def get_class(self, labels):
l = [(np.sum(labels == i), i) for i in set(labels)]
l = np.array(l, dtype=[('x', np.int16), ('y', np.int16)])
return np.sort(l, order='x')[::-1]
def predit(self, x):
topk = self.get_topk(x)
c = self.get_class(topk[1])
confidence = c[0][0] / self.k
clazz = c[0][1]
return clazz, confidence
if __name__ == '__main__':
load = Load()
train, test = load.load_train(2000), load.load_test(200)
print('获取数据成功：', train[0].shape, train[1].shape)
#预处理标签
train_labels, test_labels = np.argmax(train[1], axis=1), np.argmax(test[1],
axis=1)
classify = Classify(6, train[0], train_labels)
ylabel, y_label = np.array([]), np.array([])
for test_item, i in zip(test[0], np.arange(0, test[0].shape[0])):
x = test_item
# print(x.shape)
y = test_labels[i]
# topk=classify.get_topk(x)
#
# # print('k:',topk[1])
