from __future__ import print_function # python3 print function

__author__ = 'Joseph Cheng <indiejoseph@gmail.com>'

__version__ = '0.1'

import os

from sys import stdout

import matplotlib.pyplot as plt

from data_prepare import YahooHistorical

from datetime import date, datetime

import numpy as np

from lib.features import Features

from pybrain.structure.modules import KohonenMap

import pickle

np.random.seed(42)

symbol1 = '0005.HK'

yahoo_data1 = YahooHistorical(data_from=date(2000, 1, 1), data_to=date(2015, 12, 31))

yahoo_data1.open(os.path.join(os.path.dirname(__file__), 'data/' + symbol1 + '.csv'))

data1 = yahoo_data1.get()

dataset1 = np.asarray([n['adj_close'] for n in data1])

p = 17 # 17-day

p = 5 # 5-day

nodes = 3

som = KohonenMap(p, nodes)

# som = pickle.load(open("pattern5.p", "rb"))

som.learningrate = 0.01

epochs = 1000

training_dataset = []

result = {}

# preparation

for i in xrange(p, len(dataset1)):

training_input = dataset1[i-p:i]

mmax = np.max(training_input)

mmin = np.min(training_input)

training_input = (training_input - mmin) / (mmax - mmin)

if np.isnan(training_input).any():

training_input = np.array([0] * p)

training_dataset.append(training_input)

# training

for epoch in xrange(epochs):

for j in xrange(len(training_dataset)):

training_input = training_dataset[j]

output = som.activate(training_input)

som.backward()

print(epoch)

# export network

pickle.dump(som, open("pattern5.p", "wb"))

# test

for i in xrange(len(training_dataset)):

x = training_dataset[i]

y = som.activate(x)

key = ','.join([str(int(f)) for f in y.tolist()])

if key not in result:

result[key] = []

result[key].append(x)

print('Category: ', result.keys())

# render plot

fig = plt.figure()

idx = 0

for key, val in result.iteritems():

idx += 1

ax = fig.add_subplot(nodes, nodes, idx)

ax.set_title(key)

val = zip(*val)

ax.plot(range(0, p), val, color="#dfdfdf", label=key)

ax.plot(range(0, p), np.mean(val, axis=1), color="b", label='AVG')

plt.show()