IDL Machine Learning

Models, Classifiers, and Test Classifier

IDLmlAutoEncoder: Implements an autoencoder model that can be used for clustering purposes.

IDLmlFeedForwardNeuralNetwork: Implements a Neural Network model that can be used for classification purposes.

IDLmlKMeans: Implements a K-means model that can be used for clustering purposes.

IDLmlSoftmax: Iimplements a Softmax model that can be used for classification purposes.

IDLmlSupportVectorMachineClassification: Implements an SVM model that can be used for classification purposes.

IDLmlSupportVectorMachineRegression: Implements an SVM model that can be used for regression purposes.

IDLmlTestClassifier: Computes a confusion matrix and other metrics that indicate how well a model trained as a classifier performed against the test data.

Partition and Shuffle

IDLmlPartition: Partitions data so that it can be separated into two or more groups.

IDLmlShuffle: Shuffles features and values to create a random reordering of training data used for machine learning applications.

Normalizers

IDLmlLinearNormalizer: Implements a linear normalizer using the formula dataOut = dataIn * scale + offset.

IDLmlRangeNormalizer: Implements a normalizer that will scale data to have a range of 1.

IDLmlTanHNormalizer: Implements a Hyperbolic Tangent Normalizer which maps the data to the Tanh of the data. The normalized data will be confined to the range (-1, +1).

IDLmlUnitNormalizer: Implements a normalizer that will scale data to have a range of 1.

IDLmlVarianceNormalizer: Implements a normalizer that will scale data to have a mean of 0 and a standard deviation of 1, regardless of range.