def run():
# init the SquareMap object (which initiates pygame)
if imgFlag == 0:
# if its a grayscale similarity map use a small grid square size
squaremap = SquareMap(gridSize=(somOutputs, somOutputs), gridSquareSize=(5, 5))
else:
squaremap = SquareMap(gridSize=(somOutputs, somOutputs))
squaremap.initMap()
# Used to manage how fast the screen updates
#clock = pygame.time.Clock()
fullscreen = False
#Loop until the user presses the escape button (or otherwise quits).
done = False
while done == False:
# Limit to 15 frames per second
#clock.tick(15)
###########################
# Key events for quitting #
# & setting fullscreen #
###########################
for event in pygame.event.get():
if event.type == QUIT:
done = True
if squaremap.keyPressed(K_ESCAPE):
done = True
# toggle fullscreen and cursor on/off
elif squaremap.keyPressed(K_f):
if not fullscreen:
squaremap.fullScreen(True)
fullscreen = True
else:
squaremap.fullScreen(False)
fullscreen = False
elif squaremap.keyPressed(K_SPACE):
# load and place the images
squaremap.placeImages() # this is only for testing
###############################
# Database and SOM operations #
###############################
# get data from DB (all the rows)
rows = getData()
imgPath = ""
# initialize SOM object
som = KohonenMap(somInputs, somOutputs, name="SquareMap", outputFullMap=False)
# set the learning rate (should this be a command line arg?)
som.learningrate = learningRate
# Do SOM operations here
# do as many iterations as there is DB entries/images
#=======================================================================
# for i in range(rows):
# # 8 feature vectors
# data = [rows[i][1], rows[i][2], rows[i][3], rows[i][4], rows[i],[5], rows[i][6], rows[i][7], rows[i][8]]
# # one forward and one backward (training) pass
# som.activate(data) # feature vectors go here
# som.backward() # training
#=======================================================================
for i in range(100):
som.activate(random.random(somInputs))
som.backward()
#===================================================================
# # print & display every 10th step
# if i % 1 == 0:
# print som.neurons
# print "======================"
#===================================================================
# send data to update the square map
# if images we selected at the command line do an image similarity mapping
#otherwise do a grayscale 'blobs' similarity map
if imgFlag == 0:
squaremap.createSimilarityMap(som.neurons)
else:
#send the current img file path and the coords of the winner neuron along w/SOM's output
imgPath = rows[i][10]
squaremap.placeImages(som.neurons, imgPath, som.winner)
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
def run():
# init the HexagonMap object (which initiates pygame)
hexmap = HexagonMap(gridSize=(somOutputs, somOutputs))
hexmap.initMap()
# Used to manage how fast the screen updates
#clock = pygame.time.Clock()
fullscreen = False
#Loop until the user presses the escape button (or otherwise quits).
done = False
while done == False:
# Limit to 15 frames per second
#clock.tick(15)
###########################
# Key events for quitting #
# & setting fullscreen #
###########################
for event in pygame.event.get(): # User did something
if event.type == QUIT: # If user clicked close
done = True # Flag that we are done so we exit this loop
if hexmap.keyPressed(K_ESCAPE):
done = True
# toggle fullscreen and cursor on/off
elif hexmap.keyPressed(K_f):
if not fullscreen:
hexmap.fullScreen(True)
fullscreen = True
else:
hexmap.fullScreen(False)
fullscreen = False
elif hexmap.keyPressed(K_SPACE):
hexmap.createSimilarityMap() # this is only for testing
###############################
# Database and SOM operations #
###############################
# get data from DB (all the rows)
rows = getData()
# initialize SOM object
som = KohonenMap(somInputs,
somOutputs,
name="HexagonMap",
outputFullMap=False)
# set the learning rate (should this be a command line arg?)
som.learningrate = learningRate
# Do SOM operations here
# do as many iterations as there is DB entries/images
#=======================================================================
# for i in range(rows):
# # 8 feature vectors
# data = [rows[i][1], rows[i][2], rows[i][3], rows[i][4], rows[i],[5], rows[i][6], rows[i][7], rows[i][8]]
# # one forward and one backward (training) pass
# som.activate(data) # feature vectors go here
# som.backward() # training
#=======================================================================
for i in range(100):
som.activate(random.random(somInputs))
som.backward()
#===================================================================
# # print & display every 10th step
# if i % 10 == 0:
# print som.neurons
# print "======================"
#===================================================================
# send data to update the hex map
hexmap.createSimilarityMap(som.neurons)
