def BuildMst(cities, paths):
# Treat a random node as the start,
# Make it a forest with one node.
randomstart = random.choice(cities)
randomstart.isconnected = True
while True:
# Collect all Nodes that are part of the forest.
connected_cities = pcc.create(ConnectedCity, cities)
# If all nodes are part of the forest exit loop.
if len(connected_cities) == len(cities):
break
# Find all paths that are have not been chosen.
disconnected_paths = pcc.create(DisconnectedPath, paths)
# Find all the paths that are not chosen
# but can be connected to the existing forest.
all_nearby_discon_paths = pcc.create(NearByDisconnectedPath,
disconnected_paths,
params=(connected_cities, ))
if len(all_nearby_discon_paths) != 0:
# Choose the path with the least weight.
best_choice_path = sorted(all_nearby_discon_paths,
key=lambda x: x.distance)[0]
# Join it to the forest.
best_choice_path.isconnected = True
best_choice_path.city1.isconnected = True
best_choice_path.city2.isconnected = True
def BuildMst(cities, paths):
# Treat a random node as the start,
# Make it a forest with one node.
randomstart = random.choice(cities)
randomstart.isconnected = True
while True:
# Collect all Nodes that are part of the forest.
connected_cities = pcc.create(ConnectedCity, cities)
# If all nodes are part of the forest exit loop.
if len(connected_cities) == len(cities):
break
# Find all paths that are have not been chosen.
disconnected_paths = pcc.create(DisconnectedPath, paths)
# Find all the paths that are not chosen
# but can be connected to the existing forest.
all_nearby_discon_paths = pcc.create(NearByDisconnectedPath, disconnected_paths, params=(connected_cities,))
if len(all_nearby_discon_paths) != 0:
# Choose the path with the least weight.
best_choice_path = sorted(all_nearby_discon_paths, key=lambda x: x.distance)[0]
# Join it to the forest.
best_choice_path.isconnected = True
best_choice_path.city1.isconnected = True
best_choice_path.city2.isconnected = True
def main():
trainingSet = []
testSet = []
split = 0.67
loadDataset("iris.data", split, trainingSet, testSet)
print 'Train set: ' + repr(len(trainingSet))
print 'Test set: ' + repr(len(testSet))
predictions = []
k = 3
for one_test in testSet:
knns = pcc.create(knn, trainingSet, params = (one_test, k))
one_test.predicted_type = getResponse(knns)
print('> predicted=' + repr(one_test.predicted_type) + ', actual=' + repr(one_test.fl_type))
accuracy = getAccuracy(testSet)
print('Accuracy: ' + repr(accuracy) + '%')
def main():
trainingSet = []
testSet = []
split = 0.67
loadDataset("iris.data", split, trainingSet, testSet)
print 'Train set: ' + repr(len(trainingSet))
print 'Test set: ' + repr(len(testSet))
predictions = []
k = 3
for one_test in testSet:
knns = pcc.create(knn, trainingSet, params=(one_test, k))
one_test.predicted_type = getResponse(knns)
print('> predicted=' + repr(one_test.predicted_type) + ', actual=' +
repr(one_test.fl_type))
accuracy = getAccuracy(testSet)
print('Accuracy: ' + repr(accuracy) + '%')
def PrintTSPPath(cities, paths):
# Step 1: Building a Minimun spanning tree using Prim's Algorithm
BuildMst(cities, paths)
MST = pcc.create(ConnectedPath, paths)
# Step 2: Find all cities in the MST that have odd degree (O)
O = pcc.create(CityWithOddDegree, cities, params=(MST,))
# Step 3: Find the induced subgraph given by the vertices from O
not_MST_paths = pcc.create(DisconnectedPath, paths)
subgraph = pcc.create(PathsWithGivenCities, not_MST_paths, params=(O,))
# Step 4: Find the Minimum weight perfect matching M in the subgraph
M = pcc.create(min_weight_perfect_match, subgraph)
# Step 5: Combining the edges of M and T to form a connected multigraph H
# in which each vertex has even degree
H = pcc.create(multigraph, M, MST)
# Step 6: Form an Eulerian circuit in H.
eulertour = pcc.create(EulerTour, cities, params=(H,))
# Step 7: Making the circuit found in previous step into a Hamiltonian
# circuit by skipping repeated vertices (shortcutting).
finaltour = pcc.create(HamiltonianTour, eulertour)
# Printing out the resulting tour.
PrintConnections(finaltour)
def PrintTSPPath(cities, paths):
# Step 1: Building a Minimun spanning tree using Prim's Algorithm
BuildMst(cities, paths)
MST = pcc.create(ConnectedPath, paths)
# Step 2: Find all cities in the MST that have odd degree (O)
O = pcc.create(CityWithOddDegree, cities, params=(MST, ))
# Step 3: Find the induced subgraph given by the vertices from O
not_MST_paths = pcc.create(DisconnectedPath, paths)
subgraph = pcc.create(PathsWithGivenCities, not_MST_paths, params=(O, ))
# Step 4: Find the Minimum weight perfect matching M in the subgraph
M = pcc.create(min_weight_perfect_match, subgraph)
# Step 5: Combining the edges of M and T to form a connected multigraph H
# in which each vertex has even degree
H = pcc.create(multigraph, M, MST)
# Step 6: Form an Eulerian circuit in H.
eulertour = pcc.create(EulerTour, cities, params=(H, ))
# Step 7: Making the circuit found in previous step into a Hamiltonian
# circuit by skipping repeated vertices (shortcutting).
finaltour = pcc.create(HamiltonianTour, eulertour)
# Printing out the resulting tour.
PrintConnections(finaltour)