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import { NgIf } from "@angular/common";
export default class Model {
_id: string = '';
constructor(
public name: string = 'Novi model',
public description: string = '',
public dateCreated: Date = new Date(),
public lastUpdated: Date = new Date(),
//public experimentId: string = '',
// Neural net training settings
public type: ProblemType = ProblemType.Regression,
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 = 5,
public hiddenLayerActivationFunctions: string[] = ['sigmoid'],
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
) { }
}
export enum ProblemType {
Regression = 'regresioni',
BinaryClassification = 'binarni-klasifikacioni',
MultiClassification = 'multi-klasifikacioni'
}
// replaceMissing srednja vrednost mean, median, najcesca vrednost (mode)
// removeOutliers
export enum ActivationFunction {
// linear
Binary_Step = 'binaryStep',
// non-linear
Leaky_Relu = 'leakyRelu',
Parameterised_Relu = 'parameterisedRelu',
Exponential_Linear_Unit = 'exponentialLinearUnit',
Swish = 'swish',
//hiddenLayers
Relu = 'relu',
Sigmoid = 'sigmoid',
Tanh = 'tanh',
//outputLayer
Linear = 'linear',
//Sigmoid='sigmoid',
Softmax = 'softmax',
}
/*
export enum ActivationFunctionHiddenLayer
{
Relu='relu',
Sigmoid='sigmoid',
Tanh='tanh'
}
export enum ActivationFunctionOutputLayer
{
Linear = 'linear',
Sigmoid='sigmoid',
Softmax='softmax'
}
*/
export enum LossFunction {
// binary classification loss functions
BinaryCrossEntropy = 'binary_crossentropy',
SquaredHingeLoss = 'squared_hinge_loss',
HingeLoss = 'hinge_loss',
// multi-class classification loss functions
CategoricalCrossEntropy = 'categorical_crossentropy',
SparseCategoricalCrossEntropy = 'sparse_categorical_crossentropy',
KLDivergence = 'kullback_leibler_divergence',
// regression loss functions
MeanAbsoluteError = 'mean_absolute_error',
MeanSquaredError = 'mean_squared_error',
MeanSquaredLogarithmicError = 'mean_squared_logarithmic_error',
HuberLoss = 'Huber'
}
export enum LossFunctionRegression {
MeanAbsoluteError = 'mean_absolute_error',
MeanSquaredError = 'mean_squared_error',
MeanSquaredLogarithmicError = 'mean_squared_logarithmic_error',
}
export enum LossFunctionBinaryClassification {
BinaryCrossEntropy = 'binary_crossentropy',
SquaredHingeLoss = 'squared_hinge_loss',
HingeLoss = 'hinge_loss',
}
export enum LossFunctionMultiClassification {
CategoricalCrossEntropy = 'categorical_crossentropy',
SparseCategoricalCrossEntropy = 'sparse_categorical_crossentropy',
KLDivergence = 'kullback_leibler_divergence',
}
export enum Optimizer {
Adam = 'Adam',
Adadelta = 'Adadelta',
Adagrad = 'Adagrad',
Ftrl = 'Ftrl',
Nadam = 'Nadam',
SGD = 'SGD',
SGDMomentum = 'SGDMomentum',
RMSprop = 'RMSprop'
}
export enum NullValueOptions {
DeleteRows = 'delete_rows',
DeleteColumns = 'delete_columns',
Replace = 'replace'
}
export enum ReplaceWith {
None = 'Popuni...',
Mean = 'Srednja vrednost',
Median = 'Medijana'
}
export class NullValReplacer {
"column": string;
"option": NullValueOptions;
"value": string;
}
export enum Metrics {
MSE = 'mse',
MAE = 'mae',
RMSE = 'rmse'
}
export enum MetricsRegression {
Mse = 'mse',
Mae = 'mae',
Mape = 'mape',
Msle = 'msle',
CosineProximity = 'cosine'
}
export enum MetricsBinaryClassification {
Accuracy = 'binary_accuracy',
Auc = "AUC",
Precision = 'precision_score',
Recall = 'recall_score',
F1 = 'f1_score',
}
export enum MetricsMultiClassification {
Accuracy = 'categorical_accuracy',
Auc = "AUC",
Precision = 'precision_score',
Recall = 'recall_score',
F1 = 'f1_score',
}
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