font=dict(
family="Courier New, monospace",
size=8,
color="#7f7f7f"
)
)
fig.update_xaxes(title_text="Scores", row=row, col=column)
fig.update_yaxes(title_text="Frequency", row=row, col=column)
# Overlay both histograms
fig.update_layout(barmode='overlay')
# Reduce opacity to see both histograms
fig.update_traces(opacity=0.75)
if __name__ == "__main__":
df = readData(sys.argv)
df_numerical = describe(df)
titles = tuple(df_numerical.columns)
fig = make_subplots(
rows=ROWS, cols=COLUMNS,
subplot_titles=titles,
specs=[
# row 1
[{"secondary_y": True}, {"secondary_y": True}, {"secondary_y": True}, {"secondary_y": True},{"secondary_y": True} ],
# row 2
[{"secondary_y": True}, {"secondary_y": True}, {"secondary_y": True}, {"secondary_y": True},{"secondary_y": True} ],
# row 3
[{"secondary_y": True}, {"secondary_y": True}, {"secondary_y": True}, {"secondary_y": True},{"secondary_y": True} ]
])
r = 1
index = 0
from Queue import Queue
import os, sys
currentdir = os.path.dirname(os.path.realpath(__file__))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir)
from helpers import readData
from env import env
if __name__ == "__main__":
airbnbList = readData(env['path_airbnb'])
myQueue = Queue()
for i in range(myQueue.limit):
myQueue.insert(airbnbList[i])
myQueue.show()
#myQueue.remove()
print(
"##############################################################################################################################"
)
myQueue.tam()
myQueue.remove()
print(
"##############################################################################################################################"
)
myQueue.insert(airbnbList[0])
myQueue.show()
for i in clusters:
plt.plot(i.x(), i.y(), 'bo')
plt.show()
def iterateClusters(vectors, clusters, old=[]):
new = assignCluster(vectors, clusters)
iterateClusters(vectors, new, clusters)
if __name__ == "__main__":
vectors = []
clusters = []
k = 7 # Number of clusters
data = helpers.readData('data_1024.csv', '\t')
for coord in zip(data['Distance_Feature'], data['Speeding_Feature']):
vector = models.Point(coord, None)
vectors.append(vector)
clusters = generateClusters(vectors, k)
coordList = [
] # Used for detecting duplicates, once a duplicate is shown the loop will break.
while True:
clusters = assignCluster(vectors, clusters)
for y in clusters:
coordList.append(y.coordinates)
