示例#1
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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
示例#2
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    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