These machine learning algorithms have been adapted from algorithms written in Matlab by Professor Alex Ihler. All functions/objects include docstrings that specify parameters and return values.

• Bagged Classifier (BaggedClassify)
• Gauss-Bayes Classifier (GaussBayesClassify)
• K-Nearest Neighbor Classifier (KNNClassify)
• Linear Classifier (LinearClassify)
• Logistic Classifier (LogisticClassify)
• Logistic Mean Squared Error Classifier (LogisticMSEClassify)
• Neural Net Classifier (NNetClassify)
• Decision Tree Classifier (TreeClassify)

• Bagged Regressor (BaggedRegress)
• K-Nearest Neighbor Regressor (KNNRegress)
• Linear Regressor (LinearRegress)
• Logistic Regressor (LogisticRegress)
• Neural Net Regressor (NNetRegress)
• Decision Tree Regressor (TreeRegress)

• Hierarchical Agglomerative Clustering (agglom_cluster)
• Expectation-Maximization Clustering (em_cluster)
• K-Means Clustering (kmeans)

• Gradient Boosting (GradBoost)
• Adaptive Boosting (AdaBoost)

• load_data_from_csv
• filter_data
• bootstrap_data
• data_GMM
• gamrand
• data_gauss
• whiten
• split_data
• shuffle_data
• rescale

• fhash
• fkitchensink
• flda
• fpoly
• fpoly_mono
• fpoly_pair
• fproject
• fsubset
• fsvd

• cross_validate
• test_randomly

• to_1_of_k
• from_1_of_k
• to_index
• from_index
• optional_return

• fix auc/roc methods, retest
• remove dependency on BaseClassify
• fix linreg option in LinearClassify and test
• test train_soft in LinearClassify/LogisticClassify
• test TreeClassifytraining options
• change range to permutation (grep 'np.random.permutation\*') and retest
• add plotting
• fix LogisticRegress
• fix BaggedClassify train method
• fix BaggedClassify.__setitem__
• test logisticMseClassify

• finish and test baggedClassify
• implement logisticMseClassify
• implement and test nnetClassify
• implement and test knnRegress
• implement and test linearRegress
• implement and test treeRegress
• implement and test baggedRegress
• implement and test nnetRegress
• implement and test logisticRegress
• add plotting
• finish helpers
• add Ihler's comments

• ensure consistency of doc strings
• fix indentation in regressors
• arg error checking
• modularize __dectree_train in TreeClassifer
• make sure inheritance is optimally utilized while maintaing clarity (added to_1_of_K to Classify)
• classify.to_1_of_k is wrong, but tree/logisitc classify depend on it; make those classifiers work with the correct version in utils

• Y (class labels/data values) is flat 1 x N array
• generally use flat array instead of one row arrays ([0] vs [[0]]) or column vectors
• predictions are returned as columns
• python indices start at 0, matlab indices start at 1; inconsistent use of both in these tools
• some classifiers can't be retrained, must instantiate new object
• train methods don't have default args
• .T notation doesn't work on flat arrays