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Commit 39ffe5a6 authored by Manuel Günther's avatar Manuel Günther
Browse files

Corrected, updated and uniformized documentation.

parent d8fb6494
Hide whitespace changes
Inline Side-by-side
bob/learn/em/empca_trainer.cpp
View file @ 39ffe5a6
......@@ -29,7 +29,7 @@ static auto EMPCATrainer_doc = bob::extension::ClassDoc(
.add_prototype("","")
.add_parameter("other", ":py:class:`bob.learn.em.EMPCATrainer`", "A EMPCATrainer object to be copied.")
.add_parameter("convergence_threshold", "double", "")
.add_parameter("convergence_threshold", "float", "")
);
......@@ -227,7 +227,7 @@ static auto m_step = bob::extension::FunctionDoc(
true
)
.add_prototype("linear_machine,data")
.add_parameter("linear_machine", ":py:class:`bob.learn.em.LinearMachine`", "LinearMachine Object")
.add_parameter("linear_machine", ":py:class:`bob.learn.linear.Machine`", "LinearMachine Object")
.add_parameter("data", "array_like <float, 2D>", "Input data");
static PyObject* PyBobLearnEMEMPCATrainer_m_step(PyBobLearnEMEMPCATrainerObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
......@@ -258,7 +258,7 @@ static auto compute_likelihood = bob::extension::FunctionDoc(
true
)
.add_prototype("linear_machine,data")
.add_parameter("linear_machine", ":py:class:`bob.learn.em.LinearMachine`", "LinearMachine Object");
.add_parameter("linear_machine", ":py:class:`bob.learn.linear.Machine`", "LinearMachine Object");
static PyObject* PyBobLearnEMEMPCATrainer_compute_likelihood(PyBobLearnEMEMPCATrainerObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
......
bob/learn/em/gmm_machine.cpp
View file @ 39ffe5a6
......@@ -98,7 +98,7 @@ static int PyBobLearnEMGMMMachine_init(PyBobLearnEMGMMMachineObject* self, PyObj
// get the number of command line arguments
int nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);
switch (nargs) {
case 0: //default initializer ()
......@@ -204,23 +204,23 @@ int PyBobLearnEMGMMMachine_setMeans(PyBobLearnEMGMMMachineObject* self, PyObject
return -1;
}
auto input_ = make_safe(input);
// perform check on the input
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, means.name());
return -1;
}
}
if (input->ndim != 2){
PyErr_Format(PyExc_TypeError, "`%s' only processes 2D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, means.name());
return -1;
}
}
if (input->shape[1] != (Py_ssize_t)self->cxx->getNInputs() && input->shape[0] != (Py_ssize_t)self->cxx->getNGaussians()) {
PyErr_Format(PyExc_TypeError, "`%s' 2D `input` array should have the shape [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] not [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] for `%s`", Py_TYPE(self)->tp_name, self->cxx->getNGaussians(), self->cxx->getNInputs(), input->shape[1], input->shape[0], means.name());
return -1;
}
}
auto b = PyBlitzArrayCxx_AsBlitz<double,2>(input, "means");
if (!b) return -1;
self->cxx->setMeans(*b);
......@@ -248,23 +248,23 @@ int PyBobLearnEMGMMMachine_setVariances(PyBobLearnEMGMMMachineObject* self, PyOb
return -1;
}
auto input_ = make_safe(input);
// perform check on the input
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, variances.name());
return -1;
}
}
if (input->ndim != 2){
PyErr_Format(PyExc_TypeError, "`%s' only processes 2D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, variances.name());
return -1;
}
}
if (input->shape[1] != (Py_ssize_t)self->cxx->getNInputs() && input->shape[0] != (Py_ssize_t)self->cxx->getNGaussians()) {
PyErr_Format(PyExc_TypeError, "`%s' 2D `input` array should have the shape [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] not [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] for `%s`", Py_TYPE(self)->tp_name, self->cxx->getNGaussians(), self->cxx->getNInputs(), input->shape[1], input->shape[0], variances.name());
return -1;
}
}
auto b = PyBlitzArrayCxx_AsBlitz<double,2>(input, "variances");
if (!b) return -1;
self->cxx->setVariances(*b);
......@@ -292,22 +292,22 @@ int PyBobLearnEMGMMMachine_setWeights(PyBobLearnEMGMMMachineObject* self, PyObje
return -1;
}
auto o_ = make_safe(input);
// perform check on the input
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, weights.name());
return -1;
}
}
if (input->ndim != 1){
PyErr_Format(PyExc_TypeError, "`%s' only processes 1D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, weights.name());
return -1;
}
}
if (input->shape[0] != (Py_ssize_t)self->cxx->getNGaussians()){
PyErr_Format(PyExc_TypeError, "`%s' 1D `input` array should have %" PY_FORMAT_SIZE_T "d elements, not %" PY_FORMAT_SIZE_T "d for `%s`", Py_TYPE(self)->tp_name, self->cxx->getNGaussians(), input->shape[0], weights.name());
return -1;
}
}
auto b = PyBlitzArrayCxx_AsBlitz<double,1>(input, "weights");
if (!b) return -1;
......@@ -337,23 +337,23 @@ int PyBobLearnEMGMMMachine_setVarianceSupervector(PyBobLearnEMGMMMachineObject*
return -1;
}
auto o_ = make_safe(input);
// perform check on the input
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, variance_supervector.name());
return -1;
}
}
if (input->ndim != 1){
PyErr_Format(PyExc_TypeError, "`%s' only processes 1D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, variance_supervector.name());
return -1;
}
}
if (input->shape[0] != (Py_ssize_t)self->cxx->getNGaussians()*(Py_ssize_t)self->cxx->getNInputs()){
PyErr_Format(PyExc_TypeError, "`%s' 1D `input` array should have %" PY_FORMAT_SIZE_T "d elements, not %" PY_FORMAT_SIZE_T "d for `%s`", Py_TYPE(self)->tp_name, self->cxx->getNGaussians()*(Py_ssize_t)self->cxx->getNInputs(), input->shape[0], variance_supervector.name());
return -1;
}
}
auto b = PyBlitzArrayCxx_AsBlitz<double,1>(input, "variance_supervector");
if (!b) return -1;
self->cxx->setVarianceSupervector(*b);
......@@ -381,23 +381,23 @@ int PyBobLearnEMGMMMachine_setMeanSupervector(PyBobLearnEMGMMMachineObject* self
return -1;
}
auto o_ = make_safe(input);
// perform check on the input
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, mean_supervector.name());
return -1;
}
}
if (input->ndim != 1){
PyErr_Format(PyExc_TypeError, "`%s' only processes 1D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, mean_supervector.name());
return -1;
}
}
if (input->shape[0] != (Py_ssize_t)self->cxx->getNGaussians()*(Py_ssize_t)self->cxx->getNInputs()){
PyErr_Format(PyExc_TypeError, "`%s' 1D `input` array should have %" PY_FORMAT_SIZE_T "d elements, not %" PY_FORMAT_SIZE_T "d for `%s`", Py_TYPE(self)->tp_name, self->cxx->getNGaussians()*(Py_ssize_t)self->cxx->getNInputs(), input->shape[0], mean_supervector.name());
return -1;
}
}
auto b = PyBlitzArrayCxx_AsBlitz<double,1>(input, "mean_supervector");
if (!b) return -1;
self->cxx->setMeanSupervector(*b);
......@@ -410,7 +410,7 @@ int PyBobLearnEMGMMMachine_setMeanSupervector(PyBobLearnEMGMMMachineObject* self
/***** variance_thresholds *****/
static auto variance_thresholds = bob::extension::VariableDoc(
"variance_thresholds",
"array_like <double, 2D>",
"array_like <float, 2D>",
"Set the variance flooring thresholds in each dimension to the same vector for all Gaussian components if the argument is a 1D numpy arrray, and equal for all Gaussian components and dimensions if the parameter is a scalar. ",
""
);
......@@ -427,8 +427,8 @@ int PyBobLearnEMGMMMachine_setVarianceThresholds(PyBobLearnEMGMMMachineObject* s
return -1;
}
auto o_ = make_safe(input);
// perform check on the input
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, variance_thresholds.name());
return -1;
......@@ -437,24 +437,24 @@ int PyBobLearnEMGMMMachine_setVarianceThresholds(PyBobLearnEMGMMMachineObject* s
if (input->ndim != 2){
PyErr_Format(PyExc_TypeError, "`%s' only processes 2D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, variance_thresholds.name());
return -1;
}
}
if (input->shape[1] != (Py_ssize_t)self->cxx->getNInputs() && input->shape[0] != (Py_ssize_t)self->cxx->getNGaussians()) {
PyErr_Format(PyExc_TypeError, "`%s' 2D `input` array should have the shape [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] not [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] for `%s`", Py_TYPE(self)->tp_name, self->cxx->getNGaussians(), self->cxx->getNInputs(), input->shape[1], input->shape[0], variance_thresholds.name());
return -1;
}
}
auto b = PyBlitzArrayCxx_AsBlitz<double,2>(input, "variance_thresholds");
if (!b) return -1;
self->cxx->setVarianceThresholds(*b);
return 0;
BOB_CATCH_MEMBER("variance_thresholds could not be set", -1)
BOB_CATCH_MEMBER("variance_thresholds could not be set", -1)
}
static PyGetSetDef PyBobLearnEMGMMMachine_getseters[] = {
static PyGetSetDef PyBobLearnEMGMMMachine_getseters[] = {
{
shape.name(),
(getter)PyBobLearnEMGMMMachine_getShape,
......@@ -505,7 +505,7 @@ static PyGetSetDef PyBobLearnEMGMMMachine_getseters[] = {
mean_supervector.doc(),
0
},
{0} // Sentinel
};
......@@ -525,9 +525,9 @@ static auto save = bob::extension::FunctionDoc(
static PyObject* PyBobLearnEMGMMMachine_Save(PyBobLearnEMGMMMachineObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
// get list of arguments
char** kwlist = save.kwlist(0);
char** kwlist = save.kwlist(0);
PyBobIoHDF5FileObject* hdf5;
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, PyBobIoHDF5File_Converter, &hdf5)) return 0;
......@@ -547,12 +547,12 @@ static auto load = bob::extension::FunctionDoc(
.add_parameter("hdf5", ":py:class:`bob.io.base.HDF5File`", "An HDF5 file open for reading");
static PyObject* PyBobLearnEMGMMMachine_Load(PyBobLearnEMGMMMachineObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
char** kwlist = load.kwlist(0);
char** kwlist = load.kwlist(0);
PyBobIoHDF5FileObject* hdf5;
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, PyBobIoHDF5File_Converter, &hdf5)) return 0;
auto hdf5_ = make_safe(hdf5);
auto hdf5_ = make_safe(hdf5);
self->cxx->load(*hdf5->f);
BOB_CATCH_MEMBER("cannot load the data", 0)
......@@ -563,7 +563,7 @@ static PyObject* PyBobLearnEMGMMMachine_Load(PyBobLearnEMGMMMachineObject* self,
/*** is_similar_to ***/
static auto is_similar_to = bob::extension::FunctionDoc(
"is_similar_to",
"Compares this GMMMachine 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`` "
......@@ -588,8 +588,8 @@ static PyObject* PyBobLearnEMGMMMachine_IsSimilarTo(PyBobLearnEMGMMMachineObject
&PyBobLearnEMGMMMachine_Type, &other,
&r_epsilon, &a_epsilon)){
is_similar_to.print_usage();
return 0;
is_similar_to.print_usage();
return 0;
}
if (self->cxx->is_similar_to(*other->cxx, r_epsilon, a_epsilon))
......@@ -643,7 +643,7 @@ static PyObject* PyBobLearnEMGMMMachine_resize(PyBobLearnEMGMMMachineObject* sel
static auto log_likelihood = bob::extension::FunctionDoc(
"log_likelihood",
"Output the log likelihood of the sample, x, i.e. :math:`log(p(x|GMM))`. Inputs are checked.",
".. note:: The :py:meth:`__call__` function is an alias for this.",
".. note:: The :py:meth:`__call__` function is an alias for this.",
true
)
.add_prototype("input","output")
......@@ -651,7 +651,7 @@ static auto log_likelihood = bob::extension::FunctionDoc(
.add_return("output","float","The log likelihood");
static PyObject* PyBobLearnEMGMMMachine_loglikelihood(PyBobLearnEMGMMMachineObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
char** kwlist = log_likelihood.kwlist(0);
PyBlitzArrayObject* input = 0;
......@@ -659,25 +659,25 @@ static PyObject* PyBobLearnEMGMMMachine_loglikelihood(PyBobLearnEMGMMMachineObje
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, &PyBlitzArray_Converter, &input)) return 0;
//protects acquired resources through this scope
auto input_ = make_safe(input);
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `input`", Py_TYPE(self)->tp_name);
log_likelihood.print_usage();
return 0;
}
}
if (input->ndim != 1){
PyErr_Format(PyExc_TypeError, "`%s' only processes 1D arrays of float64", Py_TYPE(self)->tp_name);
log_likelihood.print_usage();
return 0;
}
}
if (input->shape[0] != (Py_ssize_t)self->cxx->getNInputs()){
PyErr_Format(PyExc_TypeError, "`%s' 1D `input` array should have %" PY_FORMAT_SIZE_T "d elements, not %" PY_FORMAT_SIZE_T "d", Py_TYPE(self)->tp_name, self->cxx->getNInputs(), input->shape[0]);
log_likelihood.print_usage();
return 0;
}
}
double value = self->cxx->logLikelihood(*PyBlitzArrayCxx_AsBlitz<double,1>(input));
return Py_BuildValue("d", value);
......@@ -690,7 +690,7 @@ static PyObject* PyBobLearnEMGMMMachine_loglikelihood(PyBobLearnEMGMMMachineObje
static auto log_likelihood_ = bob::extension::FunctionDoc(
"log_likelihood_",
"Output the log likelihood of the sample, x, i.e. :math:`log(p(x|GMM))`. Inputs are NOT checked.",
"",
"",
true
)
.add_prototype("input","output")
......@@ -698,7 +698,7 @@ static auto log_likelihood_ = bob::extension::FunctionDoc(
.add_return("output","float","The log likelihood");
static PyObject* PyBobLearnEMGMMMachine_loglikelihood_(PyBobLearnEMGMMMachineObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
char** kwlist = log_likelihood_.kwlist(0);
PyBlitzArrayObject* input = 0;
......@@ -706,25 +706,25 @@ static PyObject* PyBobLearnEMGMMMachine_loglikelihood_(PyBobLearnEMGMMMachineObj
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, &PyBlitzArray_Converter, &input)) return 0;
//protects acquired resources through this scope
auto input_ = make_safe(input);
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `input`", Py_TYPE(self)->tp_name);
log_likelihood.print_usage();
return 0;
}
}
if (input->ndim != 1){
PyErr_Format(PyExc_TypeError, "`%s' only processes 1D arrays of float64", Py_TYPE(self)->tp_name);
log_likelihood.print_usage();
return 0;
}
}
if (input->shape[0] != (Py_ssize_t)self->cxx->getNInputs()){
PyErr_Format(PyExc_TypeError, "`%s' 1D `input` array should have %" PY_FORMAT_SIZE_T "d elements, not %" PY_FORMAT_SIZE_T "d", Py_TYPE(self)->tp_name, self->cxx->getNInputs(), input->shape[0]);
log_likelihood.print_usage();
return 0;
}
}
double value = self->cxx->logLikelihood_(*PyBlitzArrayCxx_AsBlitz<double,1>(input));
return Py_BuildValue("d", value);
......@@ -737,7 +737,7 @@ static PyObject* PyBobLearnEMGMMMachine_loglikelihood_(PyBobLearnEMGMMMachineObj
static auto acc_statistics = bob::extension::FunctionDoc(
"acc_statistics",
"Accumulate the GMM statistics for this sample(s). Inputs are checked.",
"",
"",
true
)
.add_prototype("input,stats")
......@@ -751,18 +751,18 @@ static PyObject* PyBobLearnEMGMMMachine_accStatistics(PyBobLearnEMGMMMachineObje
PyBlitzArrayObject* input = 0;
PyBobLearnEMGMMStatsObject* stats = 0;
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&O!", kwlist, &PyBlitzArray_Converter,&input,
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&O!", kwlist, &PyBlitzArray_Converter,&input,
&PyBobLearnEMGMMStats_Type, &stats))
return 0;
//protects acquired resources through this scope
auto input_ = make_safe(input);
blitz::Array<double,2> blitz_test = *PyBlitzArrayCxx_AsBlitz<double,2>(input);
if (blitz_test.extent(1)==0)
self->cxx->accStatistics(*PyBlitzArrayCxx_AsBlitz<double,1>(input), *stats->cxx);
else
self->cxx->accStatistics(blitz_test, *stats->cxx);
self->cxx->accStatistics(blitz_test, *stats->cxx);
BOB_CATCH_MEMBER("cannot accumulate the statistics", 0)
......@@ -774,7 +774,7 @@ static PyObject* PyBobLearnEMGMMMachine_accStatistics(PyBobLearnEMGMMMachineObje
static auto acc_statistics_ = bob::extension::FunctionDoc(
"acc_statistics_",
"Accumulate the GMM statistics for this sample(s). Inputs are NOT checked.",
"",
"",
true
)
.add_prototype("input,stats")
......@@ -782,24 +782,24 @@ static auto acc_statistics_ = bob::extension::FunctionDoc(
.add_parameter("stats", ":py:class:`bob.learn.em.GMMStats`", "Statistics of the GMM");
static PyObject* PyBobLearnEMGMMMachine_accStatistics_(PyBobLearnEMGMMMachineObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
char** kwlist = acc_statistics_.kwlist(0);
PyBlitzArrayObject* input = 0;
PyBobLearnEMGMMStatsObject* stats = 0;
if(!PyArg_ParseTupleAndKeywords(args, kwargs, "O&O!", kwlist, &PyBlitzArray_Converter,&input,
if(!PyArg_ParseTupleAndKeywords(args, kwargs, "O&O!", kwlist, &PyBlitzArray_Converter,&input,
&PyBobLearnEMGMMStats_Type, &stats))
return 0;
//protects acquired resources through this scope
auto input_ = make_safe(input);
blitz::Array<double,2> blitz_test = *PyBlitzArrayCxx_AsBlitz<double,2>(input);
if (blitz_test.extent(1)==0)
self->cxx->accStatistics_(*PyBlitzArrayCxx_AsBlitz<double,1>(input), *stats->cxx);
else
self->cxx->accStatistics_(blitz_test, *stats->cxx);
self->cxx->accStatistics_(blitz_test, *stats->cxx);
BOB_CATCH_MEMBER("cannot accumulate the statistics", 0)
Py_RETURN_NONE;
......@@ -846,7 +846,7 @@ static PyObject* PyBobLearnEMGMMMachine_setVarianceThresholds_method(PyBobLearnE
static auto get_gaussian = bob::extension::FunctionDoc(
"get_gaussian",
"Get the specified Gaussian component.",
".. note:: An exception is thrown if i is out of range.",
".. note:: An exception is thrown if i is out of range.",
true
)
.add_prototype("i","gaussian")
......@@ -854,13 +854,13 @@ static auto get_gaussian = bob::extension::FunctionDoc(
.add_return("gaussian",":py:class:`bob.learn.em.Gaussian`","Gaussian object");
static PyObject* PyBobLearnEMGMMMachine_get_gaussian(PyBobLearnEMGMMMachineObject* self, PyObject* args, PyObject* kwargs) {
BOB_TRY
char** kwlist = get_gaussian.kwlist(0);
int i = 0;
if (!PyArg_ParseTupleAndKeywords(args, kwargs, "i", kwlist, &i)) return 0;
//Allocating the correspondent python object
PyBobLearnEMGaussianObject* retval =
(PyBobLearnEMGaussianObject*)PyBobLearnEMGaussian_Type.tp_alloc(&PyBobLearnEMGaussian_Type, 0);
......@@ -923,7 +923,7 @@ static PyMethodDef PyBobLearnEMGMMMachine_methods[] = {
METH_VARARGS|METH_KEYWORDS,
acc_statistics_.doc()
},
{
get_gaussian.name(),
(PyCFunction)PyBobLearnEMGMMMachine_get_gaussian,
......@@ -937,7 +937,7 @@ static PyMethodDef PyBobLearnEMGMMMachine_methods[] = {
METH_VARARGS|METH_KEYWORDS,
set_variance_thresholds.doc()
},
{0} /* Sentinel */
};
......@@ -977,4 +977,3 @@ bool init_BobLearnEMGMMMachine(PyObject* module)
Py_INCREF(&PyBobLearnEMGMMMachine_Type);
return PyModule_AddObject(module, "GMMMachine", (PyObject*)&PyBobLearnEMGMMMachine_Type) >= 0;
}
bob/learn/em/gmm_stats.cpp
View file @ 39ffe5a6
......@@ -120,7 +120,7 @@ static int PyBobLearnEMGMMStats_init(PyBobLearnEMGMMStatsObject* self, PyObject*
arg = PyList_GET_ITEM(tmp, 0);
}
/**If the constructor input is Gaussian object**/
/**If the constructor input is Gaussian object**/
if (PyBobLearnEMGMMStats_Check(arg))
return PyBobLearnEMGMMStats_init_copy(self, args, kwargs);
/**If the constructor input is a HDF5**/
......@@ -193,28 +193,28 @@ int PyBobLearnEMGMMStats_setN(PyBobLearnEMGMMStatsObject* self, PyObject* value,
return -1;
}
auto o_ = make_safe(input);
// perform check on the input
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, n.name());
return -1;
}
}
if (input->ndim != 1){
PyErr_Format(PyExc_TypeError, "`%s' only processes 1D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, n.name());
return -1;
}
}
if (input->shape[0] != (Py_ssize_t)self->cxx->n.extent(0)){
PyErr_Format(PyExc_TypeError, "`%s' 1D `input` array should have %" PY_FORMAT_SIZE_T "d elements, not %" PY_FORMAT_SIZE_T "d for `%s`", Py_TYPE(self)->tp_name, (Py_ssize_t)self->cxx->n.extent(0), input->shape[0], n.name());
return -1;
}
auto b = PyBlitzArrayCxx_AsBlitz<double,1>(input, "n");
if (!b) return -1;
self->cxx->n = *b;
return 0;
BOB_CATCH_MEMBER("n could not be set", -1)
BOB_CATCH_MEMBER("n could not be set", -1)
}
......@@ -237,28 +237,28 @@ int PyBobLearnEMGMMStats_setSum_px(PyBobLearnEMGMMStatsObject* self, PyObject* v
return -1;
}
auto o_ = make_safe(input);
// perform check on the input
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, sum_px.name());
return -1;
}
}
if (input->ndim != 2){
PyErr_Format(PyExc_TypeError, "`%s' only processes 2D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, sum_px.name());
return -1;
}
}
if (input->shape[1] != (Py_ssize_t)self->cxx->sumPx.extent(1) && input->shape[0] != (Py_ssize_t)self->cxx->sumPx.extent(0)) {
PyErr_Format(PyExc_TypeError, "`%s' 2D `input` array should have the shape [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] not [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] for `%s`", Py_TYPE(self)->tp_name, (Py_ssize_t)self->cxx->sumPx.extent(1), (Py_ssize_t)self->cxx->sumPx.extent(0), (Py_ssize_t)input->shape[1], (Py_ssize_t)input->shape[0], sum_px.name());
return -1;
}
auto b = PyBlitzArrayCxx_AsBlitz<double,2>(input, "sum_px");
if (!b) return -1;
self->cxx->sumPx = *b;
return 0;
BOB_CATCH_MEMBER("sum_px could not be set", -1)
BOB_CATCH_MEMBER("sum_px could not be set", -1)
}
......@@ -281,28 +281,28 @@ int PyBobLearnEMGMMStats_setSum_pxx(PyBobLearnEMGMMStatsObject* self, PyObject*
return -1;
}
auto o_ = make_safe(input);
// perform check on the input
// perform check on the input
if (input->type_num != NPY_FLOAT64){
PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, sum_pxx.name());
return -1;
}
}
if (input->ndim != 2){
PyErr_Format(PyExc_TypeError, "`%s' only processes 2D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, sum_pxx.name());
return -1;
}
}
if (input->shape[1] != (Py_ssize_t)self->cxx->sumPxx.extent(1) && input->shape[0] != (Py_ssize_t)self->cxx->sumPxx.extent(0)) {
PyErr_Format(PyExc_TypeError, "`%s' 2D `input` array should have the shape [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] not [%" PY_FORMAT_SIZE_T "d, %" PY_FORMAT_SIZE_T "d] for `%s`", Py_TYPE(self)->tp_name, (Py_ssize_t)self->cxx->sumPxx.extent(1), (Py_ssize_t)self->cxx->sumPxx.extent(0), (Py_ssize_t)input->shape[1], (Py_ssize_t)input->shape[0], sum_pxx.name());
return -1;
}
auto b = PyBlitzArrayCxx_AsBlitz<double,2>(input, "sum_pxx");
if (!b) return -1;
self->cxx->sumPxx = *b;
return 0;
BOB_CATCH_MEMBER("sum_pxx could not be set", -1)
BOB_CATCH_MEMBER("sum_pxx could not be set", -1)
}
......@@ -339,7 +339,7 @@ int PyBobLearnEMGMMStats_setT(PyBobLearnEMGMMStatsObject* self, PyObject* value,
/***** log_likelihood *****/
static auto log_likelihood = bob::extension::VariableDoc(
"log_likelihood",
"double",
"float",
"The accumulated log likelihood of all samples"