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QuadraticTimeMMD.h
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30 
31 #ifndef __QUADRACTIMEMMD_H_
32 #define __QUADRACTIMEMMD_H_
33 
35 
36 namespace shogun
37 {
38 
39 class CFeatures;
40 class CKernel;
41 class CCustomKernel;
42 
45 {
47 };
48 
108 {
109  public:
111 
122  CQuadraticTimeMMD(CKernel* kernel, CFeatures* p_and_q, index_t m);
123 
135  CQuadraticTimeMMD(CKernel* kernel, CFeatures* p, CFeatures* q);
136 
146  CQuadraticTimeMMD(CCustomKernel* custom_kernel, index_t m);
147 
148  virtual ~CQuadraticTimeMMD();
149 
156  virtual float64_t compute_statistic();
157 
165  virtual SGVector<float64_t> compute_statistic(bool multiple_kernels);
166 
178  virtual float64_t compute_p_value(float64_t statistic);
179 
190  virtual float64_t compute_threshold(float64_t alpha);
191 
192  virtual const char* get_name() const
193  {
194  return "QuadraticTimeMMD";
195  };
196 
199  {
200  return S_QUADRATIC_TIME_MMD;
201  }
202 
203 #ifdef HAVE_LAPACK
204 
227  index_t num_eigenvalues);
228 #endif // HAVE_LAPACK
229 
236  void set_num_samples_sepctrum(index_t num_samples_spectrum);
237 
244  void set_num_eigenvalues_spectrum(index_t num_eigenvalues_spectrum);
245 
247  void set_statistic_type(EQuadraticMMDType statistic_type);
248 
270 
271  protected:
274 
277 
278  private:
279  void init();
280 
281  protected:
284 
287 
290 };
291 
292 }
293 
294 #endif /* __QUADRACTIMEMMD_H_ */
EQuadraticMMDType m_statistic_type
virtual const char * get_name() const
int32_t index_t
Definition: common.h:60
The Custom Kernel allows for custom user provided kernel matrices.
Definition: CustomKernel.h:33
virtual float64_t compute_statistic()
void set_statistic_type(EQuadraticMMDType statistic_type)
SGVector< float64_t > fit_null_gamma()
Kernel two sample test base class. Provides an interface for performing a two-sample test using a ker...
virtual EStatisticType get_statistic_type() const
SGVector< float64_t > sample_null_spectrum(index_t num_samples, index_t num_eigenvalues)
This class implements the quadratic time Maximum Mean Statistic as described in [1]. The MMD is the distance of two probability distributions and in a RKHS .
void set_num_eigenvalues_spectrum(index_t num_eigenvalues_spectrum)
double float64_t
Definition: common.h:48
virtual float64_t compute_unbiased_statistic()
void set_num_samples_sepctrum(index_t num_samples_spectrum)
virtual float64_t compute_p_value(float64_t statistic)
The class Features is the base class of all feature objects.
Definition: Features.h:62
virtual float64_t compute_biased_statistic()
The Kernel base class.
Definition: Kernel.h:150
virtual float64_t compute_threshold(float64_t alpha)

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