df.sort_values(by=['date'], inplace=True, ascending=True) df_concat = pd.concat([df, Predict_df], axis=1) df_concat = df_concat[df_concat.index.isin(Predict_df.index)] # 画预测值和实际值 df_concat['close'].plot(colo
ax[0].scatter(x1,y) ax[0].plot(x1, p0 + p1*x1, color='r') ax[0].grid(True) # 绘制网格线 # 绘制损失值图像 ax[1].set_title('损失值图像') li = list(range(count)) print('迭代次数:', count) ax[1].plot(li,lloss) ax[1].grid(True) plt.show() 1. 2. 3. 4. 5. 6...
Add linear Ordinary Least Squares (OLS) regression trendlines or non-linear Locally Weighted Scatterplot Smoothing (LOWESS) trendlines to scatterplots in Python. Options for moving averages (rolling means) as well as exponentially-weighted and expanding functions. ...
defgradientDescent(X,y,theta,alpha,num_iters):m=y.shape[0]#print(m)# 存储历史误差 J_history=np.zeros((num_iters,1))foriterinrange(num_iters):# 对J求导,得到 alpha/m*(WX-Y)*x(i),(3,m)*(m,1)X(m,3)*(3,1)=(m,1)theta=theta-(alpha/m)*(X.T.dot(X.dot(theta)-y))J...
Univariate Linear Regression in Python Take‘lstat’ as independent and ‘medv’ as dependent variables or Using ‘lstat’ as the predictor and ‘medv’ as the response: Step 1: Load the Boston Dataset import pandas as pd data = pd.read_csv("Boston1.csv") ...
(10, 6)) plt.plot(wavelengths * 1e9, spectral_irradiance, color='green', label='Theoretical Irradiance') plt.plot(wl, intensity, color='red', label='Noisy Signal') plt.plot(wl, filtered_data, markerfacecolor='none',color='black', label='Denoised Signal') plt.annotate( str(temperature...
在前面的基础上: 迦非喵:Python+ENO3+WENO3+RK1求解一维单块(1 blocks)结构网格1-D Linear Convection equation简单测试这里继续重构: 有: w eno.pyimport numpy as np import matplotlib.pyplot as plt # …
sum_mean=0foriinrange(len(y_pred)):sum_mean+=(y_pred[i]-y_test.values[i])**2sum_erro=np.sqrt(sum_mean/10)#这个10是你测试级的数量 # calculateRMSEby handprint("RMSE by hand:",sum_erro)#做ROC曲线 plt.figure()plt.plot(range(len(y_pred)),y_pred,'b',label="predict")plt.plo...
plt.plot(x, y_pred, color='red') plt.xlabel('x') plt.ylabel('y') plt.title('Linear Regression Fit') plt.show()输出结果:斜率 (w): 2.968467510701019 截距 (b): 4.222151077447231显示如下所示:我们可以使用 score() 方法来评估模型性能,返回 R^2 值。实例...
plt.plot(X, y) plt.scatter(X, y) plt.show() pre_data=pd.DataFrame() pre_data['X'] =X pre_data['y'] =y pre_data.set_index('X', inplace=True) pre_data.head()fromstatsmodels.graphics.tsaplotsimportplot_acf plot_acf(pre_data).show()fromstatsmodels.tsa.stattoolsimportadfuller as...