test for log reg

doc/src/Splines/Splines.do.txt

@@ -3,9 +3,6 @@ AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of
 DATE: today
 
 
-!split
-===== Optimization problems, why? =====
-
 !split
 ===== Optimization, the central part of any Machine Learning algortithm =====
 
@@ -810,7 +807,7 @@ xb = np.c_[np.ones((100,1)), x]
 theta_linreg = np.linalg.inv(xb.T.dot(xb)).dot(xb.T).dot(y)
 print("Own inversion")
 print(theta_linreg)
-sgdreg = SGDRegressor(n_iter = 50, penalty=None, eta0=0.1)
+sgdreg = SGDRegressor(max_iter = 50, penalty=None, eta0=0.1)
 sgdreg.fit(x,y.ravel())
 print("sgdreg from scikit")
 print(sgdreg.intercept_, sgdreg.coef_)
@@ -853,11 +850,6 @@ for epoch in range(n_epochs):
 print("theta from own sdg")
 print(theta)
 
-
-
-
-
-
 plt.plot(xnew, ypredict, "r-")
 plt.plot(xnew, ypredict2, "b-")
 plt.plot(x, y ,'ro')
@@ -870,6 +862,9 @@ plt.show()
 !ec
 
 
+!split
+===== Logistic Regression example =====
+
 
 !split
 ===== Using gradient descent methods, limitations =====