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ara-mdk / platform / external / opencv / 6acb9a7ea3d7564944e12cbc73a857b88c1301ee / . / ml / src / mlboost.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// copy or use the software.
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//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
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//M*/
#include "_ml.h"
static inline double
log_ratio( double val )
{
const double eps = 1e-5;
val = MAX( val, eps );
val = MIN( val, 1. - eps );
return log( val/(1. - val) );
}
CvBoostParams::CvBoostParams()
{
boost_type = CvBoost::REAL;
weak_count = 100;
weight_trim_rate = 0.95;
cv_folds = 0;
max_depth = 1;
}
CvBoostParams::CvBoostParams( int _boost_type, int _weak_count,
double _weight_trim_rate, int _max_depth,
bool _use_surrogates, const float* _priors )
{
boost_type = _boost_type;
weak_count = _weak_count;
weight_trim_rate = _weight_trim_rate;
split_criteria = CvBoost::DEFAULT;
cv_folds = 0;
max_depth = _max_depth;
use_surrogates = _use_surrogates;
priors = _priors;
}
///////////////////////////////// CvBoostTree ///////////////////////////////////
CvBoostTree::CvBoostTree()
{
ensemble = 0;
}
CvBoostTree::~CvBoostTree()
{
clear();
}
void
CvBoostTree::clear()
{
CvDTree::clear();
ensemble = 0;
}
bool
CvBoostTree::train( CvDTreeTrainData* _train_data,
const CvMat* _subsample_idx, CvBoost* _ensemble )
{
clear();
ensemble = _ensemble;
data = _train_data;
data->shared = true;
return do_train( _subsample_idx );
}
bool
CvBoostTree::train( const CvMat*, int, const CvMat*, const CvMat*,
const CvMat*, const CvMat*, const CvMat*, CvDTreeParams )
{
assert(0);
return false;
}
bool
CvBoostTree::train( CvDTreeTrainData*, const CvMat* )
{
assert(0);
return false;
}
void
CvBoostTree::scale( double scale )
{
CvDTreeNode* node = root;
// traverse the tree and scale all the node values
for(;;)
{
CvDTreeNode* parent;
for(;;)
{
node->value *= scale;
if( !node->left )
break;
node = node->left;
}
for( parent = node->parent; parent && parent->right == node;
node = parent, parent = parent->parent )
;
if( !parent )
break;
node = parent->right;
}
}
void
CvBoostTree::try_split_node( CvDTreeNode* node )
{
CvDTree::try_split_node( node );
if( !node->left )
{
// if the node has not been split,
// store the responses for the corresponding training samples
double* weak_eval = ensemble->get_weak_response()->data.db;
int* labels = data->get_labels( node );
int i, count = node->sample_count;
double value = node->value;
for( i = 0; i < count; i++ )
weak_eval[labels[i]] = value;
}
}
double
CvBoostTree::calc_node_dir( CvDTreeNode* node )
{
char* dir = (char*)data->direction->data.ptr;
const double* weights = ensemble->get_subtree_weights()->data.db;
int i, n = node->sample_count, vi = node->split->var_idx;
double L, R;
assert( !node->split->inversed );
if( data->get_var_type(vi) >= 0 ) // split on categorical var
{
const int* cat_labels = data->get_cat_var_data( node, vi );
const int* subset = node->split->subset;
double sum = 0, sum_abs = 0;
for( i = 0; i < n; i++ )
{
int idx = cat_labels[i];
double w = weights[i];
int d = idx >= 0 ? CV_DTREE_CAT_DIR(idx,subset) : 0;
sum += d*w; sum_abs += (d & 1)*w;
dir[i] = (char)d;
}
R = (sum_abs + sum) * 0.5;
L = (sum_abs - sum) * 0.5;
}
else // split on ordered var
{
const CvPair32s32f* sorted = data->get_ord_var_data(node,vi);
int split_point = node->split->ord.split_point;
int n1 = node->get_num_valid(vi);
assert( 0 <= split_point && split_point < n1-1 );
L = R = 0;
for( i = 0; i <= split_point; i++ )
{
int idx = sorted[i].i;
double w = weights[idx];
dir[idx] = (char)-1;
L += w;
}
for( ; i < n1; i++ )
{
int idx = sorted[i].i;
double w = weights[idx];
dir[idx] = (char)1;
R += w;
}
for( ; i < n; i++ )
dir[sorted[i].i] = (char)0;
}
node->maxlr = MAX( L, R );
return node->split->quality/(L + R);
}
CvDTreeSplit*
CvBoostTree::find_split_ord_class( CvDTreeNode* node, int vi )
{
const float epsilon = FLT_EPSILON*2;
const CvPair32s32f* sorted = data->get_ord_var_data(node, vi);
const int* responses = data->get_class_labels(node);
const double* weights = ensemble->get_subtree_weights()->data.db;
int n = node->sample_count;
int n1 = node->get_num_valid(vi);
const double* rcw0 = weights + n;
double lcw[2] = {0,0}, rcw[2];
int i, best_i = -1;
double best_val = 0;
int boost_type = ensemble->get_params().boost_type;
int split_criteria = ensemble->get_params().split_criteria;
rcw[0] = rcw0[0]; rcw[1] = rcw0[1];
for( i = n1; i < n; i++ )
{
int idx = sorted[i].i;
double w = weights[idx];
rcw[responses[idx]] -= w;
}
if( split_criteria != CvBoost::GINI && split_criteria != CvBoost::MISCLASS )
split_criteria = boost_type == CvBoost::DISCRETE ? CvBoost::MISCLASS : CvBoost::GINI;
if( split_criteria == CvBoost::GINI )
{
double L = 0, R = rcw[0] + rcw[1];
double lsum2 = 0, rsum2 = rcw[0]*rcw[0] + rcw[1]*rcw[1];
for( i = 0; i < n1 - 1; i++ )
{
int idx = sorted[i].i;
double w = weights[idx], w2 = w*w;
double lv, rv;
idx = responses[idx];
L += w; R -= w;
lv = lcw[idx]; rv = rcw[idx];
lsum2 += 2*lv*w + w2;
rsum2 -= 2*rv*w - w2;
lcw[idx] = lv + w; rcw[idx] = rv - w;
if( sorted[i].val + epsilon < sorted[i+1].val )
{
double val = (lsum2*R + rsum2*L)/(L*R);
if( best_val < val )
{
best_val = val;
best_i = i;
}
}
}
}
else
{
for( i = 0; i < n1 - 1; i++ )
{
int idx = sorted[i].i;
double w = weights[idx];
idx = responses[idx];
lcw[idx] += w;
rcw[idx] -= w;
if( sorted[i].val + epsilon < sorted[i+1].val )
{
double val = lcw[0] + rcw[1], val2 = lcw[1] + rcw[0];
val = MAX(val, val2);
if( best_val < val )
{
best_val = val;
best_i = i;
}
}
}
}
return best_i >= 0 ? data->new_split_ord( vi,
(sorted[best_i].val + sorted[best_i+1].val)*0.5f, best_i,
0, (float)best_val ) : 0;
}
#define CV_CMP_NUM_PTR(a,b) (*(a) < *(b))
static CV_IMPLEMENT_QSORT_EX( icvSortDblPtr, double*, CV_CMP_NUM_PTR, int )
CvDTreeSplit*
CvBoostTree::find_split_cat_class( CvDTreeNode* node, int vi )
{
CvDTreeSplit* split;
const int* cat_labels = data->get_cat_var_data(node, vi);
const int* responses = data->get_class_labels(node);
int ci = data->get_var_type(vi);
int n = node->sample_count;
int mi = data->cat_count->data.i[ci];
double lcw[2]={0,0}, rcw[2]={0,0};
double* cjk = (double*)cvStackAlloc(2*(mi+1)*sizeof(cjk[0]))+2;
const double* weights = ensemble->get_subtree_weights()->data.db;
double** dbl_ptr = (double**)cvStackAlloc( mi*sizeof(dbl_ptr[0]) );
int i, j, k, idx;
double L = 0, R;
double best_val = 0;
int best_subset = -1, subset_i;
int boost_type = ensemble->get_params().boost_type;
int split_criteria = ensemble->get_params().split_criteria;
// init array of counters:
// c_{jk} - number of samples that have vi-th input variable = j and response = k.
for( j = -1; j < mi; j++ )
cjk[j*2] = cjk[j*2+1] = 0;
for( i = 0; i < n; i++ )
{
double w = weights[i];
j = cat_labels[i];
k = responses[i];
cjk[j*2 + k] += w;
}
for( j = 0; j < mi; j++ )
{
rcw[0] += cjk[j*2];
rcw[1] += cjk[j*2+1];
dbl_ptr[j] = cjk + j*2 + 1;
}
R = rcw[0] + rcw[1];
if( split_criteria != CvBoost::GINI && split_criteria != CvBoost::MISCLASS )
split_criteria = boost_type == CvBoost::DISCRETE ? CvBoost::MISCLASS : CvBoost::GINI;
// sort rows of c_jk by increasing c_j,1
// (i.e. by the weight of samples in j-th category that belong to class 1)
icvSortDblPtr( dbl_ptr, mi, 0 );
for( subset_i = 0; subset_i < mi-1; subset_i++ )
{
idx = (int)(dbl_ptr[subset_i] - cjk)/2;
const double* crow = cjk + idx*2;
double w0 = crow[0], w1 = crow[1];
double weight = w0 + w1;
if( weight < FLT_EPSILON )
continue;
lcw[0] += w0; rcw[0] -= w0;
lcw[1] += w1; rcw[1] -= w1;
if( split_criteria == CvBoost::GINI )
{
double lsum2 = lcw[0]*lcw[0] + lcw[1]*lcw[1];
double rsum2 = rcw[0]*rcw[0] + rcw[1]*rcw[1];
L += weight;
R -= weight;
if( L > FLT_EPSILON && R > FLT_EPSILON )
{
double val = (lsum2*R + rsum2*L)/(L*R);
if( best_val < val )
{
best_val = val;
best_subset = subset_i;
}
}
}
else
{
double val = lcw[0] + rcw[1];
double val2 = lcw[1] + rcw[0];
val = MAX(val, val2);
if( best_val < val )
{
best_val = val;
best_subset = subset_i;
}
}
}
if( best_subset < 0 )
return 0;
split = data->new_split_cat( vi, (float)best_val );
for( i = 0; i <= best_subset; i++ )
{
idx = (int)(dbl_ptr[i] - cjk) >> 1;
split->subset[idx >> 5] |= 1 << (idx & 31);
}
return split;
}
CvDTreeSplit*
CvBoostTree::find_split_ord_reg( CvDTreeNode* node, int vi )
{
const float epsilon = FLT_EPSILON*2;
const CvPair32s32f* sorted = data->get_ord_var_data(node, vi);
const float* responses = data->get_ord_responses(node);
const double* weights = ensemble->get_subtree_weights()->data.db;
int n = node->sample_count;
int n1 = node->get_num_valid(vi);
int i, best_i = -1;
double best_val = 0, lsum = 0, rsum = node->value*n;
double L = 0, R = weights[n];
// compensate for missing values
for( i = n1; i < n; i++ )
{
int idx = sorted[i].i;
double w = weights[idx];
rsum -= responses[idx]*w;
R -= w;
}
// find the optimal split
for( i = 0; i < n1 - 1; i++ )
{
int idx = sorted[i].i;
double w = weights[idx];
double t = responses[idx]*w;
L += w; R -= w;
lsum += t; rsum -= t;
if( sorted[i].val + epsilon < sorted[i+1].val )
{
double val = (lsum*lsum*R + rsum*rsum*L)/(L*R);
if( best_val < val )
{
best_val = val;
best_i = i;
}
}
}
return best_i >= 0 ? data->new_split_ord( vi,
(sorted[best_i].val + sorted[best_i+1].val)*0.5f, best_i,
0, (float)best_val ) : 0;
}
CvDTreeSplit*
CvBoostTree::find_split_cat_reg( CvDTreeNode* node, int vi )
{
CvDTreeSplit* split;
const int* cat_labels = data->get_cat_var_data(node, vi);
const float* responses = data->get_ord_responses(node);
const double* weights = ensemble->get_subtree_weights()->data.db;
int ci = data->get_var_type(vi);
int n = node->sample_count;
int mi = data->cat_count->data.i[ci];
double* sum = (double*)cvStackAlloc( (mi+1)*sizeof(sum[0]) ) + 1;
double* counts = (double*)cvStackAlloc( (mi+1)*sizeof(counts[0]) ) + 1;
double** sum_ptr = (double**)cvStackAlloc( mi*sizeof(sum_ptr[0]) );
double L = 0, R = 0, best_val = 0, lsum = 0, rsum = 0;
int i, best_subset = -1, subset_i;
for( i = -1; i < mi; i++ )
sum[i] = counts[i] = 0;
// calculate sum response and weight of each category of the input var
for( i = 0; i < n; i++ )
{
int idx = cat_labels[i];
double w = weights[i];
double s = sum[idx] + responses[i]*w;
double nc = counts[idx] + w;
sum[idx] = s;
counts[idx] = nc;
}
// calculate average response in each category
for( i = 0; i < mi; i++ )
{
R += counts[i];
rsum += sum[i];
sum[i] /= counts[i];
sum_ptr[i] = sum + i;
}
icvSortDblPtr( sum_ptr, mi, 0 );
// revert back to unnormalized sums
// (there should be a very little loss in accuracy)
for( i = 0; i < mi; i++ )
sum[i] *= counts[i];
for( subset_i = 0; subset_i < mi-1; subset_i++ )
{
int idx = (int)(sum_ptr[subset_i] - sum);
double ni = counts[idx];
if( ni > FLT_EPSILON )
{
double s = sum[idx];
lsum += s; L += ni;
rsum -= s; R -= ni;
if( L > FLT_EPSILON && R > FLT_EPSILON )
{
double val = (lsum*lsum*R + rsum*rsum*L)/(L*R);
if( best_val < val )
{
best_val = val;
best_subset = subset_i;
}
}
}
}
if( best_subset < 0 )
return 0;
split = data->new_split_cat( vi, (float)best_val );
for( i = 0; i <= best_subset; i++ )
{
int idx = (int)(sum_ptr[i] - sum);
split->subset[idx >> 5] |= 1 << (idx & 31);
}
return split;
}
CvDTreeSplit*
CvBoostTree::find_surrogate_split_ord( CvDTreeNode* node, int vi )
{
const float epsilon = FLT_EPSILON*2;
const CvPair32s32f* sorted = data->get_ord_var_data(node, vi);
const double* weights = ensemble->get_subtree_weights()->data.db;
const char* dir = (char*)data->direction->data.ptr;
int n1 = node->get_num_valid(vi);
// LL - number of samples that both the primary and the surrogate splits send to the left
// LR - ... primary split sends to the left and the surrogate split sends to the right
// RL - ... primary split sends to the right and the surrogate split sends to the left
// RR - ... both send to the right
int i, best_i = -1, best_inversed = 0;
double best_val;
double LL = 0, RL = 0, LR, RR;
double worst_val = node->maxlr;
double sum = 0, sum_abs = 0;
best_val = worst_val;
for( i = 0; i < n1; i++ )
{
int idx = sorted[i].i;
double w = weights[idx];
int d = dir[idx];
sum += d*w; sum_abs += (d & 1)*w;
}
// sum_abs = R + L; sum = R - L
RR = (sum_abs + sum)*0.5;
LR = (sum_abs - sum)*0.5;
// initially all the samples are sent to the right by the surrogate split,
// LR of them are sent to the left by primary split, and RR - to the right.
// now iteratively compute LL, LR, RL and RR for every possible surrogate split value.
for( i = 0; i < n1 - 1; i++ )
{
int idx = sorted[i].i;
double w = weights[idx];
int d = dir[idx];
if( d < 0 )
{
LL += w; LR -= w;
if( LL + RR > best_val && sorted[i].val + epsilon < sorted[i+1].val )
{
best_val = LL + RR;
best_i = i; best_inversed = 0;
}
}
else if( d > 0 )
{
RL += w; RR -= w;
if( RL + LR > best_val && sorted[i].val + epsilon < sorted[i+1].val )
{
best_val = RL + LR;
best_i = i; best_inversed = 1;
}
}
}
return best_i >= 0 && best_val > node->maxlr ? data->new_split_ord( vi,
(sorted[best_i].val + sorted[best_i+1].val)*0.5f, best_i,
best_inversed, (float)best_val ) : 0;
}
CvDTreeSplit*
CvBoostTree::find_surrogate_split_cat( CvDTreeNode* node, int vi )
{
const int* cat_labels = data->get_cat_var_data(node, vi);
const char* dir = (char*)data->direction->data.ptr;
const double* weights = ensemble->get_subtree_weights()->data.db;
int n = node->sample_count;
// LL - number of samples that both the primary and the surrogate splits send to the left
// LR - ... primary split sends to the left and the surrogate split sends to the right
// RL - ... primary split sends to the right and the surrogate split sends to the left
// RR - ... both send to the right
CvDTreeSplit* split = data->new_split_cat( vi, 0 );
int i, mi = data->cat_count->data.i[data->get_var_type(vi)];
double best_val = 0;
double* lc = (double*)cvStackAlloc( (mi+1)*2*sizeof(lc[0]) ) + 1;
double* rc = lc + mi + 1;
for( i = -1; i < mi; i++ )
lc[i] = rc[i] = 0;
// 1. for each category calculate the weight of samples
// sent to the left (lc) and to the right (rc) by the primary split
for( i = 0; i < n; i++ )
{
int idx = cat_labels[i];
double w = weights[i];
int d = dir[i];
double sum = lc[idx] + d*w;
double sum_abs = rc[idx] + (d & 1)*w;
lc[idx] = sum; rc[idx] = sum_abs;
}
for( i = 0; i < mi; i++ )
{
double sum = lc[i];
double sum_abs = rc[i];
lc[i] = (sum_abs - sum) * 0.5;
rc[i] = (sum_abs + sum) * 0.5;
}
// 2. now form the split.
// in each category send all the samples to the same direction as majority
for( i = 0; i < mi; i++ )
{
double lval = lc[i], rval = rc[i];
if( lval > rval )
{
split->subset[i >> 5] |= 1 << (i & 31);
best_val += lval;
}
else
best_val += rval;
}
split->quality = (float)best_val;
if( split->quality <= node->maxlr )
cvSetRemoveByPtr( data->split_heap, split ), split = 0;
return split;
}
void
CvBoostTree::calc_node_value( CvDTreeNode* node )
{
int i, count = node->sample_count;
const double* weights = ensemble->get_weights()->data.db;
const int* labels = data->get_labels(node);
double* subtree_weights = ensemble->get_subtree_weights()->data.db;
double rcw[2] = {0,0};
int boost_type = ensemble->get_params().boost_type;
//const double* priors = data->priors->data.db;
if( data->is_classifier )
{
const int* responses = data->get_class_labels(node);
for( i = 0; i < count; i++ )
{
int idx = labels[i];
double w = weights[idx]/*priors[responses[i]]*/;
rcw[responses[i]] += w;
subtree_weights[i] = w;
}
node->class_idx = rcw[1] > rcw[0];
if( boost_type == CvBoost::DISCRETE )
{
// ignore cat_map for responses, and use {-1,1},
// as the whole ensemble response is computes as sign(sum_i(weak_response_i)
node->value = node->class_idx*2 - 1;
}
else
{
double p = rcw[1]/(rcw[0] + rcw[1]);
assert( boost_type == CvBoost::REAL );
// store log-ratio of the probability
node->value = 0.5*log_ratio(p);
}
}
else
{
// in case of regression tree:
// * node value is 1/n*sum_i(Y_i), where Y_i is i-th response,
// n is the number of samples in the node.
// * node risk is the sum of squared errors: sum_i((Y_i - <node_value>)^2)
double sum = 0, sum2 = 0, iw;
const float* values = data->get_ord_responses(node);
for( i = 0; i < count; i++ )
{
int idx = labels[i];
double w = weights[idx]/*priors[values[i] > 0]*/;
double t = values[i];
rcw[0] += w;
subtree_weights[i] = w;
sum += t*w;
sum2 += t*t*w;
}
iw = 1./rcw[0];
node->value = sum*iw;
node->node_risk = sum2 - (sum*iw)*sum;
// renormalize the risk, as in try_split_node the unweighted formula
// sqrt(risk)/n is used, rather than sqrt(risk)/sum(weights_i)
node->node_risk *= count*iw*count*iw;
}
// store summary weights
subtree_weights[count] = rcw[0];
subtree_weights[count+1] = rcw[1];
}
void CvBoostTree::read( CvFileStorage* fs, CvFileNode* fnode, CvBoost* _ensemble, CvDTreeTrainData* _data )
{
CvDTree::read( fs, fnode, _data );
ensemble = _ensemble;
}
void CvBoostTree::read( CvFileStorage*, CvFileNode* )
{
assert(0);
}
void CvBoostTree::read( CvFileStorage* _fs, CvFileNode* _node,
CvDTreeTrainData* _data )
{
CvDTree::read( _fs, _node, _data );
}
/////////////////////////////////// CvBoost /////////////////////////////////////
CvBoost::CvBoost()
{
data = 0;
weak = 0;
default_model_name = "my_boost_tree";
orig_response = sum_response = weak_eval = subsample_mask =
weights = subtree_weights = 0;
clear();
}
void CvBoost::prune( CvSlice slice )
{
if( weak )
{
CvSeqReader reader;
int i, count = cvSliceLength( slice, weak );
cvStartReadSeq( weak, &reader );
cvSetSeqReaderPos( &reader, slice.start_index );
for( i = 0; i < count; i++ )
{
CvBoostTree* w;
CV_READ_SEQ_ELEM( w, reader );
delete w;
}
cvSeqRemoveSlice( weak, slice );
}
}
void CvBoost::clear()
{
if( weak )
{
prune( CV_WHOLE_SEQ );
cvReleaseMemStorage( &weak->storage );
}
if( data )
delete data;
weak = 0;
data = 0;
cvReleaseMat( &orig_response );
cvReleaseMat( &sum_response );
cvReleaseMat( &weak_eval );
cvReleaseMat( &subsample_mask );
cvReleaseMat( &weights );
have_subsample = false;
}
CvBoost::~CvBoost()
{
clear();
}
CvBoost::CvBoost( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx,
const CvMat* _sample_idx, const CvMat* _var_type,
const CvMat* _missing_mask, CvBoostParams _params )
{
weak = 0;
data = 0;
default_model_name = "my_boost_tree";
orig_response = sum_response = weak_eval = subsample_mask = weights = 0;
train( _train_data, _tflag, _responses, _var_idx, _sample_idx,
_var_type, _missing_mask, _params );
}
bool
CvBoost::set_params( const CvBoostParams& _params )
{
bool ok = false;
CV_FUNCNAME( "CvBoost::set_params" );
__BEGIN__;
params = _params;
if( params.boost_type != DISCRETE && params.boost_type != REAL &&
params.boost_type != LOGIT && params.boost_type != GENTLE )
CV_ERROR( CV_StsBadArg, "Unknown/unsupported boosting type" );
params.weak_count = MAX( params.weak_count, 1 );
params.weight_trim_rate = MAX( params.weight_trim_rate, 0. );
params.weight_trim_rate = MIN( params.weight_trim_rate, 1. );
if( params.weight_trim_rate < FLT_EPSILON )
params.weight_trim_rate = 1.f;
if( params.boost_type == DISCRETE &&
params.split_criteria != GINI && params.split_criteria != MISCLASS )
params.split_criteria = MISCLASS;
if( params.boost_type == REAL &&
params.split_criteria != GINI && params.split_criteria != MISCLASS )
params.split_criteria = GINI;
if( (params.boost_type == LOGIT || params.boost_type == GENTLE) &&
params.split_criteria != SQERR )
params.split_criteria = SQERR;
ok = true;
__END__;
return ok;
}
bool
CvBoost::train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx,
const CvMat* _sample_idx, const CvMat* _var_type,
const CvMat* _missing_mask,
CvBoostParams _params, bool _update )
{
bool ok = false;
CvMemStorage* storage = 0;
CV_FUNCNAME( "CvBoost::train" );
__BEGIN__;
int i;
set_params( _params );
if( !_update || !data )
{
clear();
data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx,
_sample_idx, _var_type, _missing_mask, _params, true, true );
if( data->get_num_classes() != 2 )
CV_ERROR( CV_StsNotImplemented,
"Boosted trees can only be used for 2-class classification." );
CV_CALL( storage = cvCreateMemStorage() );
weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage );
storage = 0;
}
else
{
data->set_data( _train_data, _tflag, _responses, _var_idx,
_sample_idx, _var_type, _missing_mask, _params, true, true, true );
}
update_weights( 0 );
for( i = 0; i < params.weak_count; i++ )
{
CvBoostTree* tree = new CvBoostTree;
if( !tree->train( data, subsample_mask, this ) )
{
delete tree;
continue;
}
//cvCheckArr( get_weak_response());
cvSeqPush( weak, &tree );
update_weights( tree );
trim_weights();
}
data->is_classifier = true;
ok = true;
__END__;
return ok;
}
void
CvBoost::update_weights( CvBoostTree* tree )
{
CV_FUNCNAME( "CvBoost::update_weights" );
__BEGIN__;
int i, count = data->sample_count;
double sumw = 0.;
if( !tree ) // before training the first tree, initialize weights and other parameters
{
const int* class_labels = data->get_class_labels(data->data_root);
// in case of logitboost and gentle adaboost each weak tree is a regression tree,
// so we need to convert class labels to floating-point values
float* responses = data->get_ord_responses(data->data_root);
int* labels = data->get_labels(data->data_root);
double w0 = 1./count;
double p[2] = { 1, 1 };
cvReleaseMat( &orig_response );
cvReleaseMat( &sum_response );
cvReleaseMat( &weak_eval );
cvReleaseMat( &subsample_mask );
cvReleaseMat( &weights );
CV_CALL( orig_response = cvCreateMat( 1, count, CV_32S ));
CV_CALL( weak_eval = cvCreateMat( 1, count, CV_64F ));
CV_CALL( subsample_mask = cvCreateMat( 1, count, CV_8U ));
CV_CALL( weights = cvCreateMat( 1, count, CV_64F ));
CV_CALL( subtree_weights = cvCreateMat( 1, count + 2, CV_64F ));
if( data->have_priors )
{
// compute weight scale for each class from their prior probabilities
int c1 = 0;
for( i = 0; i < count; i++ )
c1 += class_labels[i];
p[0] = data->priors->data.db[0]*(c1 < count ? 1./(count - c1) : 0.);
p[1] = data->priors->data.db[1]*(c1 > 0 ? 1./c1 : 0.);
p[0] /= p[0] + p[1];
p[1] = 1. - p[0];
}
for( i = 0; i < count; i++ )
{
// save original categorical responses {0,1}, convert them to {-1,1}
orig_response->data.i[i] = class_labels[i]*2 - 1;
// make all the samples active at start.
// later, in trim_weights() deactivate/reactive again some, if need
subsample_mask->data.ptr[i] = (uchar)1;
// make all the initial weights the same.
weights->data.db[i] = w0*p[class_labels[i]];
// set the labels to find (from within weak tree learning proc)
// the particular sample weight, and where to store the response.
labels[i] = i;
}
if( params.boost_type == LOGIT )
{
CV_CALL( sum_response = cvCreateMat( 1, count, CV_64F ));
for( i = 0; i < count; i++ )
{
sum_response->data.db[i] = 0;
responses[i] = orig_response->data.i[i] > 0 ? 2.f : -2.f;
}
// in case of logitboost each weak tree is a regression tree.
// the target function values are recalculated for each of the trees
data->is_classifier = false;
}
else if( params.boost_type == GENTLE )
{
for( i = 0; i < count; i++ )
responses[i] = (float)orig_response->data.i[i];
data->is_classifier = false;
}
}
else
{
// at this moment, for all the samples that participated in the training of the most
// recent weak classifier we know the responses. For other samples we need to compute them
if( have_subsample )
{
float* values = (float*)(data->buf->data.ptr + data->buf->step);
uchar* missing = data->buf->data.ptr + data->buf->step*2;
CvMat _sample, _mask;
// invert the subsample mask
cvXorS( subsample_mask, cvScalar(1.), subsample_mask );
data->get_vectors( subsample_mask, values, missing, 0 );
//data->get_vectors( 0, values, missing, 0 );
_sample = cvMat( 1, data->var_count, CV_32F );
_mask = cvMat( 1, data->var_count, CV_8U );
// run tree through all the non-processed samples
for( i = 0; i < count; i++ )
if( subsample_mask->data.ptr[i] )
{
_sample.data.fl = values;
_mask.data.ptr = missing;
values += _sample.cols;
missing += _mask.cols;
weak_eval->data.db[i] = tree->predict( &_sample, &_mask, true )->value;
}
}
// now update weights and other parameters for each type of boosting
if( params.boost_type == DISCRETE )
{
// Discrete AdaBoost:
// weak_eval[i] (=f(x_i)) is in {-1,1}
// err = sum(w_i*(f(x_i) != y_i))/sum(w_i)
// C = log((1-err)/err)
// w_i *= exp(C*(f(x_i) != y_i))
double C, err = 0.;
double scale[] = { 1., 0. };
for( i = 0; i < count; i++ )
{
double w = weights->data.db[i];
sumw += w;
err += w*(weak_eval->data.db[i] != orig_response->data.i[i]);
}
if( sumw != 0 )
err /= sumw;
C = err = -log_ratio( err );
scale[1] = exp(err);
sumw = 0;
for( i = 0; i < count; i++ )
{
double w = weights->data.db[i]*
scale[weak_eval->data.db[i] != orig_response->data.i[i]];
sumw += w;
weights->data.db[i] = w;
}
tree->scale( C );
}
else if( params.boost_type == REAL )
{
// Real AdaBoost:
// weak_eval[i] = f(x_i) = 0.5*log(p(x_i)/(1-p(x_i))), p(x_i)=P(y=1|x_i)
// w_i *= exp(-y_i*f(x_i))
for( i = 0; i < count; i++ )
weak_eval->data.db[i] *= -orig_response->data.i[i];
cvExp( weak_eval, weak_eval );
for( i = 0; i < count; i++ )
{
double w = weights->data.db[i]*weak_eval->data.db[i];
sumw += w;
weights->data.db[i] = w;
}
}
else if( params.boost_type == LOGIT )
{
// LogitBoost:
// weak_eval[i] = f(x_i) in [-z_max,z_max]
// sum_response = F(x_i).
// F(x_i) += 0.5*f(x_i)
// p(x_i) = exp(F(x_i))/(exp(F(x_i)) + exp(-F(x_i))=1/(1+exp(-2*F(x_i)))
// reuse weak_eval: weak_eval[i] <- p(x_i)
// w_i = p(x_i)*1(1 - p(x_i))
// z_i = ((y_i+1)/2 - p(x_i))/(p(x_i)*(1 - p(x_i)))
// store z_i to the data->data_root as the new target responses
const double lb_weight_thresh = FLT_EPSILON;
const double lb_z_max = 10.;
float* responses = data->get_ord_responses(data->data_root);
/*if( weak->total == 7 )
putchar('*');*/
for( i = 0; i < count; i++ )
{
double s = sum_response->data.db[i] + 0.5*weak_eval->data.db[i];
sum_response->data.db[i] = s;
weak_eval->data.db[i] = -2*s;
}
cvExp( weak_eval, weak_eval );
for( i = 0; i < count; i++ )
{
double p = 1./(1. + weak_eval->data.db[i]);
double w = p*(1 - p), z;
w = MAX( w, lb_weight_thresh );
weights->data.db[i] = w;
sumw += w;
if( orig_response->data.i[i] > 0 )
{
z = 1./p;
responses[i] = (float)MIN(z, lb_z_max);
}
else
{
z = 1./(1-p);
responses[i] = (float)-MIN(z, lb_z_max);
}
}
}
else
{
// Gentle AdaBoost:
// weak_eval[i] = f(x_i) in [-1,1]
// w_i *= exp(-y_i*f(x_i))
assert( params.boost_type == GENTLE );
for( i = 0; i < count; i++ )
weak_eval->data.db[i] *= -orig_response->data.i[i];
cvExp( weak_eval, weak_eval );
for( i = 0; i < count; i++ )
{
double w = weights->data.db[i] * weak_eval->data.db[i];
weights->data.db[i] = w;
sumw += w;
}
}
}
// renormalize weights
if( sumw > FLT_EPSILON )
{
sumw = 1./sumw;
for( i = 0; i < count; ++i )
weights->data.db[i] *= sumw;
}
__END__;
}
static CV_IMPLEMENT_QSORT_EX( icvSort_64f, double, CV_LT, int )
void
CvBoost::trim_weights()
{
CV_FUNCNAME( "CvBoost::trim_weights" );
__BEGIN__;
int i, count = data->sample_count, nz_count = 0;
double sum, threshold;
if( params.weight_trim_rate <= 0. || params.weight_trim_rate >= 1. )
EXIT;
// use weak_eval as temporary buffer for sorted weights
cvCopy( weights, weak_eval );
icvSort_64f( weak_eval->data.db, count, 0 );
// as weight trimming occurs immediately after updating the weights,
// where they are renormalized, we assume that the weight sum = 1.
sum = 1. - params.weight_trim_rate;
for( i = 0; i < count; i++ )
{
double w = weak_eval->data.db[i];
if( sum > w )
break;
sum -= w;
}
threshold = i < count ? weak_eval->data.db[i] : DBL_MAX;
for( i = 0; i < count; i++ )
{
double w = weights->data.db[i];
int f = w > threshold;
subsample_mask->data.ptr[i] = (uchar)f;
nz_count += f;
}
have_subsample = nz_count < count;
__END__;
}
float
CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
CvMat* weak_responses, CvSlice slice,
bool raw_mode ) const
{
float* buf = 0;
bool allocated = false;
float value = -FLT_MAX;
CV_FUNCNAME( "CvBoost::predict" );
__BEGIN__;
int i, weak_count, var_count;
CvMat sample, missing;
CvSeqReader reader;
double sum = 0;
int cls_idx;
int wstep = 0;
const int* vtype;
const int* cmap;
const int* cofs;
if( !weak )
CV_ERROR( CV_StsError, "The boosted tree ensemble has not been trained yet" );
if( !CV_IS_MAT(_sample) || CV_MAT_TYPE(_sample->type) != CV_32FC1 ||
_sample->cols != 1 && _sample->rows != 1 ||
_sample->cols + _sample->rows - 1 != data->var_all && !raw_mode ||
_sample->cols + _sample->rows - 1 != data->var_count && raw_mode )
CV_ERROR( CV_StsBadArg,
"the input sample must be 1d floating-point vector with the same "
"number of elements as the total number of variables used for training" );
if( _missing )
{
if( !CV_IS_MAT(_missing) || !CV_IS_MASK_ARR(_missing) ||
!CV_ARE_SIZES_EQ(_missing, _sample) )
CV_ERROR( CV_StsBadArg,
"the missing data mask must be 8-bit vector of the same size as input sample" );
}
weak_count = cvSliceLength( slice, weak );
if( weak_count >= weak->total )
{
weak_count = weak->total;
slice.start_index = 0;
}
if( weak_responses )
{
if( !CV_IS_MAT(weak_responses) ||
CV_MAT_TYPE(weak_responses->type) != CV_32FC1 ||
weak_responses->cols != 1 && weak_responses->rows != 1 ||
weak_responses->cols + weak_responses->rows - 1 != weak_count )
CV_ERROR( CV_StsBadArg,
"The output matrix of weak classifier responses must be valid "
"floating-point vector of the same number of components as the length of input slice" );
wstep = CV_IS_MAT_CONT(weak_responses->type) ? 1 : weak_responses->step/sizeof(float);
}
var_count = data->var_count;
vtype = data->var_type->data.i;
cmap = data->cat_map->data.i;
cofs = data->cat_ofs->data.i;
// if need, preprocess the input vector
if( !raw_mode && (data->cat_var_count > 0 || data->var_idx) )
{
int bufsize;
int step, mstep = 0;
const float* src_sample;
const uchar* src_mask = 0;
float* dst_sample;
uchar* dst_mask;
const int* vidx = data->var_idx && !raw_mode ? data->var_idx->data.i : 0;
bool have_mask = _missing != 0;
bufsize = var_count*(sizeof(float) + sizeof(uchar));
if( bufsize <= CV_MAX_LOCAL_SIZE )
buf = (float*)cvStackAlloc( bufsize );
else
{
CV_CALL( buf = (float*)cvAlloc( bufsize ));
allocated = true;
}
dst_sample = buf;
dst_mask = (uchar*)(buf + var_count);
src_sample = _sample->data.fl;
step = CV_IS_MAT_CONT(_sample->type) ? 1 : _sample->step/sizeof(src_sample[0]);
if( _missing )
{
src_mask = _missing->data.ptr;
mstep = CV_IS_MAT_CONT(_missing->type) ? 1 : _missing->step;
}
for( i = 0; i < var_count; i++ )
{
int idx = vidx ? vidx[i] : i;
float val = src_sample[idx*step];
int ci = vtype[i];
uchar m = src_mask ? src_mask[i] : (uchar)0;
if( ci >= 0 )
{
int a = cofs[ci], b = cofs[ci+1], c = a;
int ival = cvRound(val);
if( ival != val )
CV_ERROR( CV_StsBadArg,
"one of input categorical variable is not an integer" );
while( a < b )
{
c = (a + b) >> 1;
if( ival < cmap[c] )
b = c;
else if( ival > cmap[c] )
a = c+1;
else
break;
}
if( c < 0 || ival != cmap[c] )
{
m = 1;
have_mask = true;
}
else
{
val = (float)(c - cofs[ci]);
}
}
dst_sample[i] = val;
dst_mask[i] = m;
}
sample = cvMat( 1, var_count, CV_32F, dst_sample );
_sample = &sample;
if( have_mask )
{
missing = cvMat( 1, var_count, CV_8UC1, dst_mask );
_missing = &missing;
}
}
cvStartReadSeq( weak, &reader );
cvSetSeqReaderPos( &reader, slice.start_index );
for( i = 0; i < weak_count; i++ )
{
CvBoostTree* wtree;
double val;
CV_READ_SEQ_ELEM( wtree, reader );
val = wtree->predict( _sample, _missing, true )->value;
if( weak_responses )
weak_responses->data.fl[i*wstep] = (float)val;
sum += val;
}
cls_idx = sum >= 0;
if( raw_mode )
value = (float)cls_idx;
else
value = (float)cmap[cofs[vtype[var_count]] + cls_idx];
__END__;
if( allocated )
cvFree( &buf );
return value;
}
void CvBoost::write_params( CvFileStorage* fs )
{
CV_FUNCNAME( "CvBoost::write_params" );
__BEGIN__;
const char* boost_type_str =
params.boost_type == DISCRETE ? "DiscreteAdaboost" :
params.boost_type == REAL ? "RealAdaboost" :
params.boost_type == LOGIT ? "LogitBoost" :
params.boost_type == GENTLE ? "GentleAdaboost" : 0;
const char* split_crit_str =
params.split_criteria == DEFAULT ? "Default" :
params.split_criteria == GINI ? "Gini" :
params.boost_type == MISCLASS ? "Misclassification" :
params.boost_type == SQERR ? "SquaredErr" : 0;
if( boost_type_str )
cvWriteString( fs, "boosting_type", boost_type_str );
else
cvWriteInt( fs, "boosting_type", params.boost_type );
if( split_crit_str )
cvWriteString( fs, "splitting_criteria", split_crit_str );
else
cvWriteInt( fs, "splitting_criteria", params.split_criteria );
cvWriteInt( fs, "ntrees", params.weak_count );
cvWriteReal( fs, "weight_trimming_rate", params.weight_trim_rate );
data->write_params( fs );
__END__;
}
void CvBoost::read_params( CvFileStorage* fs, CvFileNode* fnode )
{
CV_FUNCNAME( "CvBoost::read_params" );
__BEGIN__;
CvFileNode* temp;
if( !fnode || !CV_NODE_IS_MAP(fnode->tag) )
return;
data = new CvDTreeTrainData();
CV_CALL( data->read_params(fs, fnode));
data->shared = true;
params.max_depth = data->params.max_depth;
params.min_sample_count = data->params.min_sample_count;
params.max_categories = data->params.max_categories;
params.priors = data->params.priors;
params.regression_accuracy = data->params.regression_accuracy;
params.use_surrogates = data->params.use_surrogates;
temp = cvGetFileNodeByName( fs, fnode, "boosting_type" );
if( !temp )
return;
if( temp && CV_NODE_IS_STRING(temp->tag) )
{
const char* boost_type_str = cvReadString( temp, "" );
params.boost_type = strcmp( boost_type_str, "DiscreteAdaboost" ) == 0 ? DISCRETE :
strcmp( boost_type_str, "RealAdaboost" ) == 0 ? REAL :
strcmp( boost_type_str, "LogitBoost" ) == 0 ? LOGIT :
strcmp( boost_type_str, "GentleAdaboost" ) == 0 ? GENTLE : -1;
}
else
params.boost_type = cvReadInt( temp, -1 );
if( params.boost_type < DISCRETE || params.boost_type > GENTLE )
CV_ERROR( CV_StsBadArg, "Unknown boosting type" );
temp = cvGetFileNodeByName( fs, fnode, "splitting_criteria" );
if( temp && CV_NODE_IS_STRING(temp->tag) )
{
const char* split_crit_str = cvReadString( temp, "" );
params.split_criteria = strcmp( split_crit_str, "Default" ) == 0 ? DEFAULT :
strcmp( split_crit_str, "Gini" ) == 0 ? GINI :
strcmp( split_crit_str, "Misclassification" ) == 0 ? MISCLASS :
strcmp( split_crit_str, "SquaredErr" ) == 0 ? SQERR : -1;
}
else
params.split_criteria = cvReadInt( temp, -1 );
if( params.split_criteria < DEFAULT || params.boost_type > SQERR )
CV_ERROR( CV_StsBadArg, "Unknown boosting type" );
params.weak_count = cvReadIntByName( fs, fnode, "ntrees" );
params.weight_trim_rate = cvReadRealByName( fs, fnode, "weight_trimming_rate", 0. );
__END__;
}
void
CvBoost::read( CvFileStorage* fs, CvFileNode* node )
{
CV_FUNCNAME( "CvRTrees::read" );
__BEGIN__;
CvSeqReader reader;
CvFileNode* trees_fnode;
CvMemStorage* storage;
int i, ntrees;
clear();
read_params( fs, node );
if( !data )
EXIT;
trees_fnode = cvGetFileNodeByName( fs, node, "trees" );
if( !trees_fnode || !CV_NODE_IS_SEQ(trees_fnode->tag) )
CV_ERROR( CV_StsParseError, "<trees> tag is missing" );
cvStartReadSeq( trees_fnode->data.seq, &reader );
ntrees = trees_fnode->data.seq->total;
if( ntrees != params.weak_count )
CV_ERROR( CV_StsUnmatchedSizes,
"The number of trees stored does not match <ntrees> tag value" );
CV_CALL( storage = cvCreateMemStorage() );
weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage );
for( i = 0; i < ntrees; i++ )
{
CvBoostTree* tree = new CvBoostTree();
CV_CALL(tree->read( fs, (CvFileNode*)reader.ptr, this, data ));
CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
cvSeqPush( weak, &tree );
}
__END__;
}
void
CvBoost::write( CvFileStorage* fs, const char* name )
{
CV_FUNCNAME( "CvBoost::write" );
__BEGIN__;
CvSeqReader reader;
int i;
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_BOOSTING );
if( !weak )
CV_ERROR( CV_StsBadArg, "The classifier has not been trained yet" );
write_params( fs );
cvStartWriteStruct( fs, "trees", CV_NODE_SEQ );
cvStartReadSeq( weak, &reader );
for( i = 0; i < weak->total; i++ )
{
CvBoostTree* tree;
CV_READ_SEQ_ELEM( tree, reader );
cvStartWriteStruct( fs, 0, CV_NODE_MAP );
tree->write( fs );
cvEndWriteStruct( fs );
}
cvEndWriteStruct( fs );
cvEndWriteStruct( fs );
__END__;
}
CvMat*
CvBoost::get_weights()
{
return weights;
}
CvMat*
CvBoost::get_subtree_weights()
{
return subtree_weights;
}
CvMat*
CvBoost::get_weak_response()
{
return weak_eval;
}
const CvBoostParams&
CvBoost::get_params() const
{
return params;
}
CvSeq* CvBoost::get_weak_predictors()
{
return weak;
}
/* End of file. */