"""
Programmer  :   EOF
E-mail      :   jasonleaster@163.com
Date        :   2015.11.22
File        :   weakClassifier.py

License     :   MIT License

"""

from matplotlib import pyplot
import numpy

from config import LABEL_POSITIVE
from config import LABEL_NEGATIVE


class WeakClassifier:

    def __init__(self, Mat=None, Tag=None, W=None, train=True):
        """
        Parameter:
        @Mat    :   A matrix(or two dimension array) which's size is
                    (row    = number of features, 
                    column  = number of total sample)
        @Tag    :   A vector(or one dimension array) which's size is the
                    same as the number of total sample

        @W      :   Weight of each sample in the training set.
                    A vector or a list, which's size is the same as the 
                    number of total sample.

        @train  :   A bool value. If it's False, it means that user want to
                    get a instance of this class object from cached data"""

        if train == True:
            """
            It's necessary to do this check.
            The implementation depend on numpy.ndarray heavily
            """
            assert Mat.__class__ == numpy.ndarray
            assert Tag.__class__ == numpy.ndarray
            assert W.__class__ == numpy.ndarray

            """
            It will cost a lot of memory, if I use @Mat to initialize
            the @self._mat like this:
                self._mat = numpy.array(Mat)

            constructor @numpy.array will return a new object which's
            message is the same as @Mat

            To save memory, I just set the data member @self._mat
            the same as the parameter passed into this constructor,
            which means that they point to the same address.

            Make sure this weak classifier will not modify the inputed mat.
            """
            self._mat = Mat
            self._label = Tag

            # sampleDim == the number of features
            self.sampleDim, self.sampleNum = self._mat.shape

            if W is None:
                self.numPos = numpy.count_nonzero(self._label == LABEL_POSITIVE)
                self.numNeg = numpy.count_nonzero(self._label == LABEL_NEGATIVE)
                pos_W = [1.0 / (2 * self.numPos) for i in range(self.numPos)]

                neg_W = [1.0 / (2 * self.numNeg) for i in range(self.numNeg)]
                self.weight = numpy.array(pos_W + neg_W)

            else:
                self.weight = W

            self.output = numpy.zeros(self.sampleNum, dtype=numpy.int)

            self.opt_errorRate = 1.
            self.opt_dimension = 0
            self.opt_threshold = None
            self.opt_direction = 0

    def optimal(self, d):

        # for positive sample
        idx = (self._label + LABEL_POSITIVE) / (LABEL_POSITIVE * 2)
        weight = self.weight * idx
        vector = self._mat[d] * idx
        sumPos = weight.dot(vector)
        sumPosW = weight.sum()

        # for negative sample
        idx = (self._label + LABEL_NEGATIVE) / (LABEL_NEGATIVE * 2)
        weight = self.weight * idx
        vector = self._mat[d] * idx
        sumNeg = weight.dot(vector)
        sumNegW = weight.sum()

        """
        Code beyong there is just optimal version of this one.
        ======================================================
        sumPos = 0.
        sumNeg = 0.

        sumPosW = 0.
        sumNegW = 0.

        for i in range(self.sampleNum):
            if self._label[i] == LABEL_POSITIVE:
                sumPos  += self.weight[i] * self._mat[d][i]
                sumPosW += self.weight[i]
            else:
                sumNeg  += self.weight[i] * self._mat[d][i]
                sumNegW += self.weight[i]
        """

        miuPos = sumPos / sumPosW
        miuNeg = sumNeg / sumNegW

        threshold = (miuPos + miuNeg) / 2

        minErrRate = numpy.inf
        bestDirection = None
        for direction in [-1, 1]:
            errorRate = 0.

            self.output[self._mat[d] * direction < threshold * direction] \
                = LABEL_POSITIVE

            self.output[self._mat[d] * direction >= threshold * direction] \
                = LABEL_NEGATIVE

            errorRate = self.weight[self.output != self._label].sum()

            """
            Code beyond there is just optimal version of this one.
            ======================================================

            self.output *= 0 # reset the output
            start = time.time()
            for i in range(self.sampleNum):
                if self._mat[d][i] *direction < threshold * direction:
                    self.output[i] = LABEL_POSITIVE
                else:
                    self.output[i] = LABEL_NEGATIVE

                if self.output[i] != self._label[i]:
                    errorRate += self.weight[i]
            """

            self.output *= 0  # reset the output
            if errorRate < minErrRate:
                minErrRate = errorRate
                bestDirection = direction

        return minErrRate, threshold, bestDirection

    def train(self):

        for dim in range(self.sampleDim):
            err, threshold, direction = self.optimal(dim)
            if err < self.opt_errorRate:
                self.opt_errorRate = err
                self.opt_dimension = dim
                self.opt_threshold = threshold
                self.opt_direction = direction

        assert self.opt_errorRate < 0.5

        return self.opt_errorRate

    def prediction(self, Mat):
        sampleNum = Mat.shape[1]

        dim = self.opt_dimension
        threshold = self.opt_threshold
        direction = self.opt_direction

        output = numpy.zeros(sampleNum, dtype=numpy.int)

        output[Mat[dim] * direction < direction * threshold] = LABEL_POSITIVE
        output[Mat[dim] * direction >= direction * threshold] = LABEL_NEGATIVE
        """
        Optimised for this.
        ========================================================
        for i in range(sampleNum):
            if direction * Mat[dim][i] < direction * threshold:
                output[i] = LABEL_POSITIVE
            else:
                output[i] = LABEL_NEGATIVE
        """

        return output

    def show(self, dim=None):

        if dim == None:
            dim = self.opt_dimension

        N = 10  # the number of center
        MaxVal = numpy.max(self._mat[dim])
        MinVal = numpy.min(self._mat[dim])

        scope = (MaxVal - MinVal) / N

        centers = [(MinVal - scope / 2) + scope * i for i in range(N)]
        counter = [[0, 0] for i in range(N)]

        for j in range(N):
            for i in range(self.sampleNum):
                if abs(self._mat[dim][i] - centers[j]) < scope / 2:
                    if self._label[i] == LABEL_POSITIVE:
                        counter[j][1] += 1
                    else:
                        counter[j][0] += 1

        posVal, negVal = [], []

        for i in range(N):
            posVal.append(counter[i][1])
            negVal.append(counter[i][0])

        sumPosVal = sum(posVal)
        sumNegVal = sum(negVal)

        for i in range(len(posVal)): posVal[i] /= (1. * sumPosVal)
        for i in range(len(negVal)): negVal[i] /= (1. * sumNegVal)

        pyplot.title("A simple weak classifier")
        pyplot.plot(centers, posVal, "r-o", label="Face class")
        pyplot.plot(centers, negVal, "b-o", label="Non-Face class")
        pyplot.xlabel("feature response")
        pyplot.ylabel("frequency")

        # plot threshold line
        sumPosW = 0.
        sumNegW = 0.
        sumPos = 0.
        sumNeg = 0.
        for i in range(self.sampleNum):
            if self._label[i] == LABEL_POSITIVE:
                sumPos += self.weight[i] * self._mat[dim][i]
                sumPosW += self.weight[i]
            else:
                sumNeg += self.weight[i] * self._mat[dim][i]
                sumNegW += self.weight[i]

        miuPos = sumPos / sumPosW
        miuNeg = sumNeg / sumNegW

        threshold = (miuPos + miuNeg) / 2
        pyplot.plot([threshold for i in range(10)], [i for i in numpy.arange(0.0, 0.5, 0.05)], label="threshold")
        pyplot.legend()
        pyplot.show()

    def __str__(self):

        string = "opt_errorRate:" + str(self.opt_errorRate) + "\n"
        string += "opt_threshold:" + str(self.opt_threshold) + "\n"
        string += "opt_dimension:" + str(self.opt_dimension) + "\n"
        string += "opt_direction:" + str(self.opt_direction) + "\n"
        string += "weights      :" + str(self.weight) + "\n"
        return string

    def constructor(self, dimension, direction, threshold):
        self.opt_dimension = dimension
        self.opt_threshold = threshold
        self.opt_direction = direction

        return self