def predict(models, img, t=0):
img = np.clip(img, 0, 1) * 255
img = extend_data(config['permutation'], np.array([img]))
scores = np.hstack([m.predict(img) for m in models])[0]
#print(scores.shape)
nat_labels = np.zeros(scores.shape).astype(np.float32)
nat_labels[scores >= 0.5] = 1.
rep = rep_labels[:len(scores)].T
tmp = np.repeat([nat_labels], rep.shape[0], axis=0)
dists = np.sum(np.absolute(tmp - rep), axis=-1)
min_dist = np.min(dists)
pred_labels = np.arange(len(dists))[dists == min_dist]
pred_scores = [
np.sum([
scores[k] if rep[j][k] == 1 else 1 - scores[k]
for k in np.arange(len(scores))
]) for j in pred_labels
]
pred_label = pred_labels[np.argmax(pred_scores)]
if min_dist <= 0:
return pred_label
else:
return -1
minimum = {key: [] for key in model.params}
maximum = {key: [] for key in model.params}
for i in range(iterations):
X_batch = data[0][batch_index * batch_size:(batch_index + 1) * batch_size]
Y_batch = data[1][batch_index * batch_size:(batch_index + 1) * batch_size]
batch_index = (batch_index + 1) % batches
gradients, loss = gradient_loss(model, X_batch, Y_batch)
loss = loss.asnumpy()[0]
loss_history.append(loss)
for key, value in zip(model.params.keys(), gradients):
mean[key].append(np.mean(value).asnumpy())
std[key].append(np.std(value).asnumpy())
L_2[key].append(np.mean(value**2).asnumpy())
minimum[key].append(np.min(value).asnumpy())
maximum[key].append(np.max(value).asnumpy())
updater.update(gradients)
if (i + 1) % rescaling_interval == 0:
rescale(mlp, data[2], model.params) # validation data
print 'rescaled'
if (i + 1) % interval == 0:
print 'iteration %d loss %f' % (i + 1, loss)
pickle.dump((loss_history, mean, std, L_2, minimum, maximum),
open('dr-g-norm-%d' % rescaling_interval, 'wb'))
def red5(x):
return mp.min(x, axis=0, keepdims=True)
def red4(x):
return mp.min(x, axis=0)
def red3(x):
return mp.min(x, axis=1, keepdims=True)
def red2(x):
return mp.min(x, axis=1)
def red1(x):
return mp.min(x)
print(str(feature))
dirTrain = dirTrains[0]
#将txt文件中的moments读取到矩阵f中
#矩阵F用来作中间运算
f_train = np.loadtxt(dirTrain + "image_train_features.txt", delimiter=' ')
F = np.empty(f_train.shape, dtype=float)
#与模型库中的所有图片中心矩比较l
F = f_train - feature
#求出曼哈顿距离最小的图片
F = np.abs(F)
s = np.sum(F, axis=1)
index = np.argmin(s)
m = np.min(s)
print(str(f_train[index]))
print(m)
#获得测试图片的预测偏转角度
flag = 0
#读取图像名字txt文件
image_train_f = open(dirTrain + 'image_train_list.txt', 'r')
img_name_train = image_train_f.readline()
img_name_train = img_name_train.strip('\n')
while flag < index:
flag = flag + 1
img_name_train = image_train_f.readline()
img_name_train = img_name_train.strip('\n')
image_train_f.close()
def red5(x):
return mp.min(x, axis=0, keepdims=True)
def red4(x):
return mp.min(x, axis=0)
def red3(x):
return mp.min(x, axis=1, keepdims=True)
def red2(x):
return mp.min(x, axis=1)
def red1(x):
return mp.min(x)
def barrier(S0, K, B, tau, r, q, v, M, N):
S = pricepaths(S0, tau, r, q, v, M, N)
l = np.min(S, 0) > B
payoffs = l * np.maximum(S[-1, :] - K, 0)
return math.exp(-r * tau) * np.mean(payoffs)
