From c497ee89f2ea932c5e00533cb12d06e335a13005 Mon Sep 17 00:00:00 2001
From: mhjensen <morten.hjorth-jensen@fys.uio.no>
Date: Fri, 4 May 2018 15:34:24 -0400
Subject: [PATCH] updated on how t read

---
 doc/src/How2ReadData/How2ReadData.do.txt | 29 +++++++++++++++++++++++-
 1 file changed, 28 insertions(+), 1 deletion(-)

diff --git a/doc/src/How2ReadData/How2ReadData.do.txt b/doc/src/How2ReadData/How2ReadData.do.txt
index 078f797d4..fb295b12e 100644
--- a/doc/src/How2ReadData/How2ReadData.do.txt
+++ b/doc/src/How2ReadData/How2ReadData.do.txt
@@ -348,7 +348,7 @@ plt.show()
 
 
 !split
-===== Simple regression model using gradient descent=====
+===== Simple regression model with gradient descent  =====
 Add info about the equations, play around with different learning rates
 !bc pycod
 # Importing various packages
@@ -386,10 +386,37 @@ plt.xlabel(r'$x$')
 plt.ylabel(r'$y$')
 plt.title(r'Random numbers ')
 plt.show()
+!ec
 
 
+!split
+===== Simple regression model with stochastic gradient descent  =====
+Add info about the equations, play around with different learning rates
+!bc pycod
+# Importing various packages
+from math import exp, sqrt
+from random import random, seed
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import SGDRegressor
+
+x = 2*np.random.rand(100,1)
+y = 4+3*x+np.random.randn(100,1)
+
+xb = np.c_[np.ones((100,1)), x]
+theta_linreg = np.linalg.inv(xb.T.dot(xb)).dot(xb.T).dot(y)
+print(theta_linreg)
+sgdreg = SGDRegressor(n_iter = 50, penalty=None, eta0=0.1)
+sgdreg.fit(x,y.ravel())
+print(sgdreg.intercept_, sgdreg.coef_)
+
 !ec
 
+!split
+===== Polynomial regression =====
+!bc pycod 
+
+!ec