import tensorflow as tf
import numpy
rng = numpy.random

import latticex.rosetta as rst



# Parameters
learning_rate = 0.01

# Training Data
train_X = numpy.asarray([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])
train_Y = numpy.asarray([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])
n_samples = train_X.shape[0]

# tf Graph Input
X = tf.placeholder("float64")
Y = tf.placeholder("float64")

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)

# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)

try:
    # Gradient descent
    #  Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
    print("Pass")
except Exception:
    print("Fail")


Writer = tf.summary.FileWriter("log", tf.get_default_graph())
Writer.close()
rst.deactivate()

