data = tf.data.Dataset.from_tensor_slices((labels))
data = data.map(load.loadImage)
iterator = data.make_initializable_iterator()
next_element = iterator.get_next()
with tf.Session() as sess:
sess.run(iterator.initializer)
while True:
try:
elem = next_element.eval()
next_element = iterator.get_next()
except tf.errors.OutOfRangeError:
break
clusters = KMeans(elem, 6)
img = clusters.clustering(5)
plt.figure()
plt.ion()
plt.imshow(img)
plt.colorbar()
plt.show()
plt.pause(0.001)
def input_fn():
return tf.train.limit_epochs(
tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1)
points = elem.flatten();
points = load.adjustDimension(points)
kmeans = tf.contrib.factorization.KMeansClustering(num_clusters=6, use_mini_batch=False)
num_iterations = 8
print("Clustering K-means")
for i in range(10):
print("Kmeans itr ", i)
st = time.time()
#load data into mem
hd_data = np.loadtxt(fname, delimiter=delim)
kmeans = KMeans(hd_data.tolist(), nb_cts)
#cluster
kmeans.clustering()
et = time.time()
km_avg += (et - st)
#remove data from mem
del hd_data
km_avg = km_avg / 10
#gathering data for bar charts
bar_x = np.arange(2)
c_times = [np.min(times), km_avg]
#plotting