# -*- coding: utf-8 -*-
'''
    Norms and co-norms based on the definitions in the IEEE standard.
    Each function here returns a FuzzyAggregationMethods object,
    which is really just a pair of functions: (and_func, or_func).
    I'm following Annex A in IEEE 1855-2016, definintions A.14-A.27.
    @author: james.power@mu.ie Created on Fri Aug 10 12:48:30 2018
'''


import numpy as np
from skfuzzy.control.term import FuzzyAggregationMethods


''' Reminder of what's in term.py:
    class FuzzyAggregationMethods(object):
        def __init__(self, and_func=np.fmin, or_func=np.fmax):
            self.and_func = and_func
            self.or_func = or_func
'''


def _fam_vectorise(and_func, or_func):
    '''
        Vectorise an and/or function pair; return a FuzzyAggregationMethods.
        Make sure the function names are propoagated to the resulting object.
    '''
    fam = FuzzyAggregationMethods(np.vectorize(and_func),
                                  np.vectorize(or_func))
    fam.and_func.__name__ = and_func.__name__
    fam.or_func.__name__ = or_func.__name__
    return fam


# This is the default:
# A.14: minimum t-norm, A.21: maximum t-conorm
MIN_MAX = FuzzyAggregationMethods(np.fmin, np.fmax)


def ps_prod_and(a, b):
    '''A.15: product t-norm'''
    return a*b


def ps_sum_or(a, b):
    '''A.21: Probabilistic sum t-conorm'''
    return a+b - (a*b)


PRODUCT_SUM = FuzzyAggregationMethods(ps_prod_and, ps_sum_or)


def bounded_and(a, b):
    'A.16: bounded difference t-norm'
    return np.fmax(0, a+b - 1)


def bounded_or(a, b):
    'A.22: bounded sum t-conorm'
    return np.fmin(1, a+b)


BOUNDED = FuzzyAggregationMethods(bounded_and, bounded_or)


'''This is just a synonym for bounded'''
LUCASIEWICZ = BOUNDED


def drastic_and(a, b):
    'A.17: drastic product t-norm'
    if a == 1:
        return b
    elif b == 1:
        return a
    else:
        return 0


def drastic_or(a, b):
    'A.23: drastic sum t-conorm'
    if a == 0:
        return b
    elif b == 0:
        return a
    else:
        return 1

DRASTIC = _fam_vectorise(drastic_and, drastic_or)


def einstein_and(a, b):
    'A.18: Einstein product t-norm'
    return (a*b) / (2 - (a+b - a*b))


def einstein_or(a, b):
    'A.24: Einstein sum t-conorm'
    return (a+b) / (1 + a*b)


EINSTEIN = FuzzyAggregationMethods(einstein_and, einstein_or)


# Denoninator is wrong in A.19 whihc says: ((a+b) / ((a+b) - a*b))
def hamacher_and(a, b):
    '''A.19: Hamacher product t-norm; added divide-by-zero check'''
    if a == b == 0:
        return 0.0
    else:
        return (a*b) / ((a+b) - a*b)


def hamacher_or(a, b):
    'A.25: Hamacher sum t-conorm; added divide-by-zero check'
    if a == b == 1:
        return 1.0
    else:
        return (a+b - 2*a*b) / (1 - a*b)


HAMACHER = _fam_vectorise(hamacher_and, hamacher_or)


def nilpotent_and(a, b):
    'A.20: Nilpotent minum t-norm'
    if a+b > 1:
        return np.fmin(a, b)
    else:
        return 0.0


def nilpotent_or(a, b):
    'A.26: Nilpotent maximum t-conorm'
    if a+b < 1:
        return np.fmax(a, b)
    else:
        return 1.0


NILPOTENT = _fam_vectorise(nilpotent_and, nilpotent_or)


# ################# ###
# ### Test routines ###
# ################# ###

def check_classic(fam):
    '''
        Test that norm and co-norm work like classic and/or for 0, 1 inputs.
    '''
    a = np.array([0, 0, 1, 1])
    b = np.array([0, 1, 0, 1])
    try:
        # First check that the product works like the AND function:
        expected = np.logical_and(a, b)
        fprod = fam.and_func(a, b)
        if not (fprod == expected).all():
            print('Failed check_classic', fam.and_func.__name__, '\n',
                  'Inputs:', (a, b), '\n',
                  'Product =', fprod)
        # Now check that the sum works like the OR function:
        expected = np.logical_or(a, b)
        fsum = fam.or_func(a, b)
        if not (fsum == expected).all():
            print('Failed check_classic', fam.or_func.__name__, '\n',
                  'Inputs:', (a, b), '\n',
                  'Sum =', fsum)
    # A divide-by-zero error is also a problem:
    except ZeroDivisionError as e:
        print('check_classic',
              fam.and_func.__name__, fam.or_func.__name__, '\n',
              'Inputs:', (a, b), '\n',
              'Divide by zero')


def check_duality(fam):
    '''
        Run some tests to make sure that the norm and co-norm are duals.
        That is, they obey de Morgan's law: (a and b) = not((not a) or (not b))
    '''
    # Test a range of values between 0.0 and 1.0 inclusive:
    a = np.arange(0.0, 1.1, 0.1)
    b = np.arange(0.0, 1.1, 0.1)
    try:
        prod = fam.and_func(a, b)
        # Need to round, since e.g. 1-0.7 is not 0.3 otherwise:
        dual_sum = 1 - fam.or_func(np.round(1-a, 1), np.round(1-b, 1))
        if not np.isclose(prod, dual_sum).all():
            print('Failed check_duality', fam.and_func.__name__, '\n',
                  'Inputs:', (a, b), '\n',
                  'Product =', prod, '\n',
                  'Dual Sum =', dual_sum)
    except ZeroDivisionError as e:
        print('Failed check_duality',
              fam.and_func.__name__, fam.or_func.__name__, '\n',
              'Inputs:', (a, b), '\n',
              'Divide by zero')


_all_norms = {
    'Min/Max': MIN_MAX,
    'Prod/Sum': PRODUCT_SUM,
    'Bounded': BOUNDED,
    'Drastic': DRASTIC,
    'Einstein': EINSTEIN,
    'Hamacher': HAMACHER,
    'Nilpotent': NILPOTENT,
}


import matplotlib.pyplot as plt
import skfuzzy.membership as skmemb


def visualise_all(x, y1, y2, all_norms=_all_norms):
    '''Plot the norm and conorm for the given sample inputs'''
    ncols = 3
    fig, axes = plt.subplots(nrows=len(all_norms), ncols=ncols, figsize=(8, 9))
    fig.tight_layout()
    fig.subplots_adjust(bottom=-.25)
    for row, name in enumerate(_all_norms.keys()):
        for col in range(ncols):  # so all have the same (0,1) y-axis
            axes[row][col].set_ylim([-0.05, 1.05])
        axes[row][0].set_title('Sample inputs')
        axes[row][0].plot(x, y1)
        axes[row][0].plot(x, y2)
        axes[row][1].set_title(name + ' norm')
        axes[row][1].plot(x, _all_norms[name].and_func(y1, y2))
        axes[row][2].set_title(name + ' co-norm')
        axes[row][2].plot(x, _all_norms[name].or_func(y1, y2))

if __name__ == '__main__':
    for fam in _all_norms.values():
        check_classic(fam)
        check_duality(fam)
    sample_x = np.arange(0, 100)
    sample_y1 = skmemb.trapmf(sample_x, [15, 30, 55, 75])
    sample_y2 = skmemb.trapmf(sample_x, [25, 45, 70, 85])
    visualise_all(sample_x, sample_y1, sample_y2)