#!/usr/bin/env python3
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import model_selection
import sys

gender_df = pd.read_csv('data/binary_data.csv')

# Shuffle our data
gender_df = gender_df.sample(frac=1).reset_index(drop=True)

# We'll go ahead and split the data set into training and testing parts.
# 70 per cent will go to training, the rest to testing.
train_x,test_x, train_y, test_y = model_selection.train_test_split(gender_df['HEIGHT'],gender_df['GENDER'],test_size = 0.3)

n_samples = train_x.shape[0]

# These will be the placeholders for the testing and training data
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)

# Variables for the weight and bias.
W = tf.Variable(0,dtype = tf.float32)
b = tf.Variable(0,dtype = tf.float32)

# This is our activation function to determine the probability
# of our gender based on height.
activation = tf.nn.sigmoid((W * x) + b)

# Set our alpha value for the optimizer.
learning_rate = 0.001

# cross_entropy is our cost function.
cross_entropy = tf.reduce_mean(-(y*tf.log(activation) + (1 - y) * tf.log(1-activation)))

# We'll use a standard gradient descent optimizer.
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

# Now train our jodel.
    for epoch in range(1000):
        _,l = sess.run([train_step, cross_entropy], feed_dict = {x: train_x, y:train_y})
    if epoch % 50 == 0:
            print ('loss = %f' %(l))

# Now let's see how our model performed on the test data.
    correct = tf.equal(tf.argmax(activation,1), tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct,'float'))
    print ('Accuracy: ', sess.run(accuracy,feed_dict = {x: test_x, y:test_y}))