int main() {
LinearRegression lr;
vector<DataGroup> train_set;
DataGroup train1, train2, train3, train4, train5;
//y = 3 * x1 - 5 * x2 + 3;
DataGroup test1;
//1 1 1
train1.in.push_back(1); train1.in.push_back(1); train1.out.push_back(1);
//1 -1 1
train2.in.push_back(2); train2.in.push_back(1); train2.out.push_back(4);
//-1 1 1
train3.in.push_back(-1); train3.in.push_back(3); train3.out.push_back(-15);
//0 0 1
train4.in.push_back(9); train4.in.push_back(1); train4.out.push_back(25);
//-1 -1 0
train5.in.push_back(0); train5.in.push_back(1); train5.out.push_back(-2);
train_set.push_back(train1);
train_set.push_back(train2);
train_set.push_back(train3);
train_set.push_back(train4);
train_set.push_back(train5);
lr.train(train_set, 1e-5);
test1.in.push_back(1); test1.in.push_back(1); test1.out.push_back(0);
lr.predict(test1);
cout << "result:" << test1.out[0] << endl;
return 0;
}
void test_linearity() {
Radix r;
SimpleLSystemWithBranching sls;
int b_index = 3;
int grid_size = 10;
//int grid_size = 5;
int N = 10000;
cv::Mat_<double> X(N, (grid_size - b_index - 2) * 2 + b_index);
//cv::Mat_<double> X(N, (grid_size - 3) * 2 + 1);
cv::Mat_<double> Y(N, grid_size * grid_size);
for (int i = 0; i < N; ++i) {
cv::Mat_<double> param(1, X.cols);
for (int c = 0; c < X.cols; ++c) {
param(0, c) = rand() % 3 - 1;
}
param.copyTo(X.row(i));
}
cv::Mat_<double> X2(X.rows, X.cols);
int count = 0;
for (int i = 0; i < X.rows; ++i) {
try {
//cv::Mat_<double> density = sls.computeDensity(grid_size, X.row(i), true, true);
cv::Mat_<double> density = sls.computeDensity(grid_size, X.row(i), true, false);
density.copyTo(Y.row(count));
X.row(i).copyTo(X2.row(count));
count++;
} catch (char* ex) {
//cout << "conflict" << endl;
}
}
ml::saveDataset("dataX.txt", X2(cv::Rect(0, 0, X2.cols, count)));
ml::saveDataset("dataY.txt", Y(cv::Rect(0, 0, Y.cols, count)));
//cv::Mat_<double> X, Y;
ml::loadDataset("dataX.txt", X);
ml::loadDataset("dataY.txt", Y);
cv::Mat_<double> trainX, trainY;
cv::Mat_<double> testX, testY;
ml::splitDataset(X, 0.8, trainX, testX);
ml::splitDataset(Y, 0.8, trainY, testY);
// Forward
{
LinearRegression lr;
lr.train(trainX, trainY);
cv::Mat_<double> Y_hat = lr.predict(testX);
cv::Mat_<double> Y_avg;
cv::reduce(trainY, Y_avg, 0, CV_REDUCE_AVG);
Y_avg = cv::repeat(Y_avg, testY.rows, 1);
cout << "-----------------------" << endl;
cout << "Forward:" << endl;
cout << "RMSE: " << ml::rmse(testY, Y_hat, true) << endl;
cout << "Baselime: " << ml::rmse(testY, Y_avg, true) << endl;
}
// Inverse
{
LinearRegression lr;
lr.train(trainY, trainX);
cv::Mat_<double> X_hat = lr.predict(testY);
// Xの各値を-1,0,1にdiscretizeする
{
for (int r = 0; r < X_hat.rows; ++r) {
for (int c = 0; c < X_hat.cols; ++c) {
if (X_hat(r, c) < -0.5) {
X_hat(r, c) = -1;
} else if (X_hat(r,c ) > 0.5) {
X_hat(r, c) = 1;
} else {
X_hat(r, c) = 0;
}
}
}
}
for (int i = 0; i < testX.cols; ++i) {
cout << ml::rmse(testX.col(i), X_hat.col(i), true) << endl;
}
cv::Mat_<double> Y_hat(testY.rows, testY.cols);
for (int i = 0; i < testX.rows; ++i) {
cv::Mat_<double> density_hat = sls.computeDensity(grid_size, X_hat.row(i), true, false);
density_hat.copyTo(Y_hat.row(i));
}
cv::Mat X_avg;
cv::reduce(trainX, X_avg, 0, CV_REDUCE_AVG);
double baselineX = ml::rmse(testX, cv::repeat(X_avg, testX.rows, 1), true);
cv::Mat Y_avg = sls.computeDensity(grid_size, X_avg, true, false);
//cv::reduce(trainY, Y_avg, 0, CV_REDUCE_AVG);
double baselineY = ml::rmse(testY, cv::repeat(Y_avg, testY.rows, 1), true);
cout << "-----------------------" << endl;
cout << "Inverse:" << endl;
cout << "RMSE in Parameter: " << ml::rmse(testX, X_hat, true) << endl;
cout << "Baselime: " << baselineX << endl;
cout << "RMSE in Indicator: " << ml::rmse(testY, Y_hat, true) << endl;
cout << "Baseline: " << baselineY << endl;
}
}