from library.exercise import Exercise
from library.dataset import DataSet, TrainTestData
from library.GanExamples import StupidToyListGan
from library.SimpleGan import SimpleGan
from library.Repeater import Repeater
from library.SpheredNoise import SpheredNoise

import pickle
import numpy as np
import random
from imblearn.datasets import fetch_datasets


def loadDataset(datasetName):
    pickle_in = open(f"{datasetName}.pickle", "rb")
    pickle_dict = pickle.load(pickle_in)

    myData = pickle_dict["folding"]
    k = myData[0]

    labels = np.concatenate((k[1], k[3]), axis=0).astype(float)
    features = np.concatenate((k[0], k[2]), axis=0).astype(float)
    label_1 = list(np.where(labels == 1)[0])
    label_0 = list(np.where(labels == 0)[0])
    features_1 = features[label_1]
    features_0 = features[label_0]
    
    return DataSet(data0=features_0, data1=features_1)


def getRandGen(initValue, incValue=257, multValue=101, modulus=65537):
    value = initValue
    while True:
        value = ((multValue * value) + incValue) % modulus
        yield value
            
def genShuffler():
    randGen = getRandGen(2021)

    def shuffler(data):
        data = list(data)
        size = len(data)
        shuffled = []
        while size > 0:
            p = next(randGen) % size
            size -= 1
            shuffled.append(data[p])
            data = data[0:p] + data[(p + 1):]
        return np.array(shuffled)
    return shuffler


def runExerciseForSimpleGAN(datasetName):
    ganName = "SimpleGAN"
    print()
    print()
    print("///////////////////////////////////////////")
    print(f"// Running {ganName} on {datasetName}")
    print("///////////////////////////////////////////")
    print()
    data = loadDataset(f"data_input/{datasetName}")
    gan = SimpleGan(numOfFeatures=data.data0.shape[1])
    random.seed(2021)
    shuffler = genShuffler()
    exercise = Exercise(shuffleFunction=shuffler, numOfShuffles=5, numOfSlices=5)
    exercise.run(gan, data)
    exercise.saveResultsTo(f"data_result/{datasetName}-{ganName}.csv")
    exercise.saveResultsTo(f"data_result/{ganName}-{datasetName}.csv")
    
    
def runExerciseForRepeater(datasetName):
    ganName = "Repeater"
    print()
    print()
    print("///////////////////////////////////////////")
    print(f"// Running {ganName} on {datasetName}")
    print("///////////////////////////////////////////")
    print()
    data = loadDataset(f"data_input/{datasetName}")
    gan = Repeater()
    random.seed(2021)
    shuffler = genShuffler()
    exercise = Exercise(shuffleFunction=shuffler, numOfShuffles=5, numOfSlices=5)
    exercise.run(gan, data)
    exercise.saveResultsTo(f"data_result/{datasetName}-{ganName}.csv")
    exercise.saveResultsTo(f"data_result/{ganName}-{datasetName}.csv")
    
def runExerciseForSpheredNoise(datasetName):
    ganName = "SpheredNoise"
    print()
    print()
    print("///////////////////////////////////////////")
    print(f"// Running {ganName} on {datasetName}")
    print("///////////////////////////////////////////")
    print()
    data = loadDataset(f"data_input/{datasetName}")
    gan = SpheredNoise()
    random.seed(2021)
    shuffler = genShuffler()
    exercise = Exercise(shuffleFunction=shuffler, numOfShuffles=5, numOfSlices=5)
    exercise.run(gan, data)
    exercise.saveResultsTo(f"data_result/{datasetName}-{ganName}.csv")
    exercise.saveResultsTo(f"data_result/{ganName}-{datasetName}.csv")
    
testSets = [
    "folding_abalone_17_vs_7_8_9_10",
    "folding_abalone9-18",
    "folding_car_good",
    "folding_car-vgood",
    "folding_flare-F",
    "folding_hypothyroid",
    "folding_kddcup-guess_passwd_vs_satan",
    "folding_kr-vs-k-three_vs_eleven",
    "folding_kr-vs-k-zero-one_vs_draw",
    "folding_shuttle-2_vs_5",
    "folding_winequality-red-4",
    "folding_yeast4",
    "folding_yeast5",
    "folding_yeast6"
    ]

def runAllTestSets(dataSetList):
    for dsFileName in dataSetList:
        runExerciseForSimpleGAN(dataSetList)
        runExerciseForRepeater(dataSetList)