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import numpy
import theano
from theano import tensor
from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity, Softmax
from blocks.bricks.lookup import LookupTable
from blocks.initialization import IsotropicGaussian, Constant
import data
import hdist
class Model(object):
def __init__(self, config):
# The input and the targets
x_firstk_latitude = (tensor.matrix('first_k_latitude') - data.porto_center[0]) / data.data_std[0]
x_firstk_longitude = (tensor.matrix('first_k_longitude') - data.porto_center[1]) / data.data_std[1]
x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.porto_center[0]) / data.data_std[0]
x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.porto_center[1]) / data.data_std[1]
x_client = tensor.lvector('origin_call')
x_stand = tensor.lvector('origin_stand')
y = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
tensor.vector('destination_longitude')[:, None]), axis=1)
# Define the model
client_embed_table = LookupTable(length=data.n_train_clients+1, dim=config.dim_embed, name='client_lookup')
stand_embed_table = LookupTable(length=data.n_stands+1, dim=config.dim_embed, name='stand_lookup')
mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Softmax()],
dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes')
# Create the Theano variables
client_embed = client_embed_table.apply(x_client)
stand_embed = stand_embed_table.apply(x_stand)
inputs = tensor.concatenate([x_firstk_latitude, x_firstk_longitude,
x_lastk_latitude, x_lastk_longitude,
client_embed, stand_embed],
axis=1)
# inputs = theano.printing.Print("inputs")(inputs)
cls_probas = mlp.apply(inputs)
outputs = tensor.dot(cls_probas, classes)
# outputs = theano.printing.Print("outputs")(outputs)
# y = theano.printing.Print("y")(y)
outputs.name = 'outputs'
# Calculate the cost
cost = hdist.erdist(outputs, y).mean()
cost.name = 'cost'
hcost = hdist.hdist(outputs, y).mean()
hcost.name = 'hcost'
# Initialization
client_embed_table.weights_init = IsotropicGaussian(0.001)
stand_embed_table.weights_init = IsotropicGaussian(0.001)
mlp.weights_init = IsotropicGaussian(0.01)
mlp.biases_init = Constant(0.001)
client_embed_table.initialize()
stand_embed_table.initialize()
mlp.initialize()
self.cost = cost
self.hcost = hcost
self.outputs = outputs
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