1 files changed, 72 insertions, 10 deletions

diff --git a/frontend/src/app/_data/Model.ts b/frontend/src/app/_data/Model.ts
index 7d383584..526a8290 100644
--- a/frontend/src/app/_data/Model.ts
+++ b/frontend/src/app/_data/Model.ts
@@ -1,12 +1,13 @@
 import { NgIf } from "@angular/common";
+import { FolderFile } from "./FolderFile";
 
-export default class Model {
+export default class Model extends FolderFile {
     _id: string = '';
     constructor(
-        public name: string = 'Novi model',
+        name: string = 'Novi model',
         public description: string = '',
-        public dateCreated: Date = new Date(),
-        public lastUpdated: Date = new Date(),
+        dateCreated: Date = new Date(),
+        lastUpdated: Date = new Date(),
         //public experimentId: string = '',
 
         // Neural net training settings
@@ -14,17 +15,65 @@ export default class Model {
         public optimizer: Optimizer = Optimizer.Adam,
         public lossFunction: LossFunction = LossFunction.MeanSquaredError,
         public inputNeurons: number = 1,
-        public hiddenLayerNeurons: number = 1,
         public hiddenLayers: number = 1,
-        public batchSize: number = 4,
-        public hiddenLayerActivationFunctions: string[] = ['sigmoid'],
+        public batchSize: BatchSize = BatchSize.O3,
         public outputLayerActivationFunction: ActivationFunction = ActivationFunction.Sigmoid,
         public uploaderId: string = '',
         public metrics: string[] = [], // TODO add to add-model form
-        public epochs: number = 5 // TODO add to add-model form
+        public epochs: number = 5, // TODO add to add-model form
+        public inputColNum: number = 5,
+        public learningRate: LearningRate = LearningRate.LR1,
+        public layers: Layer[] = [new Layer()],
+
+        // Test set settings
+        public randomOrder: boolean = true,
+        public randomTestSet: boolean = true,
+        public randomTestSetDistribution: number = 0.1, //0.1-0.9 (10% - 90%) JESTE OVDE ZAKUCANO 10, AL POSLATO JE KAO 0.1 BACK-U
+
+        public isPublic: boolean = false,
+        public accessibleByLink: boolean = false
+    ) {
+        super(name, dateCreated, lastUpdated);
+    }
+}
+export class Layer {
+    constructor(
+        public layerNumber: number = 0,
+        public activationFunction: ActivationFunction = ActivationFunction.Sigmoid,
+        public neurons: number = 3,
+        public regularisation: Regularisation = Regularisation.L1,
+        public regularisationRate: RegularisationRate = RegularisationRate.RR1,
     ) { }
 }
-
+export enum LearningRate {
+    LR1 = '0.00001',
+    LR2 = '0.0001',
+    LR3 = '0.001',
+    LR4 = '0.003',
+    LR5 = '0.01',
+    LR6 = '0.03',
+    LR7 = '0.1',
+    LR8 = '0.3',
+    LR9 = '1',
+    LR10 = '3',
+    LR11 = '10',
+}
+export enum Regularisation {
+    L1 = 'l1',
+    L2 = 'l2'
+}
+export enum RegularisationRate {
+    RR1 = '0',
+    RR2 = '0.001',
+    RR3 = '0.003',
+    RR4 = '0.01',
+    RR5 = '0.03',
+    RR6 = '0.1',
+    RR7 = '0.3',
+    RR8 = '1',
+    RR9 = '3',
+    RR10 = '10',
+}
 export enum ProblemType {
     Regression = 'regresioni',
     BinaryClassification = 'binarni-klasifikacioni',
@@ -94,7 +143,7 @@ export enum LossFunctionBinaryClassification {
     HingeLoss = 'hinge_loss',
 }
 export enum LossFunctionMultiClassification {
-    //CategoricalCrossEntropy = 'categorical_crossentropy',
+    CategoricalCrossEntropy = 'categorical_crossentropy',
     SparseCategoricalCrossEntropy = 'sparse_categorical_crossentropy',
     KLDivergence = 'kullback_leibler_divergence',
 }
@@ -157,3 +206,16 @@ export enum MetricsMultiClassification {
     Recall = 'recall_score',
     F1 = 'f1_score',
 }
+
+export enum BatchSize {
+    O1 = '2',
+    O2 = '4',
+    O3 = '8',
+    O4 = '16',
+    O5 = '32',
+    O6 = '64',
+    O7 = '128',
+    O8 = '256',
+    O9 = '512',
+    O10 = '1024'
+}
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