def run_predictions():
import MySQLdb as mdb
from pyfann import libfann
#from datetime import date
from network_functions import save_prediction
mydate = ""
con = None
con = mdb.connect('localhost', 'root',
'fil1202job', 'stock');
with con:
cur = con.cursor(mdb.cursors.DictCursor)
cur1 = con.cursor()
#
# Get a list of all networks
#
cur.execute("SELECT a.id, a.group, b.ticker, b.predict_data, a.net_file FROM `network`.`network` a, network.net_group b where a.group = b.id;")
rows = cur.fetchall()
for row in rows:
#
# For each network get the training data - only most recent data at the moment
#
#seldate = "select latest_prediction from network.network where id = " + str(row["id"])
#cur2.execute(seldate)
#latestdate = cur2.fetchone()
#latestdate1 = latestdate[0]
#print latestdate1
cur1.execute(row["predict_data"])
for row1 in cur1.fetchall():
#
# Extract Date
#
mydate = row1[(len(row1) - 1)]
row1b = list(row1)
del row1b[(len(row1b) - 1)]
#
# Set up network
#
ann = libfann.neural_net()
ann.create_from_file(row["net_file"])
#
# Run Prediction
#
print row1b
print ann.run(row1b)
prediction = ann.run(row1b)
prediction = str(prediction).translate(None, '[]')
#
# Store results in db - Function
#
save_prediction(row["id"], mydate, prediction)
calc_signals()
#cur2.execute(seldate)
#latestdate = cur2.fetchone()
#latestdate1 = latestdate[0]
#print latestdate1
cur1.execute(row["predict_data"])
for row1 in cur1.fetchall():
#
# Extract Date
#
mydate = row1[(len(row1) - 1)]
row1b = list(row1)
del row1b[(len(row1b) - 1)]
#
# Set up network
#
ann = libfann.neural_net()
ann.create_from_file(row["net_file"])
#
# Run Prediction
#
print ann.run(row1b)
prediction = ann.run(row1b)
prediction = str(prediction).translate(None, '[]')
#
# Store results in db - Function
#
save_prediction(row["id"], mydate, prediction)
