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Commit 8bcad55d authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
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Modifications in the load and save bind in order to support keyword arguments

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bob/learn/misc/gmm_stats.cpp
+ 84
− 78
View file @ 8bcad55d
......@@ -15,12 +15,11 @@
static auto GMMStats_doc = bob::extension::ClassDoc(
BOB_EXT_MODULE_PREFIX ".GMMStats",
"A container for GMM statistics.\n",
"With respect to Reynolds, \"Speaker Verification Using Adapted "
"Gaussian Mixture Models\", DSP, 2000:\n"
"Eq (8) is n(i)\n"
"Eq (9) is sumPx(i) / n(i)\n"
"Eq (10) is sumPxx(i) / n(i)\n"
"A container for GMM statistics",
"With respect to [Reynolds2000]_ the class computes: \n\n"
"* Eq (8) is :math:`n_i=\\sum\\limits_{t=1}^T Pr(i | x_t)`\n\n"
"* Eq (9) is :math:`sumPx=E_i(x)=\\frac{1}{n(i)}\\sum\\limits_{t=1}^T Pr(i | x_t)x_t`\n\n"
"* Eq (10) is :math:`sumPxx=E_i(x^2)=\\frac{1}{n(i)}\\sum\\limits_{t=1}^T Pr(i | x_t)x_t^2`\n\n"
).add_constructor(
bob::extension::FunctionDoc(
"__init__",
......@@ -52,11 +51,13 @@ static int PyBobLearnMiscGMMStats_init_number(PyBobLearnMiscGMMStatsObject* self
if(n_gaussians < 0){
PyErr_Format(PyExc_TypeError, "gaussians argument must be greater than or equal to zero");
GMMStats_doc.print_usage();
return -1;
}
if(n_inputs < 0){
PyErr_Format(PyExc_TypeError, "input argument must be greater than or equal to zero");
GMMStats_doc.print_usage();
return -1;
}
......@@ -69,7 +70,10 @@ static int PyBobLearnMiscGMMStats_init_copy(PyBobLearnMiscGMMStatsObject* self,
char** kwlist = GMMStats_doc.kwlist(1);
PyBobLearnMiscGMMStatsObject* tt;
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnMiscGMMStats_Type, &tt)) return -1;
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnMiscGMMStats_Type, &tt)){
GMMStats_doc.print_usage();
return -1;
}
self->cxx.reset(new bob::learn::misc::GMMStats(*tt->cxx));
return 0;
......@@ -81,21 +85,13 @@ static int PyBobLearnMiscGMMStats_init_hdf5(PyBobLearnMiscGMMStatsObject* self,
char** kwlist = GMMStats_doc.kwlist(2);
PyBobIoHDF5FileObject* config = 0;
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, &PyBobIoHDF5File_Converter, &config))
return -1;
try {
self->cxx.reset(new bob::learn::misc::GMMStats(*(config->f)));
}
catch (std::exception& ex) {
PyErr_SetString(PyExc_RuntimeError, ex.what());
return -1;
}
catch (...) {
PyErr_Format(PyExc_RuntimeError, "cannot create new object of type `%s' - unknown exception thrown", Py_TYPE(self)->tp_name);
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, &PyBobIoHDF5File_Converter, &config)){
GMMStats_doc.print_usage();
return -1;
}
self->cxx.reset(new bob::learn::misc::GMMStats(*(config->f)));
return 0;
}
......@@ -105,12 +101,13 @@ static int PyBobLearnMiscGMMStats_init(PyBobLearnMiscGMMStatsObject* self, PyObj
BOB_TRY
// get the number of command line arguments
Py_ssize_t nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);
int nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);
switch (nargs) {
case 0: //default initializer ()
self->cxx.reset(new bob::learn::misc::GMMStats());
return 0;
case 1:{
//Reading the input argument
......@@ -133,7 +130,8 @@ static int PyBobLearnMiscGMMStats_init(PyBobLearnMiscGMMStatsObject* self, PyObj
case 2:
return PyBobLearnMiscGMMStats_init_number(self, args, kwargs);
default:
PyErr_Format(PyExc_RuntimeError, "number of arguments mismatch - %s requires 0, 1 or 2 arguments, but you provided %" PY_FORMAT_SIZE_T "d (see help)", Py_TYPE(self)->tp_name, nargs);
PyErr_Format(PyExc_RuntimeError, "number of arguments mismatch - %s requires 0, 1 or 2 arguments, but you provided %d (see help)", Py_TYPE(self)->tp_name, nargs);
GMMStats_doc.print_usage();
return -1;
}
BOB_CATCH_MEMBER("cannot create GMMStats", 0)
......@@ -179,8 +177,8 @@ int PyBobLearnMiscGMMStats_Check(PyObject* o) {
/***** n *****/
static auto n = bob::extension::VariableDoc(
"n",
"array_like <double, 1D> ",
"For each Gaussian, the accumulated sum of responsibilities, i.e. the sum of P(gaussian_i|x)"
"array_like <double, 1D>",
"For each Gaussian, the accumulated sum of responsibilities, i.e. the sum of :math:`P(gaussian_i|x)`"
);
PyObject* PyBobLearnMiscGMMStats_getN(PyBobLearnMiscGMMStatsObject* self, void*){
BOB_TRY
......@@ -260,8 +258,8 @@ int PyBobLearnMiscGMMStats_setSum_pxx(PyBobLearnMiscGMMStatsObject* self, PyObje
/***** t *****/
static auto t = bob::extension::VariableDoc(
"t",
"int ",
"The accumulated number of samples"
"int",
"The number of samples"
);
PyObject* PyBobLearnMiscGMMStats_getT(PyBobLearnMiscGMMStatsObject* self, void*){
BOB_TRY
......@@ -312,6 +310,21 @@ int PyBobLearnMiscGMMStats_setLog_likelihood(PyBobLearnMiscGMMStatsObject* self,
}
/***** shape *****/
static auto shape = bob::extension::VariableDoc(
"shape",
"(int,int)",
"A tuple that represents the number of gaussians and dimensionality of each Gaussian ``(n_gaussians, dim)``.",
""
);
PyObject* PyBobLearnMiscGMMStats_getShape(PyBobLearnMiscGMMStatsObject* self, void*) {
BOB_TRY
return Py_BuildValue("(i,i)", self->cxx->sumPx.shape()[0], self->cxx->sumPx.shape()[1]);
BOB_CATCH_MEMBER("shape could not be read", 0)
}
static PyGetSetDef PyBobLearnMiscGMMStats_getseters[] = {
{
n.name(),
......@@ -347,8 +360,16 @@ static PyGetSetDef PyBobLearnMiscGMMStats_getseters[] = {
(setter)PyBobLearnMiscGMMStats_setLog_likelihood,
log_likelihood.doc(),
0
},
{
shape.name(),
(getter)PyBobLearnMiscGMMStats_getShape,
0,
shape.doc(),
0
},
{0} // Sentinel
};
......@@ -364,31 +385,20 @@ static auto save = bob::extension::FunctionDoc(
"Save the configuration of the GMMStats to a given HDF5 file"
)
.add_prototype("hdf5")
.add_parameter("hdf5", ":py:class:`bob.io.base.HDF5File`", "An HDF5 file open for writing")
;
static PyObject* PyBobLearnMiscGMMStats_Save(PyBobLearnMiscGMMStatsObject* self, PyObject* arg) {
.add_parameter("hdf5", ":py:class:`bob.io.base.HDF5File`", "An HDF5 file open for writing");
static PyObject* PyBobLearnMiscGMMStats_Save(PyBobLearnMiscGMMStatsObject* 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);
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;
}
auto hdf5_ = make_safe(hdf5);
self->cxx->save(*hdf5->f);
BOB_CATCH_MEMBER("cannot save the data", 0)
Py_RETURN_NONE;
}
......@@ -399,27 +409,17 @@ 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* PyBobLearnMiscGMMStats_Load(PyBobLearnMiscGMMStatsObject* self, PyObject* f) {
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);
static PyObject* PyBobLearnMiscGMMStats_Load(PyBobLearnMiscGMMStatsObject* self, PyObject* args, PyObject* kwargs) {
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);
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_CATCH_MEMBER("cannot load the data", 0)
Py_RETURN_NONE;
}
......@@ -429,14 +429,14 @@ static auto is_similar_to = bob::extension::FunctionDoc(
"is_similar_to",
"Compares this GMMStats 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``"
"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."
)
.add_prototype("other, [r_epsilon], [a_epsilon]","output")
.add_parameter("other", ":py:class:`bob.learn.misc.GMMStats`", "A GMMStats object to be compared.")
.add_parameter("[r_epsilon]", "float", "Relative precision.")
.add_parameter("[a_epsilon]", "float", "Absolute precision.")
.add_parameter("r_epsilon", "float", "Relative precision.")
.add_parameter("a_epsilon", "float", "Absolute precision.")
.add_return("output","bool","True if it is similar, otherwise false.");
static PyObject* PyBobLearnMiscGMMStats_IsSimilarTo(PyBobLearnMiscGMMStatsObject* self, PyObject* args, PyObject* kwds) {
......@@ -450,9 +450,11 @@ static PyObject* PyBobLearnMiscGMMStats_IsSimilarTo(PyBobLearnMiscGMMStatsObject
if (!PyArg_ParseTupleAndKeywords(args, kwds, "O!|dd", kwlist,
&PyBobLearnMiscGMMStats_Type, &other,
&r_epsilon, &a_epsilon)) return 0;
&r_epsilon, &a_epsilon)){
//auto other_ = reinterpret_cast<PyBobLearnMiscGMMStatsObject*>(other);
is_similar_to.print_usage();
return 0;
}
if (self->cxx->is_similar_to(*other->cxx, r_epsilon, a_epsilon))
Py_RETURN_TRUE;
......@@ -464,9 +466,11 @@ static PyObject* PyBobLearnMiscGMMStats_IsSimilarTo(PyBobLearnMiscGMMStatsObject
/*** resize ***/
static auto resize = bob::extension::FunctionDoc(
"resize",
" Allocates space for the statistics and resets to zero."
"Allocates space for the statistics and resets to zero.",
0,
true
)
.add_prototype("n_gaussians,n_inputs","")
.add_prototype("n_gaussians,n_inputs")
.add_parameter("n_gaussians", "int", "Number of gaussians")
.add_parameter("n_inputs", "int", "Dimensionality of the feature vector");
static PyObject* PyBobLearnMiscGMMStats_resize(PyBobLearnMiscGMMStatsObject* self, PyObject* args, PyObject* kwargs) {
......@@ -482,10 +486,12 @@ static PyObject* PyBobLearnMiscGMMStats_resize(PyBobLearnMiscGMMStatsObject* sel
if (n_gaussians <= 0){
PyErr_Format(PyExc_TypeError, "n_gaussians must be greater than zero");
resize.print_usage();
return 0;
}
if (n_inputs <= 0){
PyErr_Format(PyExc_TypeError, "n_inputs must be greater than zero");
resize.print_usage();
return 0;
}
......@@ -503,7 +509,7 @@ static auto init = bob::extension::FunctionDoc(
"init",
" Resets statistics to zero."
)
.add_prototype("","");
.add_prototype("");
static PyObject* PyBobLearnMiscGMMStats_init_method(PyBobLearnMiscGMMStatsObject* self) {
BOB_TRY
......@@ -520,13 +526,13 @@ static PyMethodDef PyBobLearnMiscGMMStats_methods[] = {
{
save.name(),
(PyCFunction)PyBobLearnMiscGMMStats_Save,
METH_O,
METH_VARARGS|METH_KEYWORDS,
save.doc()
},
{
load.name(),
(PyCFunction)PyBobLearnMiscGMMStats_Load,
METH_O,
METH_VARARGS|METH_KEYWORDS,
load.doc()
},
{
......@@ -601,7 +607,7 @@ bool init_BobLearnMiscGMMStats(PyObject* module)
PyBobLearnMiscGMMStats_Type.tp_richcompare = reinterpret_cast<richcmpfunc>(PyBobLearnMiscGMMStats_RichCompare);
PyBobLearnMiscGMMStats_Type.tp_methods = PyBobLearnMiscGMMStats_methods;
PyBobLearnMiscGMMStats_Type.tp_getset = PyBobLearnMiscGMMStats_getseters;
//PyBobLearnMiscGMMStats_Type.tp_call = reinterpret_cast<ternaryfunc>(PyBobLearnMiscGMMStats_loglikelihood);
PyBobLearnMiscGMMStats_Type.tp_call = 0;
PyBobLearnMiscGMMStats_Type.tp_as_number = &PyBobLearnMiscGMMStats_operators;
//set operators
......
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