instances = 10 draws = np.random.randint(0,2,size=(instances,step)) walk = np.where(draws>0,1,-1) #print walk walks = walk.cumsum(1) print walks hist3 = (np.abs(walks)>=3).any(1) print hist3 crossing_time = np.abs(walks[hist3]).argmax(1) crossing_time2 = (np.abs(walks[hist3])>=3).argmax(1) print crossing_time print crossing_time2 print crossing_time.mean() print crossing_time2.mean() import theano_test from theano_test import tensor print theano_test.__version__ a = tensor.dscalar() b = tensor.dscalar() c = a + b f = theano_test.function([a,b],c) print f assert 4.0 == f(1.5,2.5)
#encoding:UTF-8
__author__ = 'auroua'
import theano_test
from theano_test import pp
from theano_test import function
import theano_test.tensor as T
x = T.dscalar('x')
y = x**2
gy = T.grad(y,x)
f = function([x],y)
print f(4)
x2 = T.dmatrix('x2')
s = T.sum(1/(1+T.exp(-x2)))
gs = T.grad(s,x2)
dlogistic = function([x2],gs)
print dlogistic([[0,1],[-1,-2]])
x3 = T.dvector('x3')
y3 = x3**2
J,updates = theano_test.scan(lambda i,y,x:T.grad(y[i],x),sequences=T.arange(y3.shape[0]),non_sequences=[y3,x3])
f = function([x3],J,updates=updates)
print f([4,4])
x4 = T.dvector('x4')
y4 = x4**2
cost = y4.sum()
gy4 = T.grad(cost,x4)
H,updates2 = theano_test.scan(lambda i,gy,x4:T.grad(gy[i],x4),sequences=T.arange(gy4.shape[0]),non_sequences=[gy4,x4])
__author__ = "auroua"
import theano_test.tensor as T
from theano_test import function
from theano_test import pp
x = T.dscalar("x")
y = T.dscalar("y")
z = x + y
f = function([x, y], z)
f(2, 3)
z.eval({x: 16.3, y: 14.3})
print z
print pp(z)
xm = T.dmatrix("xm")
ym = T.dmatrix("ym")
zm = xm + ym
f2 = function((xm, ym), zm)
f2(np.array([[1, 2], [2, 3]]), np.array([[3, 4], [4, 5]]))
xv = T.dvector("xv")
yv = T.dvector("yv")
zv = xv ** 2 + yv ** 2 + 2 * xv * yv
fv = function((xv, yv), zv)
print pp(zv)
print fv([1, 2], [3, 4])
