# -*- coding: utf-8 -*-

"""
Created on Wed Nov 29 20:35:58 2017

@author: Omar Enrique Castro, E.I.T
"""

####################################
###                              ###
###    Polynomial Regression     ###
###                              ###
####################################


# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


# Importing the dataset
dataset = pd.read_csv('/Users/omare.castro/Udemy/Machine Learning - Hands On Python & R in Data Science/Part 2 - Regression/Section 6 - Polynomial Regression/Polynomial Regression/Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values


# Splitting the dataset into the Training set and Test set
"""from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"""


# Feature Scaling
"""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)"""


# Fitting Linear Regression to the Dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)


# Fitting Polynomial Regression to the Dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg2 = LinearRegression()
lin_reg2.fit(X_poly, y)


# Visualising the Linear Regression results
plt.scatter(X, y, color = 'red')
plt.plot(X, lin_reg.predict(X), color = 'blue')
plt.title('Truth or Bluff - Linear Regression')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()


# Visualising the Polynomial results
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, lin_reg2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')
plt.title('Truth or Bluff - Polynomial Regression')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()


# Predicting a new result with Linear Regression
lin_reg.predict(6.5)

# Predicting a new result with Polynomail Regression
lin_reg2.predict(poly_reg.fit_transform(6.5))


