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opencv/apps/traincascade/old_ml_boost.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// 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,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "old_ml_precomp.hpp"
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;
cv::AutoBuffer<int> inn_buf(node->sample_count);
const int* labels = data->get_cv_labels(node, inn_buf.data());
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
{
cv::AutoBuffer<int> inn_buf(n);
const int* cat_labels = data->get_cat_var_data(node, vi, inn_buf.data());
const int* subset = node->split->subset;
double sum = 0, sum_abs = 0;
for( i = 0; i < n; i++ )
{
int idx = ((cat_labels[i] == 65535) && data->is_buf_16u) ? -1 : 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
{
cv::AutoBuffer<uchar> inn_buf(2*n*sizeof(int)+n*sizeof(float));
float* values_buf = (float*)inn_buf.data();
int* sorted_indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = sorted_indices_buf + n;
const float* values = 0;
const int* sorted_indices = 0;
data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values, &sorted_indices, sample_indices_buf );
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_indices[i];
double w = weights[idx];
dir[idx] = (char)-1;
L += w;
}
for( ; i < n1; i++ )
{
int idx = sorted_indices[i];
double w = weights[idx];
dir[idx] = (char)1;
R += w;
}
for( ; i < n; i++ )
dir[sorted_indices[i]] = (char)0;
}
node->maxlr = MAX( L, R );
return node->split->quality/(L + R);
}
CvDTreeSplit*
CvBoostTree::find_split_ord_class( CvDTreeNode* node, int vi, float init_quality,
CvDTreeSplit* _split, uchar* _ext_buf )
{
const float epsilon = FLT_EPSILON*2;
const double* weights = ensemble->get_subtree_weights()->data.db;
int n = node->sample_count;
int n1 = node->get_num_valid(vi);
cv::AutoBuffer<uchar> inn_buf;
if( !_ext_buf )
inn_buf.allocate(n*(3*sizeof(int)+sizeof(float)));
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
float* values_buf = (float*)ext_buf;
int* sorted_indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = sorted_indices_buf + n;
const float* values = 0;
const int* sorted_indices = 0;
data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values, &sorted_indices, sample_indices_buf );
int* responses_buf = sorted_indices_buf + n;
const int* responses = data->get_class_labels( node, responses_buf );
const double* rcw0 = weights + n;
double lcw[2] = {0,0}, rcw[2];
int i, best_i = -1;
double best_val = init_quality;
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_indices[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_indices[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( values[i] + epsilon < values[i+1] )
{
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_indices[i];
double w = weights[idx];
idx = responses[idx];
lcw[idx] += w;
rcw[idx] -= w;
if( values[i] + epsilon < values[i+1] )
{
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;
}
}
}
}
CvDTreeSplit* split = 0;
if( best_i >= 0 )
{
split = _split ? _split : data->new_split_ord( 0, 0.0f, 0, 0, 0.0f );
split->var_idx = vi;
split->ord.c = (values[best_i] + values[best_i+1])*0.5f;
split->ord.split_point = best_i;
split->inversed = 0;
split->quality = (float)best_val;
}
return split;
}
template<typename T>
class LessThanPtr
{
public:
bool operator()(T* a, T* b) const { return *a < *b; }
};
CvDTreeSplit*
CvBoostTree::find_split_cat_class( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf )
{
int ci = data->get_var_type(vi);
int n = node->sample_count;
int mi = data->cat_count->data.i[ci];
int base_size = (2*mi+3)*sizeof(double) + mi*sizeof(double*);
cv::AutoBuffer<uchar> inn_buf((2*mi+3)*sizeof(double) + mi*sizeof(double*));
if( !_ext_buf)
inn_buf.allocate( base_size + 2*n*sizeof(int) );
uchar* base_buf = inn_buf.data();
uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
int* cat_labels_buf = (int*)ext_buf;
const int* cat_labels = data->get_cat_var_data(node, vi, cat_labels_buf);
int* responses_buf = cat_labels_buf + n;
const int* responses = data->get_class_labels(node, responses_buf);
double lcw[2]={0,0}, rcw[2]={0,0};
double* cjk = (double*)cv::alignPtr(base_buf,sizeof(double))+2;
const double* weights = ensemble->get_subtree_weights()->data.db;
double** dbl_ptr = (double**)(cjk + 2*mi);
int i, j, k, idx;
double L = 0, R;
double best_val = init_quality;
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] == 65535) && data->is_buf_16u) ? -1 : 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)
std::sort(dbl_ptr, dbl_ptr + mi, LessThanPtr<double>());
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;
}
}
}
CvDTreeSplit* split = 0;
if( best_subset >= 0 )
{
split = _split ? _split : data->new_split_cat( 0, -1.0f);
split->var_idx = vi;
split->quality = (float)best_val;
memset( split->subset, 0, (data->max_c_count + 31)/32 * sizeof(int));
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, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf )
{
const float epsilon = FLT_EPSILON*2;
const double* weights = ensemble->get_subtree_weights()->data.db;
int n = node->sample_count;
int n1 = node->get_num_valid(vi);
cv::AutoBuffer<uchar> inn_buf;
if( !_ext_buf )
inn_buf.allocate(2*n*(sizeof(int)+sizeof(float)));
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
float* values_buf = (float*)ext_buf;
int* indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = indices_buf + n;
const float* values = 0;
const int* indices = 0;
data->get_ord_var_data( node, vi, values_buf, indices_buf, &values, &indices, sample_indices_buf );
float* responses_buf = (float*)(indices_buf + n);
const float* responses = data->get_ord_responses( node, responses_buf, sample_indices_buf );
int i, best_i = -1;
double L = 0, R = weights[n];
double best_val = init_quality, lsum = 0, rsum = node->value*R;
// compensate for missing values
for( i = n1; i < n; i++ )
{
int idx = indices[i];
double w = weights[idx];
rsum -= responses[idx]*w;
R -= w;
}
// find the optimal split
for( i = 0; i < n1 - 1; i++ )
{
int idx = indices[i];
double w = weights[idx];
double t = responses[idx]*w;
L += w; R -= w;
lsum += t; rsum -= t;
if( values[i] + epsilon < values[i+1] )
{
double val = (lsum*lsum*R + rsum*rsum*L)/(L*R);
if( best_val < val )
{
best_val = val;
best_i = i;
}
}
}
CvDTreeSplit* split = 0;
if( best_i >= 0 )
{
split = _split ? _split : data->new_split_ord( 0, 0.0f, 0, 0, 0.0f );
split->var_idx = vi;
split->ord.c = (values[best_i] + values[best_i+1])*0.5f;
split->ord.split_point = best_i;
split->inversed = 0;
split->quality = (float)best_val;
}
return split;
}
CvDTreeSplit*
CvBoostTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf )
{
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];
int base_size = (2*mi+3)*sizeof(double) + mi*sizeof(double*);
cv::AutoBuffer<uchar> inn_buf(base_size);
if( !_ext_buf )
inn_buf.allocate(base_size + n*(2*sizeof(int) + sizeof(float)));
uchar* base_buf = inn_buf.data();
uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
int* cat_labels_buf = (int*)ext_buf;
const int* cat_labels = data->get_cat_var_data(node, vi, cat_labels_buf);
float* responses_buf = (float*)(cat_labels_buf + n);
int* sample_indices_buf = (int*)(responses_buf + n);
const float* responses = data->get_ord_responses(node, responses_buf, sample_indices_buf);
double* sum = (double*)cv::alignPtr(base_buf,sizeof(double)) + 1;
double* counts = sum + mi + 1;
double** sum_ptr = (double**)(counts + mi);
double L = 0, R = 0, best_val = init_quality, 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] == 65535) && data->is_buf_16u) ? -1 : 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] = fabs(counts[i]) > DBL_EPSILON ? sum[i]/counts[i] : 0;
sum_ptr[i] = sum + i;
}
std::sort(sum_ptr, sum_ptr + mi, LessThanPtr<double>());
// 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;
}
}
}
}
CvDTreeSplit* split = 0;
if( best_subset >= 0 )
{
split = _split ? _split : data->new_split_cat( 0, -1.0f);
split->var_idx = vi;
split->quality = (float)best_val;
memset( split->subset, 0, (data->max_c_count + 31)/32 * sizeof(int));
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, uchar* _ext_buf )
{
const float epsilon = FLT_EPSILON*2;
int n = node->sample_count;
cv::AutoBuffer<uchar> inn_buf;
if( !_ext_buf )
inn_buf.allocate(n*(2*sizeof(int)+sizeof(float)));
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
float* values_buf = (float*)ext_buf;
int* indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = indices_buf + n;
const float* values = 0;
const int* indices = 0;
data->get_ord_var_data( node, vi, values_buf, indices_buf, &values, &indices, sample_indices_buf );
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 = indices[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 = indices[i];
double w = weights[idx];
int d = dir[idx];
if( d < 0 )
{
LL += w; LR -= w;
if( LL + RR > best_val && values[i] + epsilon < values[i+1] )
{
best_val = LL + RR;
best_i = i; best_inversed = 0;
}
}
else if( d > 0 )
{
RL += w; RR -= w;
if( RL + LR > best_val && values[i] + epsilon < values[i+1] )
{
best_val = RL + LR;
best_i = i; best_inversed = 1;
}
}
}
return best_i >= 0 && best_val > node->maxlr ? data->new_split_ord( vi,
(values[best_i] + values[best_i+1])*0.5f, best_i,
best_inversed, (float)best_val ) : 0;
}
CvDTreeSplit*
CvBoostTree::find_surrogate_split_cat( CvDTreeNode* node, int vi, uchar* _ext_buf )
{
const char* dir = (char*)data->direction->data.ptr;
const double* weights = ensemble->get_subtree_weights()->data.db;
int n = node->sample_count;
int i, mi = data->cat_count->data.i[data->get_var_type(vi)];
int base_size = (2*mi+3)*sizeof(double);
cv::AutoBuffer<uchar> inn_buf(base_size);
if( !_ext_buf )
inn_buf.allocate(base_size + n*sizeof(int));
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
int* cat_labels_buf = (int*)ext_buf;
const int* cat_labels = data->get_cat_var_data(node, vi, cat_labels_buf);
// 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 );
double best_val = 0;
double* lc = (double*)cv::alignPtr(cat_labels_buf + n, sizeof(double)) + 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] == 65535) && data->is_buf_16u) ? -1 : 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, n = node->sample_count;
const double* weights = ensemble->get_weights()->data.db;
cv::AutoBuffer<uchar> inn_buf(n*(sizeof(int) + ( data->is_classifier ? sizeof(int) : sizeof(int) + sizeof(float))));
int* labels_buf = (int*)inn_buf.data();
const int* labels = data->get_cv_labels(node, labels_buf);
double* subtree_weights = ensemble->get_subtree_weights()->data.db;
double rcw[2] = {0,0};
int boost_type = ensemble->get_params().boost_type;
if( data->is_classifier )
{
int* _responses_buf = labels_buf + n;
const int* _responses = data->get_class_labels(node, _responses_buf);
int m = data->get_num_classes();
int* cls_count = data->counts->data.i;
for( int k = 0; k < m; k++ )
cls_count[k] = 0;
for( i = 0; i < n; i++ )
{
int idx = labels[i];
double w = weights[idx];
int r = _responses[i];
rcw[r] += w;
cls_count[r]++;
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;
float* values_buf = (float*)(labels_buf + n);
int* sample_indices_buf = (int*)(values_buf + n);
const float* values = data->get_ord_responses(node, values_buf, sample_indices_buf);
for( i = 0; i < n; 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 *= n*iw*n*iw;
}
// store summary weights
subtree_weights[n] = rcw[0];
subtree_weights[n+1] = rcw[1];
}
void CvBoostTree::read( const cv::FileNode& fnode, CvBoost* _ensemble, CvDTreeTrainData* _data )
{
CvDTree::read( fnode, _data );
ensemble = _ensemble;
}
void CvBoostTree::read( cv::FileNode& )
{
assert(0);
}
void CvBoostTree::read( cv::FileNode& _node,
CvDTreeTrainData* _data )
{
CvDTree::read( _node, _data );
}
/////////////////////////////////// CvBoost /////////////////////////////////////
CvBoost::CvBoost()
{
data = 0;
weak = 0;
default_model_name = "my_boost_tree";
active_vars = active_vars_abs = orig_response = sum_response = weak_eval =
subsample_mask = weights = subtree_weights = 0;
have_active_cat_vars = have_subsample = false;
clear();
}
void CvBoost::prune( CvSlice slice )
{
if( weak && weak->total > 0 )
{
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( &active_vars );
cvReleaseMat( &active_vars_abs );
cvReleaseMat( &orig_response );
cvReleaseMat( &sum_response );
cvReleaseMat( &weak_eval );
cvReleaseMat( &subsample_mask );
cvReleaseMat( &weights );
cvReleaseMat( &subtree_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";
active_vars = active_vars_abs = orig_response = sum_response = weak_eval =
subsample_mask = weights = subtree_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 );
cvReleaseMat( &active_vars );
cvReleaseMat( &active_vars_abs );
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 );
}
if ( (_params.boost_type == LOGIT) || (_params.boost_type == GENTLE) )
data->do_responses_copy();
update_weights( 0 );
for( i = 0; i < params.weak_count; i++ )
{
CvBoostTree* tree = new CvBoostTree;
if( !tree->train( data, subsample_mask, this ) )
{
delete tree;
break;
}
//cvCheckArr( get_weak_response());
cvSeqPush( weak, &tree );
update_weights( tree );
trim_weights();
if( cvCountNonZero(subsample_mask) == 0 )
break;
}
if(weak->total > 0)
{
get_active_vars(); // recompute active_vars* maps and condensed_idx's in the splits.
data->is_classifier = true;
data->free_train_data();
ok = true;
}
else
clear();
__END__;
return ok;
}
bool CvBoost::train( CvMLData* _data,
CvBoostParams _params,
bool update )
{
bool result = false;
CV_FUNCNAME( "CvBoost::train" );
__BEGIN__;
const CvMat* values = _data->get_values();
const CvMat* response = _data->get_responses();
const CvMat* missing = _data->get_missing();
const CvMat* var_types = _data->get_var_types();
const CvMat* train_sidx = _data->get_train_sample_idx();
const CvMat* var_idx = _data->get_var_idx();
CV_CALL( result = train( values, CV_ROW_SAMPLE, response, var_idx,
train_sidx, var_types, missing, _params, update ) );
__END__;
return result;
}
void CvBoost::initialize_weights(double (&p)[2])
{
p[0] = 1.;
p[1] = 1.;
}
void
CvBoost::update_weights( CvBoostTree* tree )
{
CV_FUNCNAME( "CvBoost::update_weights" );
__BEGIN__;
int i, n = data->sample_count;
double sumw = 0.;
int step = 0;
float* fdata = 0;
int *sample_idx_buf;
const int* sample_idx = 0;
cv::AutoBuffer<uchar> inn_buf;
size_t _buf_size = (params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? (size_t)(data->sample_count)*sizeof(int) : 0;
if( !tree )
_buf_size += n*sizeof(int);
else
{
if( have_subsample )
_buf_size += data->get_length_subbuf()*(sizeof(float)+sizeof(uchar));
}
inn_buf.allocate(_buf_size);
uchar* cur_buf_pos = inn_buf.data();
if ( (params.boost_type == LOGIT) || (params.boost_type == GENTLE) )
{
step = CV_IS_MAT_CONT(data->responses_copy->type) ?
1 : data->responses_copy->step / CV_ELEM_SIZE(data->responses_copy->type);
fdata = data->responses_copy->data.fl;
sample_idx_buf = (int*)cur_buf_pos;
cur_buf_pos = (uchar*)(sample_idx_buf + data->sample_count);
sample_idx = data->get_sample_indices( data->data_root, sample_idx_buf );
}
CvMat* dtree_data_buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
if( !tree ) // before training the first tree, initialize weights and other parameters
{
int* class_labels_buf = (int*)cur_buf_pos;
cur_buf_pos = (uchar*)(class_labels_buf + n);
const int* class_labels = data->get_class_labels(data->data_root, class_labels_buf);
// 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
double w0 = 1./ n;
double p[2] = { 1., 1. };
initialize_weights(p);
cvReleaseMat( &orig_response );
cvReleaseMat( &sum_response );
cvReleaseMat( &weak_eval );
cvReleaseMat( &subsample_mask );
cvReleaseMat( &weights );
cvReleaseMat( &subtree_weights );
CV_CALL( orig_response = cvCreateMat( 1, n, CV_32S ));
CV_CALL( weak_eval = cvCreateMat( 1, n, CV_64F ));
CV_CALL( subsample_mask = cvCreateMat( 1, n, CV_8U ));
CV_CALL( weights = cvCreateMat( 1, n, CV_64F ));
CV_CALL( subtree_weights = cvCreateMat( 1, n + 2, CV_64F ));
if( data->have_priors )
{
// compute weight scale for each class from their prior probabilities
int c1 = 0;
for( i = 0; i < n; i++ )
c1 += class_labels[i];
p[0] = data->priors->data.db[0]*(c1 < n ? 1./(n - 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];
}
if (data->is_buf_16u)
{
unsigned short* labels = (unsigned short*)(dtree_data_buf->data.s + data->data_root->buf_idx*length_buf_row +
data->data_root->offset + (size_t)(data->work_var_count-1)*data->sample_count);
for( i = 0; i < n; 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] = (unsigned short)i;
}
}
else
{
int* labels = dtree_data_buf->data.i + data->data_root->buf_idx*length_buf_row +
data->data_root->offset + (size_t)(data->work_var_count-1)*data->sample_count;
for( i = 0; i < n; 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, n, CV_64F ));
for( i = 0; i < n; i++ )
{
sum_response->data.db[i] = 0;
fdata[sample_idx[i]*step] = 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 < n; i++ )
fdata[sample_idx[i]*step] = (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*)cur_buf_pos;
cur_buf_pos = (uchar*)(values + data->get_length_subbuf());
uchar* missing = cur_buf_pos;
cur_buf_pos = missing + data->get_length_subbuf() * (size_t)CV_ELEM_SIZE(data->buf->type);
CvMat _sample, _mask;
// invert the subsample mask
cvXorS( subsample_mask, cvScalar(1.), subsample_mask );
data->get_vectors( subsample_mask, 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 < n; 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 < n; 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 < n; 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 < n; i++ )
weak_eval->data.db[i] *= -orig_response->data.i[i];
cvExp( weak_eval, weak_eval );
for( i = 0; i < n; 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_buf = data->get_resp_float_buf();
const float* responses = 0;
data->get_ord_responses(data->data_root, responses_buf, &responses);*/
/*if( weak->total == 7 )
putchar('*');*/
for( i = 0; i < n; 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 < n; 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;
fdata[sample_idx[i]*step] = (float)MIN(z, lb_z_max);
}
else
{
z = 1./(1-p);
fdata[sample_idx[i]*step] = (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 < n; i++ )
weak_eval->data.db[i] *= -orig_response->data.i[i];
cvExp( weak_eval, weak_eval );
for( i = 0; i < n; 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 < n; ++i )
weights->data.db[i] *= sumw;
}
__END__;
}
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 );
std::sort(weak_eval->data.db, weak_eval->data.db + count);
// 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 <= 0 )
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__;
}
const CvMat*
CvBoost::get_active_vars( bool absolute_idx )
{
CvMat* mask = 0;
CvMat* inv_map = 0;
CvMat* result = 0;
CV_FUNCNAME( "CvBoost::get_active_vars" );
__BEGIN__;
if( !weak )
CV_ERROR( CV_StsError, "The boosted tree ensemble has not been trained yet" );
if( !active_vars || !active_vars_abs )
{
CvSeqReader reader;
int i, j, nactive_vars;
CvBoostTree* wtree;
const CvDTreeNode* node;
assert(!active_vars && !active_vars_abs);
mask = cvCreateMat( 1, data->var_count, CV_8U );
inv_map = cvCreateMat( 1, data->var_count, CV_32S );
cvZero( mask );
cvSet( inv_map, cvScalar(-1) );
// first pass: compute the mask of used variables
cvStartReadSeq( weak, &reader );
for( i = 0; i < weak->total; i++ )
{
CV_READ_SEQ_ELEM(wtree, reader);
node = wtree->get_root();
assert( node != 0 );
for(;;)
{
const CvDTreeNode* parent;
for(;;)
{
CvDTreeSplit* split = node->split;
for( ; split != 0; split = split->next )
mask->data.ptr[split->var_idx] = 1;
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;
}
}
nactive_vars = cvCountNonZero(mask);
//if ( nactive_vars > 0 )
{
active_vars = cvCreateMat( 1, nactive_vars, CV_32S );
active_vars_abs = cvCreateMat( 1, nactive_vars, CV_32S );
have_active_cat_vars = false;
for( i = j = 0; i < data->var_count; i++ )
{
if( mask->data.ptr[i] )
{
active_vars->data.i[j] = i;
active_vars_abs->data.i[j] = data->var_idx ? data->var_idx->data.i[i] : i;
inv_map->data.i[i] = j;
if( data->var_type->data.i[i] >= 0 )
have_active_cat_vars = true;
j++;
}
}
// second pass: now compute the condensed indices
cvStartReadSeq( weak, &reader );
for( i = 0; i < weak->total; i++ )
{
CV_READ_SEQ_ELEM(wtree, reader);
node = wtree->get_root();
for(;;)
{
const CvDTreeNode* parent;
for(;;)
{
CvDTreeSplit* split = node->split;
for( ; split != 0; split = split->next )
{
split->condensed_idx = inv_map->data.i[split->var_idx];
assert( split->condensed_idx >= 0 );
}
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;
}
}
}
}
result = absolute_idx ? active_vars_abs : active_vars;
__END__;
cvReleaseMat( &mask );
cvReleaseMat( &inv_map );
return result;
}
float
CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
CvMat* weak_responses, CvSlice slice,
bool raw_mode, bool return_sum ) const
{
float value = -FLT_MAX;
CvSeqReader reader;
double sum = 0;
int wstep = 0;
const float* sample_data;
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) ||
(active_vars && _sample->cols + _sample->rows - 1 != active_vars->cols && 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 or "
"as the 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" );
}
int i, 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);
}
int var_count = active_vars->cols;
const int* vtype = data->var_type->data.i;
const int* cmap = data->cat_map->data.i;
const int* cofs = data->cat_ofs->data.i;
cv::Mat sample = cv::cvarrToMat(_sample);
cv::Mat missing;
if(!_missing)
missing = cv::cvarrToMat(_missing);
// if need, preprocess the input vector
if( !raw_mode )
{
int sstep, mstep = 0;
const float* src_sample;
const uchar* src_mask = 0;
float* dst_sample;
uchar* dst_mask;
const int* vidx = active_vars->data.i;
const int* vidx_abs = active_vars_abs->data.i;
bool have_mask = _missing != 0;
sample = cv::Mat(1, var_count, CV_32FC1);
missing = cv::Mat(1, var_count, CV_8UC1);
dst_sample = sample.ptr<float>();
dst_mask = missing.ptr<uchar>();
src_sample = _sample->data.fl;
sstep = 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[i], idx_abs = vidx_abs[i];
float val = src_sample[idx_abs*sstep];
int ci = vtype[idx];
uchar m = src_mask ? src_mask[idx_abs*mstep] : (uchar)0;
if( ci >= 0 )
{
int a = cofs[ci], b = (ci+1 >= data->cat_ofs->cols) ? data->cat_map->cols : cofs[ci+1],
c = a;
int ival = cvRound(val);
if ( (ival != val) && (!m) )
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;
}
if( !have_mask )
missing.release();
}
else
{
if( !CV_IS_MAT_CONT(_sample->type & (_missing ? _missing->type : -1)) )
CV_Error( CV_StsBadArg, "In raw mode the input vectors must be continuous" );
}
cvStartReadSeq( weak, &reader );
cvSetSeqReaderPos( &reader, slice.start_index );
sample_data = sample.ptr<float>();
if( !have_active_cat_vars && missing.empty() && !weak_responses )
{
for( i = 0; i < weak_count; i++ )
{
CvBoostTree* wtree;
const CvDTreeNode* node;
CV_READ_SEQ_ELEM( wtree, reader );
node = wtree->get_root();
while( node->left )
{
CvDTreeSplit* split = node->split;
int vi = split->condensed_idx;
float val = sample_data[vi];
int dir = val <= split->ord.c ? -1 : 1;
if( split->inversed )
dir = -dir;
node = dir < 0 ? node->left : node->right;
}
sum += node->value;
}
}
else
{
const int* avars = active_vars->data.i;
const uchar* m = !missing.empty() ? missing.ptr<uchar>() : 0;
// full-featured version
for( i = 0; i < weak_count; i++ )
{
CvBoostTree* wtree;
const CvDTreeNode* node;
CV_READ_SEQ_ELEM( wtree, reader );
node = wtree->get_root();
while( node->left )
{
const CvDTreeSplit* split = node->split;
int dir = 0;
for( ; !dir && split != 0; split = split->next )
{
int vi = split->condensed_idx;
int ci = vtype[avars[vi]];
float val = sample_data[vi];
if( m && m[vi] )
continue;
if( ci < 0 ) // ordered
dir = val <= split->ord.c ? -1 : 1;
else // categorical
{
int c = cvRound(val);
dir = CV_DTREE_CAT_DIR(c, split->subset);
}
if( split->inversed )
dir = -dir;
}
if( !dir )
{
int diff = node->right->sample_count - node->left->sample_count;
dir = diff < 0 ? -1 : 1;
}
node = dir < 0 ? node->left : node->right;
}
if( weak_responses )
weak_responses->data.fl[i*wstep] = (float)node->value;
sum += node->value;
}
}
if( return_sum )
value = (float)sum;
else
{
int cls_idx = sum >= 0;
if( raw_mode )
value = (float)cls_idx;
else
value = (float)cmap[cofs[vtype[data->var_count]] + cls_idx];
}
return value;
}
float CvBoost::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
{
float err = 0;
const CvMat* values = _data->get_values();
const CvMat* response = _data->get_responses();
const CvMat* missing = _data->get_missing();
const CvMat* sample_idx = (type == CV_TEST_ERROR) ? _data->get_test_sample_idx() : _data->get_train_sample_idx();
const CvMat* var_types = _data->get_var_types();
int* sidx = sample_idx ? sample_idx->data.i : 0;
int r_step = CV_IS_MAT_CONT(response->type) ?
1 : response->step / CV_ELEM_SIZE(response->type);
bool is_classifier = var_types->data.ptr[var_types->cols-1] == CV_VAR_CATEGORICAL;
int sample_count = sample_idx ? sample_idx->cols : 0;
sample_count = (type == CV_TRAIN_ERROR && sample_count == 0) ? values->rows : sample_count;
float* pred_resp = 0;
if( resp && (sample_count > 0) )
{
resp->resize( sample_count );
pred_resp = &((*resp)[0]);
}
if ( is_classifier )
{
for( int i = 0; i < sample_count; i++ )
{
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 );
if( pred_resp )
pred_resp[i] = r;
int d = fabs((double)r - response->data.fl[si*r_step]) <= FLT_EPSILON ? 0 : 1;
err += d;
}
err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
}
else
{
for( int i = 0; i < sample_count; i++ )
{
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 );
if( pred_resp )
pred_resp[i] = r;
float d = r - response->data.fl[si*r_step];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
}
return err;
}
void CvBoost::write_params( cv::FileStorage& fs ) const
{
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 )
fs.write( "boosting_type", boost_type_str );
else
fs.write( "boosting_type", params.boost_type );
if( split_crit_str )
fs.write( "splitting_criteria", split_crit_str );
else
fs.write( "splitting_criteria", params.split_criteria );
fs.write( "ntrees", weak->total );
fs.write( "weight_trimming_rate", params.weight_trim_rate );
data->write_params( fs );
}
void CvBoost::read_params( cv::FileNode& fnode )
{
CV_FUNCNAME( "CvBoost::read_params" );
__BEGIN__;
if( fnode.empty() || !fnode.isMap() )
return;
data = new CvDTreeTrainData();
data->read_params( 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;
cv::FileNode temp = fnode[ "boosting_type" ];
if( temp.empty() )
return;
if ( temp.isString() )
{
std::string boost_type_str = temp;
params.boost_type = (boost_type_str == "DiscreteAdaboost") ? DISCRETE :
(boost_type_str == "RealAdaboost") ? REAL :
(boost_type_str == "LogitBoost") ? LOGIT :
(boost_type_str == "GentleAdaboost") ? GENTLE : -1;
}
else
params.boost_type = temp.empty() ? -1 : (int)temp;
if( params.boost_type < DISCRETE || params.boost_type > GENTLE )
CV_ERROR( CV_StsBadArg, "Unknown boosting type" );
temp = fnode[ "splitting_criteria" ];
if( !temp.empty() && temp.isString() )
{
std::string split_crit_str = temp;
params.split_criteria = ( split_crit_str == "Default" ) ? DEFAULT :
( split_crit_str == "Gini" ) ? GINI :
( split_crit_str == "Misclassification" ) ? MISCLASS :
( split_crit_str == "SquaredErr" ) ? SQERR : -1;
}
else
params.split_criteria = temp.empty() ? -1 : (int) temp;
if( params.split_criteria < DEFAULT || params.boost_type > SQERR )
CV_ERROR( CV_StsBadArg, "Unknown boosting type" );
params.weak_count = (int) fnode[ "ntrees" ];
params.weight_trim_rate = (double)fnode["weight_trimming_rate"];
__END__;
}
void
CvBoost::read( cv::FileNode& node )
{
CV_FUNCNAME( "CvBoost::read" );
__BEGIN__;
cv::FileNodeIterator reader;
cv::FileNode trees_fnode;
CvMemStorage* storage;
int ntrees;
clear();
read_params( node );
if( !data )
EXIT;
trees_fnode = node[ "trees" ];
if( trees_fnode.empty() || !trees_fnode.isSeq() )
CV_ERROR( CV_StsParseError, "<trees> tag is missing" );
reader = trees_fnode.begin();
ntrees = (int) trees_fnode.size();
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( int i = 0; i < ntrees; i++ )
{
CvBoostTree* tree = new CvBoostTree();
tree->read( *reader, this, data );
reader++;
cvSeqPush( weak, &tree );
}
get_active_vars();
__END__;
}
void
CvBoost::write( cv::FileStorage& fs, const char* name ) const
{
CV_FUNCNAME( "CvBoost::write" );
__BEGIN__;
CvSeqReader reader;
int i;
fs.startWriteStruct( name, cv::FileNode::MAP, CV_TYPE_NAME_ML_BOOSTING );
if( !weak )
CV_ERROR( CV_StsBadArg, "The classifier has not been trained yet" );
write_params( fs );
fs.startWriteStruct( "trees", cv::FileNode::SEQ );
cvStartReadSeq(weak, &reader);
for( i = 0; i < weak->total; i++ )
{
CvBoostTree* tree;
CV_READ_SEQ_ELEM( tree, reader );
fs.startWriteStruct( 0, cv::FileNode::MAP );
tree->write( fs );
fs.endWriteStruct();
}
fs.endWriteStruct();
fs.endWriteStruct();
__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;
}
const CvDTreeTrainData* CvBoost::get_data() const
{
return data;
}
using namespace cv;
CvBoost::CvBoost( const Mat& _train_data, int _tflag,
const Mat& _responses, const Mat& _var_idx,
const Mat& _sample_idx, const Mat& _var_type,
const Mat& _missing_mask,
CvBoostParams _params )
{
weak = 0;
data = 0;
default_model_name = "my_boost_tree";
active_vars = active_vars_abs = orig_response = sum_response = weak_eval =
subsample_mask = weights = subtree_weights = 0;
train( _train_data, _tflag, _responses, _var_idx, _sample_idx,
_var_type, _missing_mask, _params );
}
bool
CvBoost::train( const Mat& _train_data, int _tflag,
const Mat& _responses, const Mat& _var_idx,
const Mat& _sample_idx, const Mat& _var_type,
const Mat& _missing_mask,
CvBoostParams _params, bool _update )
{
train_data_hdr = cvMat(_train_data);
train_data_mat = _train_data;
responses_hdr = cvMat(_responses);
responses_mat = _responses;
CvMat vidx = cvMat(_var_idx), sidx = cvMat(_sample_idx), vtype = cvMat(_var_type), mmask = cvMat(_missing_mask);
return train(&train_data_hdr, _tflag, &responses_hdr, vidx.data.ptr ? &vidx : 0,
sidx.data.ptr ? &sidx : 0, vtype.data.ptr ? &vtype : 0,
mmask.data.ptr ? &mmask : 0, _params, _update);
}
float
CvBoost::predict( const Mat& _sample, const Mat& _missing,
const Range& slice, bool raw_mode, bool return_sum ) const
{
CvMat sample = cvMat(_sample), mmask = cvMat(_missing);
/*if( weak_responses )
{
int weak_count = cvSliceLength( slice, weak );
if( weak_count >= weak->total )
{
weak_count = weak->total;
slice.start_index = 0;
}
if( !(weak_responses->data && weak_responses->type() == CV_32FC1 &&
(weak_responses->cols == 1 || weak_responses->rows == 1) &&
weak_responses->cols + weak_responses->rows - 1 == weak_count) )
weak_responses->create(weak_count, 1, CV_32FC1);
pwr = &(wr = *weak_responses);
}*/
return predict(&sample, _missing.empty() ? 0 : &mmask, 0,
slice == Range::all() ? CV_WHOLE_SEQ : cvSlice(slice.start, slice.end),
raw_mode, return_sum);
}
/* End of file. */