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SVM stands for support vector machines. Intuitively, a support vector is the vector right near the decision boundary.
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
from sklearn.datasets import load_iris
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from sklearn import svm
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from sklearn.model_selection import train_test_split
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import doctest
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# different functions implementing different types of SVM's
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def NuSVC(train_x, train_y):
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svc_NuSVC = svm.NuSVC()
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svc_NuSVC.fit(train_x, train_y)
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return svc_NuSVC
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def Linearsvc(train_x, train_y):
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svc_linear = svm.LinearSVC()
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svc_linear.fit(train_x, train_y)
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return svc_linear
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def SVC(train_x, train_y):
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# svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None)
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# various parameters like "kernal","gamma","C" can effectively tuned for a given machine learning model.
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SVC = svm.SVC(gamma="auto")
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SVC.fit(train_x, train_y)
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return SVC
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def test(X_new):
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"""
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3 test cases to be passed
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an array containing the sepal length (cm), sepal width (cm),petal length (cm),petal width (cm)
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based on which the target name will be predicted
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>>> test([1,2,1,4])
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'virginica'
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>>> test([5, 2, 4, 1])
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'versicolor'
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>>> test([6,3,4,1])
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'versicolor'
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"""
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iris = load_iris()
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# splitting the dataset to test and train
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train_x, test_x, train_y, test_y = train_test_split(
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iris["data"], iris["target"], random_state=4
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)
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# any of the 3 types of SVM can be used
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# current_model=SVC(train_x, train_y)
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# current_model=NuSVC(train_x, train_y)
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current_model = Linearsvc(train_x, train_y)
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prediction = current_model.predict([X_new])
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return iris["target_names"][prediction][0]
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if __name__ == "__main__":
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doctest.testmod()
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