import numpy as np

class GanBaseClass:
    def __init__(self):
        self.isTrained = False
        self.exampleItems = None
        self.nextIndex = 0
        pass

    def train(self, dataSet):
        if dataSet.data1.shape[0] <= 0:
            raise AttributeError("Train GAN: Expected data class 1 to contain at least one point.")

        print(
            "Train GAN with |class 0|=%d, |class 1|=%d"
            % (dataSet.data0.shape[0], dataSet.data1.shape[0])
            )
        self.isTrained = True
        self.exampleItems = dataSet.data1.copy()

    def generateData(self):
        if not self.isTrained:
            raise ValueError("Try to generate data with untrained GAN.")

        i = self.nextIndex
        self.nextIndex += 1
        if self.nextIndex >= self.exampleItems.shape[0]:
            self.nextIndex = 0

        return self.exampleItems[i]


class TesterNetworkBaseClass:
    def __init__(self):
        pass

    def train(self, data, labels):
        pass

    def predict(self, data):
        return np.zeros(data.shape[0])