target_column = m.topo.target_column
if "diabetes" in train_file and "diabetes" in test_file:
diabetes = True
else:
diabetes = False
ID = int(os.environ["DEVICE_ID"])
# Gradient descent for fine tuning
m = Messager()
m.registerCallbackSync()
m.start()
nodes = len(m.getNeighbors()) + 1
X_train, y_train, X_test, y_test = read_data(train_file,test_file,ID,nodes,target_column)
# Create the classifier
clf = svm.SVC(kernel='linear',C=1.0)
# Do the learning
clf.fit(X_train,y_train)
# Predict
predictions = clf.predict(X_test)
print("The error is {0}".format(mse(predictions,y_test)))
print("The coefficients are {0}".format(clf.coef_[:]))
m.start()
def setState(state):
m.lifeState = state
if state:
led.setRedOn()
else:
led.setRedOff()
setState(random.randint(0, 1) == True)
i = 0
while True:
i += 1
for neighbor in m.getNeighbors().keys():
message = {
'state': m.lifeState,
'sync': i
}
m.sendMessage(neighbor, message)
m.waitForMessageFromAllNeighbors(i)
c = sum(n['state'] for n in m.sync[i])
if c <= 1 or c >= 4:
setState(False)
if c == 3:
setState(True)
m.registerCallbackSync()
m.start()
def setState(state):
m.lifeState = state
if state:
led.setRedOn()
else:
led.setRedOff()
setState(random.randint(0, 1) == True)
i = 0
while True:
i += 1
for neighbor in m.getNeighbors().keys():
message = {'state': m.lifeState, 'sync': i}
m.sendMessage(neighbor, message)
m.waitForMessageFromAllNeighbors(i)
c = sum(n['state'] for n in m.sync[i])
if c <= 1 or c >= 4:
setState(False)
if c == 3:
setState(True)
# Get necessary values
ID = int(os.environ["DEVICE_ID"])
my_val = assignment[str(ID)]
w = get_weights(m.topo,ID)
# Create x vector
nodes = len(assignment)
x = np.zeros(nodes,dtype=float)
x[ID-1] = my_val # Insert own value
iterations = 10
for i in range(iterations):
# time.sleep(1)
# All of the communication to neighbors
for recipient in m.getNeighbors().keys():
message = {
'value' : my_val,
'sync' : i
}
m.sendMessage(recipient,message)
m.waitForMessageFromAllNeighbors(i)
# Construct X
for message in m.sync[i]:
node = int(message['from'])
value = float(message['value'])
x[node-1] = value
# Get necessary values
ID = int(os.environ["DEVICE_ID"])
my_val = assignment[str(ID)]
w = get_weights(m.topo, ID)
# Create x vector
nodes = len(assignment)
x = np.zeros(nodes, dtype=float)
x[ID - 1] = my_val # Insert own value
iterations = 10
for i in range(iterations):
# time.sleep(1)
# All of the communication to neighbors
for recipient in m.getNeighbors().keys():
message = {'value': my_val, 'sync': i}
m.sendMessage(recipient, message)
m.waitForMessageFromAllNeighbors(i)
# Construct X
for message in m.sync[i]:
node = int(message['from'])
value = float(message['value'])
x[node - 1] = value
my_val = np.dot(w, x)
x[ID - 1] = my_val
print(my_val)
return (learning_rate * .5), old_w
if __name__ == '__main__':
# Parse command line options
pp = pprint.PrettyPrinter()
datafile = "data/iris_mod.csv"
target_column = 4
iterations = 100
# Initialize lock and Messager objects
m = Messager()
m.registerCallbackSync()
m.start()
nodes = len(m.getNeighbors()) + 1
ID = int(os.environ["DEVICE_ID"])
w, X, y, num_samples = read_data(datafile,target_column,ID,nodes)
learning_rate = .5
import csv
test = open("test_val/test_{0}.csv".format(ID),"w")
writer = csv.writer(test)
for i in range(iterations):
new_w = w - (learning_rate) * rss_gradient(w,X,y)
learning_rate, w = update_learning_rate(learning_rate,w,new_w,X,y)
writer.writerow(tuple([i,rss_error(w,X,y)]))
# Send w to all neighbors, receive other nodes' w
