def main():
iris = datasets.load_iris()
x = iris.data[50:150]
t = iris.target[50:150]
for i in range(100):
if t[i] == 2:
t[i] = -1
skf = model_selection.StratifiedKFold(n_splits=10)
avg_accuracy = 0
for train, test in skf.split(x, t):
classifier = PerceptronClassifier(4, 0.1)
classifier.learn(t[train], x[train])
accuracy = 0.0
for test_x, test_t in zip(x[test], t[test]):
test_y = classifier.classify(test_x)
if test_y == test_t:
accuracy += 1
accuracy /= len(test)
avg_accuracy += accuracy
avg_accuracy /= 10
print 'average accuracy:', avg_accuracy * 100, '%'
class Perceptron:
def __init__(self):
iris = datasets.load_iris()
x = iris.data[0:150]
t = iris.target[0:150]
for i in range(150):
if t[i] == 0:
t[i] = -1
if t[i] == 2:
t[i] = 1
self.classifier = PerceptronClassifier(4, 0.1)
self.classifier.learn(t, x)
def predict(self, x):
y = self.classifier.classify(x)
if y == -1:
return 'setosa'
else:
return None
