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* feat: created kivy gui boilerplate * migrated docker base image to debian Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * migrated to ubuntu Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * fixed issues Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * fix: docker build? * fix: use ubuntu bionic * fix: get kivy installed * @ltcptgeneral can't spell * optim dockerfile for not installing unused packages * install basic stuff while building the container * use prebuilt image for development * install pylint on base image * rename and use new kivy * tests: added tests for Array and CorrelationTest Both are not working due to errors * fix: Array no longer has *args and CorrelationTest functions no longer have self in the arguments * use new thing * use 20.04 base * symlink pip3 to pip * use pip instead of pip3 * tra_analysis v 2.1.0-alpha.2 SVM v 1.0.1 added unvalidated SVM unit tests Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * fixed version number Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * tests: added tests for ClassificationMetric * partially fixed and commented out svm unit tests * fixed some SVM unit tests * added installing pytest to devcontainer.json * fix: small fixes to KNN Namely, removing self from parameters and passing correct arguments to KNeighborsClassifier constructor * fix, test: Added tests for KNN and NaiveBayes. Also made some small fixes in KNN, NaiveBayes, and RegressionMetric * test: finished unit tests except for StatisticalTest Also made various small fixes and style changes * StatisticalTest v 1.0.1 * fixed RegressionMetric unit test temporarily disabled CorrelationTest unit tests * tra_analysis v 2.1.0-alpha.3 * readded __all__ * fix: floating point issues in unit tests for CorrelationTest Co-authored-by: AGawde05 <agawde05@gmail.com> Co-authored-by: ltcptgeneral <learthurgo@gmail.com> Co-authored-by: Dev Singh <dev@devksingh.com> Co-authored-by: jzpan1 <panzhenyu2014@gmail.com>
63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
# Titan Robotics Team 2022: NaiveBayes submodule
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# Written by Arthur Lu
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# Notes:
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# this should be imported as a python module using 'from tra_analysis import NaiveBayes'
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# setup:
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__version__ = "1.0.0"
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__changelog__ = """changelog:
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1.0.0:
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- ported analysis.NaiveBayes() here
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"""
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__author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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)
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__all__ = [
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'gaussian',
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'multinomial'
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'bernoulli',
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'complement'
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]
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import sklearn
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from sklearn import model_selection, naive_bayes
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from . import ClassificationMetric, RegressionMetric
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def gaussian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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def multinomial(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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def bernoulli(data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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def complement(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test) |