#!/usr/bin/env python
# coding: utf-8

# # Operations
# 
# This function introduces various operations in TensorFlow
# 
# Declaring Operations

# In[1]:


import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()


# ### Open graph session

# In[2]:


sess = tf.Session()


# ### Arithmetic Operations
# TensorFlow has multiple types of arithmetic functions.  Here we illustrate the differences between `div()`, `truediv()` and `floordiv()`.
# 
# `div()` : integer of division (similar to base python `//`
# 
# `truediv()` : will convert integer to floats.
# 
# `floordiv()` : float of `div()`

# In[3]:


print(sess.run(tf.div(3,4)))
print(sess.run(tf.truediv(3,4)))
print(sess.run(tf.floordiv(3.0,4.0)))


# Mod function:

# In[4]:


print(sess.run(tf.mod(22.0,5.0)))


# Cross Product:

# In[5]:


print(sess.run(tf.cross([1.,0.,0.],[0.,1.,0.])))


# ### Trig functions
# 
# Sine, Cosine, and Tangent:

# In[6]:


print(sess.run(tf.sin(3.1416)))
print(sess.run(tf.cos(3.1416)))
print(sess.run(tf.div(tf.sin(3.1416/4.), tf.cos(3.1416/4.))))


# ### Custom operations
# 
# Here we will create a polynomial function:
# 
# `f(x) = 3 * x^2 - x + 10`

# In[7]:


test_nums = range(15)

def custom_polynomial(x_val):
    # Return 3x^2 - x + 10
    return(tf.subtract(3 * tf.square(x_val), x_val) + 10)

print(sess.run(custom_polynomial(11)))


# What should we get with list comprehension:

# In[8]:


expected_output = [3*x*x-x+10 for x in test_nums]
print(expected_output)


# TensorFlow custom function output:

# In[9]:


for num in test_nums:
    print(sess.run(custom_polynomial(num)))