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test.py
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test.py
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import numpy as np
import sys
import matplotlib.pyplot as plt
from kmeans import KMeans
from fcm import FCM
colors = np.array(['#49111c','#ee2e31','#1d7874','#7f7f7f','#050517','#231651','#ff8484'])
data = np.genfromtxt('data/545_cluster_dataset.txt')
def plotKClusters(model,k,X):
if type(model) is FCM:
results = model.classify()
elif type(model) is KMeans:
results = model.classify(X)
clusters = [X[np.where(results==i)] for i in range(k)]
for cluster,color in zip(clusters,colors[:k]):
plt.scatter(cluster[:,:1], cluster[:,1:], c=color)
# Show centroids
plt.scatter(model.centroids[:,:1], model.centroids[:,1:], marker='X',c='y')
# Runs r K-Means trials and selects model with lowest SSE
def kmeans_trials(k=3,r=1):
# Create and train r models for trials
models = [KMeans(k,data) for _ in range(r)]
training_err = [m.train(data) for m in models]
# Sort modes by sum-of-squares error
results = [(err[-1], model) for err, model in zip(training_err,models)]
results = sorted(results, key=lambda x: x[0]) # Sort asscending by sum square error
# Plot trial results
for i,trial in enumerate(results):
final_err = round(trial[0], 2)
m = trial[1]
plt.title(f'Trial {i+1} Cluster Assignments (SSE={final_err})')
plotKClusters(m, k, data)
plt.show()
# Show best model from r trials
best_sse = round(results[0][0],2)
best_model = results[0][1]
plt.title(f"Best model (SSE={best_sse})")
plotKClusters(best_model,k,data)
plt.show()
# Runs r fuzzy C-Means trials, and selects model with lowest SSE.
def fcm_trials(k, m=1.2, rtrials=5):
models = [FCM(k,m,data) for _ in range(rtrials)]
training_err = [model.train(data) for model in models]
results = [(err[-1],model) for err, model in zip(training_err, models)]
results = sorted(results, key=lambda x: x[0])
# Plot results for each FCM trial
for i, trial in enumerate(results):
final_err = round(trial[0],2)
model = trial[1]
plt.title(f'Trial {i+1} FCM Cluster Assignments (SSE={final_err})')
plotKClusters(model,k,data)
plt.show()
# Select and show bes model from r trials
lowest_sse = round(results[0][0], 2)
best_model = results[0][1]
plt.title(f'Best FCM model (SSE={lowest_sse})')
plotKClusters(best_model,k,data)
plt.show()
# Run experiments
try:
# Get algorithm and number of clusters
algo = sys.argv[1]
K = int(sys.argv[2])
# Get number of trials
try:
R = int(sys.argv[3])
except(IndexError):
R = 1
if algo=="km":
kmeans_trials(K, R)
elif algo=="fcm":
m = float(sys.argv[4])
fcm_trials(K,m,R)
else:
print("Incorrect algo. Select either: km (k-means) or fcm (fuzzy c-means)")
except(IndexError):
print('Usage:\n python3 test.py <"km"=kmeans, "fcm"=fuzzy c-means> <k=number of clusters> <r=number of trials> <m=fuzzifier (FOR FCM ONLY)>')