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asilador
Programming_Assignments
Commits
3694663a
Commit
3694663a
authored
8 years ago
by
asilador
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Cleaned up code to have run(epsilon) work in interactive python
parent
7615f6e6
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Assignment 1/assignment1.py
+15
-24
15 additions, 24 deletions
Assignment 1/assignment1.py
with
15 additions
and
24 deletions
Assignment 1/assignment1.py
+
15
−
24
View file @
3694663a
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
)
\ No newline at end of file
run
(
0.00001
)
\ No newline at end of file
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