",
true
) )
.add_prototype("input, output") .add_prototype("input, output")
.add_parameter("input", "int", "The input dimension to be set") .add_parameter("input", "int", "The input dimension to be set")
......
...@@ -167,14 +167,15 @@ static PyObject* PyBobLearnLinearPCATrainer_RichCompare ...@@ -167,14 +167,15 @@ static PyObject* PyBobLearnLinearPCATrainer_RichCompare
static auto train = bob::extension::FunctionDoc( static auto train = bob::extension::FunctionDoc(
"train", "train",
"Trains a linear machine to perform the PCA (aka. KLT)" "Trains a linear machine to perform the PCA (aka. KLT)",
"The resulting machine will have the same number of inputs as columns in ``X`` and :math:`K` eigen-vectors, where :math:`K=\\min{(S-1,F)}`, with :math:`S` being the number of rows in ``X`` (samples) and :math:`F` the number of columns (or features). " "The resulting machine will have the same number of inputs as columns in ``X`` and :math:`K` eigen-vectors, where :math:`K=\\min{(S-1,F)}`, with :math:`S` being the number of rows in ``X`` (samples) and :math:`F` the number of columns (or features). "
"The vectors are arranged by decreasing eigen-value automatically -- there is no need to sort the results.\n\n" "The vectors are arranged by decreasing eigen-value automatically -- there is no need to sort the results.\n\n"
"The user may provide or not an object of type :py:class:`Machine` that will be set by this method. " "The user may provide or not an object of type :py:class:`Machine` that will be set by this method. "
"If provided, machine should have the correct number of inputs and outputs matching, respectively, the number of columns in the input data array ``X`` and the output of the method :py:meth:`output_size`.\n\n" "If provided, machine should have the correct number of inputs and outputs matching, respectively, the number of columns in the input data array ``X`` and the output of the method :py:meth:`output_size`.\n\n"
"The input data matrix ``X`` should correspond to a 64-bit floating point array organized in such a way that every row corresponds to a new observation of the phenomena (i.e., a new sample) and every column corresponds to a different feature.\n\n" "The input data matrix ``X`` should correspond to a 64-bit floating point array organized in such a way that every row corresponds to a new observation of the phenomena (i.e., a new sample) and every column corresponds to a different feature.\n\n"
"This method returns a tuple consisting of the trained machine and a 1D 64-bit floating point array containing the eigen-values calculated while computing the KLT. " "This method returns a tuple consisting of the trained machine and a 1D 64-bit floating point array containing the eigen-values calculated while computing the KLT. "
"The eigen-value ordering matches that of eigen-vectors set in the machine." "The eigen-value ordering matches that of eigen-vectors set in the machine.",
true
) )
.add_prototype("X, [machine]", "machine, eigen_values") .add_prototype("X, [machine]", "machine, eigen_values")
.add_parameter("X", "array_like(2D, floats)", "The input data to train on") .add_parameter("X", "array_like(2D, floats)", "The input data to train on")
...@@ -232,7 +233,8 @@ static auto output_size = bob::extension::FunctionDoc( ...@@ -232,7 +233,8 @@ static auto output_size = bob::extension::FunctionDoc(
"Calculates the maximum possible rank for the covariance matrix of the given ``X``", "Calculates the maximum possible rank for the covariance matrix of the given ``X``",
"Returns the maximum number of non-zero eigen values that can be generated by this trainer, given ``X``. " "Returns the maximum number of non-zero eigen values that can be generated by this trainer, given ``X``. "
"This number (K) depends on the size of X and is calculated as follows :math:`K=\\min{(S-1,F)}`, with :math:`S` being the number of rows in ``data`` (samples) and :math:`F` the number of columns (or features).\n\n" "This number (K) depends on the size of X and is calculated as follows :math:`K=\\min{(S-1,F)}`, with :math:`S` being the number of rows in ``data`` (samples) and :math:`F` the number of columns (or features).\n\n"
"This method should be used to setup linear machines and input vectors prior to feeding them into the :py:meth:`train` function." "This method should be used to setup linear machines and input vectors prior to feeding them into the :py:meth:`train` function.",
true
) )
.add_prototype("X","size") .add_prototype("X","size")
.add_parameter("X", "array_like(2D, floats)", "The input data that should be trained on") .add_parameter("X", "array_like(2D, floats)", "The input data that should be trained on")
......
...@@ -122,7 +122,8 @@ static auto train = bob::extension::FunctionDoc( ...@@ -122,7 +122,8 @@ static auto train = bob::extension::FunctionDoc(
"The user may provide or not an object of type :py:class:`bob.learn.linear.Machine` that will be set by this method. " "The user may provide or not an object of type :py:class:`bob.learn.linear.Machine` that will be set by this method. "
"In such a case, the machine should have a shape that matches ``(X.shape[1], X.shape[1])``. " "In such a case, the machine should have a shape that matches ``(X.shape[1], X.shape[1])``. "
"If the user does not provide a machine to be set, then a new one will be allocated internally. " "If the user does not provide a machine to be set, then a new one will be allocated internally. "
"In both cases, the resulting machine is always returned." "In both cases, the resulting machine is always returned.",
true
) )
.add_prototype("X, [machine]", "machine") .add_prototype("X, [machine]", "machine")
.add_parameter("X", "[array_like(2D,float)] or array_like(3D, float)", "The training data arranged by class") .add_parameter("X", "[array_like(2D,float)] or array_like(3D, float)", "The training data arranged by class")
......
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