fromkeras.modelsimportSequentialfromkeras.layersimportLSTM,Denseimportnumpyasnp# 准备数据data=df['Traffic_Volume'].values data=data.reshape(-1,1)# 标准化数据fromsklearn.preprocessingimportMinMaxScaler scaler=MinMaxS
例如,线条 (Line2D)、文本 (Text)、矩形 (Rectangle)、图像 (AxesImage)、图例 (Legend)、坐标轴标签 (XLabel,YLabel) 等都是Artist。 Figure和Axes本身也是Artist的容器 (Figure是顶层Artist容器,Axes是其子容器)。 Matplotlib 的渲染引擎最终会将这些Artist对象绘制到输出设备上。 用户通常通过Axes对象的方法来创建...
defencircle(x,y,ax=None,**kw):ifnot ax:ax=plt.gca()p=np.c_[x,y]hull=ConvexHull(p)poly=plt.Polygon(p[hull.vertices,:],**kw)ax.add_patch(poly)# Select data to be encircled midwest_encircle_data=midwest.loc[midwest.state=='IN',:]# Draw polygon surrounding verticesencircle(midwest...
df_train = data[['Date','Close']] df_train = df_train.rename(columns={"Date":"ds","Close":"y"}) m = Prophet() m.fit(df_train) future = m.make_future_dataframe(periods=period) forecast = m.predict(future) # Show and plot foreca...
def lineplot(x_data, y_data, x_label="", y_label="", title=""): # Create the plot object _, ax = plt.subplots() # Plot the best fit line, set the linewidth (lw), color and # transparency (alpha) of the line ax.plot(x_data, y_data, lw = 2, color = '#539caf', alp...
forest.fit(X_train, Y_train) #print model accuracy on the training data. print('[0]Logistic Regression Training Accuracy:', log.score(X_train, Y_train)) print('[1]K Nearest Neighbor Training Accuracy:', knn.score(X_train, Y_train)) ...
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...
processor =Preprocess(columns_to_drop,datetime_column, dropna_columns)还可以创建一个管道来指定处理和训练数据的步骤。一旦拥有管道，我们所需要做的就是使用它对列车和测试数据应用相同的处理。pipline.fit_transform(data)易于调试 使用函数可以更容易地测试该函数是否产生期望的输出，我们可以快速地指出代码中的哪些...
3.1 Data Visualization 3.1.1 Plot2DData 3.1.2 Generate Stata Graph in Python 3.2 Scientific Computation 3.2.1 Optimization Toolbox 3.2.2 Probability Distributions 3.2.3 lllustrative Example 3.2.4 Quadrature Integration 3.2.5 Ordinary Differential...
Include my email address so I can be contacted Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly Cancel Create saved search Sign in Sign up Appearance settings Reseting focus {{ message }} cucy / pyspark_project Public ...