Linux Bash代码 利用for循环实现命令的多次执行

Linux Bash代码

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[yuanhao15@lu01 libsvm-rank-2.81]$  for ((i=0; i<=19; i++)) do ./svm-train -s 5 -c 10 -t 0 X4058_300/mytask_train.$((i)); done   [yuanhao15@lu01 libsvm-rank-2.81]$  for ((i=0; i<=19; i++)) do ./svm-predict  X4058_300/mytask_test.$((i)) mytask_train.$((i)).model output_file.$((i)); done   Accuracy = 33.9901% (69/203) (classification) Mean absolute error = 0.906404 (regression) Squared correlation coefficient = 0.194913 (regression) Accuracy = 33.0049% (67/203) (classification) Mean absolute error = 0.916256 (regression) Squared correlation coefficient = 0.15242 (regression) Accuracy = 26.601% (54/203) (classification) Mean absolute error = 1.03941 (regression) Squared correlation coefficient = 0.0883807 (regression) Accuracy = 29.5567% (60/203) (classification) Mean absolute error = 0.990148 (regression) Squared correlation coefficient = 0.105375 (regression) Accuracy = 36.4532% (74/203) (classification) Mean absolute error = 0.876847 (regression) Squared correlation coefficient = 0.185002 (regression) Accuracy = 27.5862% (56/203) (classification) Mean absolute error = 1.02463 (regression) Squared correlation coefficient = 0.0996877 (regression) Accuracy = 32.0197% (65/203) (classification) Mean absolute error = 0.931034 (regression) Squared correlation coefficient = 0.152379 (regression) Accuracy = 31.0345% (63/203) (classification) Mean absolute error = 0.965517 (regression) Squared correlation coefficient = 0.140663 (regression) Accuracy = 29.064% (59/203) (classification) Mean absolute error = 1 (regression) Squared correlation coefficient = 0.178278 (regression) Accuracy = 30.5419% (62/203) (classification) Mean absolute error = 0.945813 (regression) Squared correlation coefficient = 0.176329 (regression) Accuracy = 37.4384% (76/203) (classification) Mean absolute error = 0.832512 (regression) Squared correlation coefficient = 0.279723 (regression) Accuracy = 32.0197% (65/203) (classification) Mean absolute error = 0.945813 (regression) Squared correlation coefficient = 0.160936 (regression) Accuracy = 29.5567% (60/203) (classification) Mean absolute error = 0.975369 (regression) Squared correlation coefficient = 0.175127 (regression) Accuracy = 26.1084% (53/203) (classification) Mean absolute error = 1.0197 (regression) Squared correlation coefficient = 0.123619 (regression) Accuracy = 33.0049% (67/203) (classification) Mean absolute error = 0.990148 (regression) Squared correlation coefficient = 0.0964109 (regression) Accuracy = 32.5123% (66/203) (classification) Mean absolute error = 0.926108 (regression) Squared correlation coefficient = 0.195953 (regression) Accuracy = 28.5714% (58/203) (classification) Mean absolute error = 0.995074 (regression) Squared correlation coefficient = 0.140257 (regression) Accuracy = 33.4975% (68/203) (classification) Mean absolute error = 0.896552 (regression) Squared correlation coefficient = 0.22211 (regression) Accuracy = 39.4089% (80/203) (classification) Mean absolute error = 0.857143 (regression) Squared correlation coefficient = 0.219532 (regression) Accuracy = 34.9754% (71/203) (classification) Mean absolute error = 0.935961 (regression) Squared correlation coefficient = 0.145034 (regression)