v0.3.0

Version 0.3.0 (2016-01-31)

• The mlxtend.preprocessing.standardize function now optionally returns the parameters, which are estimated from the array, for re-use. A further improvement makes the standardize function smarter in order to avoid zero-division errors
• Added a progress bar tracker to classifier.NeuralNetMLP
• Added a function to score predicted vs. target class labels evaluate.scoring
• Added confusion matrix functions to create (evaluate.confusion_matrix) and plot (evaluate.plot_confusion_matrix) confusion matrices
• Cosmetic improvements to the evaluate.plot_decision_regions function such as hiding plot axes
• Renaming of classifier.EnsembleClassfier to classifier.EnsembleVoteClassifier
• Improved random weight initialization in Perceptron, Adaline, LinearRegression, and LogisticRegression
• Changed learning parameter of mlxtend.classifier.Adaline to solver and added "normal equation" as closed-form solution solver
• New style parameter and improved axis scaling in mlxtend.evaluate.plot_learning_curves
• Hide y-axis labels in mlxtend.evaluate.plot_decision_regions in 1 dimensional evaluations
• Added loadlocal_mnist to mlxtend.data for streaming MNIST from a local byte files into numpy arrays
• New NeuralNetMLP parameters: random_weights, shuffle_init, shuffle_epoch
• Sequential Feature Selection algorithms were unified into a single SequentialFeatureSelector class with parameters to enable floating selection and toggle between forward and backward selection.
• New SFS features such as the generation of pandas DataFrame results tables and plotting functions (with confidence intervals, standard deviation, and standard error bars)
• Added support for regression estimators in SFS
• Stratified sampling of MNIST (now 500x random samples from each of the 10 digit categories)
• Added Boston housing dataset
• Renaming mlxtend.plotting to mlxtend.general_plotting in order to distinguish general plotting function from specialized utility function such as evaluate.plot_decision_regions
• Shuffle fix and new shuffle parameter for classifier.NeuralNetMLP