fit_transform(data) # 创建训练和测试数据集 train_size = int(len(data) * 0.8) train, test = data[:train_size], data[train_size:] # 创建数据集函数 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back - 1): a = dataset[i:(i...
web自动化测试：selenium 模拟鼠标键盘：pymouse、pywinauto、pyautogui 微信自动化：wechatpy 3、自动化...
fit(X,y) # 画出通过三个点([2,3]、[3,4]和[4,4])直线z = np.linspace(-5,5,20) plt.scatter(X,y,s=80) plt.plot(z,lr.predict(z.reshape(-1,1)),c='k') plt.title('Straight Line') plt.show() # 显示这条线的斜率和截距 print('y={:.3f}'.format(lr.coef_[0]),'x',...
seaborn.regplot(x, y, data=None, x_estimator=None, x_bins=None, x_ci='ci', scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, seed=None, order=1, logistic=False, lowess=False, robust=False, logx=False, x_partial=None, y_partial=None, truncate=True, dropna=True, x_...
train_y = encoder.fit_transform(train_y) valid_y = encoder.fit_transform(valid_y) 二、特征工程 接下来是特征工程,在这一步,原始数据将被转换为特征向量,另外也会根据现有的数据创建新的特征。为了从数据集中选出重要的特征,有以下几种方式...
If your data points clearly will not fit a linear regression (a straight line through all data points), it might be ideal for polynomial regression.Polynomial regression, like linear regression, uses the relationship between the variables x and y to find the best way to draw a line through ...
clf.fit(x_train, y_train) '''测试结果的打印''' answer = clf.predict(x) print(x) print(answer) print(y) print(np.mean( answer == y)) '''准确率与召回率''' precision, recall, thresholds = precision_recall_curve(y_train, clf.predict(x_train)) answer ...
X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype = float)) 将数据分为训练集和测试集 In [0]: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) In [12]: len(y_train) Out[12]:
然后我们可以定义我们自己的配色方案并绘制散点图，代码如下所示：# Set a 3 KMeans clusteringkmeans = KMeans(n_clusters=3)# Compute cluster centers and predict cluster indicesX_clustered = kmeans.fit_predict(x_9d)# Define our own color mapLABEL_COLOR_MAP = {0: 'r',1: '...
(n_samples=100,n_features=1,n_informative=2,noise=50,random_state=8)X_train,X_test,y_train,y_test = train_test_split(X, y, random_state=8,test_size=0.3)clf = MLPRegressor(max_iter=20000).fit(X,y)title = "MLPRegressor make_regression数据集(有噪音)"myutil.draw_line(X[:,0],y...