import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn import datasets
from tensorflow.python.framework import ops

ops.reset_default_graph()
sess = tf.Session()
iris = datasets.load_iris()
x_vals = np.array([x[3] for x in iris.data])
y_vals = np.array([y[0] for y in iris.data])
batch_size = 50
x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)
y_target = tf.placeholder(shape=[None,1],dtype=tf.float32)
A = tf.Variable(tf.random_normal(shape=[1,1]))
b = tf.Variable(tf.random_normal(shape=[1,1]))
model_output = tf.add(tf.matmul(x_data,A),b)

lasso_param = tf.constant(0.9)
heavyside_step = tf.truediv(1., tf.add(1., tf.exp(tf.multiply(-50., tf.subtract(A,lasso_param)))))
regularization_param = tf.multiply(heavyside_step,99.)
loss = tf.add(tf.reduce_mean(tf.square(y_target - model_output)), regularization_param)

''' Ridge Regression
ridge_param = tf.constant(1.)
ridge_loss = tf.reduce_mean(tf.square(A))
loss = tf.expand_dims(tf.add(tf.reduce_mean(tf.square(y_target - model_output)),tf.multiply(ridge_param,ridge_loss)),0)
'''

my_opt = tf.train.GradientDescentOptimizer(0.001)
train_step = my_opt.minimize(loss)
init = tf.global_variables_initializer()
sess.run(init)

loss_vec=[]
for i in range(1500):
    rand_index = np.random.choice(len(x_vals),size=batch_size)
    rand_x = np.transpose([x_vals[rand_index]])
    rand_y = np.transpose([y_vals[rand_index]])
    sess.run(train_step, feed_dict={x_data:rand_x,y_target:rand_y})
    temp_loss = sess.run(loss, feed_dict={x_data:rand_x , y_target:rand_y})
    loss_vec.append(temp_loss[0])
    if (i+1) % 300 ==0:
        print('Step #'+str(i+1)+' A = ' + str(sess.run(A))+ ' b = '+str(sess.run(b)))
        print('Loss = '+ str(temp_loss))
        print('\n')