1,100))# 加权拟合p=np.polyfit(x,y,1,w=weights)y_fit=np.polyval(p,x)# 绘制数据和拟合直线plt.scatter(x,y,label='Data')plt.plot(x,y_fit,color='red',label='Weighted Best Fit Line')plt.legend()plt.show
a = ybar - b * xbar print('best fit line:\ny = {:.2f} + {:.2f}x'.format(a, b)) returna, b # solution a, b = best_fit(X, Y) # best fit line: # y = 0.80 + 0.92x # plot points and fit line import matplotlib.pyplotasplt plt.scatter(X, Y) yfit = [a + b * ...
linear.fit(xfit,yfit) xpre=np.linspace(19.5,20.1,num=50,endpoint=True)#创建用于预测的x值ypre=linear.predict(xpre[:,np.newaxis]) ax.plot(xpre,ypre,"-",label="degree 1")#假设数据属于多项式回归,分别对其进行拟合fromsklearn.preprocessingimportPolynomialFeaturesforiin[2,4]: PF=PolynomialFeatures...
# add a 'best fit' line y = ((1 / (np.sqrt(2 * np.pi) * sigma)) * np.exp(-0.5 * (1 / sigma * (bins - mu))**2)) ax.plot(bins, y, '--') ax.set_xlabel('Smarts') ax.set_ylabel('Probability density') ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$...
x=np.array([1,2,3,4,5])y=np.array([2,3,5,7,11])plt.scatter(x,y)# 计算最佳拟合线的参数m,b=np.polyfit(x,y,1)# 添加最佳拟合线plt.plot(x,m*x+b,color='red')plt.title("Scatter Plot with Best Fit Line - how2matplotlib.com")plt.xlabel("X Axis")plt.ylabel("Y Axis")plt...
# add a 'best fit' line y = mlab.normpdf(bins_limits, mu, sigma) ax.plot(bins_limits, y, '--') ax.set_xlabel('Smarts') ax.set_ylabel('Probability density') ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$') ...
# 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', alpha = 1) # Label the axes and provide a title ax.set_title(title) ...
# 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', alpha = 1) # Label the axes and provide a title ax.set_title(title) ...
# add a 'best fit' line y = ((1 / (np.sqrt(2 * np.pi) * sigma)) * np.exp(-0.5 * (1 / sigma * (bins - mu))**2)) ax.plot(bins, y, '--') ax.set_xlabel('Smarts') ax.set_ylabel('Probability density') ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15...
带线性回归最佳拟合线的散点图 (Scatter plot with linear regression line of best fit) 如果你想了解两个变量如何相互改变,那么最佳拟合线就是常用的方法。 抖动图 (Jittering with stripplot) 通常,多个数据点具有完全相同的 X 和 Y 值。 结果,多个点绘制会重叠并隐藏。 为避免这种情况,请将数据点稍微抖动,...