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| import matplotlib as mpl | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import tensorflow as tf | |
| import edward as ed | |
| from edward.models import Bernoulli, Beta, Binomial | |
| #Model | |
| p = Beta(1.0, 1.0) | |
| b = tf.constant("B") | |
| condition = lambda i: tf.cast(Bernoulli(probs=p), tf.bool) | |
| a = lambda next: tf.string_join([tf.constant("A"),next]) | |
| result = tf.while_loop(condition, a, [b]) | |
| shaped_result = tf.stack([result]) | |
| #Observations | |
| data = b"AAAAAAAAAAAAAAAAAAB" | |
| ##Infer | |
| qp_a = tf.Variable(1.0) | |
| qp_b = tf.Variable(1.0) | |
| qp = Beta(qp_a, qp_b) | |
| sess = ed.get_session() | |
| inference = ed.KLqp({p: qp}, {shaped_result: [data]}) | |
| inference.initialize() | |
| tf.global_variables_initializer().run() | |
| for _ in range(inference.n_iter): | |
| info_dict = inference.update() | |
| #inference.print_progress(info_dict) | |
| inference.finalize() | |
| #Results | |
| qp_samples = qp.sample(1000) | |
| mean = tf.reduce_mean(qp_samples) | |
| print(sess.run(mean)) | |
| plt.hist(sess.run(qp_samples)) |
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