Train conductor: Excuse me, mama. Ticket, please.Passenger: Sorry. I left it in the smoking section and I'll go fetch it.Train conductor: It's OK, lady._________ .A． Take your timeB． Take your seatC． You can go nowD． As you please
Train conductor: Excuse me, mama. Ticket, please.
Passenger: Sorry. I left it in the smoking section and I'll go fetch it.
Train conductor: It's OK, lady._________ .
A． Take your time
B． Take your seat
C． You can go now
D． As you please
相关考题：
Conductor: Good morning, , please?Passenger: Here you are.A． can I have your ticketB． anything to declareC． can I help youD． anything special
阅读下列C程序和程序说明，将应填入(n)处的字句写在对应栏内。【说明】下面是一个用C编写的快速排序算法。为了避免最坏情况，取基准记录pivot时，采用从left、right和mid=[(left+right)/2]中取中间值，并交换到right位置的办法。数组a存放待排序的一组记录，数据类型为T，left和right是待排序子区间的最左端点和最右端点。void quicksort (int a[], int left, int right) {int temp;if (left＜right) {hat pivot = median3 (a, left, right); //三者取中子程序int i = left, j = right-1;for(;;){while (i ＜j a[i] ＜ pivot) i++;while (i ＜j pivot ＜ a[j]) j--;if(i＜j){temp = a[i]; a[j] = a[i]; a[i] = temp;i++; j--;}else break;}if (a[i] ＞ pivot){temp = a[i]; a[i] = a[right]; a[right] = temp;}quicksort( (1) ); //递归排序左子区间quieksort(a,i+1 ,right); //递归排序右子区间}}void median3 (int a[], int left, int right){ int mid=(2);int k = left;if(a[mid] ＜ a[k])k = mid;if(a[high] ＜ a[k]) k = high; //选最小记录int temp = a[k]; a[k] = a[left]; a[left] = temp; //最小者交换到 leftif(a[mid] ＜ a[right]){temp=a[mid]; a[mid]=a[right]; a[right]=temp;}}消去第二个递归调用 quicksort (a,i+1,right)。 采用循环的办法:void quicksort (int a[], int left, int right) {int temp; int i,j;(3) {int pivot = median3(a, left, right); //三者取中子程序i = left; j = righi-1;for (;; ){while (i＜j a[i] ＜ pivot)i++;while (i＜j pivot ＜a[j]) j--;if(i ＜j) {temp = a[i]; a[j]; = a[i]; a[i]=temp;i++; j--;}else break;}if(a[i]＞pivot){(4);a[i]=pivot;}quicksoft ((5)); //递归排序左子区间left = i+1;}}
在MINST数据集中，访问训练集train_x的第4个样本，可以通过_______语句实现。A．train_x[4]B．train_x[3]C．train_x[0:3]D．train_x[:4]
下列哪些语句会开始模型的训练：A．LinearRegression().fit(x_train,y_train)B．lr_mod.predict(x_train)C．lasso_mod.fit(x_train,y_train)D．vote_mod.predict(x_train)