leopd

[alias]
    lgb = log --graph --pretty=format:'%Cred%h%Creset -%C(yellow)%d%Creset %s %Cgreen(%cr) %C(bold blue)<%an>%Creset%n' --abbrev-commit --date=relative --branches

leopd

function add_rand_field(collection) {
    collection.find().forEach(function(data) {
        collection.update({_id:data._id}, {
          $set:{rand:Math.random()}
        });
    });
    collection.ensureIndex({rand:1},{background:true})
}

leopd

class Node:
    def __init__(self,value=None,kids=[]):
        self.value = value
        self.kids = kids


def rdfs(n, previsit, postvisit):
    '''Recursive depth-first traversal'''
    previsit(n)
    for k in n.kids:

leopd

import copy
import math
import logging

def balance_cnt(n):
    """Considering all nxn matrices consisting entirely of 0s and 1s,
    return the number of matrices which have exactly n/2 ones in each column and each row.
    Solution using dynamic programming as outlined here:
    https://en.wikipedia.org/wiki/Dynamic_programming#A_type_of_balanced_0.E2.80.931_matrix
    Produces answers in this sequence (for n/2) https://oeis.org/A058527

leopd

package algostudy;

import java.util.ArrayList;
import java.util.Comparator;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.PriorityQueue;
import java.util.Queue;

leopd

#
# Cookbook Name:: imgmuck
# Recipe:: default
#
# Copyright 2012, Scaled Recognition, all rights reserved
#

include_recipe "git"
include_recipe "python"
include_recipe "application"