Dataset

impyute.dataset.mnist(missingness='mcar', thr=0.2)

Loads corrupted MNIST

Parameters:

missingness: (‘mcar’, ‘mar’, ‘mnar’)

Type of missigness you want in your dataset

th: float between [0,1]

Percentage of missing data in generated data

Returns:

numpy.ndarray

impyute.dataset.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

impyute.dataset.randu(bound=(0, 10), shape=(5, 5), missingness='mcar', thr=0.2, dtype='int')

Return randomly generated dataset of numbers with uniformly distributed values between bound[0] and bound[1]

Parameters:

bound:tuple (start,stop)

Determines the range of values in the matrix. Index 0 for start value and index 1 for stop value. Start is inclusive, stop is exclusive.

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