Updated and renamed classes for lr example

examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.cpp

@@ -0,0 +1,160 @@
+/* summator.cpp */
+
+#include <vector>
+#include <string>
+#include <iostream>
+
+#include "KomputeModelML.hpp"
+
+namespace godot {
+
+KomputeModelML::KomputeModelML() {
+    std::cout << "CALLING CONSTRUCTOR" << std::endl;
+    this->_init();
+}
+
+void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
+
+    assert(y.size() == xI.size());
+    assert(xI.size() == xJ.size());
+
+    std::vector<float> yData;
+    std::vector<float> xIData;
+    std::vector<float> xJData;
+    std::vector<float> zerosData;
+
+    for (size_t i = 0; i < yArr.size(); i++) {
+        yData.push_back(yArr[i]);
+        xIData.push_back(xIArr[i]);
+        xJData.push_back(xJArr[i]);
+        zerosData.push_back(0);
+    }
+
+    uint32_t ITERATIONS = 100;
+    float learningRate = 0.1;
+
+    std::shared_ptr<kp::Tensor> xI{ new kp::Tensor(xIData) };
+    std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor(xJData) };
+
+    std::shared_ptr<kp::Tensor> y{ new kp::Tensor(yData) };
+
+    std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
+    std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor(zerosData) };
+    std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor(zerosData) };
+
+    std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
+    std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor(zerosData) };
+
+    std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor(zerosData) };
+
+    std::vector<std::shared_ptr<kp::Tensor>> params = { xI,  xJ,    y,
+                                                        wIn, wOutI, wOutJ,
+                                                        bIn, bOut,  lOut };
+
+    {
+        kp::Manager mgr;
+
+        if (std::shared_ptr<kp::Sequence> sq =
+              mgr.getOrCreateManagedSequence("createTensors").lock()) {
+
+            sq->begin();
+
+            sq->record<kp::OpTensorCreate>(params);
+
+            sq->end();
+            sq->eval();
+
+            // Record op algo base
+            sq->begin();
+
+            sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
+
+            sq->record<kp::OpAlgoBase<>>(
+              params, std::vector<char>(LR_SHADER.begin(), LR_SHADER.end()));
+
+            sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
+
+            sq->end();
+
+            // Iterate across all expected iterations
+            for (size_t i = 0; i < ITERATIONS; i++) {
+
+                sq->eval();
+
+                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];
+                }
+            }
+        }
+    }
+
+    SPDLOG_INFO("RESULT: <<<<<<<<<<<<<<<<<<<");
+    SPDLOG_INFO(wIn->data()[0]);
+    SPDLOG_INFO(wIn->data()[1]);
+    SPDLOG_INFO(bIn->data()[0]);
+
+    this->mWeights = kp::Tensor(wIn->data());
+    this->mBias = kp::Tensor(bIn->data());
+}
+
+Array KomputeModelML::predict(Array xI, Array xJ) {
+    assert(xI.size() == xJ.size());
+
+    Array retArray;
+
+    // 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++) {
+        float xIVal = xI[i];
+        float xJVal = xJ[i];
+        float result = (xIVal * this->mWeights.data()[0]
+                + xJVal * this->mWeights.data()[1]
+                + this->mBias.data()[0]);
+
+        // Instead of using sigmoid we'll just return full numbers
+        Variant var = result > 0 ? 1 : 0;
+        retArray.push_back(var);
+    }
+
+    return retArray;
+}
+
+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);
+}
+
+}
+