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Updated and renamed classes for lr example

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This commit is contained in:
Alejandro Saucedo 2020-09-27 14:09:16 +01:00
parent 143baa4db3
commit 8959d90fa6
37 changed files with 402 additions and 60 deletions
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4
examples/godot_logistic_regression/gdnative_shared/src/KomputeGdNative.cpp
View file

@ -1,4 +1,4 @@
#include "KomputeSummator.hpp"
#include "KomputeModelML.hpp"
extern "C" void GDN_EXPORT godot_gdnative_init(godot_gdnative_init_options *o) {
godot::Godot::gdnative_init(o);
@ -11,5 +11,5 @@ extern "C" void GDN_EXPORT godot_gdnative_terminate(godot_gdnative_terminate_opt
extern "C" void GDN_EXPORT godot_nativescript_init(void *handle) {
godot::Godot::nativescript_init(handle);
godot::register_class<godot::KomputeSummator>();
godot::register_class<godot::KomputeModelML>();
}

54
examples/godot_logistic_regression/gdnative_shared/src/KomputeSummator.cpp → examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.cpp
View file

@ -4,15 +4,16 @@
#include <string>
#include <iostream>
#include "KomputeSummator.hpp"
#include "KomputeModelML.hpp"
namespace godot {
KomputeSummator::KomputeSummator() {
KomputeModelML::KomputeModelML() {
std::cout << "CALLING CONSTRUCTOR" << std::endl;
this->_init();
}
void KomputeSummator::train(Array yArr, Array xIArr, Array xJArr) {
void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
assert(y.size() == xI.size());
assert(xI.size() == xJ.size());
@ -22,7 +23,7 @@ void KomputeSummator::train(Array yArr, Array xIArr, Array xJArr) {
std::vector<float> xJData;
std::vector<float> zerosData;
for (int i = 0; i < yArr.size(); i++) {
for (size_t i = 0; i < yArr.size(); i++) {
yData.push_back(yArr[i]);
xIData.push_back(xIArr[i]);
xJData.push_back(xJArr[i]);
@ -76,11 +77,11 @@ void KomputeSummator::train(Array yArr, Array xIArr, Array xJArr) {
sq->end();
// Iterate across all expected iterations
for (int i = 0; i < ITERATIONS; i++) {
for (size_t i = 0; i < ITERATIONS; i++) {
sq->eval();
for (int j = 0; j < bOut->size(); j++) {
for (size_t 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];
@ -98,7 +99,7 @@ void KomputeSummator::train(Array yArr, Array xIArr, Array xJArr) {
this->mBias = kp::Tensor(bIn->data());
}
Array KomputeSummator::predict(Array xI, Array xJ) {
Array KomputeModelML::predict(Array xI, Array xJ) {
assert(xI.size() == xJ.size());
Array retArray;
@ -106,7 +107,7 @@ 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 (int i = 0; i < xI.size(); i++) {
for (size_t i = 0; i < xI.size(); i++) {
float xIVal = xI[i];
float xJVal = xJ[i];
float result = (xIVal * this->mWeights.data()[0]
@ -121,9 +122,38 @@ Array KomputeSummator::predict(Array xI, Array xJ) {
return retArray;
}
void KomputeSummator::_register_methods() {
register_method((char *)"train", &KomputeSummator::train);
register_method((char *)"predict", &KomputeSummator::predict);
Array KomputeModelML::get_params() {
Array retArray;
SPDLOG_INFO(this->mWeights.size() + this->mBias.size());
if(this->mWeights.size() + this->mBias.size() == 0) {
return retArray;
}
retArray.push_back(this->mWeights.data()[0]);
retArray.push_back(this->mWeights.data()[1]);
retArray.push_back(this->mBias.data()[0]);
retArray.push_back(99.0);
return retArray;
}
void KomputeModelML::_init() {
std::cout << "CALLING INIT" << std::endl;
}
void KomputeModelML::_process(float delta) {
}
void KomputeModelML::_register_methods() {
register_method((char *)"_process", &KomputeModelML::_process);
register_method((char *)"_init", &KomputeModelML::_init);
register_method((char *)"train", &KomputeModelML::train);
register_method((char *)"predict", &KomputeModelML::predict);
register_method((char *)"get_params", &KomputeModelML::get_params);
}
}

12
examples/godot_logistic_regression/gdnative_shared/src/KomputeSummator.hpp → examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.hpp
View file

@ -9,16 +9,22 @@
#include "kompute/Kompute.hpp"
namespace godot {
class KomputeSummator : public Node2D {
class KomputeModelML : public Node2D {
private:
GODOT_CLASS(KomputeSummator, Node2D);
GODOT_CLASS(KomputeModelML, Node2D);
public:
KomputeSummator();
KomputeModelML();
void train(Array y, Array xI, Array xJ);
Array predict(Array xI, Array xJ);
Array get_params();
void _process(float delta);
void _init();
static void _register_methods();
private: