diff --git a/bob/learn/misc/gaussian.cpp b/bob/learn/misc/gaussian.cpp
index f1c56407ff18724f9ac70f58b9b929b313fa8551..5ab2a7d029a378abee4fb1d310d0d1fb3d5a6033 100644
--- a/bob/learn/misc/gaussian.cpp
+++ b/bob/learn/misc/gaussian.cpp
@@ -45,6 +45,7 @@ static int PyBobLearnMiscGaussian_init_number(PyBobLearnMiscGaussianObject* self
if(n_inputs < 0){
PyErr_Format(PyExc_TypeError, "input argument must be greater than or equal to zero");
+ Gaussian_doc.print_usage();
return -1;
}
@@ -56,7 +57,10 @@ static int PyBobLearnMiscGaussian_init_copy(PyBobLearnMiscGaussianObject* self,
char** kwlist = Gaussian_doc.kwlist(1);
PyBobLearnMiscGaussianObject* tt;
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnMiscGaussian_Type, &tt)) return -1;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnMiscGaussian_Type, &tt)){
+ Gaussian_doc.print_usage();
+ return -1;
+ }
self->cxx.reset(new bob::learn::misc::Gaussian(*tt->cxx));
return 0;
@@ -67,9 +71,11 @@ static int PyBobLearnMiscGaussian_init_hdf5(PyBobLearnMiscGaussianObject* self,
char** kwlist = Gaussian_doc.kwlist(2);
PyBobIoHDF5FileObject* config = 0;
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, &PyBobIoHDF5File_Converter, &config))
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, &PyBobIoHDF5File_Converter, &config)){
+ Gaussian_doc.print_usage();
return -1;
-
+ }
+
try {
self->cxx.reset(new bob::learn::misc::Gaussian(*(config->f)));
}
@@ -118,10 +124,11 @@ static int PyBobLearnMiscGaussian_init(PyBobLearnMiscGaussianObject* self, PyObj
return PyBobLearnMiscGaussian_init_hdf5(self, args, kwargs);
else
PyErr_Format(PyExc_TypeError, "invalid input argument");
+ Gaussian_doc.print_usage();
return -1;
- BOB_CATCH_MEMBER("cannot create Gaussian", 0)
- return -0;
+ BOB_CATCH_MEMBER("cannot create Gaussian", -1)
+ return 0;
}
@@ -214,33 +221,6 @@ int PyBobLearnMiscGaussian_setVariance(PyBobLearnMiscGaussianObject* self, PyObj
BOB_CATCH_MEMBER("variance could not be set", -1)
}
-/***** dim_d *****/
-static auto dimD = bob::extension::VariableDoc(
- "dim_d",
- "int",
- "Dimensionality of the input feature space",
- ""
-);
-PyObject* PyBobLearnMiscGaussian_getdimD(PyBobLearnMiscGaussianObject* self, void*){
- BOB_TRY
- return Py_BuildValue("i", self->cxx->getNInputs());
- BOB_CATCH_MEMBER("dimD could not be read", 0)
-}
-int PyBobLearnMiscGaussian_setdimD(PyBobLearnMiscGaussianObject* self, PyObject* value, void*){
- BOB_TRY
- if (!PyInt_Check(value)){
- PyErr_Format(PyExc_RuntimeError, "%s %s expects an int", Py_TYPE(self)->tp_name, dimD.name());
- return -1;
- }
- if (PyInt_AS_LONG(value) <= 0){
- PyErr_Format(PyExc_TypeError, "dim_d must be greater than zero");
- return -1;
- }
- self->cxx->setNInputs(PyInt_AS_LONG(value));
- return 0;
- BOB_CATCH_MEMBER("dim_d could not be set", -1)
-}
-
/***** variance_thresholds *****/
static auto variance_thresholds = bob::extension::VariableDoc(
@@ -274,33 +254,14 @@ int PyBobLearnMiscGaussian_setVarianceThresholds(PyBobLearnMiscGaussianObject* s
static auto shape = bob::extension::VariableDoc(
"shape",
"(int)",
- "A tuple that represents the dimensionality of the Gaussian ``(dim_d,)``.",
+ "A tuple that represents the dimensionality of the Gaussian ``(dim,)``.",
""
);
PyObject* PyBobLearnMiscGaussian_getShape(PyBobLearnMiscGaussianObject* self, void*) {
BOB_TRY
- return Py_BuildValue("(n)", self->cxx->getNInputs());
+ return Py_BuildValue("(i)", self->cxx->getNInputs());
BOB_CATCH_MEMBER("shape could not be read", 0)
}
-int PyBobLearnMiscGaussian_setShape(PyBobLearnMiscGaussianObject* self, PyObject* o, void*){
- BOB_TRY
-
- if (!PySequence_Check(o)) {
- PyErr_Format(PyExc_TypeError, "`%s' shape can only be set using tuples (or sequences), not `%s'", Py_TYPE(self)->tp_name, Py_TYPE(o)->tp_name);
- return -1;
- }
-
- //getting the shape
- PyObject* shape = PySequence_Tuple(o);
- auto shape_ = make_safe(shape);
- Py_ssize_t dim_d = PyNumber_AsSsize_t(PyTuple_GET_ITEM(shape, 0), PyExc_OverflowError);
-
- self->cxx->setNInputs(dim_d);
- return 0;
-
- BOB_CATCH_MEMBER("variance_thresholds could not be set", -1)
-}
-
static PyGetSetDef PyBobLearnMiscGaussian_getseters[] = {
{
@@ -317,13 +278,6 @@ static PyGetSetDef PyBobLearnMiscGaussian_getseters[] = {
variance.doc(),
0
},
- {
- dimD.name(),
- (getter)PyBobLearnMiscGaussian_getdimD,
- (setter)PyBobLearnMiscGaussian_setdimD,
- dimD.doc(),
- 0
- },
{
variance_thresholds.name(),
(getter)PyBobLearnMiscGaussian_getVarianceThresholds,
@@ -334,7 +288,7 @@ static PyGetSetDef PyBobLearnMiscGaussian_getseters[] = {
{
shape.name(),
(getter)PyBobLearnMiscGaussian_getShape,
- (setter)PyBobLearnMiscGaussian_setShape,
+ 0,
shape.doc(),
0
},
@@ -364,6 +318,7 @@ static PyObject* PyBobLearnMiscGaussian_resize(PyBobLearnMiscGaussianObject* sel
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "i", kwlist, &input)) Py_RETURN_NONE;
if (input <= 0){
PyErr_Format(PyExc_TypeError, "input must be greater than zero");
+ resize.print_usage();
Py_RETURN_NONE;
}
self->cxx->setNInputs(input);
@@ -373,22 +328,16 @@ static PyObject* PyBobLearnMiscGaussian_resize(PyBobLearnMiscGaussianObject* sel
Py_RETURN_NONE;
}
-/*** log_likelihood ***/
-static auto forward = bob::extension::FunctionDoc(
- "forward",
- "Output the log likelihood of the sample, x. The input size is checked."
-)
-.add_prototype("input","output")
-.add_parameter("input", "array_like <double, 1D>", "Input vector")
-.add_return("output","double","The log likelihood");
/*** log_likelihood ***/
static auto log_likelihood = bob::extension::FunctionDoc(
"log_likelihood",
- "Output the log likelihood of the sample, x. The input size is checked."
+ "Output the log likelihood of the sample, x. The input size is checked.",
+ ".. note:: The :py:meth:`__call__` function is an alias for this.",
+ true
)
.add_prototype("input","output")
.add_parameter("input", "array_like <double, 1D>", "Input vector")
-.add_return("output","double","The log likelihood");
+.add_return("output","float","The log likelihood");
static PyObject* PyBobLearnMiscGaussian_loglikelihood(PyBobLearnMiscGaussianObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
@@ -439,28 +388,19 @@ static auto save = bob::extension::FunctionDoc(
.add_prototype("hdf5")
.add_parameter("hdf5", ":py:class:`bob.io.base.HDF5File`", "An HDF5 file open for writing")
;
-static PyObject* PyBobLearnMiscGaussian_Save(PyBobLearnMiscGaussianObject* self, PyObject* arg) {
+static PyObject* PyBobLearnMiscGaussian_Save(PyBobLearnMiscGaussianObject* self, PyObject* args, PyObject* kwargs) {
+ BOB_TRY
+
// get list of arguments
- if (!PyBobIoHDF5File_Check(arg)) {
- PyErr_Format(PyExc_TypeError, "`%s' cannot write itself to `%s', only to an HDF5 file", Py_TYPE(self)->tp_name, Py_TYPE(arg)->tp_name);
- return 0;
- }
+ char** kwlist = save.kwlist(0);
+ PyBobIoHDF5FileObject* hdf5;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, PyBobIoHDF5File_Converter, &hdf5)) return 0;
- auto hdf5 = reinterpret_cast<PyBobIoHDF5FileObject*>(arg);
+ auto hdf5_ = make_safe(hdf5);
- try {
- self->cxx->save(*hdf5->f);
- }
- catch (std::exception& e) {
- PyErr_SetString(PyExc_RuntimeError, e.what());
- return 0;
- }
- catch (...) {
- PyErr_Format(PyExc_RuntimeError, "`%s' cannot write data to file `%s' (at group `%s'): unknown exception caught", Py_TYPE(self)->tp_name,
- hdf5->f->filename().c_str(), hdf5->f->cwd().c_str());
- return 0;
- }
+ self->cxx->save(*hdf5->f);
+ BOB_CATCH_MEMBER("cannot save the data", 0)
Py_RETURN_NONE;
}
@@ -471,26 +411,18 @@ static auto load = bob::extension::FunctionDoc(
)
.add_prototype("hdf5")
.add_parameter("hdf5", ":py:class:`bob.io.base.HDF5File`", "An HDF5 file open for reading");
-static PyObject* PyBobLearnMiscGaussian_Load(PyBobLearnMiscGaussianObject* self, PyObject* f) {
+static PyObject* PyBobLearnMiscGaussian_Load(PyBobLearnMiscGaussianObject* self, PyObject* args, PyObject* kwargs) {
- if (!PyBobIoHDF5File_Check(f)) {
- PyErr_Format(PyExc_TypeError, "`%s' cannot load itself from `%s', only from an HDF5 file", Py_TYPE(self)->tp_name, Py_TYPE(f)->tp_name);
- return 0;
- }
-
- auto h5f = reinterpret_cast<PyBobIoHDF5FileObject*>(f);
- try {
- self->cxx->load(*h5f->f);
- }
- catch (std::exception& e) {
- PyErr_SetString(PyExc_RuntimeError, e.what());
- return 0;
- }
- catch (...) {
- PyErr_Format(PyExc_RuntimeError, "cannot read data from file `%s' (at group `%s'): unknown exception caught", h5f->f->filename().c_str(),
- h5f->f->cwd().c_str());
- return 0;
- }
+ BOB_TRY
+
+ char** kwlist = load.kwlist(0);
+ PyBobIoHDF5FileObject* hdf5;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, PyBobIoHDF5File_Converter, &hdf5)) return 0;
+
+ auto hdf5_ = make_safe(hdf5);
+ self->cxx->load(*hdf5->f);
+
+ BOB_CATCH_MEMBER("cannot load the data", 0)
Py_RETURN_NONE;
}
@@ -500,8 +432,9 @@ static auto is_similar_to = bob::extension::FunctionDoc(
"is_similar_to",
"Compares this Gaussian 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 ``weights``, ``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 ``weights``, ``biases`` and any other values internal to this machine.",
+ true
)
.add_prototype("other, [r_epsilon], [a_epsilon]","output")
.add_parameter("other", ":py:class:`bob.learn.misc.Gaussian`", "A gaussian to be compared.")
@@ -566,12 +499,6 @@ static PyMethodDef PyBobLearnMiscGaussian_methods[] = {
METH_VARARGS|METH_KEYWORDS,
log_likelihood.doc()
},
- {
- forward.name(),
- (PyCFunction)PyBobLearnMiscGaussian_loglikelihood,
- METH_VARARGS|METH_KEYWORDS,
- forward.doc()
- },
{
log_likelihood_.name(),
(PyCFunction)PyBobLearnMiscGaussian_loglikelihood_,
@@ -581,13 +508,13 @@ static PyMethodDef PyBobLearnMiscGaussian_methods[] = {
{
save.name(),
(PyCFunction)PyBobLearnMiscGaussian_Save,
- METH_O,
+ METH_VARARGS|METH_KEYWORDS,
save.doc()
},
{
load.name(),
(PyCFunction)PyBobLearnMiscGaussian_Load,
- METH_O,
+ METH_VARARGS|METH_KEYWORDS,
load.doc()
},
{