# Path: impyute/dataset/corrupt.py
# class Corruptor:
#     """ Adds missing values to a complete dataset.
# 
#     Attributes
#     ----------
#     data: np.ndarray
#         Matrix of values with no NaN's that you want to add NaN's to.
#     thr: float (optional)
#         The percentage of null values you want in your dataset, a number
#         between 0 and 1.
# 
#     Methods
#     -------
#     mcar()
#         Overwrite values with MCAR placed NaN's.
#     mar()
#         Overwrite values with MAR placed NaN's.
#     mnar()
#         Overwrite values with MNAR placed NaN's.
# 
#     """
#     def __init__(self, data, thr=0.2, dtype=np.float):
#         self.dtype = data.dtype
#         self.shape = np.shape(data)
#         self.data = data.astype(dtype)
#         self.thr = thr
# 
#     def mcar(self):
#         """ Overwrites values with MCAR placed NaN's """
#         data_1d = self.data.flatten()
#         n_total = len(data_1d)
#         nan_x = np.random.choice(range(n_total),
#                                   size=int(self.thr*n_total),
#                                   replace=False)
#         for x_i in nan_x:
#             data_1d[x_i] = np.nan
#         output = data_1d.reshape(self.shape)
#         return output
# 
#     def mar(self):
#         """ Overwrites values with MAR placed NaN's """
#         pass
# 
#     def mnar(self):
#         """ Overwrites values with MNAR placed NaN's """
#         pass
# 
#     def complete(self):
#         """ Do nothing to the data """
#         output = self.data
#         return output
# 
# Path: impyute/ops/error.py
# class BadInputError(Exception):
# class BadOutputError(Exception):


import itertools
import math
import random
import string
import numpy as np
from impyute.dataset.corrupt import Corruptor
from impyute.ops import error
    from sklearn.datasets import fetch_mldata
    return raw_data


def randn(theta=(0, 1), shape=(5, 5), missingness="mcar", thr=0.2, dtype="float"):
    """ Return randomly generated dataset of numbers with normally
    distributed values with given and sigma.

    Parameters
    ----------
    theta: tuple (mu, sigma)
        Determines the range of values in the matrix
    shape:tuple(optional)
        Size of the randomly generated data
    missingness: ('mcar', 'mar', 'mnar')
        Type of missingness you want in your dataset
    thr: float between [0,1]
        Percentage of missing data in generated data
    dtype: ('int','float')
        Type of data

    Returns
    -------
    numpy.ndarray
    """
    mean, sigma = theta
    data = np.random.normal(mean, sigma, size=shape)
    if dtype == "int":
        data = np.round(data)
    elif dtype == "float":
        pass

# Path: impyute/dataset/corrupt.py
# class Corruptor:
#     """ Adds missing values to a complete dataset.
# 
#     Attributes
#     ----------
#     data: np.ndarray
#         Matrix of values with no NaN's that you want to add NaN's to.
#     thr: float (optional)
#         The percentage of null values you want in your dataset, a number
#         between 0 and 1.
# 
#     Methods
#     -------
#     mcar()
#         Overwrite values with MCAR placed NaN's.
#     mar()
#         Overwrite values with MAR placed NaN's.
#     mnar()
#         Overwrite values with MNAR placed NaN's.
# 
#     """
#     def __init__(self, data, thr=0.2, dtype=np.float):
#         self.dtype = data.dtype
#         self.shape = np.shape(data)
#         self.data = data.astype(dtype)
#         self.thr = thr
# 
#     def mcar(self):
#         """ Overwrites values with MCAR placed NaN's """
#         data_1d = self.data.flatten()
#         n_total = len(data_1d)
#         nan_x = np.random.choice(range(n_total),
#                                   size=int(self.thr*n_total),
#                                   replace=False)
#         for x_i in nan_x:
#             data_1d[x_i] = np.nan
#         output = data_1d.reshape(self.shape)
#         return output
# 
#     def mar(self):
#         """ Overwrites values with MAR placed NaN's """
#         pass
# 
#     def mnar(self):
#         """ Overwrites values with MNAR placed NaN's """
#         pass
# 
#     def complete(self):
#         """ Do nothing to the data """
#         output = self.data
#         return output
# 
# Path: impyute/ops/error.py
# class BadInputError(Exception):
# class BadOutputError(Exception):


# Path: impyute/dataset/base.py
import itertools
import math
import random
import string
import numpy as np
from impyute.dataset.corrupt import Corruptor
from impyute.ops import error
    from sklearn.datasets import fetch_mldata

    return raw_data


def randn(theta=(0, 1), shape=(5, 5), missingness="mcar", thr=0.2, dtype="float"):
    """ Return randomly generated dataset of numbers with normally
    distributed values with given and sigma.

    Parameters
    ----------
    theta: tuple (mu, sigma)
        Determines the range of values in the matrix
    shape:tuple(optional)
        Size of the randomly generated data
    missingness: ('mcar', 'mar', 'mnar')
        Type of missingness you want in your dataset
    thr: float between [0,1]
        Percentage of missing data in generated data
    dtype: ('int','float')
        Type of data

    Returns
    -------
    numpy.ndarray
    """
    mean, sigma = theta
    data = np.random.normal(mean, sigma, size=shape)
    if dtype == "int":
        data = np.round(data)
    elif dtype == "float":
        pass

    corruptor = Corruptor(data, thr=thr)