from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity
from blocks.bricks.lookup import LookupTable

from blocks.initialization import IsotropicGaussian, Constant

from theano import tensor

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] + [Identity()],
                           dims=[config.dim_input] + config.dim_hidden + [config.dim_output])

        # 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)
        outputs = mlp.apply(inputs)

        # Normalize & Center
        # outputs = theano.printing.Print("normal_outputs")(outputs)
        outputs = data.data_std * outputs + data.porto_center

        # 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