# Lab 5 Logistic Regression Classifier
import tensorflow as tf
tf.set_random_seed(777)  # for reproducibility

x_data = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 3], [6, 2]]
y_data = [[0], [0], [0], [1], [1], [1]]

# placeholders for a tensor that will be always fed.
X = tf.placeholder(tf.float32, shape=[None, 2])
Y = tf.placeholder(tf.float32, shape=[None, 1])

W = tf.Variable(tf.random_normal([2, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')

# Hypothesis using sigmoid: tf.div(1., 1. + tf.exp(tf.matmul(X, W)))
hypothesis = tf.sigmoid(tf.matmul(X, W) + b)

# Cost function
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
                       tf.log(1 - hypothesis))

train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

# Accuracy computation
# True if hypothesis>0.5 else False
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))

# Launch graph
with tf.Session() as sess:
    # Initialize TensorFlow variables
    sess.run(tf.global_variables_initializer())
    feed = {X: x_data, Y: y_data}

    for step in range(10001):
        sess.run(train, feed_dict=feed)
        if step % 200 == 0:
            print(step, sess.run(cost, feed_dict=feed), sess.run(W))

    # Accuracy report
    h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict=feed)
    print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)

'''
Hypothesis:  [[ 0.03074029]
 [ 0.15884677]
 [ 0.30486736]
 [ 0.78138196]
 [ 0.93957496]
 [ 0.98016882]]
Correct (Y):  [[ 0.]
 [ 0.]
 [ 0.]
 [ 1.]
 [ 1.]
 [ 1.]]
Accuracy:  1.0
'''

tensorflow.set_random_seed tensorflow.Session tensorflow.matmul tensorflow.equal tensorflow.log tensorflow.placeholder tensorflow.global_variables_initializer tensorflow.random_normal tensorflow.cast tensorflow.train.GradientDescentOptimizer