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Updated logistic regression to include predict and train functionality

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This commit is contained in:
Alejandro Saucedo 2020-09-26 18:02:49 +01:00
parent 9308b83af4
commit 143baa4db3
2 changed files with 5 additions and 20 deletions
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22
examples/godot_logistic_regression/gdnative_shared/src/KomputeSummator.cpp
View file

@ -9,8 +9,7 @@
namespace godot {
KomputeSummator::KomputeSummator() {
std::cout << "CALLING CONSTRUCTOR" << std::endl;
this->_init();
}
void KomputeSummator::train(Array yArr, Array xIArr, Array xJArr) {
@ -23,7 +22,7 @@ void KomputeSummator::train(Array yArr, Array xIArr, Array xJArr) {
std::vector<float> xJData;
std::vector<float> zerosData;
for (size_t i = 0; i < yArr.size(); i++) {
for (int i = 0; i < yArr.size(); i++) {
yData.push_back(yArr[i]);
xIData.push_back(xIArr[i]);
xJData.push_back(xJArr[i]);
@ -77,11 +76,11 @@ void KomputeSummator::train(Array yArr, Array xIArr, Array xJArr) {
sq->end();
// Iterate across all expected iterations
for (size_t i = 0; i < ITERATIONS; i++) {
for (int i = 0; i < ITERATIONS; i++) {
sq->eval();
for (size_t j = 0; j < bOut->size(); j++) {
for (int j = 0; j < bOut->size(); j++) {
wIn->data()[0] -= learningRate * wOutI->data()[j];
wIn->data()[1] -= learningRate * wOutJ->data()[j];
bIn->data()[0] -= learningRate * bOut->data()[j];
@ -107,7 +106,7 @@ Array KomputeSummator::predict(Array xI, Array xJ) {
// We run the inference in the CPU for simplicity
// BUt you can also implement the inference on GPU
// GPU implementation would speed up minibatching
for (size_t i = 0; i < xI.size(); i++) {
for (int i = 0; i < xI.size(); i++) {
float xIVal = xI[i];
float xJVal = xJ[i];
float result = (xIVal * this->mWeights.data()[0]
@ -122,18 +121,7 @@ Array KomputeSummator::predict(Array xI, Array xJ) {
return retArray;
}
void KomputeSummator::_init() {
std::cout << "CALLING INIT" << std::endl;
}
void KomputeSummator::_process(float delta) {
}
void KomputeSummator::_register_methods() {
register_method((char *)"_process", &KomputeSummator::_process);
register_method((char *)"_init", &KomputeSummator::_init);
register_method((char *)"train", &KomputeSummator::train);
register_method((char *)"predict", &KomputeSummator::predict);
}

3
examples/godot_logistic_regression/gdnative_shared/src/KomputeSummator.hpp
View file

@ -19,9 +19,6 @@ public:
void train(Array y, Array xI, Array xJ);
Array predict(Array xI, Array xJ);
void _process(float delta);
void _init();
static void _register_methods();
private: