import os
import tensorflow as tf
import numpy as np
tf.set_random_seed(777)

xy = np.loadtxt('data-03-diabetes.csv', delimiter=',', dtype=np.float32)

x_data = xy[:, 0:-1]
y_data = xy[:, [-1]]

X = tf.placeholder(tf.float32, shape=[None, 8])
Y = tf.placeholder(tf.float32, shape=[None, 1])

W = tf.Variable(tf.random_normal([8,1]), name='weight')
b = tf.Variable(tf.random_normal([1], name='bias'))


hypothesis = tf.sigmoid(tf.matmul(X, W) + b)

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

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

predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    for step in range(1000):
        cost_val, _ = sess.run([cost, train], feed_dict={X:x_data, Y:y_data})
        if step % 20 == 0:
            print(step, cost_val)

    h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict={X:x_data, Y:y_data})
    print ("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)
