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/*****************************************************************************
 *                QuantCup 1:   Price-Time Matching Engine
 *
 * Submitted by: voyager
 * 
 * Design Overview:
 *   In this implementation, the limit order book is represented using 
 *   a flat linear array (pricePoints), indexed by the numeric price value. 
 *   Each entry in this array corresponds to a specific price point and holds 
 *   an instance of struct pricePoint. This data structure maintains a list 

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import gmpy, time, random, math

def genprime(bits):
    p=1
    while(gmpy.is_prime(p)==0):
        p = random.randrange(math.pow(2,bits-1),math.pow(2,bits))
    return p

BITS=20
found=False

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# Definition for a binary tree node.
# class TreeNode(object):
#     def __init__(self, x):
#         self.val = x
#         self.left = None
#         self.right = None

class Solution(object):
    def invertTree(self, root):
        """

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# Definition for a binary tree node.
class TreeNode(object):
     def __init__(self, x):
         self.val = x
         self.left = None
         self.right = None

class Solution(object):
    p = TreeNode(1)
    q = TreeNode(1)

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# Definition for a binary tree node.
class TreeNode(object):
     def __init__(self, x):
         self.val = x
         self.left = None
         self.right = None

class Solution(object):
    def maxDepth(self, root):
        if not root:

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"""
A Python implementation of NNLS algorithm

References:
[1]  Lawson, C.L. and R.J. Hanson, Solving Least-Squares Problems, Prentice-Hall, Chapter 23, p. 161, 1974.

Contributed by Klaus Schuch (schuch@igi.tugraz.at)
based on MATLAB's lsqnonneg function

"""

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sudo -H pip freeze --local | grep -v '^\-e' | cut -d = -f 1  | xargs -n1 sudo -H pip install -U

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#'Norm Vec
norm_vec <- function(x){
	return (sqrt(sum(x^2)))
} 

#' Gradient Step
#' 
#' @param gradf handle to function that returns gradient of objective function
#' @param x current parameter estimate
#' @param t step-size

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#'Norm Vec
norm_vec <- function(x){
	return (sqrt(sum(x^2)))
} 

#' Gradient Step
#' 
#' @param gradf handle to function that returns gradient of objective function
#' @param x current parameter estimate
#' @param t step-size

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#'Norm Vec
norm_vec <- function(x){
	return (sqrt(sum(x^2)))
} 

#' Gradient Step
#' 
#' @param gradf handle to function that returns gradient of objective function
#' @param x current parameter estimate
#' @param t step-size