"""
The model for an autoassociator for sparse inputs, using Ronan Collobert + Jason
Weston's sampling trick (2008).
"""

from graph import trainfn
import parameters
import numpy
from globals import LR

class Model:
    def __init__(self):
        self.parameters = parameters.Parameters(randomly_initialize=True)

    def update(self, instance, nonzero_indexes, zero_indexes):
        xnonzero = numpy.asarray([instance[idx] for idx in nonzero_indexes])
        print
        print "xnonzero:", xnonzero

        (ynonzero, yzero, loss, gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero) = trainfn(xnonzero, self.parameters.w1[nonzero_indexes, :], self.parameters.b1, self.parameters.w2[:, nonzero_indexes], self.parameters.w2[:, zero_indexes], self.parameters.b2[nonzero_indexes], self.parameters.b2[zero_indexes])
        print "OLD ynonzero:", ynonzero
        print "OLD yzero:", yzero
        print "OLD total loss:", loss

        # SGD update
        self.parameters.w1[nonzero_indexes, :]  -= LR * gw1nonzero
        self.parameters.b1						-= LR * gb1
        self.parameters.w2[:, nonzero_indexes]  -= LR * gw2nonzero
        self.parameters.w2[:, zero_indexes]		-= LR * gw2zero
        self.parameters.b2[nonzero_indexes]		-= LR * gb2nonzero
        self.parameters.b2[zero_indexes]		-= LR * gb2zero

        # Recompute the loss, to make sure it's descreasing
        (ynonzero, yzero, loss, gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero) = trainfn(xnonzero, self.parameters.w1[nonzero_indexes, :], self.parameters.b1, self.parameters.w2[:, nonzero_indexes], self.parameters.w2[:, zero_indexes], self.parameters.b2[nonzero_indexes], self.parameters.b2[zero_indexes])
        print "NEW ynonzero:", ynonzero
        print "NEW yzero:", yzero
        print "NEW total loss:", loss