leopd

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

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

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

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

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

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

[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

count_by = function(collection,field) {
    return collection.aggregate({
        $group: {
            _id: "$" + field,
            count: { $sum: 1 }
        }
    })
}
 
count_by(db.vid10,'char');

leopd

" turn off compatibility with the old vi
set nocompatible

set ts=4
set et

" turn syntax highlighting on by default
syntax on

" set auto-indenting on for programming

leopd

set -o vi  # Switch bash to vi mode

leopd

from functools import lru_cache
import subprocess
from torch.utils.data import Dataset

class FileReaderDataset(Dataset):
    """Exposes a line-delimited text file as a PyTorch Dataset.
    Maintains an LRU cache of lines it has read, while supporting random access into
    files too large to hold in memory. Memory requirement still scales by O(N), but just
    for pointers into the file, about 8 bytes per line.  After the file has been scanned,
    random access will be very fast - as fast as the disk plus the OS's cache of it.