Updated and renamed classes for lr example

examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.cpp

@@ -2,13 +2,13 @@
 
 #include <vector>
 
-#include "KomputeSummatorNode.h"
+#include "KomputeModelMLNode.h"
 
-KomputeSummatorNode::KomputeSummatorNode() {
+KomputeModelMLNode::KomputeModelMLNode() {
     this->_init();
 }
 
-void KomputeSummatorNode::add(float value) {
+void KomputeModelMLNode::add(float value) {
     // Set the new data in the local device
     this->mSecondaryTensor->setData({value});
     // Execute recorded sequence
@@ -20,14 +20,14 @@ void KomputeSummatorNode::add(float value) {
     }
 }
 
-void KomputeSummatorNode::reset() {
+void KomputeModelMLNode::reset() {
 }
 
-float KomputeSummatorNode::get_total() const {
+float KomputeModelMLNode::get_total() const {
     return this->mPrimaryTensor->data()[0];
 }
 
-void KomputeSummatorNode::_init() {
+void KomputeModelMLNode::_init() {
     std::cout << "CALLING INIT" << std::endl;
     this->mPrimaryTensor = this->mManager.buildTensor({ 0.0 });
     this->mSecondaryTensor = this->mManager.buildTensor({ 0.0 });
@@ -74,16 +74,16 @@ void KomputeSummatorNode::_init() {
     }
 }
 
-void KomputeSummatorNode::_process(float delta) {
+void KomputeModelMLNode::_process(float delta) {
 
 }
 
-void KomputeSummatorNode::_bind_methods() {
-    ClassDB::bind_method(D_METHOD("_process", "delta"), &KomputeSummatorNode::_process);
-    ClassDB::bind_method(D_METHOD("_init"), &KomputeSummatorNode::_init);
+void KomputeModelMLNode::_bind_methods() {
+    ClassDB::bind_method(D_METHOD("_process", "delta"), &KomputeModelMLNode::_process);
+    ClassDB::bind_method(D_METHOD("_init"), &KomputeModelMLNode::_init);
 
-    ClassDB::bind_method(D_METHOD("add", "value"), &KomputeSummatorNode::add);
-    ClassDB::bind_method(D_METHOD("reset"), &KomputeSummatorNode::reset);
-    ClassDB::bind_method(D_METHOD("get_total"), &KomputeSummatorNode::get_total);
+    ClassDB::bind_method(D_METHOD("add", "value"), &KomputeModelMLNode::add);
+    ClassDB::bind_method(D_METHOD("reset"), &KomputeModelMLNode::reset);
+    ClassDB::bind_method(D_METHOD("get_total"), &KomputeModelMLNode::get_total);
 }
 

examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.h

@@ -6,11 +6,11 @@
 
 #include "scene/main/node.h"
 
-class KomputeSummatorNode : public Node {
-    GDCLASS(KomputeSummatorNode, Node);
+class KomputeModelMLNode : public Node {
+    GDCLASS(KomputeModelMLNode, Node);
 
 public:
-    KomputeSummatorNode();
+    KomputeModelMLNode();
 
     void add(float value);
     void reset();

examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.cpp

@@ -3,10 +3,10 @@
 #include "register_types.h"
 
 #include "core/class_db.h"
-#include "KomputeSummatorNode.h"
+#include "KomputeModelMLNode.h"
 
 void register_kompute_summator_types() {
-    ClassDB::register_class<KomputeSummatorNode>();
+    ClassDB::register_class<KomputeModelMLNode>();
 }
 
 void unregister_kompute_summator_types() {

examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.h

@@ -1,6 +1,6 @@
 /* register_types.h */
 #pragma once
 
-void register_kompute_summator_types();
-void unregister_kompute_summator_types();
+void register_kompute_model_ml_types();
+void unregister_kompute_model_ml_types();
 /* yes, the word in the middle must be the same as the module folder name */

examples/godot_logistic_regression/gdnative_shared/CMakeLists.txt

@@ -16,7 +16,7 @@ find_package(Vulkan REQUIRED)
 
 add_library(kompute_godot
     SHARED
-    src/KomputeSummator.cpp
+    src/KomputeModelML.cpp
     src/KomputeGdNative.cpp)
 
 target_include_directories(

examples/godot_logistic_regression/gdnative_shared/src/KomputeGdNative.cpp

@@ -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>();
 }

examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.cpp

@@ -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);
 }
 
 }

examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.hpp

@@ -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:

examples/godot_logistic_regression/godot_resources/assets/DynamicExampleScene.tscn

@@ -1,10 +1,309 @@
-[gd_scene load_steps=3 format=2]
+[gd_scene load_steps=10 format=2]
 
 [ext_resource path="res://godot_resources/scripts/DynamicExampleScript.gd" type="Script" id=1]
 [ext_resource path="res://godot_resources/scripts/KomputeNativeClass.gdns" type="Script" id=2]
+[ext_resource path="res://godot_resources/assets/icon.png" type="Texture" id=3]
+[ext_resource path="res://godot_resources/assets/TextFormat.theme" type="Theme" id=4]
+
+[sub_resource type="GradientTexture" id=1]
+
+[sub_resource type="StyleBoxTexture" id=2]
+texture = SubResource( 1 )
+region_rect = Rect2( 0, 0, 2048, 1 )
+
+[sub_resource type="DynamicFontData" id=3]
+font_path = "res://godot_resources/assets/roboto.ttf"
+
+[sub_resource type="DynamicFont" id=4]
+size = 27
+font_data = SubResource( 3 )
+
+[sub_resource type="Theme" id=5]
+default_font = SubResource( 4 )
 
 [node name="Parent" type="Node2D"]
 script = ExtResource( 1 )
 
 [node name="KomputeNode" type="Node2D" parent="."]
 script = ExtResource( 2 )
+
+[node name="UI" type="Node" parent="."]
+
+[node name="UIVBoxContainer" type="VBoxContainer" parent="UI"]
+anchor_right = 1.0
+anchor_bottom = 1.0
+theme = ExtResource( 4 )
+__meta__ = {
+"_edit_use_anchors_": false
+}
+
+[node name="TitleLabel" type="Label" parent="UI/UIVBoxContainer"]
+margin_right = 1024.0
+margin_bottom = 60.0
+text = "Godot ML Kompute "
+align = 1
+
+[node name="LogoHBoxContainer" type="HBoxContainer" parent="UI/UIVBoxContainer"]
+margin_top = 64.0
+margin_right = 1024.0
+margin_bottom = 160.0
+alignment = 1
+
+[node name="TextureRect" type="TextureRect" parent="UI/UIVBoxContainer/LogoHBoxContainer"]
+margin_left = 464.0
+margin_right = 560.0
+margin_bottom = 96.0
+texture = ExtResource( 3 )
+
+[node name="XIHBoxContainer" type="HBoxContainer" parent="UI/UIVBoxContainer"]
+margin_top = 164.0
+margin_right = 1024.0
+margin_bottom = 234.0
+
+[node name="VSeparator" type="VSeparator" parent="UI/UIVBoxContainer/XIHBoxContainer"]
+margin_right = 20.0
+margin_bottom = 70.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="Label" type="Label" parent="UI/UIVBoxContainer/XIHBoxContainer"]
+margin_left = 24.0
+margin_top = 5.0
+margin_right = 193.0
+margin_bottom = 65.0
+text = "Xi Input"
+
+[node name="VSeparator2" type="VSeparator" parent="UI/UIVBoxContainer/XIHBoxContainer"]
+margin_left = 197.0
+margin_right = 217.0
+margin_bottom = 70.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="LineEdit" type="LineEdit" parent="UI/UIVBoxContainer/XIHBoxContainer"]
+margin_left = 221.0
+margin_right = 1000.0
+margin_bottom = 70.0
+size_flags_horizontal = 3
+text = "[ 0, 0, 1, 1, 1, 1 ]"
+align = 1
+
+[node name="VSeparator3" type="VSeparator" parent="UI/UIVBoxContainer/XIHBoxContainer"]
+margin_left = 1004.0
+margin_right = 1024.0
+margin_bottom = 70.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="XJHBoxContainer" type="HBoxContainer" parent="UI/UIVBoxContainer"]
+margin_top = 238.0
+margin_right = 1024.0
+margin_bottom = 308.0
+
+[node name="VSeparator" type="VSeparator" parent="UI/UIVBoxContainer/XJHBoxContainer"]
+margin_right = 20.0
+margin_bottom = 70.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="Label" type="Label" parent="UI/UIVBoxContainer/XJHBoxContainer"]
+margin_left = 24.0
+margin_top = 5.0
+margin_right = 193.0
+margin_bottom = 65.0
+text = "Xj Input"
+
+[node name="VSeparator2" type="VSeparator" parent="UI/UIVBoxContainer/XJHBoxContainer"]
+margin_left = 197.0
+margin_right = 217.0
+margin_bottom = 70.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="LineEdit" type="LineEdit" parent="UI/UIVBoxContainer/XJHBoxContainer"]
+margin_left = 221.0
+margin_right = 1000.0
+margin_bottom = 70.0
+size_flags_horizontal = 3
+text = "[ 0, 0, 0, 0, 1, 1 ]"
+align = 1
+
+[node name="VSeparator3" type="VSeparator" parent="UI/UIVBoxContainer/XJHBoxContainer"]
+margin_left = 1004.0
+margin_right = 1024.0
+margin_bottom = 70.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="YHBoxContainer" type="HBoxContainer" parent="UI/UIVBoxContainer"]
+margin_top = 312.0
+margin_right = 1024.0
+margin_bottom = 382.0
+
+[node name="VSeparator" type="VSeparator" parent="UI/UIVBoxContainer/YHBoxContainer"]
+margin_right = 20.0
+margin_bottom = 70.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="Label" type="Label" parent="UI/UIVBoxContainer/YHBoxContainer"]
+margin_left = 24.0
+margin_top = 5.0
+margin_right = 192.0
+margin_bottom = 65.0
+text = "Y Input "
+
+[node name="VSeparator2" type="VSeparator" parent="UI/UIVBoxContainer/YHBoxContainer"]
+margin_left = 196.0
+margin_right = 216.0
+margin_bottom = 70.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="LineEdit" type="LineEdit" parent="UI/UIVBoxContainer/YHBoxContainer"]
+margin_left = 220.0
+margin_right = 1000.0
+margin_bottom = 70.0
+size_flags_horizontal = 3
+text = "[ 0, 0, 0, 0, 1, 1 ]"
+align = 1
+
+[node name="VSeparator3" type="VSeparator" parent="UI/UIVBoxContainer/YHBoxContainer"]
+margin_left = 1004.0
+margin_right = 1024.0
+margin_bottom = 70.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="Button" type="Button" parent="UI/UIVBoxContainer"]
+margin_top = 386.0
+margin_right = 1024.0
+margin_bottom = 452.0
+text = "Kompute Train & Predict ML"
+
+[node name="Panel" type="PanelContainer" parent="UI/UIVBoxContainer"]
+margin_top = 456.0
+margin_right = 1024.0
+margin_bottom = 600.0
+size_flags_vertical = 3
+custom_styles/panel = SubResource( 2 )
+
+[node name="VBoxContainer" type="VBoxContainer" parent="UI/UIVBoxContainer/Panel"]
+margin_right = 1024.0
+margin_bottom = 144.0
+
+[node name="VSplitContainer2" type="VSplitContainer" parent="UI/UIVBoxContainer/Panel/VBoxContainer"]
+margin_right = 1024.0
+margin_bottom = 10.0
+rect_min_size = Vector2( 0, 10 )
+
+[node name="PredHBoxContainer" type="HBoxContainer" parent="UI/UIVBoxContainer/Panel/VBoxContainer"]
+margin_top = 14.0
+margin_right = 1024.0
+margin_bottom = 47.0
+theme = SubResource( 5 )
+__meta__ = {
+"_edit_use_anchors_": false
+}
+
+[node name="VSeparator3" type="VSeparator" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_right = 20.0
+margin_bottom = 33.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="Label" type="Label" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 24.0
+margin_right = 144.0
+margin_bottom = 33.0
+text = "Weight 1: "
+
+[node name="Weight1Label" type="Label" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 148.0
+margin_right = 332.0
+margin_bottom = 33.0
+size_flags_horizontal = 3
+text = "n/a"
+align = 1
+
+[node name="VSeparator4" type="VSeparator" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 336.0
+margin_right = 356.0
+margin_bottom = 33.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="VSeparator5" type="VSeparator" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 360.0
+margin_right = 380.0
+margin_bottom = 33.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="Label2" type="Label" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 384.0
+margin_right = 504.0
+margin_bottom = 33.0
+text = "Weight 2: "
+
+[node name="Weight2Label" type="Label" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 508.0
+margin_right = 692.0
+margin_bottom = 33.0
+size_flags_horizontal = 3
+text = "n/a"
+align = 1
+
+[node name="VSeparator6" type="VSeparator" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 696.0
+margin_right = 716.0
+margin_bottom = 33.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="VSeparator7" type="VSeparator" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 720.0
+margin_right = 740.0
+margin_bottom = 33.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="Label3" type="Label" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 744.0
+margin_right = 811.0
+margin_bottom = 33.0
+text = "Bias: "
+
+[node name="BiasLabel" type="Label" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 815.0
+margin_right = 999.0
+margin_bottom = 33.0
+size_flags_horizontal = 3
+text = "n/a"
+align = 1
+
+[node name="VSeparator8" type="VSeparator" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer"]
+margin_left = 1003.0
+margin_right = 1023.0
+margin_bottom = 33.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="VSplitContainer" type="VSplitContainer" parent="UI/UIVBoxContainer/Panel/VBoxContainer"]
+margin_top = 51.0
+margin_right = 1024.0
+margin_bottom = 71.0
+rect_min_size = Vector2( 0, 20 )
+
+[node name="PredHBoxContainer2" type="HBoxContainer" parent="UI/UIVBoxContainer/Panel/VBoxContainer"]
+margin_top = 75.0
+margin_right = 1024.0
+margin_bottom = 135.0
+__meta__ = {
+"_edit_use_anchors_": false
+}
+
+[node name="VSeparator3" type="VSeparator" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer2"]
+margin_right = 20.0
+margin_bottom = 60.0
+rect_min_size = Vector2( 20, 0 )
+
+[node name="Label" type="Label" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer2"]
+margin_left = 24.0
+margin_right = 399.0
+margin_bottom = 60.0
+text = "Prediction result:"
+
+[node name="PredictionsLabel" type="Label" parent="UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer2"]
+margin_left = 403.0
+margin_right = 1024.0
+margin_bottom = 60.0
+size_flags_horizontal = 3
+text = "n/a"
+align = 1
+[connection signal="pressed" from="UI/UIVBoxContainer/Button" to="." method="compute_ml"]

examples/godot_logistic_regression/godot_resources/scripts/DynamicExampleScript.gd

@@ -1,29 +1,36 @@
 extends Node2D
 
+onready var xi_node = $UI/UIVBoxContainer/XIHBoxContainer/LineEdit
+onready var xj_node = $UI/UIVBoxContainer/XJHBoxContainer/LineEdit
+onready var y_node = $UI/UIVBoxContainer/XJHBoxContainer/LineEdit
+onready var preds_node = $UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer2/PredictionsLabel
+onready var w1_node = $UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer/Weight1Label
+onready var w2_node = $UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer/Weight2Label
+onready var bias_node = $UI/UIVBoxContainer/Panel/VBoxContainer/PredHBoxContainer/BiasLabel
+
 # Called when the node enters the scene tree for the first time.
 func _ready():
+    pass
 
-    var xi = [0, 1, 1, 1, 1, 1]
-    var xj = [0, 0, 0, 1, 1, 1]
+func compute_ml():
 
-    var y_train_1 = [0, 0, 0, 1, 1, 1]
+    var xi = str2var(xi_node.text)
+    var xj = str2var(xj_node.text)
+    var y = str2var(y_node.text)
 
-    print("Training with " + str(y_train_1))
-    $KomputeNode.train(y_train_1, xi, xj)
+    var s = KomputeModelML.new()
 
-    print("Now running prediction with " + str(xi) + " and " + str(xj))
-    print($KomputeNode.predict(xi, xj))
+    s.train(y, xi, xj)
 
-    # We can also reference the class as named in editor
-    # and create a new instance
-    var s = KomputeSummator.new()
+    var preds = s.predict(xi, xj)
 
-    # We can use a new prediciton value to see how weights change
-    var y_train_2 = [0, 0, 1, 1, 1, 1]
+    preds_node.text = str(preds)
+
+    var params = s.get_params()
+
+    w1_node.set_text(str(params[0]))
+    w2_node.set_text(str(params[1]))
+    bias_node.set_text(str(params[2]))
 
-    print("\nTraining with " + str(y_train_2))
-    s.train(y_train_2, xi, xj)
 
-    print("Now running prediction with " + str(xi) + " and " + str(xj))
-    print(s.predict(xi, xj))
 

examples/godot_logistic_regression/godot_resources/scripts/KomputeNativeClass.gdns

@@ -3,6 +3,6 @@
 [ext_resource path="res://godot_resources/scripts/KomputeNativeLibrary.gdnlib" type="GDNativeLibrary" id=1]
 
 [resource]
-class_name = "KomputeSummator"
+class_name = "KomputeModelML"
 library = ExtResource( 1 )
-script_class_name = "KomputeSummator"
+script_class_name = "KomputeModelML"

examples/godot_logistic_regression/project.godot

@@ -9,13 +9,13 @@
 config_version=4
 
 _global_script_classes=[ {
-"base": "",
-"class": "KomputeSummator",
+"base": "Node2D",
+"class": "KomputeModelML",
 "language": "NativeScript",
 "path": "res://godot_resources/scripts/KomputeNativeClass.gdns"
 } ]
 _global_script_class_icons={
-"KomputeSummator": ""
+"KomputeModelML": ""
 }
 
 [application]