關(guān)于KNN的介紹可以參考：?http://blog.csdn.net/fengbingchun/article/details/78464169?

這里給出KNN的C++實(shí)現(xiàn)，用于分類。訓(xùn)練數(shù)據(jù)和測試數(shù)據(jù)均來自MNIST，關(guān)于MNIST的介紹可以參考：?http://blog.csdn.net/fengbingchun/article/details/49611549? ， 從MNIST中提取的40幅圖像，0,1,2,3四類各20張，每類的前10幅來自于訓(xùn)練樣本，用于訓(xùn)練，后10幅來自測試樣本，用于測試，如下圖：

實(shí)現(xiàn)代碼如下：

knn.hpp:

#ifndef FBC_NN_KNN_HPP_
#define FBC_NN_KNN_HPP_#include <memory>
#include <vector>namespace ANN {template<typename T>
class KNN {
public:KNN() = default;void set_k(int k);int set_train_samples(const std::vector<std::vector<T>>& samples, const std::vector<T>& labels);int predict(const std::vector<T>& sample, T& result) const;private:int k = 3;int feature_length = 0;int samples_number = 0;std::unique_ptr<T[]> samples;std::unique_ptr<T[]> labels;
};} // namespace ANN#endif // FBC_NN_KNN_HPP_

knn.cpp:

#include "knn.hpp"
#include <limits>
#include <algorithm>
#include <functional>
#include "common.hpp"namespace ANN {template<typename T>
void KNN<T>::set_k(int k)
{this->k = k;
}template<typename T>
int KNN<T>::set_train_samples(const std::vector<std::vector<T>>& samples, const std::vector<T>& labels)
{CHECK(samples.size() == labels.size());this->samples_number = samples.size();if (this->k > this->samples_number) this->k = this->samples_number;this->feature_length = samples[0].size();this->samples.reset(new T[this->feature_length * this->samples_number]);this->labels.reset(new T[this->samples_number]);T* p = this->samples.get();for (int i = 0; i < this->samples_number; ++i) {T* q = p + i * this->feature_length;for (int j = 0; j < this->feature_length; ++j) {q[j] = samples[i][j];}this->labels.get()[i] = labels[i];}
}template<typename T>
int KNN<T>::predict(const std::vector<T>& sample, T& result) const
{if (sample.size() != this->feature_length) {fprintf(stderr, "their feature length dismatch: %d, %d", sample.size(), this->feature_length);return -1;}typedef std::pair<T, T> value;std::vector<value> info;for (int i = 0; i < this->k + 1; ++i) {info.push_back(std::make_pair(std::numeric_limits<T>::max(), (T)-1.));}for (int i = 0; i < this->samples_number; ++i) {T s{ 0. };const T* p = this->samples.get() + i * this->feature_length;for (int j = 0; j < this->feature_length; ++j) {s += (p[j] - sample[j]) * (p[j] - sample[j]);}info[this->k] = std::make_pair(s, this->labels.get()[i]);std::stable_sort(info.begin(), info.end(), [](const std::pair<T, T>& p1, const std::pair<T, T>& p2) {return p1.first < p2.first; });}std::vector<T> vec(this->k);for (int i = 0; i < this->k; ++i) {vec[i] = info[i].second;}std::sort(vec.begin(), vec.end(), std::greater<T>());vec.erase(std::unique(vec.begin(), vec.end()), vec.end());std::vector<std::pair<T, int>> ret;for (int i = 0; i < vec.size(); ++i) {ret.push_back(std::make_pair(vec[i], 0));}for (int i = 0; i < this->k; ++i) {for (int j = 0; j < ret.size(); ++j) {if (info[i].second == ret[j].first) {++ret[j].second;break;}}}int max = -1, index = -1;for (int i = 0; i < ret.size(); ++i) {if (ret[i].second > max) {max = ret[i].second;index = i;}}result = ret[index].first;return 0;
}template class KNN<float>;
template class KNN<double>;} // namespace ANN

測試代碼如下：

#include "funset.hpp"
#include <iostream>
#include "perceptron.hpp"
#include "BP.hpp""
#include "CNN.hpp"
#include "linear_regression.hpp"
#include "naive_bayes_classifier.hpp"
#include "logistic_regression.hpp"
#include "common.hpp"
#include "knn.hpp"
#include <opencv2/opencv.hpp>// =========================== KNN(K-Nearest Neighbor) ======================
int test_knn_classifier_predict()
{const std::string image_path{ "E:/GitCode/NN_Test/data/images/digit/handwriting_0_and_1/" };const int K{ 3 };cv::Mat tmp = cv::imread(image_path + "0_1.jpg", 0);const int train_samples_number{ 40 }, predict_samples_number{ 40 };const int every_class_number{ 10 };cv::Mat train_data(train_samples_number, tmp.rows * tmp.cols, CV_32FC1);cv::Mat train_labels(train_samples_number, 1, CV_32FC1);float* p = (float*)train_labels.data;for (int i = 0; i < 4; ++i) {std::for_each(p + i * every_class_number, p + (i + 1)*every_class_number, [i](float& v){v = (float)i; });}// train datafor (int i = 0; i < 4; ++i) {static const std::vector<std::string> digit{ "0_", "1_", "2_", "3_" };static const std::string suffix{ ".jpg" };for (int j = 1; j <= every_class_number; ++j) {std::string image_name = image_path + digit[i] + std::to_string(j) + suffix;cv::Mat image = cv::imread(image_name, 0);CHECK(!image.empty() && image.isContinuous());image.convertTo(image, CV_32FC1);image = image.reshape(0, 1);tmp = train_data.rowRange(i * every_class_number + j - 1, i * every_class_number + j);image.copyTo(tmp);}}ANN::KNN<float> knn;knn.set_k(K);std::vector<std::vector<float>> samples(train_samples_number);std::vector<float> labels(train_samples_number);const int feature_length{ tmp.rows * tmp.cols };for (int i = 0; i < train_samples_number; ++i) {samples[i].resize(feature_length);const float* p1 = train_data.ptr<float>(i);float* p2 = samples[i].data();memcpy(p2, p1, feature_length * sizeof(float));}const float* p1 = (const float*)train_labels.data;float* p2 = labels.data();memcpy(p2, p1, train_samples_number * sizeof(float));knn.set_train_samples(samples, labels);// predict dattacv::Mat predict_data(predict_samples_number, tmp.rows * tmp.cols, CV_32FC1);for (int i = 0; i < 4; ++i) {static const std::vector<std::string> digit{ "0_", "1_", "2_", "3_" };static const std::string suffix{ ".jpg" };for (int j = 11; j <= every_class_number + 10; ++j) {std::string image_name = image_path + digit[i] + std::to_string(j) + suffix;cv::Mat image = cv::imread(image_name, 0);CHECK(!image.empty() && image.isContinuous());image.convertTo(image, CV_32FC1);image = image.reshape(0, 1);tmp = predict_data.rowRange(i * every_class_number + j - 10 - 1, i * every_class_number + j - 10);image.copyTo(tmp);}}cv::Mat predict_labels(predict_samples_number, 1, CV_32FC1);p = (float*)predict_labels.data;for (int i = 0; i < 4; ++i) {std::for_each(p + i * every_class_number, p + (i + 1)*every_class_number, [i](float& v){v = (float)i; });}std::vector<float> sample(feature_length);int count{ 0 };for (int i = 0; i < predict_samples_number; ++i) {float value1 = ((float*)predict_labels.data)[i];float value2;memcpy(sample.data(), predict_data.ptr<float>(i), feature_length * sizeof(float));CHECK(knn.predict(sample, value2) == 0);fprintf(stdout, "expected value: %f, actual value: %f\n", value1, value2);if (int(value1) == int(value2)) ++count;}fprintf(stdout, "when K = %d, accuracy: %f\n", K, count * 1.f / predict_samples_number);return 0;
}

執(zhí)行結(jié)果如下：與OpenCV中KNN結(jié)果相似。

GitHub：? https://github.com/fengbingchun/NN_Test?

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