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One-line Tree in Python

Using Python's built-in defaultdict we can easily define a tree data structure:

def tree(): return defaultdict(tree)

That's it!

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from pylab import *
from scipy.ndimage import measurements
import numpy as np
import matplotlib.pyplot as plt
import time
import sys
import signal

def signal_handler(signal, frame):
    sys.exit(0)

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Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

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A Collection of NLP notes

N-grams

Calculating unigram probabilities:

P( w_i ) = count ( w_i ) ) / count ( total number of words )

In english..

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Table Of Content

Skip to the relevant sections if needed.

• 2-min tutorial to do it the quick-and-dirty-way
• Concepts for resolving Git conflicts
• Setting up different editors / tool for using git mergetool
  • Finding out what mergetool editors are supported
• mergetool simple code example for vimdiff
  • Resolving conflict from a Git pull
• Other great references and tutorials

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Competitive Programming Syllabus

Geometry

• Graham Scan algorithm for Convex Hull O(n * log(n))
• Online construction of 3-D convex hull in O(n^2)
• Bentley Ottmann algorithm to list all intersection points of n line segments in O((n + I) * logn)
  • Suggested Reading - http://softsurfer.com/Archive/algorithm_0108/algorithm_0108.htm
• Rotating Calipers Technique
  • Suggested Reading - http://cgm.cs.mcgill.ca/~orm/rotcal.html

• Problems - Refer the article for a list of problems which can be solved using Rotating Calipers technique.

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do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.

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In this gist I'm going to list all the concept I need to learn for starting with tensorflow. This list is being made while reading official documentation.

1. Install
2. Tensor
3. Computational graph
4. tf.Session()
5. tf.constant()
6. tf.placeholder()
7. sess.run( , feed_dict = { } )

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import tensorflow as tf
import numpy as np

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

x = tf.placeholder("float", [None, 784]) #mnist data image of shape 28*28*784
y = tf.placeholder("float", [None, 10]) #0-9 digits recognition

#Create model

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import cv2
import numpy as np
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data

def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev=0.01))


def model(X, w_h, w_o):