dll.cpp

#include "utils.h"
#include <stdio.h>
#include "dll.h"

extern "C" __declspec(dllexport) int FilterBank(double* darray, double* dout,
	int s_rate, int subbands, int len, int stimulus, int electrodes, int num_samples, int debug)
{
	std::unique_ptr<Preprocess> pe = std::make_unique<Preprocess>(s_rate, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> trials = Eigen::Tensor<double, 4>(len * stimulus, subbands, electrodes, num_samples);

	Eigen::Tensor<double, 4, Eigen::RowMajor> dtensor = Eigen::TensorMap<Eigen::Tensor<double, 4, Eigen::RowMajor>>(
		darray, len, stimulus, electrodes, num_samples);
	Eigen::Tensor<double, 4> input = dtensor.swap_layout().shuffle(Eigen::array<int, 4>{3, 2, 1, 0});

	for (int block = 0; block < len; block++) {
		for (int i = 0; i < stimulus; i++) {
			Eigen::Tensor<double, 2> single_trial = pe->notch(input.chip(block, 0).chip(i, 0));
			trials.chip<0>(block * stimulus + i) = pe->filterBank(single_trial);
		}
	}
	// Convert train_trials to row-major order
	Eigen::Tensor<double, 4, Eigen::RowMajor> trials_rows = trials.swap_layout().shuffle(Eigen::array<int, 4>{3, 2, 1, 0});
	memcpy(dout, trials_rows.data(), trials_rows.size() * sizeof(double));

	if (debug != 0) {
		tensor4dToCsv(trials, "./filterBank_dll.csv");
	}

	return 0;
}

extern "C" __declspec(dllexport) int TrcaTrain(double* darray, double* pTemplate, double* pU,
	int s_rate, int subbands, int train_len, int stimulus, int electrodes, int num_samples, int debug)
{
	std::unique_ptr<Preprocess> pe = std::make_unique<Preprocess>(s_rate, subbands, electrodes, num_samples);
	std::unique_ptr<Trca> te = std::make_unique<Trca>(subbands, stimulus, electrodes, num_samples, train_len);
	Eigen::Tensor<double, 4> train_trials = Eigen::Tensor<double, 4>(train_len * stimulus, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> templates = Eigen::Tensor<double, 4>(stimulus, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> U_trca = Eigen::Tensor<double, 4>(subbands, stimulus, electrodes, 1);

	//行优先->列优先：map之后转置
	//列优先->行优先：map之后reshape，其中double需要cast到float上面才能reshape，
	//	reshape之后要赋值给tensor float变量之后才能cast到double，原因是reshape传回参数不能被cast解析
	Eigen::Tensor<double, 4, Eigen::RowMajor> dtensor = Eigen::TensorMap<Eigen::Tensor<double, 4, Eigen::RowMajor>>(
		darray, train_len, stimulus, electrodes, num_samples);
	Eigen::Tensor<double, 4> input = dtensor.swap_layout().shuffle(Eigen::array<int, 4>{3, 2, 1, 0});

	//@zikai 4维：(训练次数，目标数，电极通道数，单通道数据)
	//@zikai train_trials init，优化trials分割
	for (int block = 0; block < train_len; block++) {
		for (int i = 0; i < stimulus; i++) {
			Eigen::Tensor<double, 2> single_trial = pe->notch(input.chip(block, 0).chip(i, 0));
			train_trials.chip<0>(block * stimulus + i) = pe->filterBank(single_trial);
		}
	}
	templates = calculateTemplates(train_trials, stimulus, train_len);
	U_trca = te->fit(train_trials, templates);

	if (debug == 1) {
		tensor4dToCsv(templates, "./templates_dll.csv");
		tensor4dToCsv(U_trca, "./u_dll.csv");
	}
	else if (debug == 2) {
		tensor4dToCsv(input, "./input_dll.csv");
		tensor4dToCsv(train_trials, "./trials_fb_dll.csv");
		tensor4dToCsv(templates, "./templates_dll.csv");
		tensor4dToCsv(U_trca, "./u_dll.csv");
	}
	memcpy(pTemplate, templates.data(), templates.size() * sizeof(double));
	memcpy(pU, U_trca.data(), U_trca.size() * sizeof(double));

	return 0;
}

extern "C" __declspec(dllexport) int TrcaTrainOnly(double* darray, double* pTemplate, double* pU,
	int s_rate, int subbands, int train_len, int stimulus, int electrodes, int num_samples, int debug)
{
	std::unique_ptr<Trca> te = std::make_unique<Trca>(subbands, stimulus, electrodes, num_samples, train_len);
	Eigen::Tensor<double, 4> templates = Eigen::Tensor<double, 4>(stimulus, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> U_trca = Eigen::Tensor<double, 4>(subbands, stimulus, electrodes, 1);

	Eigen::Tensor<double, 4, Eigen::RowMajor> dtensor = Eigen::TensorMap<Eigen::Tensor<double, 4, Eigen::RowMajor>>(
		darray, train_len*stimulus, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> train_trials = dtensor.swap_layout().shuffle(Eigen::array<int, 4>{3, 2, 1, 0});

	templates = calculateTemplates(train_trials, stimulus, train_len);
	U_trca = te->fit(train_trials, templates);

	if (debug == 1) {
		tensor4dToCsv(templates, "./templates_dll.csv");
		tensor4dToCsv(U_trca, "./u_dll.csv");
	}
	else if (debug == 2) {
		tensor4dToCsv(train_trials, "./input_dll.csv");
		tensor4dToCsv(templates, "./templates_dll.csv");
		tensor4dToCsv(U_trca, "./u_dll.csv");
	}
	memcpy(pTemplate, templates.data(), templates.size() * sizeof(double));
	memcpy(pU, U_trca.data(), U_trca.size() * sizeof(double));

	return 0;
}

extern "C" __declspec(dllexport) int TrcaTest(double* darray, double* pTemplate, double* pU, double* pcoeff,
	int* pPred, int s_rate, int subbands, int test_len, int stimulus, int electrodes, int num_samples, int debug)
{
	std::unique_ptr<Preprocess> pe = std::make_unique<Preprocess>(s_rate, subbands, electrodes, num_samples);
	std::unique_ptr<Trca> te = std::make_unique<Trca>(subbands, stimulus, electrodes, num_samples);
	Eigen::Tensor<double, 4> test_trial = Eigen::Tensor<double, 4>(test_len, subbands, electrodes, num_samples);

	Eigen::Tensor<double, 4, Eigen::RowMajor> dtensor = Eigen::TensorMap<Eigen::Tensor<double, 4, Eigen::RowMajor>>(
		darray, 1, test_len, electrodes, num_samples);
	Eigen::Tensor<double, 4> input = dtensor.swap_layout().shuffle(Eigen::array<int, 4>{3, 2, 1, 0});

	Eigen::Tensor<double, 4> templates = Eigen::TensorMap<Eigen::Tensor<double, 4>>(
		pTemplate, stimulus, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> U_trca = Eigen::TensorMap<Eigen::Tensor<double, 4>>(
		pU, subbands, stimulus, electrodes, 1);

	//@zikai test_trials init
	for (int i = 0; i < test_len; i++) {
		Eigen::Tensor<double, 2> single_trial = pe->notch(input.chip(0, 0).chip(i, 0));
		test_trial.chip<0>(i) = pe->filterBank(single_trial);
	}

	std::vector<double> coeff;
	Eigen::Tensor<int, 1> pred = te->predict(test_trial, templates, U_trca, U_trca, coeff);

	if (debug != 0) {
		tensor4dToCsv(templates, "./ptr_templates_dll.csv");
		tensor4dToCsv(U_trca, "./ptr_u_dll.csv");
		tensor4dToCsv(test_trial, "./ptr_filterBank_dll.csv");
	}
	memcpy(pPred, pred.data(), pred.size() * sizeof(int));
	memcpy(pcoeff, coeff.data(), coeff.size() * sizeof(double));

	return 0;
}

extern "C" __declspec(dllexport) int TrcaTestOnly(double* darray, double* pTemplate, double* pU, double* pcoeff,
	int* pPred,	int s_rate, int subbands, int test_len, int stimulus, int electrodes, int num_samples, int debug)
{
	std::unique_ptr<Trca> te = std::make_unique<Trca>(subbands, stimulus, electrodes, num_samples);

	Eigen::Tensor<double, 4, Eigen::RowMajor> dtensor = Eigen::TensorMap<Eigen::Tensor<double, 4, Eigen::RowMajor>>(
		darray, test_len, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> test_trial = dtensor.swap_layout().shuffle(Eigen::array<int, 4>{3, 2, 1, 0});

	Eigen::Tensor<double, 4> templates = Eigen::TensorMap<Eigen::Tensor<double, 4>>(
		pTemplate, stimulus, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> U_trca = Eigen::TensorMap<Eigen::Tensor<double, 4>>(
		pU, subbands, stimulus, electrodes, 1);

	std::vector<double> coeff;
	Eigen::Tensor<int, 1> pred = te->predict(test_trial, templates, U_trca, U_trca, coeff);
	if (debug != 0) {
		tensor4dToCsv(templates, "./ptr_templates_dll.csv");
		tensor4dToCsv(U_trca, "./ptr_u_dll.csv");
		tensor4dToCsv(test_trial, "./ptr_filterBank_dll.csv");
	}
	memcpy(pPred, pred.data(), pred.size() * sizeof(int));
	memcpy(pcoeff, coeff.data(), coeff.size() * sizeof(double));
	
	return 0;
}

extern "C" __declspec(dllexport) int TrcaTestCsv(double* darray, char* pTemplate, char* pU, double* pcoeff,
	int* pPred, int s_rate, int subbands, int test_len, int stimulus, int electrodes, int num_samples, int debug)
{
	std::unique_ptr<Preprocess> pe = std::make_unique<Preprocess>(s_rate, subbands, electrodes, num_samples);
	std::unique_ptr<Trca> te = std::make_unique<Trca>(subbands, stimulus, electrodes, num_samples);
	Eigen::Tensor<double, 4> test_trial = Eigen::Tensor<double, 4>(test_len, subbands, electrodes, num_samples);

	Eigen::Tensor<double, 4, Eigen::RowMajor> dtensor = Eigen::TensorMap<Eigen::Tensor<double, 4, Eigen::RowMajor>>(
		darray, 1, test_len, electrodes, num_samples);
	Eigen::Tensor<double, 4> input = dtensor.swap_layout().shuffle(Eigen::array<int, 4>{3, 2, 1, 0});

	Eigen::Tensor<double, 4> templates = tensor4dFromCsv(pTemplate, stimulus, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> U_trca = tensor4dFromCsv(pU, subbands, stimulus, electrodes, 1);

	//@zikai test_trials init
	for (int i = 0; i < test_len; i++) {
		Eigen::Tensor<double, 2> single_trial = pe->notch(input.chip(0, 0).chip(i, 0));
		test_trial.chip<0>(i) = pe->filterBank(single_trial);
	}

	std::vector<double> coeff;
	Eigen::Tensor<int, 1> pred = te->predict(test_trial, templates, U_trca, U_trca, coeff);

	if (debug != 0) {
		tensor4dToCsv(templates, "./csv_templates_dll.csv");
		tensor4dToCsv(U_trca, "./csv_u_dll.csv");
		tensor4dToCsv(test_trial, "./csv_filterBank_dll.csv");
	}
	memcpy(pPred, pred.data(), pred.size() * sizeof(int));
	memcpy(pcoeff, coeff.data(), coeff.size() * sizeof(double));

	return 0;
}

extern "C" __declspec(dllexport) int TrcaTestOnlyCsv(double* darray, char* pTemplate, char* pU, double* pcoeff,
	int* pPred, int s_rate, int subbands, int test_len, int stimulus, int electrodes, int num_samples, int debug)
{
	std::unique_ptr<Trca> te = std::make_unique<Trca>(subbands, stimulus, electrodes, num_samples);

	Eigen::Tensor<double, 4, Eigen::RowMajor> dtensor = Eigen::TensorMap<Eigen::Tensor<double, 4, Eigen::RowMajor>>(
		darray, test_len, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> test_trial = dtensor.swap_layout().shuffle(Eigen::array<int, 4>{3, 2, 1, 0});

	Eigen::Tensor<double, 4> templates = tensor4dFromCsv(pTemplate, stimulus, subbands, electrodes, num_samples);
	Eigen::Tensor<double, 4> U_trca = tensor4dFromCsv(pU, subbands, stimulus, electrodes, 1);

	std::vector<double> coeff;
	Eigen::Tensor<int, 1> pred = te->predict(test_trial, templates, U_trca, U_trca, coeff);

	if (debug != 0) {
		tensor4dToCsv(templates, "./csv_templates_dll.csv");
		tensor4dToCsv(U_trca, "./csv_u_dll.csv");
		tensor4dToCsv(test_trial, "./csv_filterBank_dll.csv");
	}
	memcpy(pPred, pred.data(), pred.size() * sizeof(int));
	memcpy(pcoeff, coeff.data(), coeff.size() * sizeof(double));

	return 0;
}