mlxtend: v0.4.1

Version 0.4.1 (2016-05-01) New Features

• New TensorFlow estimator for Linear Regression (tf_regressor.TfLinearRegression)
• New k-means clustering estimator (cluster.Kmeans)
• New TensorFlow k-means clustering estimator (tf_cluster.Kmeans)

• Due to refactoring of the estimator classes, the init_weights parameter of the fit methods was globally renamed to init_params
• Overall performance improvements of estimators due to code clean-up and refactoring
• Added several additional checks for correct array types and more meaningful exception messages
• Added optional dropout to the tf_classifier.TfMultiLayerPerceptron classifier for regularization
• Added an optional decay parameter to the tf_classifier.TfMultiLayerPerceptron classifier for adaptive learning via an exponential decay of the learning rate eta
• Replaced old NeuralNetMLP by more streamlined MultiLayerPerceptron (classifier.MultiLayerPerceptron); now also with softmax in the output layer and categorical cross-entropy loss.
• Unified init_params parameter for fit functions to continue training where the algorithm left off (if supported)