# Path: deeppy/base.py
# class ParamMixin(object):
# class PhaseMixin(object):
# class CollectionMixin(ParamMixin, PhaseMixin):
# class Model(ParamMixin):
# class PickleMixin(object):
#     def params(self):
#     def params(self, params):
#     def phase(self):
#     def phase(self, phase):
#     def params(self):
#     def params(self, params):
#     def phase(self):
#     def phase(self, phase):
#     def setup(self, *array_shapes):
#     def update(self, *arrays):
#     def _pickle_discard(self, attr_name):
#     def __getstate__(self):
# 
# Path: deeppy/dataset/util.py
# def touch(filepath, times=None):
# def require_dir(path):
# def url_filename(url):
# def download(url, target_dir, filename=None):
# def checkpoint(filepath):
# def is_archive(filepath):
# def archive_extract(filepath, target_dir):
# def checksum(filename, method='sha1'):
# def _read_int(buf):
# def load_idx(filepath):


import os
import numpy as np
import logging
from ..base import float_, int_
from .util import (dataset_home, download, checksum, archive_extract,
                   checkpoint, load_idx)


class MNIST(object):
    '''
    THE MNIST DATABASE of handwritten digits [1]
    http://yann.lecun.com/exdb/mnist/

    References:
    [1]: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based
         learning applied to document recognition." Proceedings of the IEEE,
         86(11):2278-2324, November 1998
    '''

    def __init__(self):
        self.name = 'mnist'
        self.data_dir = os.path.join(dataset_home, self.name)
        self._npz_path = os.path.join(self.data_dir, 'mnist.npz')
        self.n_classes = 10
        self.n_test = 10000
        self.n_train = 60000
        self.img_shape = (28, 28)
        self._install()
        self._arrays = self._load()

    def arrays(self, flat=False, dp_dtypes=False):
        x_train, y_train, x_test, y_test = self._arrays
        if dp_dtypes:
            x_train = x_train.astype(float_)
            y_train = y_train.astype(int_)
            x_test = x_test.astype(float_)

# Path: deeppy/base.py
# class ParamMixin(object):
# class PhaseMixin(object):
# class CollectionMixin(ParamMixin, PhaseMixin):
# class Model(ParamMixin):
# class PickleMixin(object):
#     def params(self):
#     def params(self, params):
#     def phase(self):
#     def phase(self, phase):
#     def params(self):
#     def params(self, params):
#     def phase(self):
#     def phase(self, phase):
#     def setup(self, *array_shapes):
#     def update(self, *arrays):
#     def _pickle_discard(self, attr_name):
#     def __getstate__(self):
# 
# Path: deeppy/dataset/util.py
# def touch(filepath, times=None):
# def require_dir(path):
# def url_filename(url):
# def download(url, target_dir, filename=None):
# def checkpoint(filepath):
# def is_archive(filepath):
# def archive_extract(filepath, target_dir):
# def checksum(filename, method='sha1'):
# def _read_int(buf):
# def load_idx(filepath):


# Path: deeppy/dataset/mnist.py
import os
import numpy as np
import logging
from ..base import float_, int_
from .util import (dataset_home, download, checksum, archive_extract,
                   checkpoint, load_idx)



class MNIST(object):
    '''
    THE MNIST DATABASE of handwritten digits [1]
    http://yann.lecun.com/exdb/mnist/

    References:
    [1]: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based
         learning applied to document recognition." Proceedings of the IEEE,
         86(11):2278-2324, November 1998
    '''

    def __init__(self):
        self.name = 'mnist'
        self.data_dir = os.path.join(dataset_home, self.name)
        self._npz_path = os.path.join(self.data_dir, 'mnist.npz')
        self.n_classes = 10
        self.n_test = 10000
        self.n_train = 60000
        self.img_shape = (28, 28)
        self._install()
        self._arrays = self._load()

    def arrays(self, flat=False, dp_dtypes=False):
        x_train, y_train, x_test, y_test = self._arrays
        if dp_dtypes:
            x_train = x_train.astype(float_)
            y_train = y_train.astype(int_)
            x_test = x_test.astype(float_)

            y_test = y_test.astype(int_)