Skip to main content

Break out of more than 2 loops in python

If you want to break out of a loop in Python, just use "break". How about when you need to bread out of more than 2 loops in Python?

Here is a hint: Using Raise and Exception.

try:
   ...
   if (condition): #You want to break your loop here
      raise GetOutOfLoop
except GetOutOfLoop:
   pass
Labels:

Comments

  1. AnonymousJune 26, 2012 at 11:43 PM

    No more updates here?? Bring your work in the wide open, trigger the minds of the the laymen and everybody will understand the relevance of your fascinating job, the algorithmic analysis and the theorem of Pythagoras. All the best to you Thu!

    ReplyDelete

Post a Comment

Popular posts from this blog

Pytorch and Keras cheat sheets

Post a Comment
Read more

Python Tkinter: Changing background images using key press

Let's write a simple Python application that changes its background image everytime you click on it. Here is a short code that helps you do that: import os, sys import Tkinter import Image, ImageTk def key(event): print "pressed", repr(event.char) event.widget.quit() root = Tkinter.Tk() root.bind_all(' ', key) root.geometry('+%d+%d' % (100,100)) dirlist = os.listdir('.') old_label_image = None for f in dirlist: try: image1 = Image.open(f) root.geometry('%dx%d' % (image1.size[0],image1.size[1])) tkpi = ImageTk.PhotoImage(image1) label_image = Tkinter.Label(root, image=tkpi) label_image.place(x=0,y=0,width=image1.size[0],height=image1.size[1]) root.title(f) if old_label_image is not None: old_label_image.destroy() old_label_image = label_image root.mainloop() # wait until user clicks the window except Exception, e: # Skip a...
Post a Comment
Read more

Word embeddings

In this post, we are going to talk about word embedding (or word vector), which is how we represent words in NLP. Word embedding is used in many higher-level applications such as sentiment analysis, Q&A, etc. Let's have a look at the most currently widely used models. One-hot vector is a vector of size V, with V is the vocabulary size. It has value 1 in one position (represents the value of this word "appears") and 0 in all other positions. [0, 0, ... 1, .., 0] This is usually used as the input of a word2vec model. It is just operating as a lookup table. So this one-hot encoding treats words as independent units. In fact, we want to find the "similarity" between words for many other higher-level tasks such as document classification, Q&A, etc. The idea is: To capture the meaning of a word, we look at the words that frequently appear close-by this word. Let's have a look at some state-of-the-art architectures that give us the results of word ve...
Post a Comment
Read more