import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

df = pd.read_csv("CHD.csv", header=0)


# Parameters

learning_rate = 0.2
training_epochs = 6
batch_size = 100
display_step = 1
sess = tf.Session()
b=np.zeros((100,2))
#print pd.get_dummies(df['admit']).values[1]
print(sess.run(tf.one_hot(indices = [1, 3, 2, 4], depth=5, on_value = 1, off_value = 0, axis = 1 , name = "a")))
#print a.eval(session=sess)


# tf Graph Input

x = tf.placeholder("float", [None, 1])
y = tf.placeholder("float", [None, 2])
# Create model
# Set model weights
W = tf.Variable(tf.zeros([1, 2]))
b = tf.Variable(tf.zeros([2]))


# Construct model
activation = tf.nn.softmax(tf.matmul(x, W) + b)
# Minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(activation), reduction_indices=1)) # Cross entropy
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent


# Initializing the variables
init = tf.initialize_all_variables()


# Launch the graph

with tf.Session() as sess:
tf.train.write_graph(sess.graph, './graphs','graph.pbtxt')
sess.run(init)
writer = tf.summary.FileWriter('./graphs', sess.graph)
#Initialize the graph structure
graphnumber=321
#Generate a new graph
plt.figure(1)
#Iterate through all the epochs
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = 400/batch_size
# Loop over all batches

for i in range(int(total_batch)):
# Transform the array into a one hot format
temp=tf.one_hot(indices = df['chd'].values, depth=2, on_value = 1, off_value = 0, axis = -1 , name = "a")
batch_xs, batch_ys = (np.transpose([df['age']])-44.38)/11.721327, temp
# Fit training using batch data
sess.run(optimizer, feed_dict={x: batch_xs.astype(float), y: batch_ys.eval()})
# Compute average loss, suming the corrent cost divided by the batch total number
avg_cost += sess.run(cost, feed_dict={x: batch_xs.astype(float), y: batch_ys.eval()})/total_batch
# Display logs per epoch step

if epoch % display_step == 0:
print("Epoch:", '%05d' % (epoch+1), "cost=", "{:.8f}".format(avg_cost))
#Generate a new graph, and add it to the complete graph
trX = np.linspace(-30, 30, 100)
print (b.eval())
print (W.eval())
Wdos=2*W.eval()[0][0]/11.721327
bdos=2*b.eval()[0]
# Generate the probabiliy function
trY = np.exp(-(Wdos*trX)+bdos)/(1+np.exp(-(Wdos*trX)+bdos) )
# Draw the samples and the probability function, whithout the normalization
plt.subplot(graphnumber)
graphnumber=graphnumber+1
#Plot a scatter draw of the random datapoints
plt.scatter((df['age']),df['chd'])
plt.plot(trX+44.38,trY) #Plot a scatter draw of the random datapoints
plt.grid(True)


#Plot the final graph
plt.savefig("test.svg")
plt.show()

CHD.csv