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])