# Linear regression example - demonstrates the use of placeholder nodes
import tensorflow as tf
import numpy as np
from sklearn.datasets import fetch_california_housing

# Fetch data to use in the placeholder nodes
housing = fetch_california_housing()
m, n = housing.data.shape
housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]

# Set up the computation graph
X = tf.placeholder(tf.float32, shape=(None, n+1), name="X")
y = tf.placeholder(tf.float32, shape=(None, 1), name="y")

XT = tf.transpose(X)
theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(XT, X)), XT), y)

# Run the graph
with tf.Session() as sess:
    theta_val = theta.eval(feed_dict={X:housing_data_plus_bias,y:housing.target.reshape(-1,1)})
    print(theta_val)