if __name__ == "__main__":
i = 0
w = np.random.rand(num_features + 1, 1) * 2 - 1
w *= 5
m = LinearRegression.LinearRegressionStream(draw=False, output=output,
alpha=0.001)
x = Stream('x')
linear_regression.init_plot()
model = Stream_Learn(data_train=x, data_out=x, train_func=m.train,
predict_func=m.predict,
min_window_size=min_window_size,
max_window_size=max_window_size, step_size=step_size,
num_features=num_features, all_func=all_func)
y = model.run()
stream_func(inputs=y, f=print_stream, f_type='element', num_outputs=0)
while i < num_points:
w[1] += 0.01
x_value = np.ones((1, num_features)) * i
x_b = np.hstack((np.ones((1, 1)), x_value)).transpose()
y_value = w.transpose().dot(x_b)[0][0]
values = x_value.tolist()[0]
values.append(y_value)
x.extend([tuple(values)])
if i % 100 == 0 and i != 0:
num_centroids = 5
k = 5
max_window_size = 1000
num_points = 15000
step_size = 1
if __name__ == "__main__":
i = 0
centroids = kmeans.initialize(num_centroids, -5, 5)
x = Stream('x')
m = KMeans.KMeansStream(draw=draw, output=output, k=k)
model = Stream_Learn(data_train=x, data_out=x, train_func=m.train,
predict_func=m.predict, min_window_size=k,
max_window_size=max_window_size, step_size=step_size,
num_features=2)
y = model.run()
while i < num_points:
index = np.random.randint(0, num_centroids)
z = np.random.rand(1, 2) * 2 - 1
centroids[index] = centroids[index].reshape(1, 2) + z * 2
x.extend([tuple(kmeans.initializeDataCenter(centroids[index],
1, 1).tolist()[0])])
print i
i += 1
print "Average number of iterations: ", m.avg_iterations
print "Average error: ", m.avg_error
import requests
import json
def all_func(x, y, model, state, window_state):
if state is None:
state = Geomap.Geomap(llcrnrlat = 20, llcrnrlon = -126, urcrnrlat = 60, urcrnrlon = -65)
state.clear()
state.plot(x, kmeans.findClosestCentroids(x, model.centroids), s = 70)
# state.plot(model.centroids, color = 'Red', s = 50)
return state
x = Stream('x')
m = KMeans.KMeansStream(draw = False, output = False, k = 5)
model = Stream_Learn(x, x, m.train, m.predict, 5, 30, 1, 2, all_func = all_func)
y = model.run()
r = requests.get('http://stream.meetup.com/2/rsvps', stream=True)
i = 0
for line in r.iter_lines():
if line:
data = json.loads(line)
lat, lon = data['group']['group_lat'], data['group']['group_lon']
if data['group']['group_country'] == 'us':
x.extend([(lat, lon)])
print i
i += 1
def all_func(x, y, model, state, window_state):
if state is None:
state = Geomap.Geomap(llcrnrlat=20,
llcrnrlon=-126,
urcrnrlat=60,
urcrnrlon=-65)
state.clear()
state.plot(x, kmeans.findClosestCentroids(x, model.centroids), s=70)
# state.plot(model.centroids, color = 'Red', s = 50)
return state
x = Stream('x')
m = KMeans.KMeansStream(draw=False, output=False, k=5)
model = Stream_Learn(x, x, m.train, m.predict, 5, 30, 1, 2, all_func=all_func)
y = model.run()
r = requests.get('http://stream.meetup.com/2/rsvps', stream=True)
i = 0
for line in r.iter_lines():
if line:
data = json.loads(line)
lat, lon = data['group']['group_lat'], data['group']['group_lon']
if data['group']['group_country'] == 'us':
x.extend([(lat, lon)])
print i
i += 1
