def manual_regression(trainInputs, trainOutputs):
regressor = Regression()
regressor.fit(trainInputs, trainOutputs)
w0, w1, w2 = regressor.intercept_, regressor.coef_[0], regressor.coef_[1]
print('The learnt model is: f(x) = ', w0, ' + ', w1, ' * x1', ' + ', w2,
' * x2')
return regressor
def main():
X, y = make_regression(n_samples=100, n_features=1, noise=20)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
n_samples, n_features = np.shape(X)
model = Regression(n_iterations=100, learning_rate=0.01)
model.fit(X_train, y_train)
# Training error plot
n = len(model.training_errors)
training, = plt.plot(range(n), model.training_errors, label="Training Error")
plt.legend(handles=[training])
plt.title("Error Plot")
plt.ylabel('Mean Squared Error')
plt.xlabel('Iterations')
plt.show()
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print ("Mean squared error: %s" % (mse))
y_pred_line = model.predict(X)
# Color map
cmap = plt.get_cmap('viridis')
# Plot the results
m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
plt.plot(366 * X, y_pred_line, color='black', linewidth=2, label="Prediction")
plt.suptitle("Base Regression")
plt.title("MSE: %.2f" % mse, fontsize=10)
plt.xlabel('Day')
plt.ylabel('Temperature in Celcius')
plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
plt.show()
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.setblocking(0)
server_address = ('192.168.0.52', 10001)
server.bind(server_address)
server.listen(5)
inputs = [server]
outputs = []
pools = {}
message_queues = {}
args = parse_args()
# Start connection
# Build model
model_ufmg = Regression(args.bufmg, args.algorithm, args.features)
model_ufrgs = Regression(args.bufrgs, args.algorithm, args.features)
model_ufmg.fit()
model_ufrgs.fit()
# Train model
# Predict output
#server = Server()
#print(server.handleConnection())
while inputs:
# Wait for at least one of the sockets to be ready for processing
#print >>sys.stderr, '\nwaiting for the next event'
readable, writable, exceptional = select.select(
inputs, outputs, inputs)
# Handle inputs
# print('rms: ', rms)
# print('')
# if rms < best_rms:
# best_rms = rms
# best_x = x
# best_y = y
# best_state = state
#print('Best RMS: %f' % (best_rms))
#print('hidden_layer_sizes=(%d, %d)' % (best_x, best_y))
#print('random_state=%d' % (best_state))
#print('gama: ', best_gama)
#print('C: ', best_C)
#print('epsilon: ', best_epsilon)
best_est = 0
best_rms = 9999999.0
for est in range(1, 2000):
regression = Regression(database, 'rfr', 10)
rms = regression.fit(est)
if rms < best_rms:
best_rms = rms
best_est = est
print('estimators: ', est)
print('RMS: ', rms)
print('')
print('Best estimator: ', best_est)
print('Best RMS: ', best_rms)
def main():
reg = Regression()
google = GoogleQuery(ticker='AAPL',
dataset_id='my_dataset',
table_id='live_AAPL')
reg.fit()
from regression import REG_ALGORITHMS
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Regression Module Test Suite')
parser.add_argument('-i',
'--input',
action='store',
type=str,
required=True,
help='CSV input database path')
parser.add_argument('-a',
'--algorithm',
action='store',
type=str,
choices=REG_ALGORITHMS,
default='nnet',
help='Regression algorithm to be used for prediction')
parser.add_argument('-f',
'--features',
action='store',
type=int,
default=10,
help='Number of features in the database')
args = parser.parse_args()
# Build model
regression = Regression(args.input, args.algorithm, args.features)
rms = regression.fit()
print(rms)
from preProcessor import PreProcessor
import argparse
from regression import Regression
from picDrawer import PicDrawer
if __name__ == '__main__':
'''
python run -f [filepath] -s [filepath] -c [stock code]
output:
stock error : implement by regression.score()
picture : implement by drawer
'''
# create pre processor
data_cleaner = PreProcessor()
train_feature, train_label, test_feature, test_label = data_cleaner.run()
reg = Regression()
reg.fit(train_feature, train_label)
pred_result = reg.predict(test_feature)
score = reg.score(test_label, pred_result)
drawer = PicDrawer()
drawer.run()
