for-loop

问题 How to flip this triangle? So i was making aritmethic sequance triangle. It was upside down. How do I turn it 180 degree? for example: 1=1 1+2=3 1+2+3=6 etc... my code: package javaapplication4; public class NewClass5 { public static void main(String[] args) { int i=5,a; for(int j=i; j>=1; j--) { for(a=1; a<=i; a++) System.out.print(a +" + "); int n = 0; for(a = 1; a<=i; a++) { n = n + a; } System.out.print(" = "+ n); System.out.println(); i--; } } } 回答1: You can do it for any n, by getting

问题 How to flip this triangle? So i was making aritmethic sequance triangle. It was upside down. How do I turn it 180 degree? for example: 1=1 1+2=3 1+2+3=6 etc... my code: package javaapplication4; public class NewClass5 { public static void main(String[] args) { int i=5,a; for(int j=i; j>=1; j--) { for(a=1; a<=i; a++) System.out.print(a +" + "); int n = 0; for(a = 1; a<=i; a++) { n = n + a; } System.out.print(" = "+ n); System.out.println(); i--; } } } 回答1: You can do it for any n, by getting

问题 I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set. Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding

问题 I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set. Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding

问题 I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set. Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding

问题 I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set. Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding

问题 I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set. Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding

问题 I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set. Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding

问题 I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set. Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding

问题 I had some problems with the code that was given in an answer at this post: Can I use a nested for loop for an if-else statement with multiple conditions in python? import pprint board = { '1a': 'bking', '4e': 'bpawn', '2c': 'bpawn', '3f': 'bpawn', '5h': 'bbishop', '6d': 'wking', '7f': 'wrook', '2b': 'wqueen' } count = {} for k, v in board.items(): count[k[0]][k[1:]] = v pprint.pprint(count) I wanted to get the following dictionary: count = {'b': {'king': 1, 'pawn': 3, 'bishop': 1}, 'w': {