"""
Jaime Sendra Berenguer-2020.
MLearner Machine Learning Library Extensions
Author:Jaime Sendra Berenguer<www.linkedin.com/in/jaisenbe>
License: MIT
"""
import numpy as np
import pytest
from mlearner.data import data_uniform, data_normal, data_gamma, create_dataset
n = 1000
config = {
'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)
}
config_bad = np.array([1, 2, 3])
def test_config_bad():
with pytest.raises(TypeError):
create_dataset(config_bad, n)
def test_type_n():
with pytest.raises(TypeError):
create_dataset(config, float(n))
def test_float_n_minor():
with pytest.raises(NameError):
data_normal(mu, sd, 0)
def test_result():
data = data_normal(mu, sd, n)
np.testing.assert_allclose(np.mean(data), out, rtol=inc)
def test_float_n():
with pytest.raises(TypeError):
data_normal(mu, sd, float(n))
def test_type_n():
with pytest.raises(TypeError):
data_normal(mu, sd, str(n))
def test_type_b():
with pytest.raises(TypeError):
data_normal(mu, str(sd), n)
def test_type_a():
with pytest.raises(TypeError):
data_normal(str(mu), sd, n)
def test_data_len():
data = data_normal(mu, sd, n)
assert (len(data) == n)