pramod-io

"""
Problem Description:
You are given three vessels A, B and C of capacities 8, 5 and 3 gallons respectively. A is filled, while B and C are empty.
Divide the liquid in A into two equal quantities. 
(From Graph Theory, Narsingh Deo)
http://bit.ly/1Dnp4HA

Usage:
import decanting as dc
import networkx as nx

pramod-io

#https://www.youtube.com/watch?v=UZjlBQbV1KU
x <- seq(0, 1, length=100)
a= 0.5
b= 0.5
y <- x^(a-1) * (1-x)^(b-1)
plot(x, y, type="l", lwd=3)

pramod-io

import collections
import functools

#https://wiki.python.org/moin/PythonDecoratorLibrary#Memoize

class memoized(object):
   '''Decorator. Caches a function's return value each time it is called.
   If called later with the same arguments, the cached value is returned
   (not reevaluated).
   '''

pramod-io

def write(file_path, sequence, line_format):
    """
    Example Usage: Save squares of numbers from 1 to 100 in a csv file
    
    squares = ( (i, i * i) for i in range(1, 101))
    write("squares.txt", squares, "{},{}")
    
    """

    lines = (line_format.format(*line_variables) for line_variables in sequence)

pramod-io

package main

import (
	"fmt"
	"net/http"
	"time"
	"os/exec"
)

func IsAppPublished(url string) bool {

pramod-io

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#FlipPredictor
#A coin is drawn at random from a bag of coins of varying probabilities
#Each coin has the same chance of being drawn
#Your class FlipPredictor will be initialized with a list of the probability of 
#heads for each coin. This list of probabilities can be accessed as self.coins 
#in the functions you must write. The function update will be called after every 
#flip to enable you to update your estimate of the probability of each coin being 
#the selected coin. The function pheads may be called and any time and will 
#return your best estimate of the next flip landing on heads.

pramod-io

import os
import time

from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler

class VHandler(FileSystemEventHandler):

	def __init__(self):
		FileSystemEventHandler.__init__(self)

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#For simulations
import random 

class Item(object):
	def __init__(self, value=0, weight=0):
		self.value = value
		self.weight = weight

	def __repr__(self):
		return "({value}, {weight})".format(**self.__dict__)

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def max_weights(a):

	weights = [0] * (len(a) + 2)

	for i, v in enumerate(a):
		j = i + 2
		with_v = weights[j-2] + v
		without_v = weights[j-1]
		weights[j] = max(with_v, without_v)