svm-prediction.py 文件源码

def svr_main(X, Y):
    X_train = X[:TRAIN_SIZE]
    Y_train = Y[:TRAIN_SIZE]
    X_test = X[TRAIN_SIZE:]
    Y_test = Y[TRAIN_SIZE:]

    clf = SVR(kernel='rbf', C=1e3, gamma=0.00001)
    #clf.fit(X_train,Y_train)
    #y_pred = clf.predict(X_test)
    #plt.plot(X_test, y_pred, linestyle='-', color='red') 

    #clf = GradientBoostingRegressor(n_estimators=100,max_depth=1)
    #clf = DecisionTreeRegressor(max_depth=25)
    #clf = ExtraTreesRegressor(n_estimators=2000,max_depth=14)
    #clf = xgb.XGBRegressor(n_estimators=2000,max_depth=25)
    #clf = RandomForestRegressor(n_estimators=1000,max_depth=26,n_jobs=7)
    predict_list = []
    for i in xrange(TEST_SIZE):
        X = [ [x] for x in xrange(i, TRAIN_SIZE+i)]
        clf.fit(X, Y[i:TRAIN_SIZE+i])
        y_pred = clf.predict([TRAIN_SIZE+1+i])
        predict_list.append(y_pred)

    print "mean_squared_error:%s"%mean_squared_error(Y_test, predict_list)
    print "sqrt of mean_squared_error:%s"%np.sqrt(mean_squared_error(Y_test, predict_list))
    origin_data = Y_test
    print "origin data:%s"%origin_data
    plt.plot([ x for x in xrange(TRAIN_SIZE+1, TRAIN_SIZE+TEST_SIZE+1)], predict_list, linestyle='-', color='red', label='prediction model')  
    plt.plot(X_test, Y_test, linestyle='-', color='blue', label='actual model') 
    plt.legend(loc=1, prop={'size': 12})
    plt.show()