def test_backward(self):
A = Square()
B = Exp()
C = Square()
x = Variable(np.array(0.5))
a = A(x)
b = B(a)
y = C(b)
y.grad = np.array(1.0)
b.grad = C.backward(y.grad)
a.grad = B.backward(b.grad)
x.grad = A.backward(a.grad)
print(x.grad)
def soy_showcase(self):
# sanmartin
sanmartin = Variable('soy/afascl')
rosario = Variable('soy/bcr')
chicago = Variable('soy/chicago')
# foorecast soy sanmartin
regression = TimeRegression(self.date_list, self.day, [rosario])
fx, _, rmse = regression.pattern()
price = fx(regression.future_x)
filename = draw(regression, 'graph_soy_rosario.png')
self.tweet(('Estimación Soja Rosario para el'
' %s: AR$ %.f (RMSE: AR$ %.f)') %
(self.day.strftime('%d-%m-%Y'), price, rmse), filename)
regression = TimeRegression(self.date_list, self.day, [chicago])
fx, _, rmse = regression.pattern()
price = fx(regression.future_x)
filename = draw(regression, 'graph_soy_chicago.png')
self.tweet(('Estimación Soja Chicago para el'
' %s: U$D %.f (RMSE: U$D %.f)') %
(self.day.strftime('%d-%m-%Y'), price, rmse), filename)
regression = TimeRegression(self.date_list, self.day, [sanmartin])
fx, _, rmse = regression.pattern()
price = fx(regression.future_x)
filename = draw(regression, 'graph_soy_sanmartin.png')
self.tweet(('Estimación Soja puerto San Martín con descarga para el'
' %s: AR$ %.f (RMSE: AR$ %.f)') %
(self.day.strftime('%d-%m-%Y'), price, rmse), filename)
regression = VariableRegression(self.date_list, self.day,
[chicago, sanmartin])
fx, _, rmse = regression.pattern()
filename = draw(regression, 'graph_soy_related.png')
price = fx(regression.future_x)
x, y, dt = regression.data
pearson = regression.pearson_correlation()
m_dt = max(dt)
m_dt = int_to_date(m_dt) if isinstance(m_dt, int) else m_dt
try:
self.tweet(('Correlación Soja Chicago con pto. San Martín hasta el'
' %s: AR$ %.f (RMSE: AR$ %.f, Pearson: %.2f%%)') %
(m_dt.strftime('%d-%m-%Y'), price, rmse, pearson),
filename)
except AttributeError:
pass
self.tweet(
'El código puede ser descargado desde https://github.com/limiear/soyprice.',
[])
def test_composite_function_differential(self):
def f(x):
A = Square()
B = Exp()
C = Square()
return C(B(A(x)))
x = Variable(np.array(0.5))
dy = numerical_diff(f, x)
print(dy)
def test_composite(self):
A = Square()
B = Exp()
C = Square()
x = Variable(np.array(0.5))
a = A(x)
b = B(a)
y = C(b)
print(y.data)
def test_function_chain_backward(self):
A = Square()
B = Exp()
C = Square()
x = Variable(np.array(0.5))
a = A(x)
b = B(a)
y = C(b)
y.grad = np.array(1.0)
C = y.creator
b = C._input
b.grad = C.backward(y.grad)
B = b.creator
a = B._input
a.grad = B.backward(b.grad)
A = a.creator
x = A._input
x.grad = A.backward(a.grad)
print(x.grad)
def dollar_showcase(self):
dollars = Variable('dollar/blue')
regression = TimeRegression(self.date_list, self.day, [dollars])
fx, _, rmse = regression.pattern()
price = fx(regression.future_x)
filename = draw(regression, 'graph_dollar.png')
try:
rmse = int(rmse)
print self.day, price, rmse, filename
self.tweet(('Estimación Dollar Blue para el %s: AR$ %.2f '
'(RMSE: AR$ %.2f)') %
(self.day.strftime('%d-%m-%Y'), price, rmse), filename)
except ValueError:
pass
def test_function_chain(self):
A = Square()
B = Exp()
C = Square()
x = Variable(np.array(0.5))
a = A(x)
b = B(a)
y = C(b)
self.assertTrue(y.creator == C)
self.assertTrue(y.creator._input == b)
self.assertTrue(y.creator._input.creator == B)
self.assertTrue(y.creator._input.creator._input == a)
self.assertTrue(y.creator._input.creator._input.creator == A)
self.assertTrue(y.creator._input.creator._input.creator._input == x)
def test_differential(self):
f = Square()
x = Variable(np.array(2.0))
dy = numerical_diff(f, x)
print(dy)
def test_initialize(self):
data = np.array(1.0)
x = Variable(data)
self.assertEqual(x.data, data)
def test_function(self):
x = Variable(np.array(10))
f = Square()
y = f(x)
self.assertTrue(isinstance(y, Variable))
print(y.data)
from core import Variable
from operation import *
if __name__ == "__main__":
a = Variable(1.0)
b = add(a, a)
c = square(a)
d = add(a, b)
e = add(c, d)
e.backward()
print(e.data)
print(a.grad)
a.clearGrad()
print(a.grad)
from core import Variable from operation import * a = Variable(2) b = square(a) c = square(b) d = square(c) e = square(d) e.backward() print(a.grad)
from core import Variable
from operation import *
if __name__ == "__main__":
a = Variable(1.0)
b = add(a,a)
b.backward()
print(b.data)
print(a.grad)