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79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
# Titan Robotics Team 2022: SVM 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 SVM'
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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.SVM() 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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]
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import sklearn
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from sklearn import svm
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from . import ClassificationMetric, RegressionMetric
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class SVM:
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class CustomKernel:
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def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
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return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
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class StandardKernel:
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def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
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return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
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class PrebuiltKernel:
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class Linear:
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def __new__(cls):
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return sklearn.svm.SVC(kernel = 'linear')
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class Polynomial:
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def __new__(cls, power, r_bias):
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return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
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class RBF:
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def __new__(cls, gamma):
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return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
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class Sigmoid:
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def __new__(cls, r_bias):
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return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
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def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
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return kernel.fit(train_data, train_outputs)
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def eval_classification(self, kernel, test_data, test_outputs):
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predictions = kernel.predict(test_data)
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return ClassificationMetric(predictions, test_outputs)
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def eval_regression(self, kernel, test_data, test_outputs):
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predictions = kernel.predict(test_data)
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return RegressionMetric(predictions, test_outputs) |