mlearner version: 0.2.0

create_dataset

create_dataset(config, n)

Generate a Dataset.

Attributes

• config : dict

  Dictionary for dataset configuration: p.e.:

    dict = {

- `'A'` : data_uniform(0, 1, n),


- `'B'` : data_normal(n),


- `'C'` : data_normal(mu=5, sd=2, n=n),


- `'D'` : data_gamma(a=5, n=n)

    }

• n : int

  number of data in the dataset.

Returns

• data : Dataset

Examples

For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/data/create_dataset/

data_gamma

data_gamma(a=5, n=100)

Generate a Gamma data distribution.

Attributes

• a : int or float

  Parameter form.

• n : int

  number of data in the dataset.

Returns

• data : Uniform data distribution.

Examples

For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/data/data_gamma/

data_normal

data_normal(mu=0, sd=1, n=100)

Generate a Normal data distribution.

Attributes

• mu : int or float

  mean value.

• sd : int or float

  standard deviation.

• n : int

  number of data in the dataset.

Returns

• data : Uniform data distribution.

Examples

For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/data/data_normal/

data_uniform

data_uniform(a, b, n)

Generate a Uniform data distribution.

Attributes

• a : int or float

  manimum value of the entire dataset.

• b : int or float

  maximum value of the entire dataset.

• n : int

  number of data in the dataset.

Returns

• data : Uniform data distribution.

Examples

For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/data/data_uniform/

wine_data

wine_data()

Wine dataset.

Source: https://archive.ics.uci.edu/ml/datasets/Wine Number of samples: 178 Class labels: {0, 1, 2}, distribution: [59, 71, 48]

Data Set Information:

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

The attributes are (dontated by Riccardo Leardi, riclea@anchem.unige.it)

• 1) Alcohol
• 2) Malic acid
• 3) Ash
• 4) Alcalinity of ash
• 5) Magnesium
• 6) Total phenols
• 7) Flavanoids
• 8) Nonflavanoid phenols
• 9) Proanthocyanins
• 10) Color intensity
• 11) Hue
• 12) OD280/OD315 of diluted wines
• 13) Proline

In a classification context, this is a well posed problem with "well behaved" class structures. A good data set for first testing of a new classifier, but not very challenging.

Returns

• X, y : [n_samples, n_features], [n_class_labels]

  X is the feature matrix with 178 wine samples as rows and 13 feature columns. y is a 1-dimensional array of the 3 class labels 0, 1, 2

Examples

For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/data/wine_data

adapted from
https://github.com/rasbt/mlxtend/blob/master/mlxtend/data/wine.py
Author: Sebastian Raschka <sebastianraschka.com>
License: BSD 3 clause