kmeans_trainer.cpp 20.7 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
/**
 * @author Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
 * @date Tue 13 Jan 16:50:00 2015
 *
 * @brief Python API for bob::learn::em
 *
 * Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
 */

#include "main.h"

/******************************************************************/
/************ Constructor Section *********************************/
/******************************************************************/

// InitializationMethod type conversion
17
18
19
20
21
22
23

#if BOOST_VERSION >= 104700
  static const std::map<std::string, bob::learn::em::KMeansTrainer::InitializationMethod> IM = {{"RANDOM",  bob::learn::em::KMeansTrainer::InitializationMethod::RANDOM},  {"RANDOM_NO_DUPLICATE", bob::learn::em::KMeansTrainer::InitializationMethod::RANDOM_NO_DUPLICATE}, {"KMEANS_PLUS_PLUS", bob::learn::em::KMeansTrainer::InitializationMethod::KMEANS_PLUS_PLUS}};
#else
  static const std::map<std::string, bob::learn::em::KMeansTrainer::InitializationMethod> IM = {{"RANDOM",  bob::learn::em::KMeansTrainer::InitializationMethod::RANDOM}, {"RANDOM_NO_DUPLICATE", bob::learn::em::KMeansTrainer::InitializationMethod::RANDOM_NO_DUPLICATE}};
#endif

24
25
26
27
28
29
30
31
32
33
34
35
static inline bob::learn::em::KMeansTrainer::InitializationMethod string2IM(const std::string& o){            /* converts string to InitializationMethod type */
  auto it = IM.find(o);
  if (it == IM.end()) throw std::runtime_error("The given InitializationMethod '" + o + "' is not known; choose one of ('RANDOM', 'RANDOM_NO_DUPLICATE', 'KMEANS_PLUS_PLUS')");
  else return it->second;
}
static inline const std::string& IM2string(bob::learn::em::KMeansTrainer::InitializationMethod o){            /* converts InitializationMethod type to string */
  for (auto it = IM.begin(); it != IM.end(); ++it) if (it->second == o) return it->first;
  throw std::runtime_error("The given InitializationMethod type is not known");
}


static auto KMeansTrainer_doc = bob::extension::ClassDoc(
36
  BOB_EXT_MODULE_PREFIX ".KMeansTrainer",
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
  "Trains a KMeans machine."
  "This class implements the expectation-maximization algorithm for a k-means machine."
  "See Section 9.1 of Bishop, \"Pattern recognition and machine learning\", 2006"
  "It uses a random initialization of the means followed by the expectation-maximization algorithm"
).add_constructor(
  bob::extension::FunctionDoc(
    "__init__",
    "Creates a KMeansTrainer",
    "",
    true
  )
  .add_prototype("initialization_method","")
  .add_prototype("other","")
  .add_prototype("","")

52
  .add_parameter("initialization_method", "str", "The initialization method of the means.\nPossible values are: 'RANDOM', 'RANDOM_NO_DUPLICATE', 'KMEANS_PLUS_PLUS' ")
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
  .add_parameter("other", ":py:class:`bob.learn.em.KMeansTrainer`", "A KMeansTrainer object to be copied.")

);


static int PyBobLearnEMKMeansTrainer_init_copy(PyBobLearnEMKMeansTrainerObject* self, PyObject* args, PyObject* kwargs) {

  char** kwlist = KMeansTrainer_doc.kwlist(1);
  PyBobLearnEMKMeansTrainerObject* tt;
  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMKMeansTrainer_Type, &tt)){
    KMeansTrainer_doc.print_usage();
    return -1;
  }

  self->cxx.reset(new bob::learn::em::KMeansTrainer(*tt->cxx));
  return 0;
}

static int PyBobLearnEMKMeansTrainer_init_str(PyBobLearnEMKMeansTrainerObject* self, PyObject* args, PyObject* kwargs) {

  char** kwlist = KMeansTrainer_doc.kwlist(0);
  char* value;
  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "s", kwlist, &value)){
    KMeansTrainer_doc.print_usage();
    return -1;
  }
  self->cxx.reset(new bob::learn::em::KMeansTrainer(string2IM(std::string(value))));
  return 0;
}


static int PyBobLearnEMKMeansTrainer_init(PyBobLearnEMKMeansTrainerObject* self, PyObject* args, PyObject* kwargs) {
  BOB_TRY

  int nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);

  switch (nargs) {

    case 0:{ //default initializer ()
      self->cxx.reset(new bob::learn::em::KMeansTrainer());
      return 0;
    }
    case 1:{
      //Reading the input argument
      PyObject* arg = 0;
      if (PyTuple_Size(args))
        arg = PyTuple_GET_ITEM(args, 0);
      else {
        PyObject* tmp = PyDict_Values(kwargs);
        auto tmp_ = make_safe(tmp);
        arg = PyList_GET_ITEM(tmp, 0);
      }

      // If the constructor input is KMeansTrainer object
      if (PyBobLearnEMKMeansTrainer_Check(arg))
        return PyBobLearnEMKMeansTrainer_init_copy(self, args, kwargs);
      else if(PyString_Check(arg))
        return PyBobLearnEMKMeansTrainer_init_str(self, args, kwargs);
    }
    default:{
      PyErr_Format(PyExc_RuntimeError, "number of arguments mismatch - %s requires 0 or 1 arguments, but you provided %d (see help)", Py_TYPE(self)->tp_name, nargs);
      KMeansTrainer_doc.print_usage();
      return -1;
    }
  }
Tiago de Freitas Pereira's avatar
Tiago de Freitas Pereira committed
118
  BOB_CATCH_MEMBER("cannot create KMeansTrainer", -1)
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
  return 0;
}


static void PyBobLearnEMKMeansTrainer_delete(PyBobLearnEMKMeansTrainerObject* self) {
  self->cxx.reset();
  Py_TYPE(self)->tp_free((PyObject*)self);
}


int PyBobLearnEMKMeansTrainer_Check(PyObject* o) {
  return PyObject_IsInstance(o, reinterpret_cast<PyObject*>(&PyBobLearnEMKMeansTrainer_Type));
}


static PyObject* PyBobLearnEMKMeansTrainer_RichCompare(PyBobLearnEMKMeansTrainerObject* self, PyObject* other, int op) {
  BOB_TRY

  if (!PyBobLearnEMKMeansTrainer_Check(other)) {
    PyErr_Format(PyExc_TypeError, "cannot compare `%s' with `%s'", Py_TYPE(self)->tp_name, Py_TYPE(other)->tp_name);
    return 0;
  }
  auto other_ = reinterpret_cast<PyBobLearnEMKMeansTrainerObject*>(other);
  switch (op) {
    case Py_EQ:
      if (*self->cxx==*other_->cxx) Py_RETURN_TRUE; else Py_RETURN_FALSE;
    case Py_NE:
      if (*self->cxx==*other_->cxx) Py_RETURN_FALSE; else Py_RETURN_TRUE;
    default:
      Py_INCREF(Py_NotImplemented);
      return Py_NotImplemented;
  }
  BOB_CATCH_MEMBER("cannot compare KMeansTrainer objects", 0)
}


/******************************************************************/
/************ Variables Section ***********************************/
/******************************************************************/

/***** initialization_method *****/
static auto initialization_method = bob::extension::VariableDoc(
  "initialization_method",
  "str",
  "Initialization method.",
164
165
166
167
  "Possible values:\n"
  " `RANDOM`: Random initialization \n\n"
  " `RANDOM_NO_DUPLICATE`: Random initialization without repetition \n\n"
  " `KMEANS_PLUS_PLUS`: Apply the kmeans++ initialization http://en.wikipedia.org/wiki/K-means%2B%2B  \n\n"
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
);
PyObject* PyBobLearnEMKMeansTrainer_getInitializationMethod(PyBobLearnEMKMeansTrainerObject* self, void*) {
  BOB_TRY
  return Py_BuildValue("s", IM2string(self->cxx->getInitializationMethod()).c_str());
  BOB_CATCH_MEMBER("initialization method could not be read", 0)
}
int PyBobLearnEMKMeansTrainer_setInitializationMethod(PyBobLearnEMKMeansTrainerObject* self, PyObject* value, void*) {
  BOB_TRY

  if (!PyString_Check(value)){
    PyErr_Format(PyExc_RuntimeError, "%s %s expects an str", Py_TYPE(self)->tp_name, initialization_method.name());
    return -1;
  }
  self->cxx->setInitializationMethod(string2IM(PyString_AS_STRING(value)));

  return 0;
Tiago de Freitas Pereira's avatar
Tiago de Freitas Pereira committed
184
  BOB_CATCH_MEMBER("initialization method could not be set", -1)
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
}


/***** zeroeth_order_statistics *****/
static auto zeroeth_order_statistics = bob::extension::VariableDoc(
  "zeroeth_order_statistics",
  "array_like <float, 1D>",
  "Returns the internal statistics. Useful to parallelize the E-step",
  ""
);
PyObject* PyBobLearnEMKMeansTrainer_getZeroethOrderStatistics(PyBobLearnEMKMeansTrainerObject* self, void*){
  BOB_TRY
  return PyBlitzArrayCxx_AsConstNumpy(self->cxx->getZeroethOrderStats());
  BOB_CATCH_MEMBER("zeroeth_order_statistics could not be read", 0)
}
int PyBobLearnEMKMeansTrainer_setZeroethOrderStatistics(PyBobLearnEMKMeansTrainerObject* self, PyObject* value, void*){
  BOB_TRY
  PyBlitzArrayObject* o;
  if (!PyBlitzArray_Converter(value, &o)){
    PyErr_Format(PyExc_RuntimeError, "%s %s expects a 1D array of floats", Py_TYPE(self)->tp_name, zeroeth_order_statistics.name());
    return -1;
  }
  auto o_ = make_safe(o);
  auto b = PyBlitzArrayCxx_AsBlitz<double,1>(o, "zeroeth_order_statistics");
  if (!b) return -1;
  self->cxx->setZeroethOrderStats(*b);
  return 0;
  BOB_CATCH_MEMBER("zeroeth_order_statistics could not be set", -1)
}


/***** first_order_statistics *****/
static auto first_order_statistics = bob::extension::VariableDoc(
  "first_order_statistics",
  "array_like <float, 2D>",
  "Returns the internal statistics. Useful to parallelize the E-step",
  ""
);
PyObject* PyBobLearnEMKMeansTrainer_getFirstOrderStatistics(PyBobLearnEMKMeansTrainerObject* self, void*){
  BOB_TRY
  return PyBlitzArrayCxx_AsConstNumpy(self->cxx->getFirstOrderStats());
  BOB_CATCH_MEMBER("first_order_statistics could not be read", 0)
}
int PyBobLearnEMKMeansTrainer_setFirstOrderStatistics(PyBobLearnEMKMeansTrainerObject* self, PyObject* value, void*){
  BOB_TRY
  PyBlitzArrayObject* o;
  if (!PyBlitzArray_Converter(value, &o)){
    PyErr_Format(PyExc_RuntimeError, "%s %s expects a 1D array of floats", Py_TYPE(self)->tp_name, first_order_statistics.name());
    return -1;
  }
  auto o_ = make_safe(o);
  auto b = PyBlitzArrayCxx_AsBlitz<double,2>(o, "first_order_statistics");
  if (!b) return -1;
  self->cxx->setFirstOrderStats(*b);
  return 0;
  BOB_CATCH_MEMBER("first_order_statistics could not be set", -1)
}


/***** average_min_distance *****/
static auto average_min_distance = bob::extension::VariableDoc(
  "average_min_distance",
  "str",
  "Average min (square Euclidean) distance. Useful to parallelize the E-step.",
  ""
);
PyObject* PyBobLearnEMKMeansTrainer_getAverageMinDistance(PyBobLearnEMKMeansTrainerObject* self, void*) {
  BOB_TRY
  return Py_BuildValue("d", self->cxx->getAverageMinDistance());
  BOB_CATCH_MEMBER("Average Min Distance method could not be read", 0)
}
int PyBobLearnEMKMeansTrainer_setAverageMinDistance(PyBobLearnEMKMeansTrainerObject* self, PyObject* value, void*) {
  BOB_TRY

259
  if (!PyBob_NumberCheck(value)){
260
261
262
263
264
265
    PyErr_Format(PyExc_RuntimeError, "%s %s expects an double", Py_TYPE(self)->tp_name, average_min_distance.name());
    return -1;
  }
  self->cxx->setAverageMinDistance(PyFloat_AS_DOUBLE(value));

  return 0;
Tiago de Freitas Pereira's avatar
Tiago de Freitas Pereira committed
266
  BOB_CATCH_MEMBER("Average Min Distance could not be set", -1)
267
268
269
270
271
}




272
static PyGetSetDef PyBobLearnEMKMeansTrainer_getseters[] = {
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
  {
   initialization_method.name(),
   (getter)PyBobLearnEMKMeansTrainer_getInitializationMethod,
   (setter)PyBobLearnEMKMeansTrainer_setInitializationMethod,
   initialization_method.doc(),
   0
  },
  {
   zeroeth_order_statistics.name(),
   (getter)PyBobLearnEMKMeansTrainer_getZeroethOrderStatistics,
   (setter)PyBobLearnEMKMeansTrainer_setZeroethOrderStatistics,
   zeroeth_order_statistics.doc(),
   0
  },
  {
   first_order_statistics.name(),
   (getter)PyBobLearnEMKMeansTrainer_getFirstOrderStatistics,
   (setter)PyBobLearnEMKMeansTrainer_setFirstOrderStatistics,
   first_order_statistics.doc(),
   0
  },
  {
   average_min_distance.name(),
   (getter)PyBobLearnEMKMeansTrainer_getAverageMinDistance,
   (setter)PyBobLearnEMKMeansTrainer_setAverageMinDistance,
   average_min_distance.doc(),
   0
  },
  {0}  // Sentinel
};


/******************************************************************/
/************ Functions Section ***********************************/
/******************************************************************/

/*** initialize ***/
static auto initialize = bob::extension::FunctionDoc(
  "initialize",
  "Initialise the means randomly",
  "Data is split into as many chunks as there are means, then each mean is set to a random example within each chunk.",
  true
)
316
.add_prototype("kmeans_machine, data, [rng]")
317
.add_parameter("kmeans_machine", ":py:class:`bob.learn.em.KMeansMachine`", "KMeansMachine Object")
318
319
.add_parameter("data", "array_like <float, 2D>", "Input data")
.add_parameter("rng", ":py:class:`bob.core.random.mt19937`", "The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop.");
320
321
322
323
324
325
326
static PyObject* PyBobLearnEMKMeansTrainer_initialize(PyBobLearnEMKMeansTrainerObject* self, PyObject* args, PyObject* kwargs) {
  BOB_TRY

  /* Parses input arguments in a single shot */
  char** kwlist = initialize.kwlist(0);

  PyBobLearnEMKMeansMachineObject* kmeans_machine = 0;
327
  PyBlitzArrayObject* data                        = 0;
328
  PyBoostMt19937Object* rng = 0;
329

330
331
332
  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!O&|O!", kwlist, &PyBobLearnEMKMeansMachine_Type, &kmeans_machine,
                                                                 &PyBlitzArray_Converter, &data,
                                                                  &PyBoostMt19937_Type, &rng)) return 0;
333
  auto data_ = make_safe(data);
334
335

  // perform check on the input
336
337
338
  if (data->type_num != NPY_FLOAT64){
    PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, initialize.name());
    return 0;
339
  }
340
341
342
343

  if (data->ndim != 2){
    PyErr_Format(PyExc_TypeError, "`%s' only processes 2D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, initialize.name());
    return 0;
344
  }
345
346
347
348
349

  if (data->shape[1] != (Py_ssize_t)kmeans_machine->cxx->getNInputs() ) {
    PyErr_Format(PyExc_TypeError, "`%s' 2D `input` array should have the shape [N, %" PY_FORMAT_SIZE_T "d] not [N, %" PY_FORMAT_SIZE_T "d] for `%s`", Py_TYPE(self)->tp_name, kmeans_machine->cxx->getNInputs(), data->shape[1], initialize.name());
    return 0;
  }
350

351
  if(rng){
352
    self->cxx->setRng(rng->rng);
353
354
  }

355
356
357
358
359
360
361
362
  self->cxx->initialize(*kmeans_machine->cxx, *PyBlitzArrayCxx_AsBlitz<double,2>(data));

  BOB_CATCH_MEMBER("cannot perform the initialize method", 0)

  Py_RETURN_NONE;
}


363
364
365
366
/*** e_step ***/
static auto e_step = bob::extension::FunctionDoc(
  "e_step",
  "Compute the E-step, which is basically the distances ",
367
368
369
370
371
372
373
374
  "Accumulate across the dataset:"
  " -zeroeth and first order statistics"
  " -average (Square Euclidean) distance from the closest mean",
  true
)
.add_prototype("kmeans_machine,data")
.add_parameter("kmeans_machine", ":py:class:`bob.learn.em.KMeansMachine`", "KMeansMachine Object")
.add_parameter("data", "array_like <float, 2D>", "Input data");
375
static PyObject* PyBobLearnEMKMeansTrainer_e_step(PyBobLearnEMKMeansTrainerObject* self, PyObject* args, PyObject* kwargs) {
376
377
378
  BOB_TRY

  /* Parses input arguments in a single shot */
379
  char** kwlist = e_step.kwlist(0);
380
381
382
383

  PyBobLearnEMKMeansMachineObject* kmeans_machine;
  PyBlitzArrayObject* data = 0;
  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!O&", kwlist, &PyBobLearnEMKMeansMachine_Type, &kmeans_machine,
384
                                                                 &PyBlitzArray_Converter, &data)) return 0;
385
386
  auto data_ = make_safe(data);

387
  if (data->type_num != NPY_FLOAT64){
388
    PyErr_Format(PyExc_TypeError, "`%s' only supports 64-bit float arrays for input array `%s`", Py_TYPE(self)->tp_name, e_step.name());
389
    return 0;
390
  }
391
392

  if (data->ndim != 2){
393
    PyErr_Format(PyExc_TypeError, "`%s' only processes 2D arrays of float64 for `%s`", Py_TYPE(self)->tp_name, e_step.name());
394
    return 0;
395
  }
396
397

  if (data->shape[1] != (Py_ssize_t)kmeans_machine->cxx->getNInputs() ) {
398
    PyErr_Format(PyExc_TypeError, "`%s' 2D `input` array should have the shape [N, %" PY_FORMAT_SIZE_T "d] not [N, %" PY_FORMAT_SIZE_T "d] for `%s`", Py_TYPE(self)->tp_name, kmeans_machine->cxx->getNInputs(), data->shape[1], e_step.name());
399
400
401
    return 0;
  }

402
403
404
  self->cxx->eStep(*kmeans_machine->cxx, *PyBlitzArrayCxx_AsBlitz<double,2>(data));


405
  BOB_CATCH_MEMBER("cannot perform the e_step method", 0)
406
407
408
409
410

  Py_RETURN_NONE;
}


411
412
413
/*** m_step ***/
static auto m_step = bob::extension::FunctionDoc(
  "m_step",
414
415
416
417
  "Updates the mean based on the statistics from the E-step",
  0,
  true
)
418
.add_prototype("kmeans_machine, [data]")
419
.add_parameter("kmeans_machine", ":py:class:`bob.learn.em.KMeansMachine`", "KMeansMachine Object")
420
.add_parameter("data", "object", "Ignored.");
421
static PyObject* PyBobLearnEMKMeansTrainer_m_step(PyBobLearnEMKMeansTrainerObject* self, PyObject* args, PyObject* kwargs) {
422
423
424
  BOB_TRY

  /* Parses input arguments in a single shot */
425
  char** kwlist = m_step.kwlist(0);
426
427

  PyBobLearnEMKMeansMachineObject* kmeans_machine;
428
429
430
  PyObject* data = 0;
  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!|O", kwlist, &PyBobLearnEMKMeansMachine_Type, &kmeans_machine,
                                                                 &data)) return 0;
431
432
  self->cxx->mStep(*kmeans_machine->cxx);

433
  BOB_CATCH_MEMBER("cannot perform the m_step method", 0)
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454

  Py_RETURN_NONE;
}


/*** computeLikelihood ***/
static auto compute_likelihood = bob::extension::FunctionDoc(
  "compute_likelihood",
  "This functions returns the average min (Square Euclidean) distance (average distance to the closest mean)",
  0,
  true
)
.add_prototype("kmeans_machine")
.add_parameter("kmeans_machine", ":py:class:`bob.learn.em.KMeansMachine`", "KMeansMachine Object");
static PyObject* PyBobLearnEMKMeansTrainer_compute_likelihood(PyBobLearnEMKMeansTrainerObject* self, PyObject* args, PyObject* kwargs) {
  BOB_TRY

  /* Parses input arguments in a single shot */
  char** kwlist = compute_likelihood.kwlist(0);

  PyBobLearnEMKMeansMachineObject* kmeans_machine;
455
  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMKMeansMachine_Type, &kmeans_machine)) return 0;
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479

  double value = self->cxx->computeLikelihood(*kmeans_machine->cxx);
  return Py_BuildValue("d", value);

  BOB_CATCH_MEMBER("cannot perform the computeLikelihood method", 0)
}


/*** reset_accumulators ***/
static auto reset_accumulators = bob::extension::FunctionDoc(
  "reset_accumulators",
  "Reset the statistics accumulators to the correct size and a value of zero.",
  0,
  true
)
.add_prototype("kmeans_machine")
.add_parameter("kmeans_machine", ":py:class:`bob.learn.em.KMeansMachine`", "KMeansMachine Object");
static PyObject* PyBobLearnEMKMeansTrainer_reset_accumulators(PyBobLearnEMKMeansTrainerObject* self, PyObject* args, PyObject* kwargs) {
  BOB_TRY

  /* Parses input arguments in a single shot */
  char** kwlist = reset_accumulators.kwlist(0);

  PyBobLearnEMKMeansMachineObject* kmeans_machine;
480
  if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnEMKMeansMachine_Type, &kmeans_machine)) return 0;
481

482
483
  self->cxx->resetAccumulators(*kmeans_machine->cxx);
  Py_RETURN_NONE;
484
485
486
487
488
489
490
491
492
493
494
495
496

  BOB_CATCH_MEMBER("cannot perform the reset_accumulators method", 0)
}


static PyMethodDef PyBobLearnEMKMeansTrainer_methods[] = {
  {
    initialize.name(),
    (PyCFunction)PyBobLearnEMKMeansTrainer_initialize,
    METH_VARARGS|METH_KEYWORDS,
    initialize.doc()
  },
  {
497
498
    e_step.name(),
    (PyCFunction)PyBobLearnEMKMeansTrainer_e_step,
499
    METH_VARARGS|METH_KEYWORDS,
500
    e_step.doc()
501
502
  },
  {
503
504
    m_step.name(),
    (PyCFunction)PyBobLearnEMKMeansTrainer_m_step,
505
    METH_VARARGS|METH_KEYWORDS,
506
    m_step.doc()
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
  },
  {
    compute_likelihood.name(),
    (PyCFunction)PyBobLearnEMKMeansTrainer_compute_likelihood,
    METH_VARARGS|METH_KEYWORDS,
    compute_likelihood.doc()
  },
  {
    reset_accumulators.name(),
    (PyCFunction)PyBobLearnEMKMeansTrainer_reset_accumulators,
    METH_VARARGS|METH_KEYWORDS,
    reset_accumulators.doc()
  },
  {0} /* Sentinel */
};


/******************************************************************/
/************ Module Section **************************************/
/******************************************************************/

// Define the Gaussian type struct; will be initialized later
PyTypeObject PyBobLearnEMKMeansTrainer_Type = {
  PyVarObject_HEAD_INIT(0,0)
  0
};

bool init_BobLearnEMKMeansTrainer(PyObject* module)
{
  // initialize the type struct
  PyBobLearnEMKMeansTrainer_Type.tp_name = KMeansTrainer_doc.name();
  PyBobLearnEMKMeansTrainer_Type.tp_basicsize = sizeof(PyBobLearnEMKMeansTrainerObject);
  PyBobLearnEMKMeansTrainer_Type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE;//Enable the class inheritance
  PyBobLearnEMKMeansTrainer_Type.tp_doc = KMeansTrainer_doc.doc();

  // set the functions
  PyBobLearnEMKMeansTrainer_Type.tp_new = PyType_GenericNew;
  PyBobLearnEMKMeansTrainer_Type.tp_init = reinterpret_cast<initproc>(PyBobLearnEMKMeansTrainer_init);
  PyBobLearnEMKMeansTrainer_Type.tp_dealloc = reinterpret_cast<destructor>(PyBobLearnEMKMeansTrainer_delete);
  PyBobLearnEMKMeansTrainer_Type.tp_richcompare = reinterpret_cast<richcmpfunc>(PyBobLearnEMKMeansTrainer_RichCompare);
  PyBobLearnEMKMeansTrainer_Type.tp_methods = PyBobLearnEMKMeansTrainer_methods;
  PyBobLearnEMKMeansTrainer_Type.tp_getset = PyBobLearnEMKMeansTrainer_getseters;
  PyBobLearnEMKMeansTrainer_Type.tp_call = reinterpret_cast<ternaryfunc>(PyBobLearnEMKMeansTrainer_compute_likelihood);


  // check that everything is fine
  if (PyType_Ready(&PyBobLearnEMKMeansTrainer_Type) < 0) return false;

  // add the type to the module
  Py_INCREF(&PyBobLearnEMKMeansTrainer_Type);
557
  return PyModule_AddObject(module, "KMeansTrainer", (PyObject*)&PyBobLearnEMKMeansTrainer_Type) >= 0;
558
}