import shelve

import matplotlib.pyplot as plt

from keras.layers.core import Activation, Dense, Dropout

from keras.models import Sequential

from sklearn.cross_validation import train_test_split

from sklearn.manifold import (TSNE, Isomap, LocallyLinearEmbedding,

SpectralEmbedding)

import createDataset02 as createDataset

DATASET = shelve.open('DB.shlv')

#def dienen(object):

# def __init__(self):

# pass

#

# def fit(X, Y):

# pass

#

#

#def main():

# createDataset.createEntireDataset()

# X = DATASET['geometries']

# Y = DATASET['coeficients']

# E = DATASET['energies']

# print('Datasets retrieved')

# dimred = TSNE(n_components=2)

# XX = dimred.fit_transform(X)

# sc = plt.scatter(XX[:, 0],

# XX[:, 1],

# c=E,

# cmap="Spectral",

# alpha=0.5,

# edgecolors='none')

# plt.colorbar(sc)

# plt.show()

#

#

#if __name__ == '__main__':

# main()

#

createDataset.createEntireDataset()

X = DATASET['geometries']

#Y = DATASET['coeficients']

#E = DATASET['energies']

print('Datasets retrieved')

print(X)

#dimred = TSNE(n_components=2)

#XX = dimred.fit_transform(X)

#sc = plt.scatter(XX[:, 0],

# XX[:, 1],

# c=E,

# cmap="Spectral",

# alpha=0.5,

# edgecolors='none')

#plt.colorbar(sc)

#plt.show()

#