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r
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[
2
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[
3
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is
s
h
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F
i
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1.
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.
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Evaluation Warning : The document was created with Spire.PDF for Python.
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r
e
e
f
f
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c
t
i
v
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t
h
a
n
a
ny
c
o
nv
e
n
t
i
o
n
a
l
e
x
pe
r
t
s
c
h
e
m
e
.
S
o
m
e
of
t
h
e
wo
r
l
d
’
s
m
o
s
t
a
dv
a
n
c
e
d
c
o
m
put
e
r
s
m
a
ke
to
da
y
’
s
we
a
t
h
e
r
f
o
r
e
c
a
s
t
s
.
E
a
c
h
s
e
c
o
n
d,
t
h
e
g
i
g
a
n
ti
c
m
a
c
hi
ne
s
do
m
i
ll
i
o
ns
of
c
a
l
c
u
l
a
t
i
o
ns
to
s
o
l
v
e
e
qua
t
i
o
ns
t
h
a
t
pr
e
di
c
t
t
e
m
pe
r
a
t
ur
e
,
wi
n
d
s
,
pr
e
c
i
p
i
t
a
t
i
o
n
,
a
n
d
ot
h
e
r
e
n
vi
r
o
nm
e
n
t
a
l
e
v
e
n
t
s
.
E
v
e
n
f
o
r
t
h
e
m
o
s
t
a
dv
a
n
c
e
d
c
o
m
put
e
r
s
a
r
e
pu
t
to
t
h
e
t
e
s
t
wh
e
n
s
pe
e
d
a
n
d
a
c
c
u
r
a
c
y
a
r
e
r
e
qui
r
e
d
in
a
f
o
r
e
c
a
s
t
.
It
i
s
po
s
s
i
bl
e
t
h
a
t
th
e
wo
r
l
d
w
i
ll
t
a
ke
a
dr
a
s
t
i
c
a
ll
y
d
if
f
e
r
e
n
t
d
i
r
e
c
t
i
o
n
.
A
r
t
i
f
i
c
i
a
l
i
n
t
e
ll
i
g
e
n
c
e
(A
I)
m
o
de
l
s
o
f
f
e
r
pot
e
n
t
i
a
l
to
pr
o
vi
de
f
a
s
t
e
r
,
m
o
r
e
a
c
c
ur
a
t
e
we
a
t
h
e
r
f
o
r
e
c
a
s
t
s
,
as
s
h
o
wn
in
a
c
o
l
l
a
b
o
r
a
t
i
o
n
be
t
we
e
n
t
h
e
Uni
ve
r
s
i
t
y
of
W
a
s
hi
n
gto
n
a
n
d
M
i
c
r
o
s
o
f
t
R
e
s
e
a
r
c
h
.
UW
Ne
ws
,
2020
[
4
].
In
or
de
r
to
i
n
t
e
gr
a
t
e
s
uc
c
e
s
s
f
u
l
AI
m
e
t
h
o
ds
w
i
t
h
we
a
t
h
e
r
f
o
r
e
c
a
s
t
i
n
g,
t
h
e
r
e
s
e
a
r
c
h
e
r
s
p
l
a
c
e
d
t
h
e
s
i
x
s
i
d
e
s
of
a
c
u
b
e
o
n
t
o
t
h
e
g
l
o
b
e
E
a
r
t
h
a
n
d
t
h
e
n
f
l
a
t
t
e
n
e
d
o
u
t
t
h
e
c
ub
e
’
s
s
i
x
f
a
c
e
s
,
l
i
ke
in
an
a
r
c
hi
t
e
c
t
ur
e
pa
pe
r
m
o
de
l
.
B
e
c
a
us
e
of
t
h
e
i
r
pa
r
t
i
c
u
l
a
r
s
i
g
ni
f
i
c
a
nc
e
in
t
h
e
we
a
t
h
e
r
,
t
h
e
a
ut
h
o
r
s
h
a
n
d
l
e
d
t
h
e
po
l
a
r
f
a
c
e
s
d
i
f
f
e
r
e
n
t
l
y
as
a
m
e
a
ns
to
e
n
h
a
n
c
e
pr
e
d
i
c
t
i
o
n
a
c
c
ur
a
c
y
.
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h
e
a
ut
ho
r
s
t
h
e
n
put
t
h
e
i
r
m
o
de
l
to
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h
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e
s
t
by
e
s
t
i
m
a
t
i
n
g
t
h
e
wo
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l
d
w
i
de
he
i
g
h
t
of
t
h
e
500
h
e
c
t
o
pa
s
c
a
l
pr
e
s
s
ur
e
e
v
e
r
y
12
h
o
ur
s
f
o
r
a
y
e
a
r
.
W
e
a
t
h
e
r
B
e
n
c
h
is
a
b
e
nc
hm
a
r
k
t
e
s
t
f
o
r
we
a
t
h
e
r
f
o
r
e
c
a
s
t
s
b
a
s
e
d
on
da
t
a
.
On
e
of
t
h
e
t
o
p
pe
r
f
o
r
m
e
r
s
in
t
h
a
t
f
o
r
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c
a
s
t
i
n
g
t
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s
t,
whi
c
h
wa
s
m
a
de
f
o
r
t
h
r
e
e
-
da
y
e
s
t
i
m
a
t
e
s
,
wa
s
t
hi
s
n
e
w
m
o
de
l
.
T
he
a
ut
h
o
r
s
n
o
t
e
t
h
a
t
m
o
r
e
i
nf
o
r
m
a
t
i
o
n
wo
u
l
d
be
n
e
e
de
d
f
o
r
t
h
e
da
t
a
-
dr
i
v
e
n
m
o
de
l
b
e
f
o
r
e
it
c
o
ul
d
s
t
a
r
t
c
o
m
pe
t
i
n
g
w
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t
h
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ur
r
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n
t
o
pe
r
a
t
i
o
n
a
l
f
o
r
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c
a
s
t
s
,
b
ut
t
h
e
i
de
a
ha
s
pr
o
m
i
s
e
as
a
f
r
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s
h
a
ppr
o
a
c
h
to
pr
o
duc
i
n
g
we
a
t
h
e
r
f
o
r
e
c
a
s
t
s
,
pa
r
t
i
c
u
l
a
r
l
y
g
i
ve
n
t
h
e
gr
o
w
i
n
g
a
m
o
u
n
t
of
hi
s
t
or
i
c
a
l
f
o
r
e
c
a
s
t
s
a
n
d
w
e
a
t
h
e
r
f
i
nd
i
ngs
.
U
n
de
r
s
t
a
nd
i
n
g
t
h
e
e
c
o
n
o
m
i
c
i
m
pa
c
t
of
s
e
ve
r
e
we
a
t
h
e
r
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v
e
n
t
s
e
m
p
ha
s
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z
e
s
t
h
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n
e
e
d
f
o
r
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c
c
ur
a
t
e
we
a
t
h
e
r
pr
e
d
i
c
t
i
o
n
to
mi
t
i
ga
t
e
f
i
na
n
c
i
a
l
r
i
s
k
s
[
5]
.
T
h
e
s
uc
c
e
s
s
of
m
a
c
hi
ne
l
e
a
r
ni
ng
in
de
v
e
l
o
p
i
ng
a
C
OV
I
D
-
19
wa
r
ni
n
g
s
ys
t
e
m
[
6]
s
h
o
wc
a
s
e
s
t
h
e
pot
e
n
t
i
a
l
of
s
im
i
l
a
r
a
ppr
o
a
c
h
e
s
in
we
a
t
h
e
r
pr
e
d
i
c
t
i
o
n
.
A
d
v
a
n
c
e
d
pr
e
d
i
c
t
i
o
n
s
uc
h
as
m
u
l
t
i
c
l
a
s
s
pr
e
d
i
c
t
i
o
n
h
a
s
be
e
n
a
l
s
o
us
e
d
[7
]
,
[
8]
.
K
e
y
a
s
p
e
c
t
s
a
n
d
n
o
v
e
l
e
l
e
m
e
n
t
s
:
t
h
i
s
s
t
ud
y
i
n
t
r
oduc
e
s
a
uni
que
a
ppr
o
a
c
h
to
we
a
t
h
e
r
pr
e
di
c
t
i
o
n
.
It
e
m
p
l
o
y
s
A
NN
to
a
n
a
l
y
z
e
a
n
d
f
o
r
e
c
a
s
t
we
a
t
h
e
r
pa
tt
e
r
n
s
.
T
h
e
i
nn
o
v
a
t
i
v
e
a
s
pe
c
t
l
i
e
s
in
t
h
e
ut
i
li
z
a
t
i
o
n
of
s
m
a
r
t
c
o
m
put
i
n
g
de
vi
c
e
s
f
o
r
da
t
a
c
o
l
l
e
c
t
i
o
n
a
n
d
a
n
a
lys
i
s
.
T
h
e
s
e
de
vi
c
e
s
,
e
qu
i
ppe
d
w
i
t
h
va
r
i
o
us
s
e
ns
o
r
s
,
ga
t
h
e
r
r
e
a
l
-
t
i
m
e
e
nvi
r
o
nm
e
n
t
a
l
da
t
a
.
T
hi
s
da
t
a
f
o
r
m
s
t
h
e
f
o
u
n
da
t
i
o
n
f
o
r
t
h
e
n
e
ur
a
l
ne
t
wor
k
’
s
t
r
a
i
ni
ng
a
n
d
p
r
e
d
i
c
t
i
o
n
pr
o
c
e
s
s
e
s
.
T
h
e
f
r
a
m
e
wo
r
k
di
f
f
e
r
s
f
r
o
m
t
r
a
di
t
i
o
n
a
l
we
a
t
h
e
r
pr
e
di
c
t
i
o
n
m
e
t
h
o
ds
by
it
i
s
r
e
l
i
a
nc
e
on
d
i
r
e
c
t
da
t
a
i
nput
f
r
o
m
I
o
T
-
e
n
a
bl
e
d
de
vi
c
e
s
,
e
nha
n
c
i
ng
pr
e
d
i
c
t
i
o
n
a
c
c
ur
a
c
y
.
An
o
t
h
e
r
n
o
v
e
l
e
l
e
m
e
n
t
is
t
h
e
i
n
t
e
gr
a
t
i
o
n
of
m
a
c
hi
ne
l
e
a
r
ni
ng
a
l
go
r
i
t
hm
s
w
i
t
h
t
r
a
di
t
i
o
n
a
l
we
a
t
h
e
r
f
o
r
e
c
a
s
t
i
n
g
t
e
c
h
ni
que
s
.
T
hi
s
c
o
m
bi
na
t
i
o
n
o
f
f
e
r
s
a
m
o
r
e
d
y
n
a
mi
c
a
n
a
ly
s
i
s
of
we
a
t
h
e
r
pa
tt
e
r
n
s
.
T
h
e
s
t
udy
a
l
s
o
e
x
p
l
o
r
e
s
t
h
e
i
m
pa
c
t
of
c
l
im
a
t
e
c
h
a
n
ge
on
pr
e
d
i
c
t
i
o
n
m
o
de
l
s
.
It
a
c
kn
o
w
l
e
dge
s
t
h
e
i
n
c
r
e
a
s
i
ng
un
pr
e
d
i
c
t
a
bi
li
t
y
of
we
a
t
h
e
r
pa
tt
e
r
n
s
a
n
d
a
da
pt
s
it
i
s
m
e
t
ho
d
o
l
o
g
y
a
c
c
o
r
di
n
g
ly
.
T
h
e
r
e
s
e
a
r
c
h
a
i
m
s
to
i
m
pr
o
v
e
pr
e
d
i
c
t
i
o
n
a
c
c
ur
a
c
y
a
n
d
r
e
l
i
a
bil
i
t
y
,
f
o
c
us
i
ng
on
n
e
x
t
-
da
y
we
a
t
h
e
r
f
o
r
e
c
a
s
t
s
.
It
hi
g
hli
g
h
t
s
t
h
e
s
i
g
ni
f
i
c
a
n
c
e
of
a
dv
a
n
c
e
d
c
o
m
put
i
n
g
a
n
d
a
r
t
i
f
i
c
i
a
l
i
n
t
e
ll
i
ge
n
c
e
in
a
ddr
e
s
s
i
ng
c
o
m
p
l
e
x
e
nvi
r
o
nm
e
n
t
a
l
c
h
a
ll
e
n
ge
s
.
T
hi
s
a
ppr
o
a
c
h
r
e
pr
e
s
e
n
t
s
a
f
o
r
wa
r
d
-
t
hi
n
k
i
ng
c
o
n
t
r
i
b
ut
i
o
n
to
t
h
e
f
i
e
l
d
of
we
a
t
h
e
r
pr
e
di
c
t
i
o
n
,
pr
o
m
i
s
i
ng
e
nh
a
n
c
e
d
a
c
c
ur
a
c
y
a
n
d
e
f
f
i
c
i
e
nc
y
in
f
o
r
e
c
a
s
t
s
.
2.
RE
L
AT
E
D
L
I
T
E
RA
T
U
RE
2
.
1
.
Cur
r
e
n
t
r
e
s
e
a
r
c
h
T
h
e
a
ut
h
o
r
s
e
x
a
m
i
ne
d
312
pa
pe
r
s
a
n
d
j
o
ur
n
a
l
pu
bli
c
a
t
i
o
n
s
f
r
o
m
I
E
E
E
,
A
C
M
,
a
n
d
E
l
s
e
vi
e
r
as
we
l
l
as
ot
h
e
r
r
e
f
e
r
e
n
c
e
d
j
o
ur
n
a
l
s
t
h
a
t
h
a
v
e
b
e
e
n
publi
s
h
e
d
dur
i
n
g
2018
to
da
t
e
.
T
h
e
publ
i
c
a
t
i
o
ns
we
r
e
c
a
t
e
g
o
r
i
s
e
d
b
a
s
e
d
on
r
e
s
e
a
r
c
h
e
v
a
l
u
a
t
i
o
ns
,
ke
y
wo
r
ds
,
a
n
d
f
i
n
d
i
n
gs
to
m
a
t
c
h
t
h
e
pe
r
t
i
n
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9]
c
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al.
[
10]
us
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d
m
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d
da
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pr
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da
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pe
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Dur
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al.
[
11]
,
p
r
o
p
o
s
e
d
a
s
m
a
r
t
we
a
t
h
e
r
s
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v
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o
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in
C
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da
[
12]
,
pr
o
vi
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d
a
r
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f
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c
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l
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ll
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t
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pr
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us
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r
a
ns
a
c
t
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o
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s
.
J
a
d
h
a
v
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al.
[
13]
,
s
ugge
s
t
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d
a
s
i
m
p
l
e
,
e
a
s
y
-
to
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us
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o
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c
c
ur
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ur
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pr
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s
in
t
h
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f
a
c
e
of
c
h
a
n
g
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m
e
t
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or
o
l
o
gi
c
a
l
c
o
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d
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t
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.
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ng
da
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ll
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o
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us
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di
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c
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pr
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h
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m
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al.
[
14]
,
r
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vi
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we
d
t
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l
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t
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r
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t
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on
s
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us
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.
In
r
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2
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2
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ks
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ga
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h
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dd
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ll
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pr
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a
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c
ur
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y
in
we
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t
h
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f
o
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e
c
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t
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de
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t
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T
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s
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o
t
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Our
c
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s
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3.
RE
S
E
AR
CH
M
E
T
HO
D
T
h
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c
l
im
a
t
e
is
de
f
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by
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m
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hil
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pe
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3.
1.
S
t
e
p
1:
l
oad
in
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:
Start
for var in wind_attributes:
df[var]=df[var].map(
wind_angles)
df[var+‘_cos’]=np.cos(df[var])
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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do
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End
F
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k
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f
o
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e
of
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da
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ll
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s
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t
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d
in
F
i
g
ur
e
6.
F
i
g
ur
e
5
.
B
e
h
a
vi
o
ur
of
n
o
de
a
c
t
i
v
a
t
i
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n
f
u
n
c
t
i
o
n
F
i
g
ur
e
6
.
C
a
r
di
n
a
l
d
i
r
e
c
t
i
o
ns
3.
3.
S
t
e
p
3:
d
ivi
d
e
t
h
e
d
at
as
e
t
i
n
t
o
t
e
s
t
an
d
t
r
ain
s
e
t
s
In
t
h
i
s
s
t
e
p,
t
h
e
a
u
t
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p
l
i
t
t
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a
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o
t
r
a
i
ni
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a
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t
i
n
g
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e
t
s
a
n
d
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o
r
m
a
l
i
z
e
i
t
.
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u
t
b
e
f
o
r
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pe
r
f
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m
i
ng
t
h
a
t
s
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e
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ge
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l
a
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s
R
a
i
nT
o
m
o
r
r
o
w
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s
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pa
r
a
t
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d,
whi
c
h
is
t
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o
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va
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u
e
to
pr
e
di
c
t
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o
m
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ll
t
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ot
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r
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o
l
u
m
ns
of
da
t
a
.
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t
a
r
ge
t
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l
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l
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ngs
to
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tpu
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wor
k,
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ot
th
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nput
l
a
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r
.
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i
d
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a
is
to
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c
t
r
o
ws
of
da
t
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at
r
a
n
do
m
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n
d
s
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t
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m
a
s
i
de
f
o
r
l
a
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r
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e
s
e
a
r
e
n
ot
c
o
n
s
i
d
e
r
e
d
at
a
ll
whil
e
t
r
a
i
ni
ng
t
h
e
n
e
ur
a
l
n
e
t
wor
k.
W
h
e
n
r
e
f
e
r
r
i
n
g
to
t
h
e
m
l
a
t
e
r
,
it
i
s
t
h
e
s
a
m
e
as
f
i
nd
i
ng
e
xa
m
p
l
e
s
of
we
a
t
h
e
r
c
o
n
d
i
t
i
o
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s
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o
t
s
e
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n
b
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f
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r
e
.
T
h
e
pr
o
p
o
s
e
d
m
o
de
l
c
a
n
a
c
c
ur
a
t
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l
y
pr
e
d
i
c
t
wh
e
t
h
e
r
it
r
a
i
ns
t
h
e
n
e
x
t
da
y
.
s
kl
e
a
r
n
h
a
s
a
c
o
nv
e
ni
e
n
t
f
u
nc
t
i
o
n
c
a
l
l
e
d
t
r
a
i
n
_t
e
s
t
_s
p
l
i
t
.
T
h
e
ps
e
udo
-
c
o
de
f
o
r
t
h
e
di
vi
s
i
o
n
of
t
h
e
da
t
a
s
e
t
i
n
t
o
t
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s
t
a
n
d
t
r
a
i
n
is
g
i
ve
n
:
Begin
Train_set, Test_set, train_y, test_y=testtrain_splitt(
X,
y,
testsize=0.33,
randomstate=0
)
print(‘Train_set’, Train_set.shape)
print(‘Test_set’, Test_set.shape)
End
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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do
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J
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N:
2
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2
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4
7
52
A
r
ti
f
icial
int
e
ll
igenc
e
-
bas
e
d
w
e
ather
pr
e
diction
f
r
ame
w
or
k
us
ing
ne
ur
al
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w
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k
s
(
K
e
s
ha
v
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aus
hik
)
1843
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h
e
o
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t
pu
t
of
t
hi
s
s
t
e
p
is
-
X_
t
r
a
i
n
(
2026,
23
)
,
X
_t
e
s
t
(
999,
23)
.
T
h
e
r
e
a
f
t
e
r
,
s
c
a
l
i
ng
of
da
t
a
is
do
n
e
b
e
f
o
r
e
t
r
a
i
ni
ng
t
h
e
n
e
ur
a
l
n
e
t
wo
r
k
.
T
h
e
ps
e
udo
-
c
o
de
f
o
r
s
c
a
l
i
ng
is
as
f
o
l
l
o
w
s
:
Begin
Set_scaler=StandardScaler()
scaler.fit(Train_set)
Train_set=scaler.transform(Train_set)
Test_set=scaler.transform(Test_set)
End
No
w,
t
h
e
c
a
l
c
u
l
a
t
i
o
n
of
s
c
a
l
i
ng
f
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c
t
or
s
to
m
u
l
t
i
p
ly
t
h
e
v
a
l
ue
s
w
i
t
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,
so
t
h
e
s
e
lie
in
t
h
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s
a
m
e
r
a
n
g
e
of
s
m
a
l
l
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l
ue
s
.
No
r
t
h
a
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a
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c
h
do
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s
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f
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r
to
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s
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y
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,
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h
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s
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t
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of
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of
z
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nd
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s
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da
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vi
a
t
i
o
n
of
o
n
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.
In
t
h
e
n
e
x
t
s
t
e
p,
t
h
e
a
ut
h
o
r
s
t
r
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n
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d
t
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n
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ur
a
l
ne
t
wor
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on
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t
r
a
i
ni
ng
s
e
t
a
n
d
de
t
e
r
m
i
ne
d
t
h
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a
c
c
ur
a
c
y
to
m
a
ke
pr
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d
i
c
t
i
o
n
s
.
3.
4.
S
t
e
p
4:
p
r
e
d
ict
in
g
u
s
in
g
t
r
ain
e
d
n
e
u
r
al
n
e
t
wor
k
In
t
hi
s
s
t
e
p,
t
h
e
pr
o
p
o
s
e
d
n
e
ur
a
l
n
e
t
wo
r
k
is
tr
a
i
n
e
d
us
i
n
g
an
m
u
l
t
i
-
l
a
y
e
r
pe
r
c
e
pt
r
o
n
(
M
L
P
C
l
a
s
s
i
f
i
e
r
)
.
T
h
e
t
a
r
ge
t
c
l
a
ss
is
pr
e
d
i
c
t
e
d
us
i
n
g
t
h
e
M
L
P
C
l
a
s
s
if
ier
wi
t
h
t
h
e
f
o
l
l
o
w
i
ng
ps
e
udo
-
c
o
de
:
Begin
classifier=MLPClassifier(
hidden_layer_sizes=(50,50),
iteration_max=500,
set_ randomstate=0
)
classifier.fit(Train_set, train_y)
End
X
h
a
s
a
ll
t
h
e
23
a
tt
r
i
b
ut
e
s
a
n
d
is
s
p
li
t
i
n
t
o
t
wo
-
t
hi
r
ds
,
o
n
e
-
t
hi
r
d
s
u
bs
e
t
s
.
In
a
dd
i
t
i
o
n
,
y
ha
s
o
nly
o
n
e
v
a
l
ue
,
t
h
e
t
a
r
ge
t
c
l
a
s
s
.
To
t
r
a
i
n
a
n
e
ur
a
l
ne
t
wor
k,
e
a
c
h
r
o
w
of
t
h
e
t
r
a
i
ni
ng
da
t
a
is
pl
ugg
e
d
i
n
t
o
t
h
e
i
n
p
ut
l
a
y
e
r
,
o
n
e
-
r
o
w
-
at
-
a
-
t
i
m
e
,
a
n
d
c
h
e
c
k
if
t
h
e
o
u
t
pu
t
is
c
o
r
r
e
c
t
.
If
t
h
e
da
t
a
s
e
t
m
e
n
t
i
o
n
s
t
h
e
r
e
wa
s
r
a
i
n
to
m
o
r
r
o
w,
t
h
e
n
t
h
e
n
e
t
wo
r
k
’
s
o
u
t
pu
t
to
be
o
n
e
,
ot
h
e
r
w
i
s
e
c
o
n
s
i
de
r
e
d
z
e
r
o
.
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h
e
r
e
f
o
r
e
,
wh
e
n
t
h
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n
e
t
wo
r
k
o
u
t
pu
t
is
wr
o
n
g,
t
h
e
a
ut
h
o
r
s
a
dj
us
t
e
d
t
h
e
we
i
g
h
t
s
a
n
d
bi
a
s
e
s
a
l
i
t
t
l
e
bi
t
in
t
h
e
d
i
r
e
c
t
i
o
n
to
m
a
k
e
it
r
i
g
h
t
.
T
h
e
l
a
r
ge
r
t
h
e
n
e
t
wo
r
k
,
t
h
e
m
o
r
e
i
n
t
r
i
c
a
t
e
t
h
e
i
n
t
e
r
a
c
t
i
o
n
s
b
e
t
we
e
n
t
h
e
i
n
pu
t
a
n
d
o
u
t
pu
t
m
a
y
be
c
a
pt
ur
e
d.
H
o
we
v
e
r
,
if
t
hi
s
is
do
n
e
e
x
c
e
s
s
i
ve
ly
,
t
h
e
m
o
de
l
m
a
y
o
ve
r
f
i
t
t
h
e
da
t
a
f
o
r
t
r
a
i
ni
ng
to
c
a
t
c
h
too
m
a
ny
t
i
ny
f
e
a
t
ur
e
s
.
T
h
e
n
it
wo
ul
d
ge
n
e
r
a
li
z
e
e
x
t
r
e
m
e
ly
w
e
l
l
to
we
a
t
h
e
r
c
i
r
c
u
m
s
t
a
n
c
e
s
it
h
a
d
n
e
v
e
r
e
x
pe
r
i
e
n
c
e
d
b
e
f
o
r
e
.
It
is
a
l
w
a
y
s
a
g
oo
d
i
de
a
to
s
e
t
t
h
e
s
e
t
_
r
a
n
do
m
s
t
a
t
e
a
n
d
c
o
n
s
i
d
e
r
t
h
e
m
a
xim
u
m
n
u
m
be
r
of
e
po
c
h
s
t
h
a
t
it
is
a
l
l
o
we
d
to
t
a
ke
is
5
00.
T
hi
s
n
e
ur
a
l
n
e
t
wo
r
k
(
ps
e
udo
c
o
de
gi
ve
n
be
l
o
w)
can
pr
e
d
i
c
t
wh
e
t
h
e
r
it
w
i
ll
r
a
i
n
t
o
m
o
r
r
o
w
in
P
e
r
t
h
w
i
t
h
89.
38%
a
c
c
ur
a
c
y
.
In
t
h
e
ne
x
t
s
t
e
p,
t
h
e
r
e
s
u
l
t
s
a
r
e
i
m
pr
o
v
e
d
by
c
h
a
n
g
i
ng
t
h
e
l
a
y
o
ut
of
t
h
e
n
e
ur
a
l
n
e
t
wo
r
k
.
Predicting_y=classifier.predict(Test_set)
print(accuracy_score(test_y, Predicting_y))
3.
5.
S
t
e
p
5:
p
e
r
f
o
r
m
in
g
gr
id
s
e
a
r
c
h
to
op
t
im
iz
e
t
h
e
r
e
s
u
l
t
s
In
t
hi
s
f
i
na
l
s
t
e
p,
t
h
e
pr
o
p
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t
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l
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y
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s
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n
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of
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2
,
000
f
o
r
t
hi
s
to
w
o
r
k
a
n
d
s
e
t
t
h
e
r
a
n
do
m
_s
t
a
t
e
to
z
e
r
o
a
ga
i
n
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h
e
ps
e
udo
-
c
o
de
f
o
r
th
e
s
a
m
e
is
g
i
ve
n
:
print(gridsearch.cv_results_[‘params’])
print(gridsearch.cv_results_[‘mean_test_score’])
best_nn=gridsearch.best_estimator_
y_pred=best_nn.predict(Test_set)
print(accuracy_score(test_y, Predicting_y))
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2
5
0
2
-
4
7
52
In
do
n
e
s
i
a
n
J
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l
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c
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&
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o
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3
6
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3
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De
c
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e
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20
24
:
1
8
36
-
1
8
48
1844
4.
RE
S
UL
T
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n
it
wo
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t
be
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bl
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to
ge
n
e
r
a
l
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z
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a
b
o
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s
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it
h
a
s
n
’
t
e
n
c
o
un
t
e
r
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d
b
e
f
o
r
e
.
T
h
e
o
p
t
i
m
a
l
ne
t
wor
k
l
a
y
o
ut
is
c
h
o
s
e
n
o
n
c
e
t
h
e
m
o
de
l
h
a
s
b
e
e
n
t
r
a
i
n
e
d.
t
h
e
ps
e
udo
-
c
o
de
f
o
r
t
h
e
s
a
m
e
is
g
i
v
e
n
:
Start
parameters=
{
‘hidden_layer_sizes’:
(
(2,), (10,), (50,50),
)
}
nn=MLPClassifier(max_iter=2000,
random_state=0)
gridsearch=GridSearchCV(nn, parameters, cv=3)
gridsearch.fit(Train_set, train_y)
End
He
r
e
in
t
h
e
a
b
o
v
e
ps
e
udo
-
c
o
de
,
we
h
a
v
e
us
e
d
t
h
e
m
u
l
t
i
-
l
a
y
e
r
pe
r
c
e
pt
r
o
n
c
l
a
s
s
if
i
e
r
a
n
d
us
e
d
t
h
e
Gr
i
dS
e
a
r
c
h
CV
m
e
t
h
o
d
to
t
a
ke
t
h
e
pa
r
a
m
e
t
e
r
s
.
T
h
e
r
e
a
f
t
e
r
,
t
h
e
gr
i
d
r
e
s
u
l
t
s
a
r
e
d
i
s
p
l
a
y
e
d
us
i
ng
t
h
e
ps
e
udo
c
o
de
gi
v
e
n
:
print(gridsearch.cv_results_[‘params’])
print(gridsearch.cv_results_[‘mean_test_score’])
t
he
a
c
c
ur
a
c
y
of
v
a
r
i
o
us
m
o
de
l
s
c
a
m
e
o
u
t
to
b
e
:
[{‘hidden_layer_sizes’: (2,)}, {‘
hidden_layer_sizes’: (10,)}, {‘hidden_layer_sizes’: (50, 50)}]
[0.90424355 0.89487179 0.88450873]
i
n
t
h
e
f
i
na
l
s
t
e
p,
we
s
e
l
e
c
t
e
d
t
h
e
b
e
s
t
n
e
ur
a
l
n
e
t
wo
r
k
f
o
r
pr
e
di
c
t
i
o
n
us
i
ng
t
h
e
ps
e
udo
c
o
de
gi
v
e
n
:
best_nn=gridsearch.best_estimator_
y_pred=best_nn.predict(Test_set)
print(accuracy_score(test_y, Predicting_y))
T
h
e
m
a
t
h
e
m
a
t
i
c
a
l
c
a
l
c
u
l
a
t
i
o
n
do
n
e
to
f
i
nd
o
u
t
t
h
e
a
c
c
ur
a
c
y
is
g
i
v
e
n
(
1)
.
S
i
n
c
e
we
h
a
ve
us
e
d
t
h
e
m
u
l
t
i
-
l
a
y
e
r
pe
r
c
e
pt
r
o
n
m
o
de
l
,
t
h
e
f
e
e
d
-
f
o
r
wa
r
d
n
e
ur
a
l
n
e
t
wor
k
is
us
e
d.
T
h
e
pr
o
c
e
dur
e
to
c
a
l
c
u
l
a
t
e
t
h
e
di
s
c
o
n
t
i
n
uo
us
f
u
n
c
t
i
o
n
is
a
l
s
o
g
i
v
e
n
:
=
+
∗
(
−
)
∗
(
1
)
He
r
e
,
in
t
h
e
(
1)
,
w
r
e
pr
e
s
e
n
t
s
we
i
g
h
t
,
LR
r
e
pr
e
s
e
n
t
s
t
h
e
r
a
t
e
of
l
e
a
r
ni
ng,
E
r
e
p
r
e
s
e
n
t
s
t
h
e
e
x
pe
c
t
e
d
v
a
l
ue
,
P
r
e
pr
e
s
e
n
t
s
t
h
e
pr
e
d
i
c
t
e
d
v
a
l
u
e
:
=
(
[
0
]
[
0
]
+
[
1
]
[
1
]
)
/
.
(
)
(
2
)
=
[
0
]
[
0
]
/
(
[
0
]
[
0
]
+
[
0
]
[
1
]
)
(
3
)
=
[
0
]
[
0
]
/
(
[
0
]
[
0
]
+
[
1
]
[
0
]
)
(
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2
5
0
2
-
4
7
52
A
r
ti
f
icial
int
e
ll
igenc
e
-
bas
e
d
w
e
ather
pr
e
diction
f
r
ame
w
or
k
us
ing
ne
ur
al
ne
t
w
o
r
k
s
(
K
e
s
ha
v
K
aus
hik
)
1845
=
[
1
]
[
1
]
/
(
[
1
]
[
1
]
+
[
0
]
[
1
]
)
(
5
)
1
=
2
∗
[
0
]
[
0
]
/
(
2
∗
[
0
]
[
0
]
+
[
1
]
[
0
]
+
[
0
]
[
1
]
)
(
6
)
a
c
c
ur
a
c
y
f
o
r
t
h
e
pr
o
p
o
s
e
d
we
a
t
h
e
r
pr
e
di
c
t
i
o
n
m
o
de
l
is
c
a
l
c
u
l
a
t
e
d
us
i
n
g
t
h
e
f
o
r
m
u
l
a
:
=
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