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scikit_test.py
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scikit_test.py
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from sklearn import svm
from sklearn.neighbors.nearest_centroid import NearestCentroid
from sklearn import tree
class Learn(object):
def test(self, X, Y, test):
t = [self.sup_vm(X, Y, test),
self.nn_centroid(X, Y, test),
self.d_tree(X, Y, test)]
print("\n")
return t
def d_tree(self, X, y, test):
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, y)
t = clf.predict(test)
print("tree:", t)
return t
def sup_vm(self, X, y, test):
clf = svm.SVC()
clf.fit(X, y)
t = clf.predict(test)
print("svm:", t)
return t
def nn_centroid(self, X, y, test):
clf = NearestCentroid()
clf.fit(X, y)
t = clf.predict(test)
print("nn_centroid:", t)
return t
def try_methods(self, X, Y, test):
# print "X =", X, "\nY =", Y, "\ntest =", test, "\n"
self.sup_vm(X, Y, test)
self.nn_centroid(X, Y, test)
self.d_tree(X, Y, test)
print("\n")
if __name__ == "__main__":
a = Learn()
X1 = [[0, 0], [0, 2], [2, 2], [2, 0]]
Y3 = [0, 1, 2, 3]
tx3 = [[1.000000000000001, 1.000000000000001]] # - dtree
a.try_methods(X1, Y3, tx3)
X2 = [[0, 0], [0, 2], [2, 2], [2, 0]]
Y2 = [0, 1, 0, 1]
tx2 = [[1, 1]]
a.try_methods(X2, Y2, tx2) # - nn_centroid
a.try_methods([[0, 0], [1, 1]], [0, 1], [[2., 2.]])