Vlad Shakhuro
11 years ago
parent
3e0c58c581
commit
56ea364cb2
4 changed files with 226 additions and 204 deletions
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#include "stump.hpp" |
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namespace cv |
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{ |
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namespace adas |
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{ |
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/* Cumulative sum by rows */ |
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static void cumsum(const Mat_<float>& src, Mat_<float> dst) |
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{ |
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CV_Assert(src.cols > 0); |
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for( int row = 0; row < src.rows; ++row ) |
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{ |
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dst(row, 0) = src(row, 0); |
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for( int col = 1; col < src.cols; ++col ) |
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{ |
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dst(row, col) = dst(row, col - 1) + src(row, col); |
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} |
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} |
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} |
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int Stump::train(const Mat& data, const Mat& labels, const Mat& weights) |
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{ |
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CV_Assert(labels.rows == 1 && labels.cols == data.cols); |
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CV_Assert(weights.rows == 1 && weights.cols == data.cols); |
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/* Assert that data and labels have int type */ |
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/* Assert that weights have float type */ |
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/* Prepare labels for each feature rearranged according to sorted order */ |
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Mat sorted_labels(data.rows, data.cols, labels.type()); |
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Mat sorted_weights(data.rows, data.cols, weights.type()); |
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Mat indices; |
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sortIdx(data, indices, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING); |
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for( int row = 0; row < indices.rows; ++row ) |
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{ |
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for( int col = 0; col < indices.cols; ++col ) |
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{ |
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sorted_labels.at<int>(row, col) = |
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labels.at<int>(0, indices.at<int>(row, col)); |
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sorted_weights.at<float>(row, col) = |
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weights.at<float>(0, indices.at<float>(row, col)); |
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} |
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} |
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/* Sort feature values */ |
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Mat sorted_data(data.rows, data.cols, data.type()); |
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sort(data, sorted_data, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING); |
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/* Split positive and negative weights */ |
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Mat pos_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols, |
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sorted_weights.type()); |
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Mat neg_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols, |
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sorted_weights.type()); |
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for( int row = 0; row < data.rows; ++row ) |
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{ |
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for( int col = 0; col < data.cols; ++col ) |
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{ |
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if( sorted_labels.at<int>(row, col) == +1 ) |
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{ |
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pos_weights.at<float>(row, col) = |
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sorted_weights.at<float>(row, col); |
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} |
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else |
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{ |
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neg_weights.at<float>(row, col) = |
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sorted_weights.at<float>(row, col); |
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} |
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} |
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} |
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/* Compute cumulative sums for fast stump error computation */ |
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Mat pos_cum_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols, |
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sorted_weights.type()); |
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Mat neg_cum_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols, |
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sorted_weights.type()); |
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cumsum(pos_weights, pos_cum_weights); |
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cumsum(neg_weights, neg_cum_weights); |
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/* Compute total weights of positive and negative samples */ |
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float pos_total_weight = pos_cum_weights.at<float>(0, weights.cols - 1); |
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float neg_total_weight = neg_cum_weights.at<float>(0, weights.cols - 1); |
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float eps = 1. / 4 * labels.cols; |
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/* Compute minimal error */ |
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float min_err = FLT_MAX; |
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int min_row = -1; |
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int min_col = -1; |
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int min_polarity = 0; |
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float min_pos_value = 1, min_neg_value = -1; |
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for( int row = 0; row < sorted_weights.rows; ++row ) |
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{ |
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for( int col = 0; col < sorted_weights.cols - 1; ++col ) |
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{ |
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float err, h_pos, h_neg; |
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// Direct polarity
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float pos_wrong = pos_cum_weights.at<float>(row, col); |
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float pos_right = pos_total_weight - pos_wrong; |
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float neg_right = neg_cum_weights.at<float>(row, col); |
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float neg_wrong = neg_total_weight - neg_right; |
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h_pos = .5 * log((pos_right + eps) / (pos_wrong + eps)); |
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h_neg = .5 * log((neg_wrong + eps) / (neg_right + eps)); |
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err = sqrt(pos_right * neg_wrong) + sqrt(pos_wrong * neg_right); |
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if( err < min_err ) |
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{ |
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min_err = err; |
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min_row = row; |
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min_col = col; |
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min_polarity = +1; |
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min_pos_value = h_pos; |
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min_neg_value = h_neg; |
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} |
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// Opposite polarity
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swap(pos_right, pos_wrong); |
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swap(neg_right, neg_wrong); |
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h_pos = -h_pos; |
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h_neg = -h_neg; |
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err = sqrt(pos_right * neg_wrong) + sqrt(pos_wrong * neg_right); |
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if( err < min_err ) |
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{ |
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min_err = err; |
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min_row = row; |
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min_col = col; |
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min_polarity = -1; |
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min_pos_value = h_pos; |
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min_neg_value = h_neg; |
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} |
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} |
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} |
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/* Compute threshold, store found values in fields */ |
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threshold_ = ( sorted_data.at<int>(min_row, min_col) + |
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sorted_data.at<int>(min_row, min_col + 1) ) / 2; |
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polarity_ = min_polarity; |
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pos_value_ = min_pos_value; |
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neg_value_ = min_neg_value; |
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return min_row; |
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} |
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float Stump::predict(int value) |
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{ |
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return polarity_ * (value - threshold_) > 0 ? pos_value_ : neg_value_; |
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} |
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} /* namespace adas */ |
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} /* namespace cv */ |
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@ -0,0 +1,58 @@ |
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#ifndef __OPENCV_ADAS_STUMP_HPP__ |
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#define __OPENCV_ADAS_STUMP_HPP__ |
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#include <opencv2/core.hpp> |
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namespace cv |
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{ |
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namespace adas |
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{ |
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class Stump |
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{ |
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public: |
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/* Initialize zero stump */ |
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Stump(): threshold_(0), polarity_(1), pos_value_(1), neg_value_(-1) {} |
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/* Initialize stump with given threshold, polarity
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and classification values */ |
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Stump(int threshold, int polarity, float pos_value, float neg_value): |
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threshold_(threshold), polarity_(polarity), |
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pos_value_(pos_value), neg_value_(neg_value) {} |
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/* Train stump for given data
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data — matrix of feature values, size M x N, one feature per row |
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labels — matrix of sample class labels, size 1 x N. Labels can be from |
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{-1, +1} |
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weights — matrix of sample weights, size 1 x N |
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Returns chosen feature index. Feature enumeration starts from 0 |
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*/ |
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int train(const Mat& data, const Mat& labels, const Mat& weights); |
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/* Predict object class given
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value — feature value. Feature must be the same as was chosen |
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during training stump |
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Returns real value, sign(value) means class
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*/ |
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float predict(int value); |
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private: |
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/* Stump decision threshold */ |
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int threshold_; |
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/* Stump polarity, can be from {-1, +1} */ |
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int polarity_; |
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/* Classification values for positive and negative classes */ |
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float pos_value_, neg_value_; |
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}; |
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} /* namespace adas */ |
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} /* namespace cv */ |
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#endif /* __OPENCV_ADAS_STUMP_HPP__ */ |
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