24 using namespace Eigen;
35 m_tolerance = tolerance;
36 m_store_covs = store_covs;
53 m_tolerance = tolerance;
62 m_store_covs = store_covs;
73 m_tolerance = tolerance;
74 m_store_covs = store_covs;
111 SG_ERROR(
"Specified features are not of type CDotFeatures\n")
127 VectorXd norm2(num_vecs*m_num_classes);
131 for (
int k = 0; k < m_num_classes; k++)
134 for (
int i = 0; i < num_vecs; i++)
136 vec = rf->get_feature_vector(i);
142 X.row(i) = Evec - Em_means_col;
144 rf->free_feature_vector(vec, i);
150 for (
int i = 0; i < num_vecs; i++)
151 norm2(i + k*num_vecs) = A.row(i).array().square().sum();
155 for (
int i = 0; i < num_vecs; i++)
156 for (
int k = 0; k < m_num_classes; k++)
158 norm2[i + k*num_vecs] += m_slog[k];
159 norm2[i + k*num_vecs] *= -0.5;
165 for (
int i = 0 ; i < num_vecs; i++)
174 SG_ERROR(
"No labels allocated in QDA training\n")
179 SG_ERROR(
"Speficied features are not of type CDotFeatures\n")
185 SG_ERROR(
"No features allocated in QDA training\n")
190 SG_ERROR(
"No train_labels allocated in QDA training\n")
198 if (num_vec != train_labels.
vlen)
199 SG_ERROR(
"Dimension mismatch between features and labels in QDA training")
201 int32_t* class_idxs = SG_MALLOC(int32_t, num_vec*m_num_classes);
202 int32_t* class_nums = SG_MALLOC(int32_t, m_num_classes);
203 memset(class_nums, 0, m_num_classes*
sizeof(int32_t));
206 for (
int i = 0; i < train_labels.
vlen; i++)
208 class_idx = train_labels.
vector[i];
210 if (class_idx < 0 || class_idx >= m_num_classes)
212 SG_ERROR(
"found label out of {0, 1, 2, ..., num_classes-1}...")
217 class_idxs[ class_idx*num_vec + class_nums[class_idx]++ ] = i;
221 for (
int i = 0; i < m_num_classes; i++)
223 if (class_nums[i] <= 0)
225 SG_ERROR(
"What? One class with no elements\n")
236 cov_dims[2] = m_num_classes;
247 rot_dims[2] = m_num_classes;
255 for (
int k = 0; k < m_num_classes; k++)
257 MatrixXd buffer(class_nums[k], m_dim);
259 for (
int i = 0; i < class_nums[k]; i++)
261 vec = rf->get_feature_vector(class_idxs[k*num_vec + i]);
266 buffer.row(i) = Evec;
268 rf->free_feature_vector(vec, class_idxs[k*num_vec + i]);
271 Em_means /= class_nums[k];
273 for (
int i = 0; i < class_nums[k]; i++)
274 buffer.row(i) -= Em_means;
280 Eigen::JacobiSVD<MatrixXd> eSvd;
281 eSvd.compute(buffer,Eigen::ComputeFullV);
282 sg_memcpy(col, eSvd.singularValues().data(), m_dim*
sizeof(
float64_t));
283 sg_memcpy(rot_mat, eSvd.matrixV().data(), m_dim*m_dim*
sizeof(
float64_t));
292 for (
int i = 0; i < m_dim; i++)
293 for (
int j = 0; j < m_dim; j++)
294 M(i,j)*=scalings[k*m_dim + j];
297 resE = MEig * rotE.transpose();
309 M_dims[2] = m_num_classes;
316 for (
int k = 0; k < m_num_classes; k++)
318 for (
int j = 0; j < m_dim; j++)
320 sinvsqrt[j] = 1.0 /
CMath::sqrt(scalings[k*m_dim + j]);
321 m_slog[k] +=
CMath::log(scalings[k*m_dim + j]);
324 for (
int i = 0; i < m_dim; i++)
325 for (
int j = 0; j < m_dim; j++)
327 idx = k*m_dim*m_dim + i + j*m_dim;
328 m_M[idx] = rotations[idx] * sinvsqrt[j];
T * get_matrix(index_t matIdx) const
static int32_t arg_max(T *vec, int32_t inc, int32_t len, T *maxv_ptr=NULL)
experimental abstract native multiclass machine class
The class Labels models labels, i.e. class assignments of objects.
bool has_property(EFeatureProperty p) const
virtual int32_t get_num_vectors() const =0
Features that support dot products among other operations.
virtual int32_t get_dim_feature_space() const =0
bool set_label(int32_t idx, float64_t label)
static void vector_multiply(T *target, const T *v1, const T *v2, int32_t len)
Compute vector multiplication.
Multiclass Labels for multi-class classification.
T * get_column_vector(index_t col) const
Class SGObject is the base class of all shogun objects.
virtual CMulticlassLabels * apply_multiclass(CFeatures *data=NULL)
static void scale_vector(T alpha, T *vec, int32_t len)
Scale vector inplace.
all of classes and functions are contained in the shogun namespace
virtual void set_features(CDotFeatures *feat)
The class Features is the base class of all feature objects.
static float64_t log(float64_t v)
virtual bool train_machine(CFeatures *data=NULL)
static float32_t sqrt(float32_t x)
virtual void set_labels(CLabels *lab)