ax.plot(ridgeWeights) # 每个特征 回归系数 变化 plot.show()#显示 # 标准化数据 def regularize(xMat):#regularize by columns inMat = xMat.copy() inMeans = mean(inMat,0) #calc mean then subtract it off inVar = var(inMat,0) #calc variance of Xi then divide by it inMat = (inMat...
variance = np.array(variance) # 预测数据归一化处理 x = (x - average) / variance # 添加偏置系数1 X = np.hstack(([1],x)) # 模型预测 y = X @ Theta print("预测结果y={0}".format(y)) #绘制特征图 def plot_X(X): plt.figure() (n, m) = np.shape(X) if m == 1: #一元...
print('the mean sqare error:%.2f' %np.mean((regr.predict(diabetes_X_test)-diabetes_y_test)**2)) print('Variance score:%.2f' %regr.score(diabetes_X_test,diabetes_y_test)) ##散点图 plt.scatter(diabetes_X_test,diabetes_y_test,color='black') plt.plot(diabetes_X_test,regr.predict(...
plt.plot(diabetes_X_test,regr.predict(diabetes_X_test),color='blue',linewidth=3) plt.xticks() plt.yticks() b=time.time() print('the running time is %.2f' %(b-a)) plt.show() 实验结果: Coefficients: [ 938.23786125] Residual sum of squares: 2548.07 Variance score: 0.47the running ...
When using the lineup2() plan, users have reported that the plan appears to run successfully and print a report of the peak statistics. Yet the motor has not been moved to the expected position after lineup2() finishes. The only possible...
90% Line - 90% of the samples took no more than this time. The remaining samples at least as long as this.
The scatter plot below shows the result of price prediction for parts of house with model. Each point on x-axis represents the median of the real price of the same type of house, and the y-axis represents the result of the linear regression model based on the feature prediction. When ...
Depicted are the first two principal components, explaining > 75% of the variance in the data. Data was power-transformed and standardized before the analysis. B Pair-plot, depicting the pairwise Spearman correlation between each pair of samples, and a histogram of normalized gene expression...
variance = np.var(data)print(f"数据:{data}")print(f"方差:{variance}") 输出结果: 数据: [2, 4, 6, 8, 10]方差: 8.0 方差为8.0,这是标准差(2.83)的平方。 5. 相关系数 相关系数衡量两个变量之间的线性关系强度。它的值在-1到1之间,1表示完全正相关,-1表示完全负相关,0表示无线性相关。
prediction_variance_degree2.py robust_regression.py run_opt_to_find_best_number_of_line_segments.py sinWaveFit.png sinWaveFit16.png sineWave.py sineWave_custom_opt_bounds.py sineWave_degrees.py sineWave_time_compare.py slope_constraint_demo.ipynb ...