lqdc

# -*- coding: utf-8 -*-
# @Author: roman
# @Date:   2016-05-09 19:59:31
# @Last Modified by:   roman
# @Last Modified time: 2016-05-09 21:08:37


import numpy as np

from sklearn.cross_validation import train_test_split

lqdc

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lqdc

import numpy as np
import pylab as pl


def calc_lv(x, y, coefs):
    x_new = x * (coefs['a'] - coefs['b'] * y - coefs['c'])
    y_new = y * (coefs['e'] * coefs['b'] * x - coefs['d'])
    return x_new, y_new

lqdc

function wordcount(text)
     words=split(readall(text), Set([' ','\n','\r','\t','-','.',',',':','_','"',';','!']),false)
     counts=Dict()
     for w in words
         counts[w] = get(counts,w,0)+1
     end          
     return counts
end
tic();open(wordcount, "input1.txt");toc()

lqdc

import time

def initTrades(trades):
    for i, trade in enumerate(trades):
        if i % 2:
            trade["side"] = 'S'
        else:
            trade["side"] = 'B'
        trade["tradeId"] = i
        trade["clientId"] = 1

lqdc

# Author: Roman Sinayev
# License: BSD Style.
# Viterbi algorithm implementation and example written in Julia Language
#
# The idea is to decode the most probable order of a hidden markov model 
# based on indirect observations.

# In the example below, we attempt to determine where a person is based on
# their cell phone service strength.  We also have fake probabilities associated
# with each state(location) and with transitions between locations and with

lqdc

# -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
#          Mathieu Blondel <mathieu@mblondel.org>
#          Lars Buitinck <L.J.Buitinck@uva.nl>
#          Robert Layton <robertlayton@gmail.com>
#          Jochen Wersdörfer <jochen@wersdoerfer.de>
#          Roman Sinayev <roman.sinayev@gmail.com>
#
# License: BSD Style.
"""