# import regression as lr lr.ridgeTestPlot() # import regression as lr lr.stageWiseTestPlot() from numpy import * # txt 文件数据提取 以TAB键值分割 def loadDataSet(fileName): numFeat = len(open(fileName).readline().split('\t')) - 1 #样本数据维度 最后一个为标签 dataMat = []; labe...
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 ...
A Principal component analysis projection of the time-series data. 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 ...
array( [data[0] for data in infer_data]).astype("float32") # extract data in test dataset infer_label = numpy.array( [data[1] for data in infer_data]).astype("float32") # extract label in test dataset assert feed_target_names[0] == 'x' results = infer_exe.run(inference_...
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 ...
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。