bool SimpleNN::predict(const Mat_<double> &test_X, Mat_<double> &result, string &err_msg){
Mat_<double> input_data = test_X.reshape(0, test_X.rows*test_X.cols); // make it column vector
if (input_data.rows != this->structure[0]){
err_msg = "wrong input size";
return false;
}
for (int row_index = 1; row_index < this->layers[0].rows; ++row_index){
this->layers[0](row_index, 0) = input_data(row_index-1, 0);
}
int num_layers = (int) this->layers.size();
for (int layer_id = 0; layer_id < num_layers - 2; ++layer_id){
Mat_<double> product = tanh(this->weights[layer_id]*this->layers[layer_id]);
for (int row_index = 1; row_index < this->layers[layer_id+1].rows; ++row_index){
this->layers[layer_id+1](row_index, 0) = product(row_index-1, 0);
}
}
// compute the output layer
{
int layer_id = num_layers - 2;
this->layers[layer_id + 1] = tanh(this->weights[layer_id] * this->layers[layer_id]);
}
result = this->layers[num_layers - 1]; // return last layers (output layer).
cout << "result:\n" << result << endl;
err_msg = "";
return true;
}
int BaseDecisionTree::fit(Mat_<double> _X,
Mat_<double> _y,
Mat_<double> sample_weight)
{
// Validation
if (_X.rows == 0 || _X.cols == 0)
return 1;
// Determine output setting
_n_samples = _X.rows;
_n_features = _X.cols;
// Reshape y to shape[n_samples, 1]
_y = _y.reshape(1, _y.total());
// Validation
if (_y.rows != _n_samples)
return 2;
// Calculate class_weight
Mat expended_class_weight(0, 0, CV_32F);
// Get class_weight
if (_class_weight.total() != 0)
expended_class_weight = compute_sample_weight(_class_weight, _y);
// Validation
if (_max_depth <= 0)
_max_depth = static_cast<int>(pow(2, 31) - 1);
if (_max_leaf_nodes <= 0)
_max_leaf_nodes = -1;
if (_max_features <= 0)
_max_features = _n_features;
if (_max_leaf_nodes > -1 && _max_leaf_nodes < 2)
return 3;
if (_min_samples_split <= 0)
return 4;
if (_min_samples_leaf <= 0)
return 5;
if (_min_weight_fraction_leaf >= 0 && _min_weight_fraction_leaf <= 0.5)
return 6;
// Set samples' weight
if (expended_class_weight.total())
{
for (int i = 0; i < sample_weight.total(); i++)
{
sample_weight.at<double>(i, 0) = sample_weight.at<double>(i, 0) * \
expended_class_weight.at<double>(i, 0);
}
}
else
{
sample_weight = expended_class_weight;
}
// Set min_weight_fraction_leaf
if (_min_weight_fraction_leaf != 0.)
_min_weight_fraction_leaf = _min_weight_fraction_leaf * cv::sum(sample_weight);
else
_min_weight_fraction_leaf = 0.;
// Set min_samples_split
_min_samples_split = max(_min_samples_split, 2 * _min_samples_leaf);
}
