Error Codes (Analytics and Machine Learning Toolkit)
- Updated2023-02-21
- 7 minute(s) read
Error Codes (Analytics and Machine Learning Toolkit)
The Analytics and Machine Learning VIs can return the following error codes. Refer to the KnowledgeBase for more information about correcting errors in LabVIEW.
| Code | Description |
|---|---|
| −388414 | The number of rows in the first batch of data must be greater than or equal to number of components. |
| −388413 | The number of columns of mean values must equal the number of columns in the current batch of input data. |
| −388412 | The length of min values or max values must equal the number of features in the current batch of input data. |
| −388411 | The length of mean values must equal the number of features in the current batch of input data. |
| −388410 | The array size of weights or biases must equal the number of hidden layers. The array size of weight or bias must equal the number of neurons in each layer. |
| −388409 | The array size of hidden layer type must equal the array size of number of hidden neurons. |
| −388408 | The model fitting does not converge before reaching the maximum number of iterations. |
| −388407 | The SVM model training fails. Normalize the training data or change the SVM parameters. |
| −388406 | The GMM model training fails. Increase regulator. |
| −388405 | The number of data samples must be greater than 1. |
| −388404 | There must be at least one cluster in predicted labels that has more than one data samples for the Davies Bouldin Index metric and the Dunn Index metric. |
| −388403 | predicted labels must have more than one clusters for the Davies Bouldin Index metric and the Dunn Index metric. |
| −388402 | The number of predicted labels in predicted labels must equal the number of data samples. |
| −388401 | The number of data samples must be no less than the number of clusters. |
| −388400 | The covariance matrix in covariance matrix must be positive definite. |
| −388399 | The covariance matrix in initial covariance matrix must be positive definite. |
| −388397 | The covariance matrix in initial covariance matrix must be symmetric. |
| −388396 | The number of rows or columns of initial covariance matrix must equal the number of features in the training data. |
| −388395 | initial weights must be between 0 and 1. |
| −388394 | The number of columns of initial mean values must equal the number of features in data. |
| −388393 | threshold for T2 must be greater than or equal to 0. |
| −388392 | The wrong Load Data (2D Array) VI instance is used to load data. |
| −388391 | The array length of number of support vectors for each class must equal number of classes. |
| −388390 | You must perform feature manipulation before training clustering, classification, and anomaly detection models. |
| −388389 | nu is invalid. Reduce the value of nu. |
| −388388 | The array size of variance must equal the number of eigenvectors. |
| −388387 | MQE threshold must be greater than or equal to 0. |
| −388386 | The number of features in the input data must equal the number of columns of support vectors. |
| −388385 | The number of nodes, which equals row * column, must equal the number of columns of map vectors. |
| −388384 | confidence level must be greater than 0 and less than 1. |
| −388383 | initial learning rate must be greater than 0. |
| −388382 | initial radius must be greater than 0. |
| −388381 | The value of row and col must be greater than or equal to 0. |
| −388380 | The number of features in the input data must equal the number of features in map vectors. |
| −388379 | covariance matrix must be symmetric. |
| −388378 | You must not choose the AIC metric or the BIC metric to evaluate the DBSCAN model and the K-Means model. |
| −388377 | threshold for Q must be greater than or equal to 0. |
| −388376 | The number of columns of the input data must equal the number of columns of centroids. |
| −388375 | The number of columns of the input data must equal the number of columns of core samples. |
| −388374 | The number of columns of the input data must equal the number of columns of mean values. |
| −388372 | The size of predicted labels must equal the size of input labels. |
| −388371 | centroids must not be empty and the number of rows of centroids must equal number of clusters. |
| −388370 | The size of covariance values must equal number of clusters and the number of rows/columns of covariance matrix must equal the size of mean values. |
| −388369 | The size of weights must equal number of clusters and the sum of weights must equal 1. |
| −388368 | The number of rows of mean values must equal number of clusters. |
| −388367 | The size of core samples labels must be greater than 0. |
| −388366 | max must be greater than min. |
| −388365 | The size of core sample labels must equal the number of rows of core samples. |
| −388364 | initial method must not be empty. |
| −388362 | attempts must be greater than 0. |
| −388361 | p must be greater than 0. |
| −388360 | min samples must be greater than 0. |
| −388359 | max distance must be greater than 0. |
| −388358 | number of clusters must be greater than 0. |
| −388357 | regulator must be greater than 0. |
| −388356 | Out of memory. |
| −388355 | The sum of number of support vectors for each class must equal number of support vectors. |
| −388354 | output layer type is empty. |
| −388353 | The array size of probB must equal number of classes * (number of classes - 1)/2. |
| −388352 | The array size of probA must equal number of classes * (number of classes - 1)/2. |
| −388351 | The array size of decision function constants must equal number of classes * (number of classes - 1)/2. |
| −388350 | The number of rows of coefficients of support vectors must be one less than number of classes, and the number of columns of coefficients of support vectors must equal number of support vectors. |
| −388349 | The number of rows of support vectors must equal number of support vectors. |
| −388348 | number of support vectors must be greater than 0. |
| −388347 | The number of rows of H to O coefficients must equal number of output neurons, and the number of columns of H to O coefficients must be one greater than number of hidden neurons. |
| −388346 | The number of rows of I to H coefficients must equal number of hidden neurons, and the number of columns of I to H coefficients must be one greater than number of input neurons. |
| −388345 | number of output neurons must equal number of classes. |
| −388344 | The number of features in the input data must equal number of input neurons. |
| −388343 | number of input neurons must be greater than 0. |
| −388342 | For multi-class classification, the number of rows of coefficients of support vectors must equal number of classes. For two-class classification, the number of rows of coefficients of support vectors must equal 1. |
| −388341 | The array size of labels of each class must equal number of classes. |
| −388340 | number of classes must be greater than 1. |
| −388339 | The number of features in the input data must be one less than the number of columns of coefficients of support vectors. |
| −388338 | The model is untrained before deployment. |
| −388337 | cost function type must not be empty. |
| −388336 | You must initialize the model before training. |
| −388335 | coef0 must not be empty. |
| −388334 | kernel type must not be empty. |
| −388333 | SVM type must not be empty. |
| −388332 | hidden layer type must not be empty. |
| −388331 | There are repeated class labels in weighted c. |
| −388330 | class weight of weighted c must be greater than 0. |
| −388329 | label of weighted c cannot be found in the labels of the training data. |
| −388328 | nu must be greater than 0 and less than or equal to 1. |
| −388327 | c must be greater than 0. |
| −388326 | number of hidden neurons must be greater than 0. |
| −388325 | number of searches must be greater than 0 when search method is random search. |
| −388324 | positive label cannot be found in the labels of the input data. |
| −388323 | number of folds must be greater than 1 and equal to or less than the number of samples in the training data. |
| −388322 | tolerance must be greater than 0. |
| −388321 | max iteration must be greater than 0. |
| −388320 | The input data must not be empty. |
| −388319 | ratio of variance must be greater than 0 and no greater than 1. |
| −388318 | The number of classes in the training data must be no greater than 2. |
| −388317 | number of components must be greater than 0 and no greater than the number of features in training data. |
| −388316 | The number of samples in the training data must equal number of labels. |
| −388315 | The number of features in the input data must equal the number of rows of eigenvectors. |
| −388314 | The number of rows of eigenvectors must equal the array size of mean values, and be no less than the number of columns of eigenvectors. |
| −388313 | mean values and eigenvectors must not be empty. |
| −388312 | selected feature index must be less than the number of features in the input data. |
| −388311 | The selected feature indexes in selected feature index must be different from each other. |
| −388310 | selected feature index must not be empty. |
| −388309 | The number of features in the input data must equal the array size of min values or max values. |
| −388308 | The number of features in the input data must equal the array size of mean values. |
| −388307 | min values and max values must not be empty. The size of min values and max values must be the same. |
| −388306 | mean values and standard deviation values must not be empty. The size of mean values and standard deviation values must be the same. |
| −388305 | The number of features in the input data must equal that in the source data. |
| −388304 | gamma must be greater than 0. |
| −388303 | degree must be greater than 0. |
| −388302 | The array sizes of source data, eigenvectors, and variance in the input model are not compatible with each other. |
| −388301 | source data, eigenvectors, and variance in the input model must not be empty. |
| −388300 | The data must be loaded. |
| 388301 | The PCA T2Q model is trained based on the data that is already transformed with PCA feature manipulation. |