def main():
X_train, X_train_lr, y_train, y_train_lr,X_test, y_test=generate_data()
config=Config()
gbdt=GBDT(config=config)
gbdt.buildGbdt(X_train,y_train)
trainDataFeatures=gbdt.generateFeatures(X_train_lr)
testDataFeatures=gbdt.generateFeatures(X_test)
print len(trainDataFeatures[0])
lrModel = LogisticRegression()
lrModel.fit(trainDataFeatures,y_train_lr)
#test model
testLabel = lrModel.predict(testDataFeatures)
accuracy = np.sum((np.array(testLabel)==np.array(y_test)))*1.0/len(y_test)
print ("the accuracy is % f"%accuracy)
def test():
x = [[0.5, 0.6, 0.7], [0.4, 0.5, 0.5], [1.2, 1.3, 1.0], [1.4, 1.5, 0.8],
[1.5, 1.3, 1.3]]
y = [0, 0, 1, 1, 1]
c = Config()
gbdt = GBDT(config=c)
gbdt.buildGbdt(x, y)
data_features = gbdt.generateFeatures(x)
print len(data_features[0])