import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
sess=tf.Session()
x_vals = np.random.normal(1, 0.1, 100)
y_vals = np.repeat(10., 100)
x_data = tf.placeholder(shape=[None,1], dtype=tf.float32)
y_target = tf.placeholder(shape=[None,1], dtype=tf.float32)
train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False)
test_indices = np.array(list(set(range(len(x_vals)))-set(train_indices)))
x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]
A = tf.Variable(tf.random_normal(shape=[1,1]))

my_output = tf.matmul(x_data,A)
loss = tf.reduce_mean(tf.square(my_output - y_target))
my_opt = tf.train.GradientDescentOptimizer(0.02)
train_step = my_opt.minimize(loss)
init = tf.global_variables_initializer()
sess.run(init)
batch_size = 20
for i in range(100):
    rand_index = np.random.choice(len(x_vals_train), size=batch_size)
    rand_x = np.transpose([x_vals_train[rand_index]])
    rand_y = np.transpose([y_vals_train[rand_index]])
    sess.run(train_step, feed_dict={x_data:rand_x, y_target:rand_y})
    if (i+1)%25==0:
        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))
        print('Loss = ' + str(sess.run(loss, feed_dict={x_data:rand_x, y_target:rand_y})))

mse_test = sess.run(loss, feed_dict={x_data:np.transpose([x_vals_test]), y_target:np.transpose([y_vals_test])})
mse_train = sess.run(loss, feed_dict={x_data:np.transpose([x_vals_train]), y_target:np.transpose([y_vals_train])})
print('MSE on test: '+str(np.round(mse_test,2)))
print('MSE on train: '+str(np.round(mse_train,2)))

from tensorflow.python.framework import ops
ops.reset_default_graph()
sess= tf.Session()
batch_size=25
x_vals = np.concatenate((np.random.normal(-1,1,50), np.random.normal((2,1,50))))
y_vals = np.concatenate((np.repeat(0.,50), np.repeat(1.,50)))
x_data = tf.placeholder(shape=[1,None], dtype=tf.float32)
y_target = tf.placeholder(shape=[1,None], dtype=tf.float32)
train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False)
test_indices = np.array(list(set(range(len(x_vals)))-set(train_indices)))
x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]
A = tf.Variable(tf.random_normal(mean=10, shape=[1]))

my_output = tf.add(x_data, A)
xentropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output, labels=y_target))
my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(xentropy)
init = tf.global_variables_initializer()
sess.run(init)

for i in range(1800):
    rand_index = np.random.choice(len(x_vals_train), size = batch_size)
    rand_x = [x_vals_train[rand_index]]
    rand_y = [y_vals_train[rand_index]]
    sess.run(train_step, feed_dict={x_data:rand_x, y_target:rand_y})
    if (i+1)%200 ==0:
        print('Step #'+str(i+1) + ' A = '+str(sess.run(A)))
        print('Loss = '+str(sess.run(xentropy, feed_dict={x_data: rand_x, y_target:rand_y})))


y_prediction = tf.squeeze(tf.round(tf.nn.sigmoid(tf.add(x_data,A))))
correct_prediction = tf.equal(y_prediction, y_target)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
acc_value_test = sess.run(accuracy, feed_dict={x_data:[x_vals_test], y_target:[y_vals_test]})
acc_value_train = sess.run(accuracy, feed_dict={x_data:[x_vals_train], y_target:[y_vals_train]})
print('Accuracy on train set : '+ str(acc_value_train))
print('Accuracy on test set : '+ str(acc_value_test))

A_result = -sess.run(A)
bins = np.linspace(-5,5,50)
plt.hist(x_vals[0:50],bins, alpha=0.5, label='N(-1,1)', color='blue')
plt.hist(x_vals[50:100], bins[0:50], alpha=0.5, label='N(2,1)',color='red')
plt.plot((A_result, A_result),(0,8),'k--', linewidth=3, label='A ='+str(np.round(A_result,2)))
plt.legend(loc='upper right')
plt.title('Binary Classifier, Accuracy = '+str(np.round(acc_value_test,2)))
plt.show()