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

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')


# # Linear Regression Plot

# A function to plot linear regression fits. 

# > from mlxtend.plotting import plot_linear_regression

# ## Overview

# The `plot_linear_regression` is a convenience function that uses scikit-learn's `linear_model.LinearRegression` to fit a linear model and SciPy's `stats.pearsonr` to calculate the correlation coefficient. 

# ### References
# 
# - -

# ## Example 1 - Ordinary Least Squares Simple Linear Regression

# In[2]:


import matplotlib.pyplot as plt
from mlxtend.plotting import plot_linear_regression
import numpy as np

X = np.array([4, 8, 13, 26, 31, 10, 8, 30, 18, 12, 20, 5, 28, 18, 6, 31, 12,
   12, 27, 11, 6, 14, 25, 7, 13,4, 15, 21, 15])

y = np.array([14, 24, 22, 59, 66, 25, 18, 60, 39, 32, 53, 18, 55, 41, 28, 61, 35,
   36, 52, 23, 19, 25, 73, 16, 32, 14, 31, 43, 34])

intercept, slope, corr_coeff = plot_linear_regression(X, y)
plt.show()


# ## API

# In[3]:


with open('../../api_modules/mlxtend.plotting/plot_linear_regression.md', 'r') as f:
    print(f.read())