"""
This module contains some example Generative Adversarial Networks for testing.

The classes StupidToyPointGan and StupidToyListGan are not really Networks. This classes are used
for testing the interface. Hope your actually GAN will perform better than this two.

The class SimpleGan is a simple standard Generative Adversarial Network.
"""


import numpy as np
import tensorflow as tf

from library.interfaces import GanBaseClass


def dist(x, y):
    return tf.sqrt(tf.reduce_sum(tf.square(x - y)))

def minDistPointToSet(x, setB):
    m = None
    for y in setB:
        d = dist(x,y)
        if m is None or m > d:
            m = d
    return m

def minDistSetToSet(setA, setB):
    m = None
    for x in setA:
        d = minDistPointToSet(x,setB)
        if m is None or m > d:
            m = d
    return m

def createSquare(pointCount, noiseSize):
    noise = []
    while len(noise) < pointCount:
        nPointsToAdd = max(100, pointCount - len(noise))
        noiseDimension = [nPointsToAdd, noiseSize]
        noise.extend(list(filter(
            lambda x: tf.reduce_max(tf.square(x)) < 1,
            np.random.normal(0, 1, noiseDimension))))
    return np.array(noise[0:pointCount])

def createDisc(pointCount, noiseSize):
    noise = []
    while len(noise) < pointCount:
        nPointsToAdd = max(100, pointCount - len(noise))
        noiseDimension = [nPointsToAdd, noiseSize]
        noise.extend(list(filter(
            lambda x: tf.reduce_sum(tf.square(x)) < 1,
            np.random.normal(0, 1, noiseDimension))))
    return np.array(noise[0:pointCount])

class SpheredNoise(GanBaseClass):
    """
    A class for a simple GAN.
    """
    def __init__(self, noiseSize=101):
        self.isTrained = False
        self.noiseSize = noiseSize
        self.disc = []
        self.reset()

    def reset(self):
        """
        Resets the trained GAN to an random state.
        """
        self.pointDists = []
        self.nextId = 0
        self.numPoints = 0
        self.nextDiscPoint = 0

    def train(self, dataset):
        majoritySet = dataset.data0
        minoritySet = dataset.data1
        trainDataSize = minoritySet.shape[0]
        numOfFeatures = minoritySet.shape[1]
        
        if minoritySet.shape[0] <= 0 or majoritySet.shape[0] <= 0:
            raise AttributeError("Train: Expected each data class to contain at least one point.")

        if numOfFeatures <= 0:
            raise AttributeError("Train: Expected at least one feature.")

        self.disc = createDisc(self.noiseSize, minoritySet.shape[1])
        self.pointDists = [(x, minDistPointToSet(x, majoritySet)) for x in minoritySet]
        self.nextId = 0
        self.numPoints = len(self.pointDists)
        self.isTrained = True
        minD = None
        maxD = None
        for (x, d) in self.pointDists:
            if minD is None or minD > d:
                minD = d
            if maxD is None or maxD < d:
                maxD = d
        print(f"trained {trainDataSize} points min:{minD} max:{maxD}")

    def generateDataPoint(self):
        (x, d) = self.pointDists[self.nextId]
        self.nextId = (self.nextId + 1) % self.numPoints
        disc = (0.5 * d) * self.disc
        p = disc[self.nextDiscPoint]
        self.nextDiscPoint = (self.nextDiscPoint + 1) % disc.shape[0]
        return p


    def generateData(self, numOfSamples=1):
        return np.array([self.generateDataPoint() for n in range(numOfSamples)])