def create_net (group_id, num_neurons_hidden):
from pyfann import libfann
from network_functions import save_tests
from network_functions import create_net_record
import MySQLdb as mdb
print "Create Net - " + str(group_id) + ", " + str(num_neurons_hidden)
# To Do
# Needs to querry Net_Group for training file name, test filename, ticker, input_nurons, output_neurons
con = mdb.connect('localhost', 'root', 'fil1202job', 'network')
sql = "select a.ticker, a.train_data, a.test_data, b.no_input, b.no_output from network.net_group a, network.net_type b where a.type = b.id and a.id =" + str(group_id)
cursor = con.cursor()
cursor.execute(sql)
for row in cursor.fetchall():
ticker = str(row[0])
train_file = str(row[1])
test_file = str(row[2])
num_input = row[3]
num_output = row[4]
# disconnect from server
#con.close()
#Parameters that will be passed in
#group_id = 191
# create empty net record and get number
net_id = create_net_record(group_id)
print "Net ID = " + str(net_id)
#train_file = "/home/user/Documents/TrainandTestdata/trainingdataFANN-ACN_out-train.dat"
#test_file = "/home/user/Documents/TrainandTestdata/testdataFANN-ACN_out-train.dat"
#ticker = "ACN"
###
# create file name as ticker_netgroup_netnumber.net
net_name = "/home/user/Documents/Networks/" + str(ticker) + "_" + str(group_id) + "_" + str(net_id) + ".net"
sql2 = "UPDATE `network`.`network` SET `net_file` = \"" + net_name + "\" where id = " + str(net_id)
#print sql2
cursor2 = con.cursor()
cursor2.execute(sql2)
con.commit()
connection_rate = 1
learning_rate = 0.7
#num_input = 7
#num_neurons_hidden = 7
#num_output = 1
desired_error = 0.0001
max_iterations = 100000
iterations_between_reports = 10000
ann = libfann.neural_net()
#ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden, num_output))
ann.create_standard_array([num_input, num_neurons_hidden, num_neurons_hidden, num_output])
ann.set_learning_rate(learning_rate)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
train_data = libfann.training_data()
train_data.read_train_from_file(train_file)
test_data = libfann.training_data()
test_data.read_train_from_file(test_file)
ann.train_on_file(train_file, max_iterations, iterations_between_reports, desired_error)
print "\nTrain error: %f, Test error: %f\n\n" %( ann.test_data(train_data),ann.test_data(test_data))
#print "Testing network again"
ann.reset_MSE()
vinput = test_data.get_input()
output = test_data.get_output()
for i in range(len(vinput)):
#print "###"
#print ann.test(vinput[i], output[i])
#print "###'"
outval = ann.test(vinput[i], output[i])
outval = str(outval)
outval = outval.translate(None, '[]')
targetout = str(output[i])
#print "###"
#print targetout
targetout = targetout.translate(None, '[]')
#print targetout
# Store test output in net_test table as [netNumber, record(test row number), prediction, target]
# Stillinserts into temp table net_tests2
save_tests(net_id, i + 1, outval, targetout)
#printingstring = 'Output number ' + str(i) + ' is '+ outval + 'Should have been: ' + targetout
#print printingstring
#print "MSE error on test data: %f" % ann.get_MSE()
ann.save(net_name)
train_data = libfann.training_data()
train_data.read_train_from_file(train_file)
test_data = libfann.training_data()
test_data.read_train_from_file(test_file)
ann.train_on_file(train_file, max_iterations, iterations_between_reports, desired_error)
print "\nTrain error: %f, Test error: %f\n\n" %( ann.test_data(train_data),ann.test_data(test_data))
print "Testing network again"
ann.reset_MSE()
vinput = test_data.get_input()
output = test_data.get_output()
for i in range(len(vinput)):
outval = ann.test(vinput[i], output[i])
outval = str(outval)
outval = outval.translate(None, '[]')
targetout = str(output[i])
targetout = targetout.translate(None, '[]')
# Store test output in net_test table as [netNumber, record(test row number), prediction, target]
# Stillinserts into temp table net_tests2
save_tests(net_id, i + 1, outval, targetout)
printingstring = 'Output number ' + str(i) + ' is '+ outval + 'Should have been: ' + targetout
print printingstring
print "MSE error on test data: %f" % ann.get_MSE()
ann.save(net_name)