Cleaned up code to have run(epsilon) work in interactive python

Assignment 1/assignment1.py

 import numpy as np
-# Initialize iteration counter
+
 # Import text files 
 Q = np.asmatrix(np.loadtxt('Q.txt'))
 b = np.asmatrix(np.loadtxt('b.txt'))
 c = np.asmatrix(np.loadtxt('c.txt'))
 b = np.transpose(b) #make b a column vector
-D = np.asmatrix(np.ones(np.size(b)))
 m = 1
 
-# Make a guess for x vector
-#x = np.asmatrix(np.zeros(np.size(b)))
-#x = np.transpose(x) #make column vector 
-#x=np.matrix(np.random.rand(5,1))
-x = np.transpose(np.asmatrix(np.ones(np.size(b)))*1)
-
-alpha0 = 1.0
-count = 0
 
+# Initialize iteration counter
+global x
+global count 
 # Define f(x)
 def f(Q,b,c,x): 
 	return np.transpose(x)*Q*x + np.transpose(b)*x + c
@@ -26,7 +20,7 @@ def gradf(Q,b,x):
 	return 2*Q*x + b
 
 # Define algorithm for Armijos rule
-def armijo(alpha0,Q,b,c,D,m):
+def armijo(alpha0,Q,b,c,D,m,x):
 	
 	alpha = alpha0
 	#print('alpha is ', alpha)
@@ -41,13 +35,6 @@ def armijo(alpha0,Q,b,c,D,m):
 	return alpha
 		
 		
-		
-		
-def xval():
-	return x
-
-def countval():
-	return count
 
 # Begin Gradient Descent Algorithm
 def grad_opt(epsilon,x,count,alpha):
@@ -55,7 +42,7 @@ def grad_opt(epsilon,x,count,alpha):
 	while np.linalg.norm(gradf(Q,b,x))>= epsilon:
 		D = -1*np.transpose(gradf(Q,b,x))/np.linalg.norm(gradf(Q,b,x))
 		#print('D is ', D)
-		alpha = armijo(alpha,Q,b,c,D,m)
+		alpha = armijo(alpha,Q,b,c,D,m,x)
 		#print('alpha0 is ', alpha)
 		count += 1
 		if count%1000==0:
@@ -71,11 +58,15 @@ def grad_opt(epsilon,x,count,alpha):
 	print 'epsilon is ', epsilon
 	return 0
 
-
 def run(epsilon):
-	xstart = xval()
-	countstart = countval()
-	grad_opt(epsilon,xstart,countstart,alpha0)
+	# Make a guess for x vector
+	#x = np.asmatrix(np.zeros(np.size(b)))
+	#x = np.transpose(x) #make column vector 
+	#x=np.matrix(np.random.rand(5,1))	
+	x = np.transpose(np.asmatrix(np.ones(np.size(b)))*1)
+	count = 0
+	alpha0 = 1.0
+	grad_opt(epsilon,x,count,alpha0)
 	return 0 
 	
-run(0.1)
+run(0.00001)
\ No newline at end of file