"""

Code developed for application #3 question 1 of the course

Algorithmic Thinking II

"""

# imports needed for this code

import random

import time

import alg_cluster as cl

import alg_project3_solution as sol

import matplotlib.pyplot as plt

# Set timeout for CodeSkulptor (only if this code is run in Code Skulptor)

#import codeskulptor

#codeskulptor.set_timeout(20)

###################################

# Function that generates random clusters for the analyses

def gen_random_clusters(num_clusters):

return _cluster_list

#####################################

# Code for answering question 1 of the application

# Measure the running times for the functions slow_closest_pair and fast_closest_pair

num_clusters = []

running_times_slow = []

running_times_fast = []

for index in range(2,201):

cluster_list = gen_random_clusters(index)

num_clusters.append(index)

start_time = time.time()

distance = sol.slow_closest_pair(cluster_list)

end_time = time.time()

running_times_slow.append(end_time - start_time)

start_time = time.time()

distance = sol.fast_closest_pair(cluster_list)

end_time = time.time()

running_times_fast.append(end_time - start_time)

if index % 10 == 0:

print "=====> Covered clusterlist up to", index

# Create plot of the result

plt.plot(num_clusters, running_times_slow, '-b', label='slow_closest_pair')

plt.plot(num_clusters, running_times_fast, '-r', label='fast_closest_pair')

plt.legend(loc='upper left')

plt.grid(True)

plt.title("Plot 9: running times of slow_closest_pair and fast_closest_pair")

plt.xlabel("number of clusters processed")

plt.ylabel("runnning time (sec.)")

plt.show()