"""Provides LogisticRegression
"""
import numpy
import theano
from theano.compile.sandbox import shared
from theano.tensor import nnet
from pylearn.shared.layers.util import update_locals, add_logging

class LogisticRegression(object):
    def __init__(self, input, w, b, params=[]):
        output=nnet.softmax(theano.dot(input, w)+b)
        l1=abs(w).sum()
        l2_sqr = (w**2).sum()
        argmax=theano.tensor.argmax(theano.dot(input, w)+b, axis=input.ndim-1)
        update_locals(self, locals())

    @classmethod
    def new(cls, input, n_in, n_out, dtype=None):
        if dtype is None:
            dtype = input.dtype
        cls._debug('allocating params w, b', n_in, n_out, dtype)
        w = shared(numpy.zeros((n_in, n_out), dtype=dtype))
        b = shared(numpy.zeros((n_out,), dtype=dtype))
        return cls(input, w, b, params=[w,b])


    def nll(self, target):
        """Return the negative log-likelihood of the prediction of this model under a given
        target distribution.  Passing symbolic integers here means 1-hot.
        WRITEME
        """
        return nnet.categorical_crossentropy(self.output, target)

    def errors(self, target):
        """Return a vector of 0s and 1s, with 1s on every line that was mis-classified.
        """
        if target.ndim != self.argmax.ndim:
            raise TypeError('target should have the same shape as self.argmax', ('target', target.type,
                'argmax', self.argmax.type))
        if target.dtype.startswith('int'):
            return theano.tensor.neq(self.argmax, target)
        else:
            raise NotImplementedError()

add_logging(LogisticRegression)