# -*- coding: utf-8 -*-
import tensorflow as tf

# Parameters.
learning_rate = 0.01
training_steps = 1000
display_step = 50

X = tf.constant([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                7.042,10.791,5.313,7.997,5.654,9.27,3.1], name='X')
Y = tf.constant([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                2.827,3.465,1.65,2.904,2.42,2.94,1.3], name='Y')

# Weight and Bias, initialized randomly.
W = tf.Variable(tf.random.normal(shape=[1], mean=0.0, stddev=1.0,
                                 dtype=tf.dtypes.float32,
                                 seed=None, name=None), name="weight")
b = tf.Variable(tf.random.normal(shape=[1], mean=0.0, stddev=1.0,
                                 dtype=tf.dtypes.float32,
                                 seed=None, name=None), name="bias")

# Linear regression (Wx + b).
def linear_regression(x):
    return W * x + b

# Mean square error.
def mean_square(y_pred, y_true):
    return tf.reduce_mean(tf.square(y_pred - y_true))

# Stochastic Gradient Descent Optimizer.
optimizer = tf.optimizers.SGD(learning_rate)

# Optimization process. 
def run_optimization():
    # Wrap computation inside a GradientTape for automatic differentiation.
    with tf.GradientTape() as g:
        pred = linear_regression(X)
        loss = mean_square(pred, Y)

    # Compute gradients.
    gradients = g.gradient(loss, [W, b])
    
    # Update W and b following gradients.
    optimizer.apply_gradients(zip(gradients, [W, b]))

# Run training for the given number of steps.
for step in range(1, training_steps + 1):
    # Run the optimization to update W and b values.
    run_optimization()
    
    if step % display_step == 0:
        pred = linear_regression(X)
        loss = mean_square(pred, Y)
        print("step: %i, loss: %f, W: %f, b: %f" % (step, loss, W.numpy(), b.numpy()))
